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Commit 006693ed authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.11.2' into v0.11.2-ori

parents 4b51e6f1 275de341
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.buildkite/check-wheel-size.py
View file @ 006693ed
......@@ -5,11 +5,11 @@ import os
import sys
import zipfile
# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 450 MiB
# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 500 MiB
# Note that we have 800 MiB quota, please use it wisely.
# See https://github.com/pypi/support/issues/6326 .
# Please also sync the value with the one in Dockerfile.
VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 450))
VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 500))
def print_top_10_largest_files(zip_file):
......
.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-W8A16-compressed-tensors.yaml → .buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml
View file @ 006693ed
# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise -b "auto" -l 1000 -f 5 -t 1
model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
model_name: "HandH1998/QQQ-Llama-3-8b-g128"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.595
value: 0.419
- name: "exact_match,flexible-extract"
value: 0.582
value: 0.416
limit: 1000
num_fewshot: 5
.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml 0 → 100644
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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
backend: "vllm-vlm"
tasks:
- name: "chartqa"
metrics:
- name: "relaxed_accuracy,none"
# TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
value: 0.80
limit: 100
num_fewshot: 0
.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml 0 → 100644
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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
tasks:
- name: "mmlu_pro"
metrics:
- name: "exact_match,custom-extract"
value: 0.80
limit: 250 # will run on 250 * 14 subjects = 3500 samples
num_fewshot: 5
.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
View file @ 006693ed
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -b auto -l 1319 -f 5 -t 1
# For vllm script, with -t option (tensor parallel size)
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
tasks:
- name: "gsm8k"
......
.buildkite/lm-eval-harness/configs/Qwen2.5-VL-7B-Instruct.yaml 0 → 100644
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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m Qwen/Qwen2.5-VL-7B-Instruct -l 2500 -t 1
model_name: "Qwen/Qwen2.5-VL-7B-Instruct"
backend: "vllm-vlm"
tasks:
- name: "chartqa"
metrics:
- name: "relaxed_accuracy,none"
value: 0.855
limit: 2500
num_fewshot: 0
.buildkite/lm-eval-harness/configs/Qwen3-235B-A22B-Instruct-2507-FP8.yaml 0 → 100644
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model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
tasks:
- name: "mmlu_pro"
metrics:
- name: "exact_match,custom-extract"
value: 0.82
limit: 250 # will run on 250 * 14 subjects = 3500 samples
num_fewshot: 5
enforce_eager: false # we use false to speed up the eval process
kv_cache_dtype: fp8 # we use fp8 to speed up the eval process
max_model_len: 40960
apply_chat_template: true
fewshot_as_multiturn: true
gen_kwargs: "temperature=0,top_p=1,top_k=0,max_gen_toks=5632,until=<|ENDANSWER|>"
.buildkite/lm-eval-harness/configs/models-mm-large-h100.txt 0 → 100644
View file @ 006693ed
Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml
.buildkite/lm-eval-harness/configs/models-mm-small.txt 0 → 100644
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Qwen2.5-VL-7B-Instruct.yaml
\ No newline at end of file
.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh 0 → 100755
View file @ 006693ed
#!/bin/bash
# We can use this script to compute baseline accuracy on chartqa for vllm.
#
# Make sure you have lm-eval-harness installed:
# pip install lm-eval==0.4.9
usage() {
echo``
echo "Runs lm eval harness on ChartQA using multimodal vllm."
echo "This pathway is intended to be used to create baselines for "
echo "our correctness tests in vllm's CI."
echo
echo "usage: ${0} <options>"
echo
echo " -m - huggingface stub or local directory of the model"
echo " -l - limit number of samples to run"
echo " -t - tensor parallel size to run at"
echo
}
while getopts "m:l:t:" OPT; do
case ${OPT} in
m )
MODEL="$OPTARG"
;;
l )
LIMIT="$OPTARG"
;;
t )
TP_SIZE="$OPTARG"
;;
\? )
usage
exit 1
;;
esac
done
lm_eval --model vllm-vlm \
--model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE" \
--tasks chartqa \
--batch_size auto \
--apply_chat_template \
--limit $LIMIT
.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh 100644 → 100755
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File mode changed from 100644 to 100755
.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh 0 → 100644
View file @ 006693ed
#!/bin/bash
# We can use this script to compute baseline accuracy on MMLUPRO for vllm.
# We use this for fp8, which HF does not support.
#
# Make sure you have lm-eval-harness installed:
# pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
usage() {
echo``
echo "Runs lm eval harness on MMLU Pro using huggingface transformers."
echo "This pathway is intended to be used to create baselines for "
echo "our automated nm-test-accuracy workflow"
echo
echo "usage: ${0} <options>"
echo
echo " -m - huggingface stub or local directory of the model"
echo " -l - limit number of samples to run"
echo " -f - number of fewshot samples to use"
echo " -t - tensor parallel size to run at"
echo
}
while getopts "m:b:l:f:t:" OPT; do
case ${OPT} in
m )
MODEL="$OPTARG"
;;
b )
BATCH_SIZE="$OPTARG"
;;
l )
LIMIT="$OPTARG"
;;
f )
FEWSHOT="$OPTARG"
;;
t )
TP_SIZE="$OPTARG"
;;
\? )
usage
exit 1
;;
esac
done
lm_eval --model vllm \
--model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true,trust_remote_code=true,max_model_len=4096" \
--tasks mmlu_pro --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
--batch_size auto
.buildkite/lm-eval-harness/test_lm_eval_correctness.py
View file @ 006693ed
......@@ -19,21 +19,35 @@ RTOL = 0.08
def launch_lm_eval(eval_config, tp_size):
trust_remote_code = eval_config.get("trust_remote_code", False)
max_model_len = eval_config.get("max_model_len", 4096)
batch_size = eval_config.get("batch_size", "auto")
backend = eval_config.get("backend", "vllm")
enforce_eager = eval_config.get("enforce_eager", "true")
kv_cache_dtype = eval_config.get("kv_cache_dtype", "auto")
model_args = (
f"pretrained={eval_config['model_name']},"
f"tensor_parallel_size={tp_size},"
f"enforce_eager=true,"
f"enforce_eager={enforce_eager},"
f"kv_cache_dtype={kv_cache_dtype},"
f"add_bos_token=true,"
f"trust_remote_code={trust_remote_code},"
f"max_model_len={max_model_len}"
f"max_model_len={max_model_len},"
)
results = lm_eval.simple_evaluate(
model="vllm",
model=backend,
model_args=model_args,
tasks=[task["name"] for task in eval_config["tasks"]],
num_fewshot=eval_config["num_fewshot"],
limit=eval_config["limit"],
batch_size="auto",
# TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
# text models. however, this is regressing measured strict-match for
# existing text models in CI, so only apply it for mm, or explicitly set
apply_chat_template=eval_config.get(
"apply_chat_template", backend == "vllm-vlm"
),
fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
# Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
gen_kwargs=eval_config.get("gen_kwargs"),
batch_size=batch_size,
)
return results
......
.buildkite/nightly-benchmarks/benchmark-pipeline.yaml deleted 100644 → 0
View file @ 4b51e6f1
steps:
- label: "Wait for container to be ready"
key: wait-for-container-image
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
containers:
- image: badouralix/curl-jq
command:
- sh .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
- label: "Cleanup H100"
agents:
queue: H100
depends_on: ~
command: docker system prune -a --volumes --force
- label: "A100"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: A100
depends_on: wait-for-container-image
if: build.branch == "main"
plugins:
- kubernetes:
podSpec:
priorityClassName: perf-benchmark
containers:
- image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
command:
- bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
resources:
limits:
nvidia.com/gpu: 8
volumeMounts:
- name: devshm
mountPath: /dev/shm
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
nodeSelector:
nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
volumes:
- name: devshm
emptyDir:
medium: Memory
- label: "H200"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: H200
depends_on: wait-for-container-image
if: build.branch == "main"
plugins:
- docker#v5.12.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
command:
- bash
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
mount-buildkite-agent: true
propagate-environment: true
ipc: host
gpus: 4,5,6,7
volumes:
- /data/benchmark-hf-cache:/root/.cache/huggingface
environment:
- VLLM_USAGE_SOURCE
- HF_TOKEN
#- block: "Run H100 Benchmark"
#key: block-h100
#depends_on: ~
- label: "H100"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: H100
depends_on: wait-for-container-image
if: build.branch == "main"
plugins:
- docker#v5.12.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
command:
- bash
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
mount-buildkite-agent: true
propagate-environment: true
ipc: host
gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
volumes:
- /data/benchmark-hf-cache:/root/.cache/huggingface
environment:
- VLLM_USAGE_SOURCE
- HF_TOKEN
# Premerge benchmark
- label: "A100"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: A100
depends_on: wait-for-container-image
if: build.branch != "main"
plugins:
- kubernetes:
podSpec:
priorityClassName: perf-benchmark
containers:
- image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
command:
- bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
resources:
limits:
nvidia.com/gpu: 8
volumeMounts:
- name: devshm
mountPath: /dev/shm
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
nodeSelector:
nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
volumes:
- name: devshm
emptyDir:
medium: Memory
- label: "H200"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: H200
depends_on: wait-for-container-image
if: build.branch != "main"
plugins:
- docker#v5.12.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
command:
- bash
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
mount-buildkite-agent: true
propagate-environment: true
ipc: host
gpus: 4,5,6,7
volumes:
- /data/benchmark-hf-cache:/root/.cache/huggingface
environment:
- VLLM_USAGE_SOURCE
- HF_TOKEN
#- block: "Run H100 Benchmark"
#key: block-h100
#depends_on: ~
- label: "H100"
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
agents:
queue: H100
depends_on: wait-for-container-image
if: build.branch != "main"
plugins:
- docker#v5.12.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
command:
- bash
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
mount-buildkite-agent: true
propagate-environment: true
ipc: host
gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
volumes:
- /data/benchmark-hf-cache:/root/.cache/huggingface
environment:
- VLLM_USAGE_SOURCE
- HF_TOKEN
.buildkite/nightly-benchmarks/nightly-annotation.md deleted 100644 → 0
View file @ 4b51e6f1
# Nightly benchmark annotation
## Description
This file contains the downloading link for benchmarking results.
- [benchmarking pipeline](artifact://nightly-pipeline.yaml)
- [benchmarking results](artifact://results.zip)
- [benchmarking code](artifact://nightly-benchmarks.zip)
Please download the visualization scripts in the post
## Results reproduction
- Find the docker we use in `benchmarking pipeline`
- Deploy the docker, and inside the docker:
- Download `nightly-benchmarks.zip`.
- In the same folder, run the following code:
```bash
export HF_TOKEN=<your HF token>
apt update
apt install -y git
unzip nightly-benchmarks.zip
VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
```
And the results will be inside `./benchmarks/results`.
.buildkite/nightly-benchmarks/nightly-descriptions.md deleted 100644 → 0
View file @ 4b51e6f1
# Nightly benchmark
This benchmark aims to:
- Provide performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and SGLang) leads in performance in what workload.
- Be reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions.
Latest results: [results link](https://blog.vllm.ai/2024/09/05/perf-update.html), scroll to the end.
Latest reproduction guide: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
## Setup
- Docker images:
- vLLM: `vllm/vllm-openai:v0.6.2`
- SGLang: `lmsysorg/sglang:v0.3.2-cu121`
- LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
- TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
- *NOTE: we use r24.07 as the current implementation only works for this version. We are going to bump this up.*
- Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
- Hardware
- 8x Nvidia A100 GPUs
- Workload:
- Dataset
- ShareGPT dataset
- Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
- Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
- Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
- Models: llama-3 8B, llama-3 70B.
- We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
- Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
- Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
- Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
## Known issues
- TRT-LLM crashes with Llama 3.1 8B [issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105).
- TGI does not support `ignore-eos` flag.
.buildkite/nightly-benchmarks/nightly-pipeline.yaml deleted 100644 → 0
View file @ 4b51e6f1
common_pod_spec: &common_pod_spec
priorityClassName: perf-benchmark
nodeSelector:
nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
volumes:
- name: devshm
emptyDir:
medium: Memory
- name: hf-cache
hostPath:
path: /root/.cache/huggingface
type: Directory
common_container_settings: &common_container_settings
command:
- bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
resources:
limits:
nvidia.com/gpu: 8
volumeMounts:
- name: devshm
mountPath: /dev/shm
- name: hf-cache
mountPath: /root/.cache/huggingface
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: HF_HOME
value: /root/.cache/huggingface
- name: VLLM_SOURCE_CODE_LOC
value: /workspace/build/buildkite/vllm/performance-benchmark
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
steps:
- block: ":rocket: Ready for comparing vllm against alternatives? This will take 4 hours."
- label: "A100 vllm step 10"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: vllm/vllm-openai:v0.6.2
<<: *common_container_settings
- label: "A100 sglang benchmark"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: lmsysorg/sglang:v0.3.2-cu121
<<: *common_container_settings
- label: "A100 lmdeploy benchmark"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: openmmlab/lmdeploy:v0.6.1-cu12
<<: *common_container_settings
- label: "A100 trt llama-8B"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
<<: *common_container_settings
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: HF_HOME
value: /root/.cache/huggingface
- name: VLLM_SOURCE_CODE_LOC
value: /workspace/build/buildkite/vllm/performance-benchmark
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
- name: TEST_SELECTOR
value: "llama8B"
- label: "A100 trt llama-70B"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
<<: *common_container_settings
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: HF_HOME
value: /root/.cache/huggingface
- name: VLLM_SOURCE_CODE_LOC
value: /workspace/build/buildkite/vllm/performance-benchmark
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
- name: TEST_SELECTOR
value: "llama70B"
# FIXME(Kuntai): uncomment this after NVIDIA gives us their test docker image
# - label: "A100 trt benchmark"
# priority: 100
# agents:
# queue: A100
# plugins:
# - kubernetes:
# podSpec:
# <<: *common_pod_spec
# containers:
# - image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
# <<: *common_container_settings
# FIXME(Kuntai): uncomment this after TGI supports `--ignore-eos`.
# - label: "A100 tgi benchmark"
# priority: 100
# agents:
# queue: A100
# plugins:
# - kubernetes:
# podSpec:
# <<: *common_pod_spec
# containers:
# - image: ghcr.io/huggingface/text-generation-inference:2.2.0
# <<: *common_container_settings
- wait
- label: "Collect the results"
priority: 100
agents:
queue: A100
plugins:
- kubernetes:
podSpec:
<<: *common_pod_spec
containers:
- image: vllm/vllm-openai:v0.5.0.post1
command:
- bash .buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
resources:
limits:
nvidia.com/gpu: 8
volumeMounts:
- name: devshm
mountPath: /dev/shm
env:
- name: VLLM_USAGE_SOURCE
value: ci-test
- name: VLLM_SOURCE_CODE_LOC
value: /workspace/build/buildkite/vllm/performance-benchmark
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
- block: ":rocket: check the results!"
\ No newline at end of file
.buildkite/nightly-benchmarks/scripts/download-tokenizer.py deleted 100644 → 0
View file @ 4b51e6f1
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import argparse
from transformers import AutoTokenizer
def main(model, cachedir):
# Load the tokenizer and save it to the specified directory
tokenizer = AutoTokenizer.from_pretrained(model)
tokenizer.save_pretrained(cachedir)
print(f"Tokenizer saved to {cachedir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Download and save Hugging Face tokenizer"
)
parser.add_argument("--model", type=str, required=True, help="Name of the model")
parser.add_argument(
"--cachedir", type=str, required=True, help="Directory to save the tokenizer"
)
args = parser.parse_args()
main(args.model, args.cachedir)
.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py deleted 100644 → 0
View file @ 4b51e6f1
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
from tabulate import tabulate
def parse_arguments():
parser = argparse.ArgumentParser(
description="Parse command line arguments for summary-nightly-results script."
)
parser.add_argument(
"--results-folder",
type=str,
required=True,
help="The folder where the results are stored.",
)
parser.add_argument(
"--description", type=str, required=True, help="Description of the results."
)
args = parser.parse_args()
return args
def get_perf(df, method, model, metric):
means = []
for qps in [2, 4, 8, 16, "inf"]:
target = df["Test name"].str.contains(model)
target = target & df["Engine"].str.contains(method)
target = target & df["Test name"].str.contains("qps_" + str(qps))
filtered_df = df[target]
if filtered_df.empty:
means.append(0.0)
else:
means.append(filtered_df[metric].values[0])
return np.array(means)
def get_perf_w_std(df, method, model, metric):
if metric in ["TTFT", "ITL"]:
mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
mean = mean.tolist()
std = get_perf(df, method, model, "Std " + metric + " (ms)")
if std.mean() == 0:
std = None
success = get_perf(df, method, model, "Successful req.")
if std is not None:
std = std / np.sqrt(success)
std = std.tolist()
else:
assert metric == "Tput"
mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
df, method, model, "Output Tput (tok/s)"
)
mean = mean.tolist()
std = None
return mean, std
def main(args):
results_folder = Path(args.results_folder)
results = []
# collect results
for test_file in results_folder.glob("*_nightly_results.json"):
with open(test_file) as f:
results = results + json.loads(f.read())
# generate markdown table
df = pd.DataFrame.from_dict(results)
md_table = tabulate(df, headers="keys", tablefmt="pipe", showindex=False)
with open(args.description) as f:
description = f.read()
description = description.format(nightly_results_benchmarking_table=md_table)
with open("nightly_results.md", "w") as f:
f.write(description)
if __name__ == "__main__":
args = parse_arguments()
main(args)
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