Compare commits
2 Commits
v0.11.1rc5
...
v0.2.1.pos
| Author | SHA1 | Date | |
|---|---|---|---|
|
3d40c834f0 | ||
|
|
d0fb047de3 |
@@ -1,53 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import os
|
||||
import sys
|
||||
import zipfile
|
||||
|
||||
# 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", 500))
|
||||
|
||||
|
||||
def print_top_10_largest_files(zip_file):
|
||||
"""Print the top 10 largest files in the given zip file."""
|
||||
with zipfile.ZipFile(zip_file, "r") as z:
|
||||
file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
|
||||
file_sizes.sort(key=lambda x: x[1], reverse=True)
|
||||
for f, size in file_sizes[:10]:
|
||||
print(f"{f}: {size / (1024 * 1024):.2f} MBs uncompressed.")
|
||||
|
||||
|
||||
def check_wheel_size(directory):
|
||||
"""Check the size of .whl files in the given directory."""
|
||||
for root, _, files in os.walk(directory):
|
||||
for file_name in files:
|
||||
if file_name.endswith(".whl"):
|
||||
wheel_path = os.path.join(root, file_name)
|
||||
wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
|
||||
if wheel_size_mb > VLLM_MAX_SIZE_MB:
|
||||
print(
|
||||
f"Not allowed: Wheel {wheel_path} is larger "
|
||||
f"({wheel_size_mb:.2f} MB) than the limit "
|
||||
f"({VLLM_MAX_SIZE_MB} MB)."
|
||||
)
|
||||
print_top_10_largest_files(wheel_path)
|
||||
return 1
|
||||
else:
|
||||
print(
|
||||
f"Wheel {wheel_path} is within the allowed size "
|
||||
f"({wheel_size_mb:.2f} MB)."
|
||||
)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python check-wheel-size.py <directory>")
|
||||
sys.exit(1)
|
||||
|
||||
directory = sys.argv[1]
|
||||
sys.exit(check_wheel_size(directory))
|
||||
@@ -1,46 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
template = """<!DOCTYPE html>
|
||||
<html>
|
||||
<body>
|
||||
<h1>Links for vLLM</h1/>
|
||||
<a href="../{x86_wheel_html_escaped}">{x86_wheel}</a><br/>
|
||||
<a href="../{arm_wheel_html_escaped}">{arm_wheel}</a><br/>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--wheel", help="The wheel path.", required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
filename = os.path.basename(args.wheel)
|
||||
|
||||
with open("index.html", "w") as f:
|
||||
print(f"Generated index.html for {args.wheel}")
|
||||
# sync the abi tag with .buildkite/scripts/upload-wheels.sh
|
||||
if "x86_64" in filename:
|
||||
x86_wheel = filename
|
||||
arm_wheel = filename.replace("x86_64", "aarch64").replace(
|
||||
"manylinux1", "manylinux2014"
|
||||
)
|
||||
elif "aarch64" in filename:
|
||||
x86_wheel = filename.replace("aarch64", "x86_64").replace(
|
||||
"manylinux2014", "manylinux1"
|
||||
)
|
||||
arm_wheel = filename
|
||||
else:
|
||||
raise ValueError(f"Unsupported wheel: {filename}")
|
||||
# cloudfront requires escaping the '+' character
|
||||
f.write(
|
||||
template.format(
|
||||
x86_wheel=x86_wheel,
|
||||
x86_wheel_html_escaped=x86_wheel.replace("+", "%2B"),
|
||||
arm_wheel=arm_wheel,
|
||||
arm_wheel_html_escaped=arm_wheel.replace("+", "%2B"),
|
||||
)
|
||||
)
|
||||
@@ -1,13 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m deepseek-ai/DeepSeek-V2-Lite-Chat -b "auto" -l 1000 -f 5 -t 2
|
||||
model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.671
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.664
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
trust_remote_code: True
|
||||
@@ -1,12 +0,0 @@
|
||||
# For hf script, without -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5
|
||||
model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.905
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.905
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For hf script, without -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-70B-Instruct -b 32 -l 250 -f 5
|
||||
model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.892
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.892
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors -b auto -l 1000 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.752
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.754
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.753
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.753
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.755
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.755
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
|
||||
model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.753
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.753
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.764
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.764
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.728
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.728
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
|
||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.758
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.759
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For hf script, without -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5
|
||||
model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.756
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.752
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# 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: 0.419
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.416
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,11 +0,0 @@
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Llama-3.2-1B-Instruct-FP8 -b "auto" -l 1319 -f 5 -t 1
|
||||
model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.335
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.323
|
||||
limit: 1319
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
|
||||
model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.356
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.358
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# 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
|
||||
@@ -1,10 +0,0 @@
|
||||
# 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
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
|
||||
model_name: "mgoin/Minitron-4B-Base-FP8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.231
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.22
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
|
||||
model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.86
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.86
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
|
||||
model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.624
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.624
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For hf script, without -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5
|
||||
model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.616
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.632
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
|
||||
model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.30
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.465
|
||||
limit: 1319
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# 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-FP8W8 -b auto -l 1000 -f 5 -t 1
|
||||
model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.578
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.585
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
|
||||
model_name: "neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.593
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.588
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# 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"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.595
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.582
|
||||
limit: 1000
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2-57B-A14B-Instruct -b "auto" -l 250 -f 5 -t 4
|
||||
model_name: "Qwen/Qwen2-57B-A14B-Instruct"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.792
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.824
|
||||
limit: 250
|
||||
num_fewshot: 5
|
||||
@@ -1,11 +0,0 @@
|
||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2.5-1.5B-Instruct -b auto -l 1319 -f 5 -t 1
|
||||
model_name: "Qwen/Qwen2.5-1.5B-Instruct"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.54
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.59
|
||||
limit: 1319
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# 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"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.47
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.64
|
||||
limit: 1319
|
||||
num_fewshot: 5
|
||||
@@ -1,12 +0,0 @@
|
||||
# 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
|
||||
@@ -1,14 +0,0 @@
|
||||
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|>"
|
||||
@@ -1,12 +0,0 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
|
||||
model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
|
||||
tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.6353
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.637
|
||||
limit: null
|
||||
num_fewshot: null
|
||||
@@ -1 +0,0 @@
|
||||
Qwen3-235B-A22B-Instruct-2507-FP8.yaml
|
||||
@@ -1,5 +0,0 @@
|
||||
Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform.yaml
|
||||
Meta-Llama-3-70B-Instruct.yaml
|
||||
Mixtral-8x7B-Instruct-v0.1.yaml
|
||||
Qwen2-57B-A14-Instruct.yaml
|
||||
DeepSeek-V2-Lite-Chat.yaml
|
||||
@@ -1 +0,0 @@
|
||||
Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml
|
||||
@@ -1 +0,0 @@
|
||||
Qwen2.5-VL-7B-Instruct.yaml
|
||||
@@ -1,6 +0,0 @@
|
||||
Qwen2.5-1.5B-Instruct.yaml
|
||||
Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
|
||||
Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
|
||||
Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
|
||||
Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
|
||||
Qwen1.5-MoE-W4A16-compressed-tensors.yaml
|
||||
@@ -1,44 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
def pytest_addoption(parser):
|
||||
parser.addoption(
|
||||
"--config-list-file",
|
||||
action="store",
|
||||
help="Path to the file listing model config YAMLs (one per line)",
|
||||
)
|
||||
parser.addoption(
|
||||
"--tp-size",
|
||||
action="store",
|
||||
default="1",
|
||||
help="Tensor parallel size to use for evaluation",
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def config_list_file(pytestconfig, config_dir):
|
||||
rel_path = pytestconfig.getoption("--config-list-file")
|
||||
return config_dir / rel_path
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def tp_size(pytestconfig):
|
||||
return pytestconfig.getoption("--tp-size")
|
||||
|
||||
|
||||
def pytest_generate_tests(metafunc):
|
||||
if "config_filename" in metafunc.fixturenames:
|
||||
rel_path = metafunc.config.getoption("--config-list-file")
|
||||
config_list_file = Path(rel_path).resolve()
|
||||
config_dir = config_list_file.parent
|
||||
with open(config_list_file, encoding="utf-8") as f:
|
||||
configs = [
|
||||
config_dir / line.strip()
|
||||
for line in f
|
||||
if line.strip() and not line.startswith("#")
|
||||
]
|
||||
metafunc.parametrize("config_filename", configs)
|
||||
@@ -1,44 +0,0 @@
|
||||
#!/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
|
||||
@@ -1,46 +0,0 @@
|
||||
#!/bin/bash
|
||||
# We can use this script to compute baseline accuracy on GSM for transformers.
|
||||
#
|
||||
# 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 GSM8k 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 " -b - batch size to run the evaluation at"
|
||||
echo " -l - limit number of samples to run"
|
||||
echo " -f - number of fewshot samples to use"
|
||||
echo
|
||||
}
|
||||
|
||||
while getopts "m:b:l:f:" OPT; do
|
||||
case ${OPT} in
|
||||
m )
|
||||
MODEL="$OPTARG"
|
||||
;;
|
||||
b )
|
||||
BATCH_SIZE="$OPTARG"
|
||||
;;
|
||||
l )
|
||||
LIMIT="$OPTARG"
|
||||
;;
|
||||
f )
|
||||
FEWSHOT="$OPTARG"
|
||||
;;
|
||||
\? )
|
||||
usage
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
lm_eval --model hf \
|
||||
--model_args "pretrained=$MODEL,parallelize=True" \
|
||||
--tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
|
||||
--batch_size "$BATCH_SIZE"
|
||||
@@ -1,51 +0,0 @@
|
||||
#!/bin/bash
|
||||
# We can use this script to compute baseline accuracy on GSM 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 GSM8k 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 " -b - batch size to run the evaluation at"
|
||||
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 gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
|
||||
--batch_size "$BATCH_SIZE"
|
||||
@@ -1,50 +0,0 @@
|
||||
#!/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
|
||||
@@ -1,71 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
"""
|
||||
LM eval harness on model to compare vs HF baseline computed offline.
|
||||
Configs are found in configs/$MODEL.yaml
|
||||
|
||||
pytest -s -v test_lm_eval_correctness.py \
|
||||
--config-list-file=configs/models-small.txt \
|
||||
--tp-size=1
|
||||
"""
|
||||
|
||||
import lm_eval
|
||||
import numpy as np
|
||||
import yaml
|
||||
|
||||
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={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},"
|
||||
)
|
||||
results = lm_eval.simple_evaluate(
|
||||
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"],
|
||||
# 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
|
||||
|
||||
|
||||
def test_lm_eval_correctness_param(config_filename, tp_size):
|
||||
eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
|
||||
|
||||
results = launch_lm_eval(eval_config, tp_size)
|
||||
|
||||
success = True
|
||||
for task in eval_config["tasks"]:
|
||||
for metric in task["metrics"]:
|
||||
ground_truth = metric["value"]
|
||||
measured_value = results["results"][task["name"]][metric["name"]]
|
||||
print(
|
||||
f"{task['name']} | {metric['name']}: "
|
||||
f"ground_truth={ground_truth} | measured={measured_value}"
|
||||
)
|
||||
success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
|
||||
|
||||
assert success
|
||||
@@ -1,133 +0,0 @@
|
||||
# vLLM benchmark suite
|
||||
|
||||
## Introduction
|
||||
|
||||
This directory contains a benchmarking suite for **developers** to run locally and gain clarity on whether their PR improves/degrades vllm's performance.
|
||||
vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](https://perf.vllm.ai/), hosted under PyTorch CI HUD.
|
||||
|
||||
## Performance benchmark quick overview
|
||||
|
||||
**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100 and Intel® Xeon® Processors, with different models.
|
||||
|
||||
**Benchmarking Duration**: about 1hr.
|
||||
|
||||
**For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
|
||||
|
||||
## Trigger the benchmark
|
||||
|
||||
The benchmark needs to be triggered manually:
|
||||
|
||||
```bash
|
||||
bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
|
||||
```
|
||||
|
||||
Runtime environment variables:
|
||||
|
||||
- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
|
||||
- `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
|
||||
- `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
|
||||
- `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
|
||||
- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
|
||||
- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
|
||||
|
||||
## Performance benchmark details
|
||||
|
||||
See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
|
||||
> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
|
||||
>
|
||||
### Latency test
|
||||
|
||||
Here is an example of one test inside `latency-tests.json`:
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"test_name": "latency_llama8B_tp1",
|
||||
"parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B",
|
||||
"tensor_parallel_size": 1,
|
||||
"load_format": "dummy",
|
||||
"num_iters_warmup": 5,
|
||||
"num_iters": 15
|
||||
}
|
||||
},
|
||||
]
|
||||
```
|
||||
|
||||
In this example:
|
||||
|
||||
- The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
|
||||
- The `parameters` attribute control the command line arguments to be used for `vllm bench latency`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `vllm bench latency`. For example, the corresponding command line arguments for `vllm bench latency` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
|
||||
|
||||
Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
|
||||
|
||||
WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
|
||||
|
||||
### Throughput test
|
||||
|
||||
The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
|
||||
|
||||
The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
|
||||
|
||||
### Serving test
|
||||
|
||||
We test the throughput by using `vllm bench serve` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B",
|
||||
"tensor_parallel_size": 1,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
]
|
||||
```
|
||||
|
||||
Inside this example:
|
||||
|
||||
- The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
|
||||
- The `server-parameters` includes the command line arguments for vLLM server.
|
||||
- The `client-parameters` includes the command line arguments for `vllm bench serve`.
|
||||
- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
|
||||
|
||||
The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
|
||||
|
||||
WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
|
||||
|
||||
### Visualizing the results
|
||||
|
||||
The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.
|
||||
You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
|
||||
If you do not see the table, please wait till the benchmark finish running.
|
||||
The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
|
||||
The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
|
||||
|
||||
The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
|
||||
When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
|
||||
`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
|
||||
If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
|
||||
|
||||
Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
|
||||
`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
|
||||
|
||||
| | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio |
|
||||
|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
|
||||
| 0 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982 | 156.526018 | 1.097396 |
|
||||
| 1 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334 | 294.018783 | 1.216863 |
|
||||
|
||||
A comparison diagram will be generated below the table.
|
||||
Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
|
||||
<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
|
||||
@@ -1,65 +0,0 @@
|
||||
# Performance benchmarks descriptions
|
||||
|
||||
## Latency tests
|
||||
|
||||
- Input length: 32 tokens.
|
||||
- Output length: 128 tokens.
|
||||
- Batch size: fixed (8).
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Evaluation metrics: end-to-end latency (mean, median, p99).
|
||||
|
||||
{latency_tests_markdown_table}
|
||||
|
||||
## Throughput tests
|
||||
|
||||
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
||||
- Output length: the corresponding output length of these 200 prompts.
|
||||
- Batch size: dynamically determined by vllm to achieve maximum throughput.
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Evaluation metrics: throughput.
|
||||
|
||||
{throughput_tests_markdown_table}
|
||||
|
||||
## Serving tests
|
||||
|
||||
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
||||
- Output length: the corresponding output length of these 200 prompts.
|
||||
- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
|
||||
- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
|
||||
- For CPU, we added random dataset tests to benchmark fixed input/output length with 100 prompts.
|
||||
|
||||
{serving_tests_markdown_table}
|
||||
|
||||
## Platform Information
|
||||
|
||||
{platform_markdown_table}
|
||||
|
||||
## json version of the benchmarking tables
|
||||
|
||||
This section contains the data of the markdown tables above in JSON format.
|
||||
You can load the benchmarking tables into pandas dataframes as follows:
|
||||
|
||||
```python
|
||||
import json
|
||||
import pandas as pd
|
||||
|
||||
benchmarking_results_json = """The json string"""
|
||||
benchmarking_results = json.loads(benchmarking_results_json)
|
||||
latency_results = pd.DataFrame.from_dict(benchmarking_results["latency"])
|
||||
throughput_results = pd.DataFrame.from_dict(benchmarking_results["throughput"])
|
||||
serving_results = pd.DataFrame.from_dict(benchmarking_results["serving"])
|
||||
```
|
||||
|
||||
The json string for all benchmarking tables:
|
||||
|
||||
```json
|
||||
{benchmarking_results_in_json_string}
|
||||
```
|
||||
|
||||
You can also check the raw experiment data in the Artifact tab of the Buildkite page.
|
||||
@@ -1,456 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
from importlib import util
|
||||
|
||||
import pandas as pd
|
||||
|
||||
pd.options.display.float_format = "{:.2f}".format
|
||||
plotly_found = util.find_spec("plotly.express") is not None
|
||||
|
||||
|
||||
def compare_data_columns(
|
||||
files, name_column, data_column, info_cols, drop_column, debug=False
|
||||
):
|
||||
"""
|
||||
Align concatenation by keys derived from info_cols instead of row order.
|
||||
- Pick one canonical key list: subset of info_cols present in ALL files.
|
||||
- For each file: set index to those keys, aggregate duplicates
|
||||
- (mean for metric, first for names).
|
||||
- Concat along axis=1 (indexes align), then reset_index so callers can
|
||||
- group by columns.
|
||||
- If --debug, add a <file_label>_name column per file.
|
||||
"""
|
||||
print("\ncompare_data_column:", data_column)
|
||||
|
||||
frames = []
|
||||
raw_data_cols = []
|
||||
compare_frames = []
|
||||
|
||||
# 1) choose a canonical key list from info_cols that exists in ALL files
|
||||
cols_per_file = []
|
||||
for f in files:
|
||||
try:
|
||||
df_tmp = pd.read_json(f, orient="records")
|
||||
except Exception as err:
|
||||
raise ValueError(f"Failed to read {f}") from err
|
||||
cols_per_file.append(set(df_tmp.columns))
|
||||
|
||||
key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
|
||||
if not key_cols:
|
||||
# soft fallback: use any info_cols present in the first file
|
||||
key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
|
||||
if not key_cols:
|
||||
raise ValueError(
|
||||
"No common key columns found from info_cols across the input files."
|
||||
)
|
||||
|
||||
# 2) build a single "meta" block (keys as columns) once, aligned by the key index
|
||||
meta_added = False
|
||||
|
||||
for file in files:
|
||||
df = pd.read_json(file, orient="records")
|
||||
|
||||
# Keep rows that actually have the compared metric (same as original behavior)
|
||||
if drop_column in df.columns:
|
||||
df = df.dropna(subset=[drop_column], ignore_index=True)
|
||||
|
||||
# Stabilize numeric key columns (harmless if missing)
|
||||
for c in (
|
||||
"Input Len",
|
||||
"Output Len",
|
||||
"TP Size",
|
||||
"PP Size",
|
||||
"# of max concurrency.",
|
||||
"qps",
|
||||
):
|
||||
if c in df.columns:
|
||||
df[c] = pd.to_numeric(df[c], errors="coerce")
|
||||
|
||||
# Ensure all key columns exist
|
||||
for c in key_cols:
|
||||
if c not in df.columns:
|
||||
df[c] = pd.NA
|
||||
|
||||
# Set index = key_cols and aggregate duplicates → unique MultiIndex
|
||||
df_idx = df.set_index(key_cols, drop=False)
|
||||
|
||||
# meta (key columns), unique per key
|
||||
meta = df_idx[key_cols]
|
||||
if not meta.index.is_unique:
|
||||
meta = meta.groupby(level=key_cols, dropna=False).first()
|
||||
|
||||
# metric series for this file, aggregated to one row per key
|
||||
file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
|
||||
s = df_idx[data_column]
|
||||
if not s.index.is_unique:
|
||||
s = s.groupby(level=key_cols, dropna=False).mean()
|
||||
s.name = file_label # column label like original
|
||||
|
||||
# add meta once (from first file) so keys are the leftmost columns
|
||||
if not meta_added:
|
||||
frames.append(meta)
|
||||
meta_added = True
|
||||
|
||||
# (NEW) debug: aligned test-name column per file
|
||||
if debug and name_column in df_idx.columns:
|
||||
name_s = df_idx[name_column]
|
||||
if not name_s.index.is_unique:
|
||||
name_s = name_s.groupby(level=key_cols, dropna=False).first()
|
||||
name_s.name = f"{file_label}_name"
|
||||
frames.append(name_s)
|
||||
|
||||
frames.append(s)
|
||||
raw_data_cols.append(file_label)
|
||||
compare_frames.append(s)
|
||||
|
||||
# Generalize ratio: for any file N>=2, add ratio (fileN / file1)
|
||||
if len(compare_frames) >= 2:
|
||||
base = compare_frames[0]
|
||||
current = compare_frames[-1]
|
||||
if "P99" in data_column or "Median" in data_column:
|
||||
ratio = base / current # for latency
|
||||
else:
|
||||
ratio = current / base
|
||||
ratio = ratio.mask(base == 0) # avoid inf when baseline is 0
|
||||
ratio.name = f"Ratio 1 vs {len(compare_frames)}"
|
||||
frames.append(ratio)
|
||||
|
||||
# 4) concat on columns with aligned MultiIndex;
|
||||
# then reset_index to return keys as columns
|
||||
concat_df = pd.concat(frames, axis=1)
|
||||
concat_df = concat_df.reset_index(drop=True).reset_index()
|
||||
if "index" in concat_df.columns:
|
||||
concat_df = concat_df.drop(columns=["index"])
|
||||
|
||||
# Ensure key/info columns appear first (in your info_cols order)
|
||||
front = [c for c in info_cols if c in concat_df.columns]
|
||||
rest = [c for c in concat_df.columns if c not in front]
|
||||
concat_df = concat_df[front + rest]
|
||||
|
||||
print(raw_data_cols)
|
||||
return concat_df, raw_data_cols
|
||||
|
||||
|
||||
def split_json_by_tp_pp(
|
||||
input_file: str = "benchmark_results.json", output_root: str = "."
|
||||
) -> list[str]:
|
||||
"""
|
||||
Split a benchmark JSON into separate folders by (TP Size, PP Size).
|
||||
|
||||
Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
|
||||
Returns: list of file paths written.
|
||||
"""
|
||||
# Load JSON data into DataFrame
|
||||
with open(input_file, encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
# If the JSON is a dict with a list under common keys, use that list
|
||||
if isinstance(data, dict):
|
||||
for key in ("results", "serving_results", "benchmarks", "data"):
|
||||
if isinstance(data.get(key), list):
|
||||
data = data[key]
|
||||
break
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# Keep only "serving" tests
|
||||
name_col = next(
|
||||
(c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
|
||||
)
|
||||
if name_col:
|
||||
df = df[
|
||||
df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
|
||||
].copy()
|
||||
|
||||
# Handle alias column names
|
||||
rename_map = {
|
||||
"tp_size": "TP Size",
|
||||
"tensor_parallel_size": "TP Size",
|
||||
"pp_size": "PP Size",
|
||||
"pipeline_parallel_size": "PP Size",
|
||||
}
|
||||
df.rename(
|
||||
columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
|
||||
)
|
||||
|
||||
# Ensure TP/PP columns exist (default to 1 if missing)
|
||||
if "TP Size" not in df.columns:
|
||||
df["TP Size"] = 1
|
||||
if "PP Size" not in df.columns:
|
||||
df["PP Size"] = 1
|
||||
|
||||
# make sure TP/PP are numeric ints with no NaN
|
||||
df["TP Size"] = (
|
||||
pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
|
||||
)
|
||||
df["PP Size"] = (
|
||||
pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
|
||||
)
|
||||
|
||||
# Split into separate folders
|
||||
saved_paths: list[str] = []
|
||||
for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
|
||||
folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
|
||||
os.makedirs(folder_name, exist_ok=True)
|
||||
filepath = os.path.join(folder_name, "benchmark_results.json")
|
||||
group_df.to_json(filepath, orient="records", indent=2, force_ascii=False)
|
||||
print(f"Saved: {filepath}")
|
||||
saved_paths.append(filepath)
|
||||
|
||||
return saved_paths
|
||||
|
||||
|
||||
def _add_limit_line(fig, y_value, label):
|
||||
# Visible dashed line + annotation
|
||||
fig.add_hline(
|
||||
y=y_value,
|
||||
line_dash="dash",
|
||||
line_color="red" if "ttft" in label.lower() else "blue",
|
||||
annotation_text=f"{label}: {y_value} ms",
|
||||
annotation_position="top left",
|
||||
)
|
||||
# Optional: add a legend item (as a transparent helper trace)
|
||||
if plot and plotly_found:
|
||||
import plotly.graph_objects as go
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[None],
|
||||
y=[None],
|
||||
mode="lines",
|
||||
line=dict(
|
||||
dash="dash", color="red" if "ttft" in label.lower() else "blue"
|
||||
),
|
||||
name=f"{label}",
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _find_concurrency_col(df: pd.DataFrame) -> str:
|
||||
for c in [
|
||||
"# of max concurrency.",
|
||||
"# of max concurrency",
|
||||
"Max Concurrency",
|
||||
"max_concurrency",
|
||||
"Concurrency",
|
||||
]:
|
||||
if c in df.columns:
|
||||
return c
|
||||
# Fallback: guess an integer-like column (harmless if unused)
|
||||
for c in df.columns:
|
||||
if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
|
||||
return c
|
||||
return "# of max concurrency."
|
||||
|
||||
|
||||
def _highlight_threshold(
|
||||
df: pd.DataFrame, threshold: float
|
||||
) -> "pd.io.formats.style.Styler":
|
||||
"""Highlight numeric per-configuration columns with value <= threshold."""
|
||||
conc_col = _find_concurrency_col(df)
|
||||
key_cols = [
|
||||
c
|
||||
for c in ["Model", "Dataset Name", "Input Len", "Output Len", conc_col]
|
||||
if c in df.columns
|
||||
]
|
||||
conf_cols = [
|
||||
c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
|
||||
]
|
||||
conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
|
||||
return df.style.map(
|
||||
lambda v: "background-color:#e6ffe6;font-weight:bold;"
|
||||
if pd.notna(v) and v <= threshold
|
||||
else "",
|
||||
subset=conf_cols,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-f", "--file", action="append", type=str, help="input file name"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--debug", action="store_true", help="show all information for debugging"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--plot",
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=True,
|
||||
help="plot perf diagrams or not --no-plot --plot",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-x",
|
||||
"--xaxis",
|
||||
type=str,
|
||||
default="# of max concurrency.",
|
||||
help="column name to use as X Axis in comparison graph",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-l",
|
||||
"--latency",
|
||||
type=str,
|
||||
default="p99",
|
||||
help="take median|p99 for latency like TTFT/TPOT",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ttft-max-ms",
|
||||
type=float,
|
||||
default=3000.0,
|
||||
help="Reference limit for TTFT plots (ms)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tpot-max-ms",
|
||||
type=float,
|
||||
default=100.0,
|
||||
help="Reference limit for TPOT plots (ms)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
drop_column = "P99"
|
||||
name_column = "Test name"
|
||||
info_cols = [
|
||||
"Model",
|
||||
"Dataset Name",
|
||||
"Input Len",
|
||||
"Output Len",
|
||||
"TP Size",
|
||||
"PP Size",
|
||||
"# of max concurrency.",
|
||||
"qps",
|
||||
]
|
||||
|
||||
if "median" in args.latency:
|
||||
data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
|
||||
html_msgs_for_data_cols = [
|
||||
"Compare Output Tokens /n",
|
||||
"Median TTFT /n",
|
||||
"Median TPOT /n",
|
||||
]
|
||||
drop_column = "P99"
|
||||
elif "p99" in args.latency:
|
||||
data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
|
||||
html_msgs_for_data_cols = [
|
||||
"Compare Output Tokens /n",
|
||||
"P99 TTFT /n",
|
||||
"P99 TPOT /n",
|
||||
]
|
||||
|
||||
if len(args.file) == 1:
|
||||
files = split_json_by_tp_pp(args.file[0], output_root="splits")
|
||||
info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
|
||||
else:
|
||||
files = args.file
|
||||
print("comparing : " + ", ".join(files))
|
||||
debug = args.debug
|
||||
plot = args.plot
|
||||
# For Plot feature, assign y axis from one of info_cols
|
||||
y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
|
||||
with open("perf_comparison.html", "w") as text_file:
|
||||
for i in range(len(data_cols_to_compare)):
|
||||
output_df, raw_data_cols = compare_data_columns(
|
||||
files,
|
||||
name_column,
|
||||
data_cols_to_compare[i],
|
||||
info_cols,
|
||||
drop_column,
|
||||
debug=debug,
|
||||
)
|
||||
|
||||
# For Plot feature, insert y axis from one of info_cols
|
||||
raw_data_cols.insert(0, info_cols[y_axis_index])
|
||||
|
||||
filtered_info_cols = info_cols[:-2]
|
||||
existing_group_cols = [
|
||||
c for c in filtered_info_cols if c in output_df.columns
|
||||
]
|
||||
if not existing_group_cols:
|
||||
raise ValueError(
|
||||
f"No valid group-by columns "
|
||||
f"Expected subset: {filtered_info_cols}, "
|
||||
f"but DataFrame has: {list(output_df.columns)}"
|
||||
)
|
||||
# output_df_sorted = output_df.sort_values(by=existing_group_cols)
|
||||
output_df_sorted = output_df.sort_values(by=args.xaxis)
|
||||
output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
|
||||
for name, group in output_groups:
|
||||
group_name = (
|
||||
",".join(map(str, name)).replace(",", "_").replace("/", "-")
|
||||
)
|
||||
group_html_name = "perf_comparison_" + group_name + ".html"
|
||||
|
||||
metric_name = str(data_cols_to_compare[i]).lower()
|
||||
if "tok/s" in metric_name:
|
||||
html = group.to_html()
|
||||
elif "ttft" in metric_name:
|
||||
styler = _highlight_threshold(group, args.ttft_max_ms).format(
|
||||
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
|
||||
na_rep="—",
|
||||
)
|
||||
html = styler.to_html(
|
||||
table_attributes='border="1" class="dataframe"'
|
||||
)
|
||||
elif (
|
||||
"tpot" in metric_name
|
||||
or "median" in metric_name
|
||||
or "p99" in metric_name
|
||||
):
|
||||
styler = _highlight_threshold(group, args.tpot_max_ms).format(
|
||||
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
|
||||
na_rep="—",
|
||||
)
|
||||
html = styler.to_html(
|
||||
table_attributes='border="1" class="dataframe"'
|
||||
)
|
||||
|
||||
text_file.write(html_msgs_for_data_cols[i])
|
||||
text_file.write(html)
|
||||
with open(group_html_name, "a+") as sub_text_file:
|
||||
sub_text_file.write(html_msgs_for_data_cols[i])
|
||||
sub_text_file.write(html)
|
||||
|
||||
if plot and plotly_found:
|
||||
import plotly.express as px
|
||||
|
||||
df = group[raw_data_cols]
|
||||
df_sorted = df.sort_values(by=info_cols[y_axis_index])
|
||||
# Melt DataFrame for plotting
|
||||
df_melted = df_sorted.melt(
|
||||
id_vars=info_cols[y_axis_index],
|
||||
var_name="Configuration",
|
||||
value_name=data_cols_to_compare[i],
|
||||
)
|
||||
title = (
|
||||
data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
|
||||
)
|
||||
# Create Plotly line chart
|
||||
fig = px.line(
|
||||
df_melted,
|
||||
x=info_cols[y_axis_index],
|
||||
y=data_cols_to_compare[i],
|
||||
color="Configuration",
|
||||
title=title,
|
||||
markers=True,
|
||||
)
|
||||
|
||||
# ---- Add threshold lines based on metric name ----
|
||||
if "ttft" in metric_name:
|
||||
_add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
|
||||
elif (
|
||||
"tpot" in metric_name
|
||||
or "median" in metric_name
|
||||
or "p99" in metric_name
|
||||
):
|
||||
_add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
|
||||
|
||||
# Export to HTML
|
||||
text_file.write(
|
||||
fig.to_html(full_html=True, include_plotlyjs="cdn")
|
||||
)
|
||||
sub_text_file.write(
|
||||
fig.to_html(full_html=True, include_plotlyjs="cdn")
|
||||
)
|
||||
@@ -1,414 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import shlex
|
||||
from importlib import util
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import pandas as pd
|
||||
import psutil
|
||||
import regex as re
|
||||
from tabulate import tabulate
|
||||
|
||||
# latency results and the keys that will be printed into markdown
|
||||
latency_results = []
|
||||
latency_column_mapping = {
|
||||
"test_name": "Test name",
|
||||
"gpu_type": "GPU",
|
||||
"avg_latency": "Mean latency (ms)",
|
||||
# "P10": "P10 (s)",
|
||||
# "P25": "P25 (s)",
|
||||
"P50": "Median latency (ms)",
|
||||
# "P75": "P75 (s)",
|
||||
# "P90": "P90 (s)",
|
||||
"P99": "P99 latency (ms)",
|
||||
}
|
||||
|
||||
# throughput tests and the keys that will be printed into markdown
|
||||
throughput_results = []
|
||||
throughput_results_column_mapping = {
|
||||
"test_name": "Test name",
|
||||
"gpu_type": "GPU",
|
||||
"num_requests": "# of req.",
|
||||
"total_num_tokens": "Total # of tokens",
|
||||
"elapsed_time": "Elapsed time (s)",
|
||||
"requests_per_second": "Tput (req/s)",
|
||||
"tokens_per_second": "Tput (tok/s)",
|
||||
}
|
||||
|
||||
# serving results and the keys that will be printed into markdown
|
||||
serving_results = []
|
||||
serving_column_mapping = {
|
||||
"test_name": "Test name",
|
||||
"model_id": "Model",
|
||||
"dataset_name": "Dataset Name",
|
||||
"input_len": "Input Len",
|
||||
"output_len": "Output Len",
|
||||
"tp_size": "TP Size",
|
||||
"pp_size": "PP Size",
|
||||
"dtype": "dtype",
|
||||
"gpu_type": "GPU",
|
||||
"completed": "# of req.",
|
||||
"qps": "qps",
|
||||
"max_concurrency": "# of max concurrency.",
|
||||
"request_throughput": "Tput (req/s)",
|
||||
"total_token_throughput": "Total Token Tput (tok/s)",
|
||||
"output_throughput": "Output Tput (tok/s)",
|
||||
# "total_input_tokens": "Total input tokens",
|
||||
# "total_output_tokens": "Total output tokens",
|
||||
"mean_ttft_ms": "Mean TTFT (ms)",
|
||||
"median_ttft_ms": "Median TTFT (ms)",
|
||||
"p99_ttft_ms": "P99 TTFT (ms)",
|
||||
"std_ttft_ms": "STD TTFT (ms)",
|
||||
"mean_tpot_ms": "Mean TPOT (ms)",
|
||||
"median_tpot_ms": "Median",
|
||||
"p99_tpot_ms": "P99",
|
||||
"std_tpot_ms": "STD TPOT (ms)",
|
||||
"mean_itl_ms": "Mean ITL (ms)",
|
||||
"median_itl_ms": "Median ITL (ms)",
|
||||
"p99_itl_ms": "P99 ITL (ms)",
|
||||
}
|
||||
|
||||
|
||||
def read_markdown(file):
|
||||
if os.path.exists(file):
|
||||
with open(file) as f:
|
||||
return f.read() + "\n"
|
||||
else:
|
||||
return f"{file} not found.\n"
|
||||
|
||||
|
||||
def results_to_json(latency, throughput, serving):
|
||||
return json.dumps(
|
||||
{
|
||||
"latency": latency.to_dict(),
|
||||
"throughput": throughput.to_dict(),
|
||||
"serving": serving.to_dict(),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def get_size_with_unit(bytes, suffix="B"):
|
||||
"""
|
||||
Scale bytes to its proper format
|
||||
e.g:
|
||||
1253656 => '1.20MB'
|
||||
1253656678 => '1.17GB'
|
||||
"""
|
||||
factor = 1024
|
||||
for unit in ["", "K", "M", "G", "T", "P"]:
|
||||
if bytes < factor:
|
||||
return f"{bytes:.2f}{unit}{suffix}"
|
||||
bytes /= factor
|
||||
|
||||
|
||||
def _coerce(val: str) -> Any:
|
||||
"""Best-effort type coercion from string to Python types."""
|
||||
low = val.lower()
|
||||
if low == "null":
|
||||
return None
|
||||
if low == "true":
|
||||
return True
|
||||
if low == "false":
|
||||
return False
|
||||
# integers
|
||||
if re.fullmatch(r"[+-]?\d+", val):
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
pass
|
||||
# floats (keep 'inf'/'-inf'/'nan' as strings)
|
||||
if re.fullmatch(r"[+-]?\d*\.\d+", val):
|
||||
try:
|
||||
return float(val)
|
||||
except ValueError:
|
||||
pass
|
||||
return val
|
||||
|
||||
|
||||
def parse_client_command(cmd: str) -> dict[str, Any]:
|
||||
"""Parse the client_command shell string into {executable, script, args}."""
|
||||
toks = shlex.split(cmd)
|
||||
if len(toks) < 2:
|
||||
raise ValueError("client_command must include an executable and a script")
|
||||
executable, script = toks[0], toks[1]
|
||||
args: dict[str, Any] = {}
|
||||
|
||||
i = 2
|
||||
while i < len(toks):
|
||||
t = toks[i]
|
||||
if t.startswith("--"):
|
||||
# --key=value or --key (value) or boolean flag
|
||||
if "=" in t:
|
||||
key, val = t.split("=", 1)
|
||||
if key == "--metadata":
|
||||
md = {}
|
||||
if val:
|
||||
if "=" in val:
|
||||
k, v = val.split("=", 1)
|
||||
md[k] = _coerce(v)
|
||||
else:
|
||||
md[val] = True
|
||||
args[key] = md
|
||||
else:
|
||||
args[key] = _coerce(val)
|
||||
i += 1
|
||||
continue
|
||||
|
||||
key = t
|
||||
|
||||
# Special: consume metadata k=v pairs until next --flag
|
||||
if key == "--metadata":
|
||||
i += 1
|
||||
md = {}
|
||||
while i < len(toks) and not toks[i].startswith("--"):
|
||||
pair = toks[i]
|
||||
if "=" in pair:
|
||||
k, v = pair.split("=", 1)
|
||||
md[k] = _coerce(v)
|
||||
else:
|
||||
md[pair] = True
|
||||
i += 1
|
||||
args[key] = md
|
||||
continue
|
||||
|
||||
# Standard: check if next token is a value (not a flag)
|
||||
if i + 1 < len(toks) and not toks[i + 1].startswith("--"):
|
||||
args[key] = _coerce(toks[i + 1])
|
||||
i += 2
|
||||
else:
|
||||
# lone flag -> True
|
||||
args[key] = True
|
||||
i += 1
|
||||
else:
|
||||
# unexpected positional; skip
|
||||
i += 1
|
||||
|
||||
return {"executable": executable, "script": script, "args": args}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-r",
|
||||
"--result",
|
||||
type=str,
|
||||
default="results",
|
||||
help="Folder name for benchmark output results.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
results_folder = Path(args.result)
|
||||
if not results_folder.exists():
|
||||
raise FileNotFoundError(f"results folder does not exist: {results_folder}")
|
||||
# collect results
|
||||
for test_file in results_folder.glob("*.json"):
|
||||
with open(test_file) as f:
|
||||
raw_result = json.loads(f.read())
|
||||
|
||||
if "serving" in str(test_file):
|
||||
# this result is generated via `vllm bench serve` command
|
||||
# attach the benchmarking command to raw_result
|
||||
try:
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
except OSError as e:
|
||||
print(e)
|
||||
continue
|
||||
# Parse Server Command Arg
|
||||
out: dict[str, Any] = {
|
||||
"server_command": parse_client_command(command["server_command"])
|
||||
}
|
||||
parse_args = [
|
||||
"--tensor-parallel-size",
|
||||
"--pipeline-parallel-size",
|
||||
"--dtype",
|
||||
]
|
||||
col_mapping = ["tp_size", "pp_size", "dtype"]
|
||||
for index, arg in enumerate(parse_args):
|
||||
if arg in out["server_command"]["args"]:
|
||||
raw_result.update(
|
||||
{col_mapping[index]: out["server_command"]["args"][arg]}
|
||||
)
|
||||
|
||||
# Parse Client Command Arg
|
||||
out: dict[str, Any] = {
|
||||
"client_command": parse_client_command(command["client_command"])
|
||||
}
|
||||
parse_args = [
|
||||
"--dataset-name",
|
||||
"--random-input-len",
|
||||
"--random-output-len",
|
||||
"--request-rate",
|
||||
]
|
||||
col_mapping = ["dataset_name", "input_len", "output_len", "qps"]
|
||||
|
||||
for index, arg in enumerate(parse_args):
|
||||
if arg in out["client_command"]["args"]:
|
||||
raw_result.update(
|
||||
{col_mapping[index]: out["client_command"]["args"][arg]}
|
||||
)
|
||||
# Add Server, Client command
|
||||
raw_result.update(command)
|
||||
|
||||
# update the test name of this result
|
||||
raw_result.update({"test_name": test_file.stem})
|
||||
# add the result to raw_result
|
||||
serving_results.append(raw_result)
|
||||
continue
|
||||
|
||||
elif "latency" in f.name:
|
||||
# this result is generated via `vllm bench latency` command
|
||||
|
||||
# attach the benchmarking command to raw_result
|
||||
try:
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
except OSError as e:
|
||||
print(e)
|
||||
continue
|
||||
|
||||
raw_result.update(command)
|
||||
|
||||
# update the test name of this result
|
||||
raw_result.update({"test_name": test_file.stem})
|
||||
|
||||
# get different percentiles
|
||||
for perc in [10, 25, 50, 75, 90, 99]:
|
||||
# Multiply 1000 to convert the time unit from s to ms
|
||||
raw_result.update(
|
||||
{f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]}
|
||||
)
|
||||
raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
|
||||
|
||||
# add the result to raw_result
|
||||
latency_results.append(raw_result)
|
||||
continue
|
||||
|
||||
elif "throughput" in f.name:
|
||||
# this result is generated via `vllm bench throughput` command
|
||||
|
||||
# attach the benchmarking command to raw_result
|
||||
try:
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
except OSError as e:
|
||||
print(e)
|
||||
continue
|
||||
|
||||
raw_result.update(command)
|
||||
|
||||
# update the test name of this result
|
||||
raw_result.update({"test_name": test_file.stem})
|
||||
|
||||
# add the result to raw_result
|
||||
throughput_results.append(raw_result)
|
||||
continue
|
||||
|
||||
print(f"Skipping {test_file}")
|
||||
|
||||
latency_results = pd.DataFrame.from_dict(latency_results)
|
||||
serving_results = pd.DataFrame.from_dict(serving_results)
|
||||
throughput_results = pd.DataFrame.from_dict(throughput_results)
|
||||
|
||||
svmem = psutil.virtual_memory()
|
||||
platform_data = {
|
||||
"Physical cores": [psutil.cpu_count(logical=False)],
|
||||
"Total cores": [psutil.cpu_count(logical=True)],
|
||||
"Total Memory": [get_size_with_unit(svmem.total)],
|
||||
}
|
||||
|
||||
if util.find_spec("numa") is not None:
|
||||
from numa import info
|
||||
|
||||
platform_data["Total NUMA nodes"] = [info.get_num_configured_nodes()]
|
||||
|
||||
if util.find_spec("cpuinfo") is not None:
|
||||
from cpuinfo import get_cpu_info
|
||||
|
||||
platform_data["CPU Brand"] = [get_cpu_info()["brand_raw"]]
|
||||
|
||||
platform_results = pd.DataFrame.from_dict(
|
||||
platform_data, orient="index", columns=["Platform Info"]
|
||||
)
|
||||
|
||||
raw_results_json = results_to_json(
|
||||
latency_results, throughput_results, serving_results
|
||||
)
|
||||
|
||||
# remapping the key, for visualization purpose
|
||||
if not latency_results.empty:
|
||||
latency_results = latency_results[list(latency_column_mapping.keys())].rename(
|
||||
columns=latency_column_mapping
|
||||
)
|
||||
if not serving_results.empty:
|
||||
valid_columns = [
|
||||
col for col in serving_column_mapping if col in serving_results.columns
|
||||
]
|
||||
serving_results = serving_results[valid_columns].rename(
|
||||
columns=serving_column_mapping
|
||||
)
|
||||
if not throughput_results.empty:
|
||||
throughput_results = throughput_results[
|
||||
list(throughput_results_column_mapping.keys())
|
||||
].rename(columns=throughput_results_column_mapping)
|
||||
|
||||
processed_results_json = results_to_json(
|
||||
latency_results, throughput_results, serving_results
|
||||
)
|
||||
|
||||
for df in [latency_results, serving_results, throughput_results]:
|
||||
if df.empty:
|
||||
continue
|
||||
|
||||
# Sort all dataframes by their respective "Test name" columns
|
||||
df.sort_values(by="Test name", inplace=True)
|
||||
|
||||
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
|
||||
# we want to turn it into "8xGPUTYPE"
|
||||
df["GPU"] = df["GPU"].apply(
|
||||
lambda x: "{}x{}".format(len(x.split("\n")), x.split("\n")[0])
|
||||
)
|
||||
|
||||
# get markdown tables
|
||||
latency_md_table = tabulate(
|
||||
latency_results, headers="keys", tablefmt="pipe", showindex=False
|
||||
)
|
||||
serving_md_table = tabulate(
|
||||
serving_results, headers="keys", tablefmt="pipe", showindex=False
|
||||
)
|
||||
throughput_md_table = tabulate(
|
||||
throughput_results, headers="keys", tablefmt="pipe", showindex=False
|
||||
)
|
||||
platform_md_table = tabulate(
|
||||
platform_results, headers="keys", tablefmt="pipe", showindex=True
|
||||
)
|
||||
|
||||
# document the result
|
||||
md_file = "benchmark_results.md"
|
||||
json_file = "benchmark_results.json"
|
||||
with open(results_folder / md_file, "w") as f:
|
||||
results = read_markdown(
|
||||
"../.buildkite/performance-benchmarks/"
|
||||
+ "performance-benchmarks-descriptions.md"
|
||||
)
|
||||
results = results.format(
|
||||
latency_tests_markdown_table=latency_md_table,
|
||||
throughput_tests_markdown_table=throughput_md_table,
|
||||
serving_tests_markdown_table=serving_md_table,
|
||||
platform_markdown_table=platform_md_table,
|
||||
benchmarking_results_in_json_string=processed_results_json,
|
||||
)
|
||||
f.write(results)
|
||||
|
||||
# document benchmarking results in json
|
||||
with open(results_folder / json_file, "w") as f:
|
||||
results = (
|
||||
latency_results.to_dict(orient="records")
|
||||
+ throughput_results.to_dict(orient="records")
|
||||
+ serving_results.to_dict(orient="records")
|
||||
)
|
||||
f.write(json.dumps(results))
|
||||
@@ -1,224 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Currently FP8 benchmark is NOT enabled.
|
||||
|
||||
set -x
|
||||
server_params=$1
|
||||
common_params=$2
|
||||
|
||||
json2args() {
|
||||
# transforms the JSON string to command line args, and '_' is replaced to '-'
|
||||
# example:
|
||||
# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
|
||||
# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
|
||||
local json_string=$1
|
||||
local args=$(
|
||||
echo "$json_string" | jq -r '
|
||||
to_entries |
|
||||
map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
|
||||
join(" ")
|
||||
'
|
||||
)
|
||||
echo "$args"
|
||||
}
|
||||
|
||||
launch_trt_server() {
|
||||
|
||||
model_path=$(echo "$common_params" | jq -r '.model')
|
||||
model_name="${model_path#*/}"
|
||||
model_type=$(echo "$server_params" | jq -r '.model_type')
|
||||
model_dtype=$(echo "$server_params" | jq -r '.model_dtype')
|
||||
model_tp_size=$(echo "$common_params" | jq -r '.tp')
|
||||
max_batch_size=$(echo "$server_params" | jq -r '.max_batch_size')
|
||||
max_input_len=$(echo "$server_params" | jq -r '.max_input_len')
|
||||
max_seq_len=$(echo "$server_params" | jq -r '.max_seq_len')
|
||||
max_num_tokens=$(echo "$server_params" | jq -r '.max_num_tokens')
|
||||
trt_llm_version=$(echo "$server_params" | jq -r '.trt_llm_version')
|
||||
|
||||
# create model caching directory
|
||||
cd ~
|
||||
rm -rf models
|
||||
mkdir -p models
|
||||
cd models
|
||||
models_dir=$(pwd)
|
||||
trt_model_path=${models_dir}/${model_name}-trt-ckpt
|
||||
trt_engine_path=${models_dir}/${model_name}-trt-engine
|
||||
|
||||
# clone tensorrt backend
|
||||
cd /
|
||||
rm -rf tensorrtllm_backend
|
||||
git clone https://github.com/triton-inference-server/tensorrtllm_backend.git
|
||||
git lfs install
|
||||
cd tensorrtllm_backend
|
||||
git checkout "$trt_llm_version"
|
||||
git submodule update --init --recursive
|
||||
|
||||
# build trtllm engine
|
||||
cd /tensorrtllm_backend
|
||||
cd "./tensorrt_llm/examples/${model_type}"
|
||||
python3 convert_checkpoint.py \
|
||||
--model_dir "${model_path}" \
|
||||
--dtype "${model_dtype}" \
|
||||
--tp_size "${model_tp_size}" \
|
||||
--output_dir "${trt_model_path}"
|
||||
trtllm-build \
|
||||
--checkpoint_dir "${trt_model_path}" \
|
||||
--use_fused_mlp \
|
||||
--reduce_fusion disable \
|
||||
--workers 8 \
|
||||
--gpt_attention_plugin "${model_dtype}" \
|
||||
--gemm_plugin "${model_dtype}" \
|
||||
--tp_size "${model_tp_size}" \
|
||||
--max_batch_size "${max_batch_size}" \
|
||||
--max_input_len "${max_input_len}" \
|
||||
--max_seq_len "${max_seq_len}" \
|
||||
--max_num_tokens "${max_num_tokens}" \
|
||||
--output_dir "${trt_engine_path}"
|
||||
|
||||
# handle triton protobuf files and launch triton server
|
||||
cd /tensorrtllm_backend
|
||||
mkdir triton_model_repo
|
||||
cp -r all_models/inflight_batcher_llm/* triton_model_repo/
|
||||
cd triton_model_repo
|
||||
rm -rf ./tensorrt_llm/1/*
|
||||
cp -r "${trt_engine_path}"/* ./tensorrt_llm/1
|
||||
python3 ../tools/fill_template.py -i tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,engine_dir:/tensorrtllm_backend/triton_model_repo/tensorrt_llm/1,decoupled_mode:true,batching_strategy:inflight_fused_batching,batch_scheduler_policy:guaranteed_no_evict,exclude_input_in_output:true,triton_max_batch_size:2048,max_queue_delay_microseconds:0,max_beam_width:1,max_queue_size:2048,enable_kv_cache_reuse:false
|
||||
python3 ../tools/fill_template.py -i preprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,preprocessing_instance_count:5"
|
||||
python3 ../tools/fill_template.py -i postprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,postprocessing_instance_count:5,skip_special_tokens:false"
|
||||
python3 ../tools/fill_template.py -i ensemble/config.pbtxt triton_max_batch_size:"$max_batch_size"
|
||||
python3 ../tools/fill_template.py -i tensorrt_llm_bls/config.pbtxt "triton_max_batch_size:$max_batch_size,decoupled_mode:true,accumulate_tokens:False,bls_instance_count:1"
|
||||
cd /tensorrtllm_backend
|
||||
python3 scripts/launch_triton_server.py \
|
||||
--world_size="${model_tp_size}" \
|
||||
--model_repo=/tensorrtllm_backend/triton_model_repo &
|
||||
|
||||
}
|
||||
|
||||
launch_tgi_server() {
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
|
||||
echo "Key 'fp8' exists in common params."
|
||||
server_command="/tgi-entrypoint.sh \
|
||||
--model-id $model \
|
||||
--num-shard $tp \
|
||||
--port $port \
|
||||
--quantize fp8 \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="/tgi-entrypoint.sh \
|
||||
--model-id $model \
|
||||
--num-shard $tp \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
|
||||
}
|
||||
|
||||
launch_lmdeploy_server() {
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
server_command="lmdeploy serve api_server $model \
|
||||
--tp $tp \
|
||||
--server-port $port \
|
||||
$server_args"
|
||||
|
||||
# run the server
|
||||
echo "Server command: $server_command"
|
||||
bash -c "$server_command" &
|
||||
}
|
||||
|
||||
launch_sglang_server() {
|
||||
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
|
||||
echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
|
||||
model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
|
||||
server_command="python3 \
|
||||
-m sglang.launch_server \
|
||||
--tp $tp \
|
||||
--model-path $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="python3 \
|
||||
-m sglang.launch_server \
|
||||
--tp $tp \
|
||||
--model-path $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
# run the server
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
}
|
||||
|
||||
launch_vllm_server() {
|
||||
|
||||
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
||||
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
|
||||
echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
|
||||
model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
|
||||
server_command="vllm serve $model \
|
||||
-tp $tp \
|
||||
--port $port \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="vllm serve $model \
|
||||
-tp $tp \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
# run the server
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
}
|
||||
|
||||
main() {
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "trt" ]]; then
|
||||
launch_trt_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "tgi" ]]; then
|
||||
launch_tgi_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
|
||||
launch_lmdeploy_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "sglang" ]]; then
|
||||
launch_sglang_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == *"vllm"* ]]; then
|
||||
launch_vllm_server
|
||||
fi
|
||||
}
|
||||
|
||||
main
|
||||
@@ -1,491 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script should be run inside the CI process
|
||||
# This script assumes that we are already inside the vllm/ directory
|
||||
# Benchmarking results will be available inside vllm/benchmarks/results/
|
||||
|
||||
# Do not set -e, as the mixtral 8x22B model tends to crash occasionally
|
||||
# and we still want to see other benchmarking results even when mixtral crashes.
|
||||
set -x
|
||||
set -o pipefail
|
||||
|
||||
check_gpus() {
|
||||
if command -v nvidia-smi; then
|
||||
# check the number of GPUs and GPU type.
|
||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
||||
elif command -v amd-smi; then
|
||||
declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
|
||||
fi
|
||||
|
||||
if [[ $gpu_count -gt 0 ]]; then
|
||||
echo "GPU found."
|
||||
else
|
||||
echo "Need at least 1 GPU to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
if command -v nvidia-smi; then
|
||||
declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
|
||||
elif command -v amd-smi; then
|
||||
declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
|
||||
fi
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
check_cpus() {
|
||||
# check the number of CPUs and NUMA Node and GPU type.
|
||||
declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
|
||||
if [[ $numa_count -gt 0 ]]; then
|
||||
echo "NUMA found."
|
||||
echo $numa_count
|
||||
else
|
||||
echo "Need at least 1 NUMA to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
declare -g gpu_type="cpu"
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
check_hf_token() {
|
||||
# check if HF_TOKEN is available and valid
|
||||
if [[ -z "$HF_TOKEN" ]]; then
|
||||
echo "Error: HF_TOKEN is not set."
|
||||
exit 1
|
||||
elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
|
||||
echo "Error: HF_TOKEN does not start with 'hf_'."
|
||||
exit 1
|
||||
else
|
||||
echo "HF_TOKEN is set and valid."
|
||||
fi
|
||||
}
|
||||
|
||||
ensure_sharegpt_downloaded() {
|
||||
local FILE=ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
if [ ! -f "$FILE" ]; then
|
||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/$FILE
|
||||
else
|
||||
echo "$FILE already exists."
|
||||
fi
|
||||
}
|
||||
|
||||
json2args() {
|
||||
# transforms the JSON string to command line args, and '_' is replaced to '-'
|
||||
# example:
|
||||
# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
|
||||
# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
|
||||
local json_string=$1
|
||||
local args=$(
|
||||
echo "$json_string" | jq -r '
|
||||
to_entries |
|
||||
map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
|
||||
join(" ")
|
||||
'
|
||||
)
|
||||
echo "$args"
|
||||
}
|
||||
|
||||
json2envs() {
|
||||
# transforms the JSON string to environment variables.
|
||||
# example:
|
||||
# input: { "VLLM_CPU_KVCACHE_SPACE": 5 }
|
||||
# output: VLLM_CPU_KVCACHE_SPACE=5
|
||||
local json_string=$1
|
||||
local args=$(
|
||||
echo "$json_string" | jq -r '
|
||||
to_entries |
|
||||
map((.key ) + "=" + (.value | tostring)) |
|
||||
join(" ")
|
||||
'
|
||||
)
|
||||
echo "$args"
|
||||
}
|
||||
|
||||
wait_for_server() {
|
||||
# wait for vllm server to start
|
||||
# return 1 if vllm server crashes
|
||||
timeout 1200 bash -c '
|
||||
until curl -X POST localhost:8000/v1/completions; do
|
||||
sleep 1
|
||||
done' && return 0 || return 1
|
||||
}
|
||||
|
||||
kill_processes_launched_by_current_bash() {
|
||||
# Kill all python processes launched from current bash script
|
||||
current_shell_pid=$$
|
||||
processes=$(ps -eo pid,ppid,command | awk -v ppid="$current_shell_pid" -v proc="$1" '$2 == ppid && $3 ~ proc {print $1}')
|
||||
if [ -n "$processes" ]; then
|
||||
echo "Killing the following processes matching '$1':"
|
||||
echo "$processes"
|
||||
echo "$processes" | xargs kill -9
|
||||
else
|
||||
echo "No processes found matching '$1'."
|
||||
fi
|
||||
}
|
||||
|
||||
kill_gpu_processes() {
|
||||
|
||||
ps -aux
|
||||
lsof -t -i:8000 | xargs -r kill -9
|
||||
pgrep python3 | xargs -r kill -9
|
||||
# vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
|
||||
pgrep VLLM | xargs -r kill -9
|
||||
|
||||
# wait until GPU memory usage smaller than 1GB
|
||||
if command -v nvidia-smi; then
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
elif command -v amd-smi; then
|
||||
while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
fi
|
||||
|
||||
# remove vllm config file
|
||||
rm -rf ~/.config/vllm
|
||||
|
||||
}
|
||||
|
||||
upload_to_buildkite() {
|
||||
# upload the benchmarking results to buildkite
|
||||
|
||||
# if the agent binary is not found, skip uploading the results, exit 0
|
||||
# Check if buildkite-agent is available in the PATH or at /workspace/buildkite-agent
|
||||
if command -v buildkite-agent >/dev/null 2>&1; then
|
||||
BUILDKITE_AGENT_COMMAND="buildkite-agent"
|
||||
elif [ -f /workspace/buildkite-agent ]; then
|
||||
BUILDKITE_AGENT_COMMAND="/workspace/buildkite-agent"
|
||||
else
|
||||
echo "buildkite-agent binary not found. Skip uploading the results."
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Use the determined command to annotate and upload artifacts
|
||||
$BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" < "$RESULTS_FOLDER/benchmark_results.md"
|
||||
$BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
|
||||
}
|
||||
|
||||
run_latency_tests() {
|
||||
# run latency tests using `vllm bench latency` command
|
||||
# $1: a json file specifying latency test cases
|
||||
|
||||
local latency_test_file
|
||||
latency_test_file=$1
|
||||
|
||||
# Iterate over latency tests
|
||||
jq -c '.[]' "$latency_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^latency_ ]]; then
|
||||
echo "In latency-test.json, test_name must start with \"latency_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get arguments
|
||||
latency_params=$(echo "$params" | jq -r '.parameters')
|
||||
latency_args=$(json2args "$latency_params")
|
||||
latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
|
||||
latency_envs=$(json2envs "$latency_environment_variables")
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
|
||||
if [ "$ON_CPU" == "1" ]; then
|
||||
pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
|
||||
world_size=$(($tp*$pp))
|
||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
else
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
fi
|
||||
|
||||
latency_command=" $latency_envs vllm bench latency \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$latency_args"
|
||||
|
||||
echo "Running test case $test_name"
|
||||
echo "Latency command: $latency_command"
|
||||
|
||||
# recoding benchmarking command ang GPU command
|
||||
jq_output=$(jq -n \
|
||||
--arg latency "$latency_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
latency_command: $latency,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
|
||||
|
||||
# run the benchmark
|
||||
eval "$latency_command"
|
||||
|
||||
kill_gpu_processes
|
||||
|
||||
done
|
||||
}
|
||||
|
||||
run_throughput_tests() {
|
||||
# run throughput tests using `vllm bench throughput`
|
||||
# $1: a json file specifying throughput test cases
|
||||
|
||||
local throughput_test_file
|
||||
throughput_test_file=$1
|
||||
|
||||
# Iterate over throughput tests
|
||||
jq -c '.[]' "$throughput_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^throughput_ ]]; then
|
||||
echo "In throughput-test.json, test_name must start with \"throughput_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get arguments
|
||||
throughput_params=$(echo "$params" | jq -r '.parameters')
|
||||
throughput_args=$(json2args "$throughput_params")
|
||||
throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
|
||||
throughput_envs=$(json2envs "$throughput_environment_variables")
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
|
||||
if [ "$ON_CPU" == "1" ]; then
|
||||
pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
|
||||
world_size=$(($tp*$pp))
|
||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
else
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
fi
|
||||
|
||||
throughput_command=" $throughput_envs vllm bench throughput \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$throughput_args"
|
||||
|
||||
echo "Running test case $test_name"
|
||||
echo "Throughput command: $throughput_command"
|
||||
# recoding benchmarking command ang GPU command
|
||||
jq_output=$(jq -n \
|
||||
--arg command "$throughput_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
throughput_command: $command,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
|
||||
|
||||
# run the benchmark
|
||||
eval "$throughput_command"
|
||||
|
||||
kill_gpu_processes
|
||||
|
||||
done
|
||||
}
|
||||
|
||||
run_serving_tests() {
|
||||
# run serving tests using `vllm bench serve` command
|
||||
# $1: a json file specifying serving test cases
|
||||
|
||||
local serving_test_file
|
||||
serving_test_file=$1
|
||||
|
||||
# Iterate over serving tests
|
||||
jq -c '.[]' "$serving_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^serving_ ]]; then
|
||||
echo "In serving-test.json, test_name must start with \"serving_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get client and server arguments
|
||||
server_params=$(echo "$params" | jq -r '.server_parameters')
|
||||
server_envs=$(echo "$params" | jq -r '.server_environment_variables')
|
||||
client_params=$(echo "$params" | jq -r '.client_parameters')
|
||||
server_args=$(json2args "$server_params")
|
||||
server_envs=$(json2envs "$server_envs")
|
||||
client_args=$(json2args "$client_params")
|
||||
qps_list=$(echo "$params" | jq -r '.qps_list')
|
||||
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
||||
echo "Running over qps list $qps_list"
|
||||
max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
|
||||
if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
|
||||
num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
|
||||
max_concurrency_list="[$num_prompts]"
|
||||
fi
|
||||
max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
|
||||
echo "Running over max concurrency list $max_concurrency_list"
|
||||
|
||||
# check if there is enough resources to run the test
|
||||
tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
|
||||
if [ "$ON_CPU" == "1" ]; then
|
||||
pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
|
||||
world_size=$(($tp*$pp))
|
||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
else
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
fi
|
||||
|
||||
# check if server model and client model is aligned
|
||||
server_model=$(echo "$server_params" | jq -r '.model')
|
||||
client_model=$(echo "$client_params" | jq -r '.model')
|
||||
if [[ $server_model != "$client_model" ]]; then
|
||||
echo "Server model and client model must be the same. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
server_command="$server_envs vllm serve \
|
||||
$server_args"
|
||||
|
||||
# run the server
|
||||
echo "Running test case $test_name"
|
||||
echo "Server command: $server_command"
|
||||
# support remote vllm server
|
||||
client_remote_args=""
|
||||
if [[ -z "${REMOTE_HOST}" ]]; then
|
||||
bash -c "$server_command" &
|
||||
server_pid=$!
|
||||
# wait until the server is alive
|
||||
if wait_for_server; then
|
||||
echo ""
|
||||
echo "vLLM server is up and running."
|
||||
else
|
||||
echo ""
|
||||
echo "vLLM failed to start within the timeout period."
|
||||
fi
|
||||
else
|
||||
server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
|
||||
if [[ ${REMOTE_PORT} ]]; then
|
||||
client_remote_args=" --host=$REMOTE_HOST --port=$REMOTE_PORT "
|
||||
else
|
||||
client_remote_args=" --host=$REMOTE_HOST "
|
||||
fi
|
||||
fi
|
||||
|
||||
# iterate over different QPS
|
||||
for qps in $qps_list; do
|
||||
# remove the surrounding single quote from qps
|
||||
if [[ "$qps" == *"inf"* ]]; then
|
||||
echo "qps was $qps"
|
||||
qps="inf"
|
||||
echo "now qps is $qps"
|
||||
fi
|
||||
|
||||
# iterate over different max_concurrency
|
||||
for max_concurrency in $max_concurrency_list; do
|
||||
new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
|
||||
echo " new test name $new_test_name"
|
||||
# pass the tensor parallel size to the client so that it can be displayed
|
||||
# on the benchmark dashboard
|
||||
client_command="vllm bench serve \
|
||||
--save-result \
|
||||
--result-dir $RESULTS_FOLDER \
|
||||
--result-filename ${new_test_name}.json \
|
||||
--request-rate $qps \
|
||||
--max-concurrency $max_concurrency \
|
||||
--metadata "tensor_parallel_size=$tp" \
|
||||
$client_args $client_remote_args "
|
||||
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
|
||||
bash -c "$client_command"
|
||||
|
||||
# record the benchmarking commands
|
||||
jq_output=$(jq -n \
|
||||
--arg server "$server_command" \
|
||||
--arg client "$client_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
server_command: $server,
|
||||
client_command: $client,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
|
||||
|
||||
done
|
||||
done
|
||||
|
||||
# clean up
|
||||
kill -9 $server_pid
|
||||
kill_gpu_processes
|
||||
done
|
||||
}
|
||||
|
||||
main() {
|
||||
local ARCH
|
||||
ARCH=''
|
||||
if [ "$ON_CPU" == "1" ];then
|
||||
check_cpus
|
||||
ARCH='-cpu'
|
||||
else
|
||||
check_gpus
|
||||
fi
|
||||
check_hf_token
|
||||
|
||||
# dependencies
|
||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||
(which jq) || (apt-get update && apt-get -y install jq)
|
||||
(which lsof) || (apt-get update && apt-get install -y lsof)
|
||||
|
||||
# get the current IP address, required by `vllm bench serve` command
|
||||
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
||||
# turn of the reporting of the status of each request, to clean up the terminal output
|
||||
export VLLM_LOGGING_LEVEL="WARNING"
|
||||
|
||||
# prepare for benchmarking
|
||||
cd benchmarks || exit 1
|
||||
ensure_sharegpt_downloaded
|
||||
declare -g RESULTS_FOLDER=results/
|
||||
mkdir -p $RESULTS_FOLDER
|
||||
QUICK_BENCHMARK_ROOT=../.buildkite/performance-benchmarks/
|
||||
|
||||
# dump vllm info via vllm collect-env
|
||||
env_output=$(vllm collect-env)
|
||||
|
||||
echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"
|
||||
|
||||
# benchmarking
|
||||
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
|
||||
run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
|
||||
run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
|
||||
|
||||
# postprocess benchmarking results
|
||||
pip install tabulate pandas
|
||||
python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
|
||||
|
||||
upload_to_buildkite
|
||||
}
|
||||
|
||||
main "$@"
|
||||
@@ -1,21 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "llama8B_tp1_genai_perf",
|
||||
"qps_list": [4,8,16,32],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
|
||||
"tp": 1,
|
||||
"port": 8000,
|
||||
"num_prompts": 500,
|
||||
"reuse_server": false
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"genai_perf_input_parameters": {
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,26 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "latency_llama8B_tp2",
|
||||
"environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"num_iters_warmup": 5,
|
||||
"num_iters": 15
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,32 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "latency_llama8B_tp1",
|
||||
"parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"load_format": "dummy",
|
||||
"num_iters_warmup": 5,
|
||||
"num_iters": 15
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "latency_llama70B_tp4",
|
||||
"parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"load_format": "dummy",
|
||||
"num-iters-warmup": 5,
|
||||
"num-iters": 15
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "latency_mixtral8x7B_tp2",
|
||||
"parameters": {
|
||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
"tensor_parallel_size": 2,
|
||||
"load_format": "dummy",
|
||||
"num-iters-warmup": 5,
|
||||
"num-iters": 15
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,311 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "llama8B_tp1_sharegpt",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
|
||||
"tp": 1,
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"reuse_server": false
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"enable_torch_compile": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "llama8B_tp1_sonnet_512_16",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
|
||||
"tp": 1,
|
||||
"dataset_name": "sonnet",
|
||||
"dataset_path": "./sonnet_4x.txt",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"sonnet_input_len": 512,
|
||||
"sonnet_output_len": 16,
|
||||
"sonnet_prefix_len": 50,
|
||||
"reuse_server": true
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"enable_torch_compile": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "llama8B_tp1_sonnet_512_256",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
|
||||
"tp": 1,
|
||||
"dataset_name": "sonnet",
|
||||
"dataset_path": "./sonnet_4x.txt",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"sonnet_input_len": 512,
|
||||
"sonnet_output_len": 256,
|
||||
"sonnet_prefix_len": 50,
|
||||
"reuse_server": true
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"enable_torch_compile": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "llama70B_tp4_sharegpt",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-70B-Instruct",
|
||||
"tp": 4,
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"reuse_server": false
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "llama70B_tp4_sonnet_512_16",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-70B-Instruct",
|
||||
"tp": 4,
|
||||
"dataset_name": "sonnet",
|
||||
"dataset_path": "./sonnet_4x.txt",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"sonnet_input_len": 512,
|
||||
"sonnet_output_len": 16,
|
||||
"sonnet_prefix_len": 50,
|
||||
"reuse_server": true
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "llama70B_tp4_sonnet_512_256",
|
||||
"qps_list": [4,8,16,32,"inf"],
|
||||
"common_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3-70B-Instruct",
|
||||
"tp": 4,
|
||||
"dataset_name": "sonnet",
|
||||
"dataset_path": "./sonnet_4x.txt",
|
||||
"num_prompts": 500,
|
||||
"port": 8000,
|
||||
"sonnet_input_len": 512,
|
||||
"sonnet_output_len": 256,
|
||||
"sonnet_prefix_len": 50,
|
||||
"reuse_server": true
|
||||
},
|
||||
"lmdeploy_server_parameters": {
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"lmdeploy_client_parameters": {
|
||||
},
|
||||
"tgi_server_parameters": {
|
||||
},
|
||||
"tgi_client_parameters": {
|
||||
"endpoint": "/generate_stream"
|
||||
},
|
||||
"trt_server_parameters": {
|
||||
"model_type": "llama",
|
||||
"model_dtype": "bfloat16",
|
||||
"max_batch_size": 2048,
|
||||
"max_input_len": 4096,
|
||||
"max_seq_len": 6144,
|
||||
"max_num_tokens": 16384,
|
||||
"trt_llm_version": "v0.11.0"
|
||||
},
|
||||
"trt_client_parameters": {
|
||||
"endpoint": "/v2/models/ensemble/generate_stream"
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"vllm_client_parameters": {
|
||||
},
|
||||
"sglang_server_parameters": {
|
||||
"disable_radix_cache": "",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"sglang_client_parameters": {
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,610 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp1_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp2_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp4_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp1_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp2_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp4_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp1_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp2_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp4_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp1_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp2_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int8_tp4_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp1_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp2_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp4_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp1_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp2_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp4_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
}
|
||||
]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,276 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp2_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_random_128_128",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp2_random_128_128",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_random_128_2048",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 2048,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp2_random_128_2048",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 2048,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_random_2048_128",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 2048,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp2_random_2048_128",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [32],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 2048,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 32
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,77 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama70B_tp4_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_mixtral8x7B_tp2_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"server_parameters": {
|
||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
"tensor_parallel_size": 2,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama70B_tp4_sharegpt_specdecode",
|
||||
"qps_list": [2],
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"swap_space": 16,
|
||||
"speculative_config": {
|
||||
"model": "turboderp/Qwama-0.5B-Instruct",
|
||||
"num_speculative_tokens": 4,
|
||||
"draft_tensor_parallel_size": 1
|
||||
}
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,27 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "throughput_llama8B_tp2",
|
||||
"environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200,
|
||||
"backend": "vllm"
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,35 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "throughput_llama8B_tp1",
|
||||
"parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"load_format": "dummy",
|
||||
"dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200,
|
||||
"backend": "vllm"
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "throughput_llama70B_tp4",
|
||||
"parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"load_format": "dummy",
|
||||
"dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200,
|
||||
"backend": "vllm"
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "throughput_mixtral8x7B_tp2",
|
||||
"parameters": {
|
||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
"tensor_parallel_size": 2,
|
||||
"load_format": "dummy",
|
||||
"dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200,
|
||||
"backend": "vllm"
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -1,201 +0,0 @@
|
||||
steps:
|
||||
# aarch64 + CUDA builds
|
||||
- label: "Build arm64 wheel - CUDA 12.9"
|
||||
depends_on: ~
|
||||
id: build-wheel-arm64-cuda-12-9
|
||||
agents:
|
||||
queue: arm64_cpu_queue_postmerge
|
||||
commands:
|
||||
# #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
|
||||
# https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg VLLM_MAIN_CUDA_VERSION=12.9 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||
- "mkdir artifacts"
|
||||
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
# aarch64 build
|
||||
- label: "Build arm64 CPU wheel"
|
||||
depends_on: ~
|
||||
id: build-wheel-arm64-cpu
|
||||
agents:
|
||||
queue: arm64_cpu_queue_postmerge
|
||||
commands:
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
|
||||
- "mkdir artifacts"
|
||||
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
# x86 + CUDA builds
|
||||
- label: "Build wheel - CUDA 12.8"
|
||||
depends_on: ~
|
||||
id: build-wheel-cuda-12-8
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||
- "mkdir artifacts"
|
||||
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- label: "Build wheel - CUDA 12.9"
|
||||
depends_on: ~
|
||||
id: build-wheel-cuda-12-9
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||
- "mkdir artifacts"
|
||||
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- label: "Build wheel - CUDA 13.0"
|
||||
depends_on: ~
|
||||
id: build-wheel-cuda-13-0
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||
- "mkdir artifacts"
|
||||
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
# Build release images (12.9)
|
||||
- label: "Build release image (x86)"
|
||||
depends_on: ~
|
||||
id: build-release-image-x86
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
|
||||
# re-tag to default image tag and push, just in case arm64 build fails
|
||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
||||
|
||||
- label: "Build release image (arm64)"
|
||||
depends_on: ~
|
||||
id: build-release-image-arm64
|
||||
agents:
|
||||
queue: arm64_cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
|
||||
|
||||
# Add job to create multi-arch manifest
|
||||
- label: "Create multi-arch manifest"
|
||||
depends_on:
|
||||
- build-release-image-x86
|
||||
- build-release-image-arm64
|
||||
id: create-multi-arch-manifest
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
|
||||
- "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
||||
|
||||
- label: "Annotate release workflow"
|
||||
depends_on:
|
||||
- create-multi-arch-manifest
|
||||
- build-wheel-cuda-12-8
|
||||
id: annotate-release-workflow
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "bash .buildkite/scripts/annotate-release.sh"
|
||||
|
||||
- label: "Build and publish TPU release image"
|
||||
depends_on: ~
|
||||
if: build.env("NIGHTLY") == "1"
|
||||
agents:
|
||||
queue: tpu_queue_postmerge
|
||||
commands:
|
||||
- "yes | docker system prune -a"
|
||||
- "git fetch --all"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
|
||||
- "docker push vllm/vllm-tpu:nightly"
|
||||
- "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
|
||||
plugins:
|
||||
- docker-login#v3.0.0:
|
||||
username: vllmbot
|
||||
password-env: DOCKERHUB_TOKEN
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- input: "Provide Release version here"
|
||||
id: input-release-version
|
||||
fields:
|
||||
- text: "What is the release version?"
|
||||
key: release-version
|
||||
|
||||
- block: "Build CPU release image"
|
||||
key: block-cpu-release-image-build
|
||||
depends_on: ~
|
||||
|
||||
- label: "Build and publish CPU release image"
|
||||
depends_on: block-cpu-release-image-build
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- block: "Build arm64 CPU release image"
|
||||
key: block-arm64-cpu-release-image-build
|
||||
depends_on: ~
|
||||
|
||||
- label: "Build and publish arm64 CPU release image"
|
||||
depends_on: block-arm64-cpu-release-image-build
|
||||
agents:
|
||||
queue: arm64_cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- label: "Build and publish nightly multi-arch image to DockerHub"
|
||||
depends_on:
|
||||
- create-multi-arch-manifest
|
||||
if: build.env("NIGHTLY") == "1"
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64"
|
||||
- "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64"
|
||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 vllm/vllm-openai:nightly-x86_64"
|
||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 vllm/vllm-openai:nightly-aarch64"
|
||||
- "docker push vllm/vllm-openai:nightly-x86_64"
|
||||
- "docker push vllm/vllm-openai:nightly-aarch64"
|
||||
- "docker manifest create vllm/vllm-openai:nightly vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
|
||||
- "docker manifest create vllm/vllm-openai:nightly-$BUILDKITE_COMMIT vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
|
||||
- "docker manifest push vllm/vllm-openai:nightly"
|
||||
- "docker manifest push vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
|
||||
# Clean up old nightly builds (keep only last 14)
|
||||
- "bash .buildkite/scripts/cleanup-nightly-builds.sh"
|
||||
plugins:
|
||||
- docker-login#v3.0.0:
|
||||
username: vllmbot
|
||||
password-env: DOCKERHUB_TOKEN
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
DOCKERHUB_USERNAME: "vllmbot"
|
||||
@@ -1,46 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -ex
|
||||
|
||||
# Get release version and strip leading 'v' if present
|
||||
RELEASE_VERSION=$(buildkite-agent meta-data get release-version | sed 's/^v//')
|
||||
|
||||
if [ -z "$RELEASE_VERSION" ]; then
|
||||
echo "Error: RELEASE_VERSION is empty. 'release-version' metadata might not be set or is invalid."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
|
||||
To download the wheel:
|
||||
\`\`\`
|
||||
aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
|
||||
aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
|
||||
|
||||
aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu126/vllm-${RELEASE_VERSION}+cu126-cp38-abi3-manylinux1_x86_64.whl .
|
||||
aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu129/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
|
||||
\`\`\`
|
||||
|
||||
To download and upload the image:
|
||||
|
||||
\`\`\`
|
||||
docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
|
||||
docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
|
||||
|
||||
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
|
||||
docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
|
||||
docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
|
||||
docker push vllm/vllm-openai:latest-x86_64
|
||||
docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
|
||||
|
||||
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
|
||||
docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
|
||||
docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
|
||||
docker push vllm/vllm-openai:latest-aarch64
|
||||
docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
|
||||
|
||||
docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64 --amend
|
||||
docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64 --amend
|
||||
docker manifest push vllm/vllm-openai:latest
|
||||
docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
|
||||
\`\`\`
|
||||
EOF
|
||||
@@ -1,17 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Usage: ./ci_clean_log.sh ci.log
|
||||
# This script strips timestamps and color codes from CI log files.
|
||||
|
||||
# Check if argument is given
|
||||
if [ $# -lt 1 ]; then
|
||||
echo "Usage: $0 ci.log"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
INPUT_FILE="$1"
|
||||
|
||||
# Strip timestamps
|
||||
sed -i 's/^\[[0-9]\{4\}-[0-9]\{2\}-[0-9]\{2\}T[0-9]\{2\}:[0-9]\{2\}:[0-9]\{2\}Z\] //' "$INPUT_FILE"
|
||||
|
||||
# Strip colorization
|
||||
sed -i -r 's/\x1B\[[0-9;]*[mK]//g' "$INPUT_FILE"
|
||||
@@ -1,120 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -ex
|
||||
|
||||
# Clean up old nightly builds from DockerHub, keeping only the last 14 builds
|
||||
# This script uses DockerHub API to list and delete old tags with "nightly-" prefix
|
||||
|
||||
# DockerHub API endpoint for vllm/vllm-openai repository
|
||||
REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
|
||||
|
||||
# Get DockerHub credentials from environment
|
||||
if [ -z "$DOCKERHUB_TOKEN" ]; then
|
||||
echo "Error: DOCKERHUB_TOKEN environment variable is not set"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ -z "$DOCKERHUB_USERNAME" ]; then
|
||||
echo "Error: DOCKERHUB_USERNAME environment variable is not set"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Get DockerHub bearer token
|
||||
echo "Getting DockerHub bearer token..."
|
||||
set +x
|
||||
BEARER_TOKEN=$(curl -s -X POST \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \
|
||||
"https://hub.docker.com/v2/users/login" | jq -r '.token')
|
||||
set -x
|
||||
|
||||
if [ -z "$BEARER_TOKEN" ] || [ "$BEARER_TOKEN" = "null" ]; then
|
||||
echo "Error: Failed to get DockerHub bearer token"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Function to get all tags from DockerHub
|
||||
get_all_tags() {
|
||||
local page=1
|
||||
local all_tags=""
|
||||
|
||||
while true; do
|
||||
set +x
|
||||
local response=$(curl -s -H "Authorization: Bearer $BEARER_TOKEN" \
|
||||
"$REPO_API_URL?page=$page&page_size=100")
|
||||
set -x
|
||||
|
||||
# Get both last_updated timestamp and tag name, separated by |
|
||||
local tags=$(echo "$response" | jq -r '.results[] | select(.name | startswith("nightly-")) | "\(.last_updated)|\(.name)"')
|
||||
|
||||
if [ -z "$tags" ]; then
|
||||
break
|
||||
fi
|
||||
|
||||
all_tags="$all_tags$tags"$'\n'
|
||||
page=$((page + 1))
|
||||
done
|
||||
|
||||
# Sort by timestamp (newest first) and extract just the tag names
|
||||
echo "$all_tags" | sort -r | cut -d'|' -f2
|
||||
}
|
||||
|
||||
delete_tag() {
|
||||
local tag_name="$1"
|
||||
echo "Deleting tag: $tag_name"
|
||||
|
||||
local delete_url="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags/$tag_name"
|
||||
set +x
|
||||
local response=$(curl -s -X DELETE -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
|
||||
set -x
|
||||
|
||||
if echo "$response" | jq -e '.detail' > /dev/null 2>&1; then
|
||||
echo "Warning: Failed to delete tag $tag_name: $(echo "$response" | jq -r '.detail')"
|
||||
else
|
||||
echo "Successfully deleted tag: $tag_name"
|
||||
fi
|
||||
}
|
||||
|
||||
# Get all nightly- prefixed tags, sorted by last_updated timestamp (newest first)
|
||||
echo "Fetching all tags from DockerHub..."
|
||||
all_tags=$(get_all_tags)
|
||||
|
||||
if [ -z "$all_tags" ]; then
|
||||
echo "No tags found to clean up"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Count total tags
|
||||
total_tags=$(echo "$all_tags" | wc -l)
|
||||
echo "Found $total_tags tags"
|
||||
|
||||
# Keep only the last 14 builds (including the current one)
|
||||
tags_to_keep=14
|
||||
tags_to_delete=$((total_tags - tags_to_keep))
|
||||
|
||||
if [ $tags_to_delete -le 0 ]; then
|
||||
echo "No tags need to be deleted (only $total_tags tags found, keeping $tags_to_keep)"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
echo "Will delete $tags_to_delete old tags, keeping the newest $tags_to_keep"
|
||||
|
||||
# Get tags to delete (skip the first $tags_to_keep tags)
|
||||
tags_to_delete_list=$(echo "$all_tags" | tail -n +$((tags_to_keep + 1)))
|
||||
|
||||
if [ -z "$tags_to_delete_list" ]; then
|
||||
echo "No tags to delete"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Delete old tags
|
||||
echo "Deleting old tags..."
|
||||
while IFS= read -r tag; do
|
||||
if [ -n "$tag" ]; then
|
||||
delete_tag "$tag"
|
||||
# Add a small delay to avoid rate limiting
|
||||
sleep 1
|
||||
fi
|
||||
done <<< "$tags_to_delete_list"
|
||||
|
||||
echo "Cleanup completed successfully"
|
||||
@@ -1,231 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script runs test inside the corresponding ROCm docker container.
|
||||
set -o pipefail
|
||||
|
||||
# Export Python path
|
||||
export PYTHONPATH=".."
|
||||
|
||||
# Print ROCm version
|
||||
echo "--- Confirming Clean Initial State"
|
||||
while true; do
|
||||
sleep 3
|
||||
if grep -q clean /opt/amdgpu/etc/gpu_state; then
|
||||
echo "GPUs state is \"clean\""
|
||||
break
|
||||
fi
|
||||
done
|
||||
|
||||
echo "--- ROCm info"
|
||||
rocminfo
|
||||
|
||||
# cleanup older docker images
|
||||
cleanup_docker() {
|
||||
# Get Docker's root directory
|
||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
||||
if [ -z "$docker_root" ]; then
|
||||
echo "Failed to determine Docker root directory."
|
||||
exit 1
|
||||
fi
|
||||
echo "Docker root directory: $docker_root"
|
||||
# Check disk usage of the filesystem where Docker's root directory is located
|
||||
disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
|
||||
# Define the threshold
|
||||
threshold=70
|
||||
if [ "$disk_usage" -gt "$threshold" ]; then
|
||||
echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
|
||||
# Remove dangling images (those that are not tagged and not used by any container)
|
||||
docker image prune -f
|
||||
# Remove unused volumes / force the system prune for old images as well.
|
||||
docker volume prune -f && docker system prune --force --filter "until=72h" --all
|
||||
echo "Docker images and volumes cleanup completed."
|
||||
else
|
||||
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||
fi
|
||||
}
|
||||
|
||||
# Call the cleanup docker function
|
||||
cleanup_docker
|
||||
|
||||
echo "--- Resetting GPUs"
|
||||
|
||||
echo "reset" > /opt/amdgpu/etc/gpu_state
|
||||
|
||||
while true; do
|
||||
sleep 3
|
||||
if grep -q clean /opt/amdgpu/etc/gpu_state; then
|
||||
echo "GPUs state is \"clean\""
|
||||
break
|
||||
fi
|
||||
done
|
||||
|
||||
echo "--- Pulling container"
|
||||
image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
|
||||
container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
|
||||
docker pull "${image_name}"
|
||||
|
||||
remove_docker_container() {
|
||||
docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
echo "--- Running container"
|
||||
|
||||
HF_CACHE="$(realpath ~)/huggingface"
|
||||
mkdir -p "${HF_CACHE}"
|
||||
HF_MOUNT="/root/.cache/huggingface"
|
||||
|
||||
commands=$@
|
||||
echo "Commands:$commands"
|
||||
|
||||
if [[ $commands == *"pytest -v -s basic_correctness/test_basic_correctness.py"* ]]; then
|
||||
commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s basic_correctness/test_basic_correctness.py"}
|
||||
fi
|
||||
|
||||
if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
|
||||
commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
|
||||
fi
|
||||
|
||||
if [[ $commands == *"pytest -v -s compile/test_basic_correctness.py"* ]]; then
|
||||
commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s compile/test_basic_correctness.py"}
|
||||
fi
|
||||
|
||||
if [[ $commands == *"pytest -v -s lora"* ]]; then
|
||||
commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
|
||||
fi
|
||||
|
||||
#ignore certain kernels tests
|
||||
if [[ $commands == *" kernels/core"* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/core/test_fused_quant_layernorm.py \
|
||||
--ignore=kernels/core/test_permute_cols.py"
|
||||
fi
|
||||
|
||||
if [[ $commands == *" kernels/attention"* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/attention/test_attention_selector.py \
|
||||
--ignore=kernels/attention/test_encoder_decoder_attn.py \
|
||||
--ignore=kernels/attention/test_flash_attn.py \
|
||||
--ignore=kernels/attention/test_flashinfer.py \
|
||||
--ignore=kernels/attention/test_prefix_prefill.py \
|
||||
--ignore=kernels/attention/test_cascade_flash_attn.py \
|
||||
--ignore=kernels/attention/test_mha_attn.py \
|
||||
--ignore=kernels/attention/test_lightning_attn.py \
|
||||
--ignore=kernels/attention/test_attention.py"
|
||||
fi
|
||||
|
||||
if [[ $commands == *" kernels/quantization"* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/quantization/test_int8_quant.py \
|
||||
--ignore=kernels/quantization/test_machete_mm.py \
|
||||
--ignore=kernels/quantization/test_block_fp8.py \
|
||||
--ignore=kernels/quantization/test_block_int8.py \
|
||||
--ignore=kernels/quantization/test_marlin_gemm.py \
|
||||
--ignore=kernels/quantization/test_cutlass_scaled_mm.py \
|
||||
--ignore=kernels/quantization/test_int8_kernel.py"
|
||||
fi
|
||||
|
||||
if [[ $commands == *" kernels/mamba"* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/mamba/test_mamba_mixer2.py \
|
||||
--ignore=kernels/mamba/test_causal_conv1d.py \
|
||||
--ignore=kernels/mamba/test_mamba_ssm_ssd.py"
|
||||
fi
|
||||
|
||||
if [[ $commands == *" kernels/moe"* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/moe/test_moe.py \
|
||||
--ignore=kernels/moe/test_cutlass_moe.py \
|
||||
--ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
|
||||
fi
|
||||
|
||||
#ignore certain Entrypoints/openai tests
|
||||
if [[ $commands == *" entrypoints/openai "* ]]; then
|
||||
commands=${commands//" entrypoints/openai "/" entrypoints/openai \
|
||||
--ignore=entrypoints/openai/test_audio.py \
|
||||
--ignore=entrypoints/openai/test_shutdown.py \
|
||||
--ignore=entrypoints/openai/test_completion.py \
|
||||
--ignore=entrypoints/openai/test_sleep.py \
|
||||
--ignore=entrypoints/openai/test_models.py \
|
||||
--ignore=entrypoints/openai/test_lora_adapters.py \
|
||||
--ignore=entrypoints/openai/test_return_tokens_as_ids.py \
|
||||
--ignore=entrypoints/openai/test_root_path.py \
|
||||
--ignore=entrypoints/openai/test_tokenization.py \
|
||||
--ignore=entrypoints/openai/test_prompt_validation.py "}
|
||||
fi
|
||||
|
||||
#ignore certain Entrypoints/llm tests
|
||||
if [[ $commands == *" entrypoints/llm "* ]]; then
|
||||
commands=${commands//" entrypoints/llm "/" entrypoints/llm \
|
||||
--ignore=entrypoints/llm/test_chat.py \
|
||||
--ignore=entrypoints/llm/test_accuracy.py \
|
||||
--ignore=entrypoints/llm/test_init.py \
|
||||
--ignore=entrypoints/llm/test_prompt_validation.py "}
|
||||
fi
|
||||
|
||||
# --ignore=entrypoints/openai/test_encoder_decoder.py \
|
||||
# --ignore=entrypoints/openai/test_embedding.py \
|
||||
# --ignore=entrypoints/openai/test_oot_registration.py
|
||||
# --ignore=entrypoints/openai/test_accuracy.py \
|
||||
# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13
|
||||
|
||||
|
||||
PARALLEL_JOB_COUNT=8
|
||||
MYPYTHONPATH=".."
|
||||
|
||||
# check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs.
|
||||
if [[ $commands == *"--shard-id="* ]]; then
|
||||
# assign job count as the number of shards used
|
||||
commands=${commands//"--num-shards= "/"--num-shards=${PARALLEL_JOB_COUNT} "}
|
||||
for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
|
||||
# assign shard-id for each shard
|
||||
commands_gpu=${commands//"--shard-id= "/"--shard-id=${GPU} "}
|
||||
echo "Shard ${GPU} commands:$commands_gpu"
|
||||
echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
|
||||
docker run \
|
||||
--device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
|
||||
--network=host \
|
||||
--shm-size=16gb \
|
||||
--rm \
|
||||
-e HIP_VISIBLE_DEVICES="${GPU}" \
|
||||
-e HF_TOKEN \
|
||||
-e AWS_ACCESS_KEY_ID \
|
||||
-e AWS_SECRET_ACCESS_KEY \
|
||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||
-e "HF_HOME=${HF_MOUNT}" \
|
||||
-e "PYTHONPATH=${MYPYTHONPATH}" \
|
||||
--name "${container_name}_${GPU}" \
|
||||
"${image_name}" \
|
||||
/bin/bash -c "${commands_gpu}" \
|
||||
|& while read -r line; do echo ">>Shard $GPU: $line"; done &
|
||||
PIDS+=($!)
|
||||
done
|
||||
#wait for all processes to finish and collect exit codes
|
||||
for pid in "${PIDS[@]}"; do
|
||||
wait "${pid}"
|
||||
STATUS+=($?)
|
||||
done
|
||||
for st in "${STATUS[@]}"; do
|
||||
if [[ ${st} -ne 0 ]]; then
|
||||
echo "One of the processes failed with $st"
|
||||
exit "${st}"
|
||||
fi
|
||||
done
|
||||
else
|
||||
echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
|
||||
docker run \
|
||||
--device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
|
||||
--network=host \
|
||||
--shm-size=16gb \
|
||||
--rm \
|
||||
-e HIP_VISIBLE_DEVICES=0 \
|
||||
-e HF_TOKEN \
|
||||
-e AWS_ACCESS_KEY_ID \
|
||||
-e AWS_SECRET_ACCESS_KEY \
|
||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||
-e "HF_HOME=${HF_MOUNT}" \
|
||||
-e "PYTHONPATH=${MYPYTHONPATH}" \
|
||||
--name "${container_name}" \
|
||||
"${image_name}" \
|
||||
/bin/bash -c "${commands}"
|
||||
fi
|
||||
@@ -1,52 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() {
|
||||
if [[ -n "$container_id" ]]; then
|
||||
podman stop --all -t0
|
||||
podman rm -f "$container_id" || true
|
||||
fi
|
||||
podman system prune -f
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Try building the docker image
|
||||
podman build -t cpu-test-ubi9-ppc -f docker/Dockerfile.ppc64le .
|
||||
|
||||
# Run the image
|
||||
container_id=$(podman run -itd --entrypoint /bin/bash -v /tmp/:/root/.cache/huggingface --privileged=true --network host -e HF_TOKEN cpu-test-ubi9-ppc)
|
||||
|
||||
function cpu_tests() {
|
||||
|
||||
# offline inference
|
||||
podman exec -it "$container_id" bash -c "
|
||||
set -xve
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log
|
||||
|
||||
# Run basic model test
|
||||
podman exec -it "$container_id" bash -c "
|
||||
set -evx
|
||||
pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
|
||||
pip install sentence-transformers datamodel_code_generator
|
||||
|
||||
# Note: disable Bart until supports V1
|
||||
# pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
|
||||
pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
|
||||
pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
|
||||
pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
|
||||
pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
|
||||
# TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
|
||||
# pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
|
||||
}
|
||||
|
||||
# All of CPU tests are expected to be finished less than 40 mins.
|
||||
|
||||
export container_id
|
||||
export -f cpu_tests
|
||||
timeout 120m bash -c cpu_tests
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f cpu-test || true; docker system prune -f; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t cpu-test -f docker/Dockerfile.s390x .
|
||||
@@ -1,119 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# allow to bind to different cores
|
||||
CORE_RANGE=${CORE_RANGE:-48-95}
|
||||
# used for TP/PP E2E test
|
||||
OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
|
||||
NUMA_NODE=${NUMA_NODE:-1}
|
||||
|
||||
export CMAKE_BUILD_PARALLEL_LEVEL=32
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() {
|
||||
set -e;
|
||||
docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Try building the docker image
|
||||
numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
|
||||
numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
|
||||
|
||||
# Run the image, setting --shm-size=4g for tensor parallel.
|
||||
docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
|
||||
docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
|
||||
|
||||
function cpu_tests() {
|
||||
set -e
|
||||
export NUMA_NODE=$2
|
||||
|
||||
# list packages
|
||||
docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
|
||||
set -e
|
||||
pip list"
|
||||
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pip list"
|
||||
|
||||
# offline inference
|
||||
docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
|
||||
set -e
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
|
||||
|
||||
# Run kernel tests
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -x -v -s tests/kernels/test_onednn.py"
|
||||
|
||||
# Run basic model test
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
# Note: disable until supports V1
|
||||
# pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
|
||||
# pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
|
||||
|
||||
pytest -x -v -s tests/models/language/generation -m cpu_model
|
||||
VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model
|
||||
|
||||
pytest -x -v -s tests/models/language/pooling -m cpu_model
|
||||
pytest -x -v -s tests/models/multimodal/generation \
|
||||
--ignore=tests/models/multimodal/generation/test_pixtral.py \
|
||||
-m cpu_model"
|
||||
|
||||
# Run compressed-tensor test
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -x -s -v \
|
||||
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
|
||||
|
||||
# Note: disable it until supports V1
|
||||
# Run AWQ test
|
||||
# docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
# set -e
|
||||
# VLLM_USE_V1=0 pytest -x -s -v \
|
||||
# tests/quantization/test_ipex_quant.py"
|
||||
|
||||
# Run multi-lora tests
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -x -s -v \
|
||||
tests/lora/test_qwen2vl.py"
|
||||
|
||||
# online serving: tp+pp
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c '
|
||||
set -e
|
||||
VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
|
||||
server_pid=$!
|
||||
timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
|
||||
vllm bench serve \
|
||||
--backend vllm \
|
||||
--dataset-name random \
|
||||
--model meta-llama/Llama-3.2-3B-Instruct \
|
||||
--num-prompts 20 \
|
||||
--endpoint /v1/completions
|
||||
kill -s SIGTERM $server_pid &'
|
||||
|
||||
# online serving: tp+dp
|
||||
docker exec cpu-test-"$NUMA_NODE" bash -c '
|
||||
set -e
|
||||
VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
|
||||
server_pid=$!
|
||||
timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
|
||||
vllm bench serve \
|
||||
--backend vllm \
|
||||
--dataset-name random \
|
||||
--model meta-llama/Llama-3.2-3B-Instruct \
|
||||
--num-prompts 20 \
|
||||
--endpoint /v1/completions
|
||||
kill -s SIGTERM $server_pid &'
|
||||
}
|
||||
|
||||
# All of CPU tests are expected to be finished less than 40 mins.
|
||||
export -f cpu_tests
|
||||
timeout 2h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
|
||||
@@ -1,29 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the GH200 docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Skip the new torch installation during build since we are using the specified version for arm64 in the Dockerfile
|
||||
python3 use_existing_torch.py
|
||||
|
||||
# Try building the docker image
|
||||
DOCKER_BUILDKIT=1 docker build . \
|
||||
--file docker/Dockerfile \
|
||||
--target vllm-openai \
|
||||
--platform "linux/arm64" \
|
||||
-t gh200-test \
|
||||
--build-arg max_jobs=66 \
|
||||
--build-arg nvcc_threads=2 \
|
||||
--build-arg RUN_WHEEL_CHECK=false \
|
||||
--build-arg torch_cuda_arch_list="9.0+PTX"
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f gh200-test || true; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Run the image and test offline inference
|
||||
docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
|
||||
python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
|
||||
'
|
||||
@@ -1,56 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -exuo pipefail
|
||||
|
||||
# Try building the docker image
|
||||
cat <<EOF | docker build -t hpu-plugin-v1-test-env -f - .
|
||||
FROM gaudi-base-image:latest
|
||||
|
||||
COPY ./ /workspace/vllm
|
||||
|
||||
WORKDIR /workspace/vllm
|
||||
|
||||
ENV no_proxy=localhost,127.0.0.1
|
||||
ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
|
||||
|
||||
RUN VLLM_TARGET_DEVICE=empty pip install .
|
||||
RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
|
||||
|
||||
# install development dependencies (for testing)
|
||||
RUN python3 -m pip install -e tests/vllm_test_utils
|
||||
|
||||
WORKDIR /workspace/
|
||||
|
||||
RUN git clone https://github.com/vllm-project/vllm-gaudi.git
|
||||
|
||||
RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
|
||||
|
||||
EOF
|
||||
|
||||
# Setup cleanup
|
||||
# certain versions of HPU software stack have a bug that can
|
||||
# override the exit code of the script, so we need to use
|
||||
# separate remove_docker_containers and remove_docker_containers_and_exit
|
||||
# functions, while other platforms only need one remove_docker_container
|
||||
# function.
|
||||
EXITCODE=1
|
||||
remove_docker_containers() { docker rm -f hpu-plugin-v1-test || true; }
|
||||
trap 'remove_docker_containers; exit $EXITCODE;' EXIT
|
||||
remove_docker_containers
|
||||
|
||||
echo "Running HPU plugin v1 test"
|
||||
docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
|
||||
-e HABANA_VISIBLE_DEVICES=all \
|
||||
hpu-plugin-v1-test-env \
|
||||
/bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
|
||||
|
||||
EXITCODE=$?
|
||||
if [ $EXITCODE -eq 0 ]; then
|
||||
echo "Test with basic model passed"
|
||||
else
|
||||
echo "Test with basic model FAILED with exit code: $EXITCODE" >&2
|
||||
fi
|
||||
|
||||
# The trap will handle the container removal and final exit.
|
||||
@@ -1,191 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the Ascend NPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Base ubuntu image with basic ascend development libraries and python installed
|
||||
VLLM_ASCEND_REPO="https://github.com/vllm-project/vllm-ascend.git"
|
||||
CONFIG_FILE_REMOTE_PATH="tests/e2e/vllm_interface/vllm_test.cfg"
|
||||
TEST_RUN_CONFIG_FILE="vllm_test.cfg"
|
||||
VLLM_ASCEND_TMP_DIR=
|
||||
# Get the test run configuration file from the vllm-ascend repository
|
||||
fetch_vllm_test_cfg() {
|
||||
VLLM_ASCEND_TMP_DIR=$(mktemp -d)
|
||||
# Ensure that the temporary directory is cleaned up when an exception occurs during configuration file retrieval
|
||||
cleanup() {
|
||||
rm -rf "${VLLM_ASCEND_TMP_DIR}"
|
||||
}
|
||||
trap cleanup EXIT
|
||||
|
||||
GIT_TRACE=1 git clone -v --depth 1 "${VLLM_ASCEND_REPO}" "${VLLM_ASCEND_TMP_DIR}"
|
||||
if [ ! -f "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" ]; then
|
||||
echo "Error: file '${CONFIG_FILE_REMOTE_PATH}' does not exist in the warehouse" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# If the file already exists locally, just overwrite it
|
||||
cp "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" "${TEST_RUN_CONFIG_FILE}"
|
||||
echo "Copied ${CONFIG_FILE_REMOTE_PATH} to ${TEST_RUN_CONFIG_FILE}"
|
||||
|
||||
# Since the trap will be overwritten later, and when it is executed here, the task of cleaning up resources
|
||||
# when the trap is abnormal has been completed, so the temporary resources are manually deleted here.
|
||||
rm -rf "${VLLM_ASCEND_TMP_DIR}"
|
||||
trap - EXIT
|
||||
}
|
||||
|
||||
# Downloads test run configuration file from a remote URL.
|
||||
# Loads the configuration into the current script environment.
|
||||
get_config() {
|
||||
if [ ! -f "${TEST_RUN_CONFIG_FILE}" ]; then
|
||||
echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2
|
||||
exit 1
|
||||
fi
|
||||
source "${TEST_RUN_CONFIG_FILE}"
|
||||
echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}"
|
||||
return 0
|
||||
}
|
||||
|
||||
# get test running configuration.
|
||||
fetch_vllm_test_cfg
|
||||
get_config
|
||||
# Check if the function call was successful. If not, exit the script.
|
||||
if [ $? -ne 0 ]; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
image_name="npu/vllm-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
|
||||
container_name="npu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
|
||||
|
||||
# BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards
|
||||
agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
|
||||
echo "agent_idx: ${agent_idx}"
|
||||
builder_name="cachebuilder${agent_idx}"
|
||||
builder_cache_dir="/mnt/docker-cache${agent_idx}"
|
||||
mkdir -p ${builder_cache_dir}
|
||||
|
||||
# Try building the docker image
|
||||
cat <<EOF | DOCKER_BUILDKIT=1 docker build \
|
||||
--add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_HOST} \
|
||||
--builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
|
||||
--cache-to type=local,dest=${builder_cache_dir},mode=max \
|
||||
--progress=plain --load -t ${image_name} -f - .
|
||||
FROM ${BASE_IMAGE_NAME}
|
||||
|
||||
# Define environments
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
|
||||
RUN pip config set global.index-url http://cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_PORT}/pypi/simple && \
|
||||
pip config set global.trusted-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local && \
|
||||
apt-get update -y && \
|
||||
apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev && \
|
||||
rm -rf /var/cache/apt/* && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install for pytest to make the docker build cache layer always valid
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
pip install pytest>=6.0 modelscope
|
||||
|
||||
WORKDIR /workspace/vllm
|
||||
|
||||
# Install vLLM dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid.
|
||||
COPY requirements/common.txt /workspace/vllm/requirements/common.txt
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
pip install -r requirements/common.txt
|
||||
|
||||
COPY . .
|
||||
|
||||
# Install vLLM
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /workspace/vllm/ --extra-index https://download.pytorch.org/whl/cpu/ && \
|
||||
python3 -m pip uninstall -y triton
|
||||
|
||||
# Install vllm-ascend
|
||||
WORKDIR /workspace
|
||||
ARG VLLM_ASCEND_REPO=https://github.com/vllm-project/vllm-ascend.git
|
||||
ARG VLLM_ASCEND_TAG=main
|
||||
RUN git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/" && \
|
||||
git clone --depth 1 \$VLLM_ASCEND_REPO --branch \$VLLM_ASCEND_TAG /workspace/vllm-ascend
|
||||
|
||||
# Install vllm dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid.
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
pip install -r /workspace/vllm-ascend/requirements.txt
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
|
||||
source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
|
||||
source /usr/local/Ascend/nnal/atb/set_env.sh && \
|
||||
export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
|
||||
python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/
|
||||
|
||||
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
|
||||
ENV VLLM_USE_MODELSCOPE=True
|
||||
|
||||
WORKDIR /workspace/vllm-ascend
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
|
||||
EOF
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() {
|
||||
docker rm -f "${container_name}" || true;
|
||||
docker image rm -f "${image_name}" || true;
|
||||
docker system prune -f || true;
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Generate corresponding --device args based on BUILDKITE_AGENT_NAME
|
||||
# Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
|
||||
# e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
|
||||
# returns --device /dev/davinci0 --device /dev/davinci1
|
||||
parse_and_gen_devices() {
|
||||
local input="$1"
|
||||
local index cards_num
|
||||
if [[ "$input" =~ ([0-9]+)-([0-9]+)cards$ ]]; then
|
||||
index="${BASH_REMATCH[1]}"
|
||||
cards_num="${BASH_REMATCH[2]}"
|
||||
else
|
||||
echo "parse error" >&2
|
||||
return 1
|
||||
fi
|
||||
|
||||
local devices=""
|
||||
local i=0
|
||||
while (( i < cards_num )); do
|
||||
local dev_idx=$(((index - 1)*cards_num + i ))
|
||||
devices="$devices --device /dev/davinci${dev_idx}"
|
||||
((i++))
|
||||
done
|
||||
|
||||
# trim leading space
|
||||
devices="${devices#"${devices%%[![:space:]]*}"}"
|
||||
# Output devices: assigned to the caller variable
|
||||
printf '%s' "$devices"
|
||||
}
|
||||
|
||||
devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
|
||||
|
||||
# Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
|
||||
# This test checks whether the OOT platform interface is functioning properly in conjunction with
|
||||
# the hardware plugin vllm-ascend.
|
||||
model_cache_dir=/mnt/modelscope${agent_idx}
|
||||
mkdir -p ${model_cache_dir}
|
||||
docker run \
|
||||
${devices} \
|
||||
--device /dev/davinci_manager \
|
||||
--device /dev/devmm_svm \
|
||||
--device /dev/hisi_hdc \
|
||||
-v /usr/local/dcmi:/usr/local/dcmi \
|
||||
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
|
||||
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
|
||||
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
|
||||
-v /etc/ascend_install.info:/etc/ascend_install.info \
|
||||
-v ${model_cache_dir}:/root/.cache/modelscope \
|
||||
--entrypoint="" \
|
||||
--name "${container_name}" \
|
||||
"${image_name}" \
|
||||
bash -c '
|
||||
set -e
|
||||
pytest -v -s tests/e2e/vllm_interface/
|
||||
'
|
||||
@@ -1,166 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -xu
|
||||
|
||||
|
||||
remove_docker_container() {
|
||||
docker rm -f tpu-test || true;
|
||||
}
|
||||
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Remove the container that might not be cleaned up in the previous run.
|
||||
remove_docker_container
|
||||
|
||||
# Build the docker image.
|
||||
docker build -f docker/Dockerfile.tpu -t vllm-tpu .
|
||||
|
||||
# Set up cleanup.
|
||||
cleanup_docker() {
|
||||
# Get Docker's root directory
|
||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
||||
if [ -z "$docker_root" ]; then
|
||||
echo "Failed to determine Docker root directory."
|
||||
exit 1
|
||||
fi
|
||||
echo "Docker root directory: $docker_root"
|
||||
# Check disk usage of the filesystem where Docker's root directory is located
|
||||
disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
|
||||
# Define the threshold
|
||||
threshold=70
|
||||
if [ "$disk_usage" -gt "$threshold" ]; then
|
||||
echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
|
||||
# Remove dangling images (those that are not tagged and not used by any container)
|
||||
docker image prune -f
|
||||
# Remove unused volumes / force the system prune for old images as well.
|
||||
docker volume prune -f && docker system prune --force --filter "until=72h" --all
|
||||
echo "Docker images and volumes cleanup completed."
|
||||
else
|
||||
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||
fi
|
||||
}
|
||||
cleanup_docker
|
||||
|
||||
# For HF_TOKEN.
|
||||
source /etc/environment
|
||||
|
||||
docker run --privileged --net host --shm-size=16G -it \
|
||||
-e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
|
||||
vllm-tpu /bin/bash -c '
|
||||
set -e # Exit immediately if a command exits with a non-zero status.
|
||||
set -u # Treat unset variables as an error.
|
||||
|
||||
echo "--- Starting script inside Docker container ---"
|
||||
|
||||
# Create results directory
|
||||
RESULTS_DIR=$(mktemp -d)
|
||||
# If mktemp fails, set -e will cause the script to exit.
|
||||
echo "Results will be stored in: $RESULTS_DIR"
|
||||
|
||||
# Install dependencies
|
||||
echo "--- Installing Python dependencies ---"
|
||||
python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
|
||||
&& python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
|
||||
&& python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
|
||||
&& python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
|
||||
echo "--- Python dependencies installed ---"
|
||||
|
||||
export VLLM_XLA_CHECK_RECOMPILATION=1
|
||||
export VLLM_XLA_CACHE_PATH=
|
||||
|
||||
echo "--- Hardware Information ---"
|
||||
# tpu-info
|
||||
echo "--- Starting Tests ---"
|
||||
set +e
|
||||
overall_script_exit_code=0
|
||||
|
||||
# --- Test Definitions ---
|
||||
# If a test fails, this function will print logs and will not cause the main script to exit.
|
||||
run_test() {
|
||||
local test_num=$1
|
||||
local test_name=$2
|
||||
local test_command=$3
|
||||
local log_file="$RESULTS_DIR/test_${test_num}.log"
|
||||
local actual_exit_code
|
||||
|
||||
echo "--- TEST_$test_num: Running $test_name ---"
|
||||
|
||||
# Execute the test command.
|
||||
eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
|
||||
actual_exit_code=$?
|
||||
|
||||
echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
|
||||
echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
|
||||
|
||||
if [ "$actual_exit_code" -ne 0 ]; then
|
||||
echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
|
||||
echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
|
||||
if [ -f "$log_file" ]; then
|
||||
cat "$log_file" >&2
|
||||
else
|
||||
echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
|
||||
fi
|
||||
echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
|
||||
return "$actual_exit_code" # Return the failure code
|
||||
else
|
||||
echo "TEST_$test_num ($test_name) PASSED."
|
||||
return 0 # Return success
|
||||
fi
|
||||
}
|
||||
|
||||
# Helper function to call run_test and update the overall script exit code
|
||||
run_and_track_test() {
|
||||
local test_num_arg="$1"
|
||||
local test_name_arg="$2"
|
||||
local test_command_arg="$3"
|
||||
|
||||
# Run the test
|
||||
run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
|
||||
local test_specific_exit_code=$?
|
||||
|
||||
# If the test failed, set the overall script exit code to 1
|
||||
if [ "$test_specific_exit_code" -ne 0 ]; then
|
||||
# No need for extra echo here, run_test already logged the failure.
|
||||
overall_script_exit_code=1
|
||||
fi
|
||||
}
|
||||
|
||||
# --- Actual Test Execution ---
|
||||
run_and_track_test 1 "test_struct_output_generate.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
|
||||
run_and_track_test 2 "test_moe_pallas.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
|
||||
run_and_track_test 3 "test_lora.py" \
|
||||
"VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
|
||||
run_and_track_test 4 "test_tpu_qkv_linear.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
|
||||
run_and_track_test 5 "test_spmd_model_weight_loading.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
|
||||
run_and_track_test 6 "test_kv_cache_update_kernel.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
|
||||
run_and_track_test 7 "test_tpu_int8.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_int8.py"
|
||||
|
||||
# After all tests have been attempted, exit with the overall status.
|
||||
if [ "$overall_script_exit_code" -ne 0 ]; then
|
||||
echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
|
||||
else
|
||||
echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
|
||||
fi
|
||||
exit "$overall_script_exit_code"
|
||||
' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
|
||||
|
||||
# Capture the exit code of the docker run command
|
||||
DOCKER_RUN_EXIT_CODE=$?
|
||||
|
||||
# The trap will run for cleanup.
|
||||
# Exit the main script with the Docker run command's exit code.
|
||||
if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
|
||||
echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
|
||||
exit "$DOCKER_RUN_EXIT_CODE"
|
||||
else
|
||||
echo "Docker run command completed successfully."
|
||||
exit 0
|
||||
fi
|
||||
# TODO: This test fails because it uses RANDOM_SEED sampling
|
||||
# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
|
||||
@@ -1,174 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -xu
|
||||
|
||||
|
||||
remove_docker_container() {
|
||||
docker rm -f tpu-test || true;
|
||||
}
|
||||
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Remove the container that might not be cleaned up in the previous run.
|
||||
remove_docker_container
|
||||
|
||||
# Build the docker image.
|
||||
docker build -f docker/Dockerfile.tpu -t vllm-tpu .
|
||||
|
||||
# Set up cleanup.
|
||||
cleanup_docker() {
|
||||
# Get Docker's root directory
|
||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
||||
if [ -z "$docker_root" ]; then
|
||||
echo "Failed to determine Docker root directory."
|
||||
exit 1
|
||||
fi
|
||||
echo "Docker root directory: $docker_root"
|
||||
# Check disk usage of the filesystem where Docker's root directory is located
|
||||
disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
|
||||
# Define the threshold
|
||||
threshold=70
|
||||
if [ "$disk_usage" -gt "$threshold" ]; then
|
||||
echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
|
||||
# Remove dangling images (those that are not tagged and not used by any container)
|
||||
docker image prune -f
|
||||
# Remove unused volumes / force the system prune for old images as well.
|
||||
docker volume prune -f && docker system prune --force --filter "until=72h" --all
|
||||
echo "Docker images and volumes cleanup completed."
|
||||
else
|
||||
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||
fi
|
||||
}
|
||||
cleanup_docker
|
||||
|
||||
# For HF_TOKEN.
|
||||
source /etc/environment
|
||||
|
||||
docker run --privileged --net host --shm-size=16G -it \
|
||||
-e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
|
||||
vllm-tpu /bin/bash -c '
|
||||
set -e # Exit immediately if a command exits with a non-zero status.
|
||||
set -u # Treat unset variables as an error.
|
||||
|
||||
echo "--- Starting script inside Docker container ---"
|
||||
|
||||
# Create results directory
|
||||
RESULTS_DIR=$(mktemp -d)
|
||||
# If mktemp fails, set -e will cause the script to exit.
|
||||
echo "Results will be stored in: $RESULTS_DIR"
|
||||
|
||||
# Install dependencies
|
||||
echo "--- Installing Python dependencies ---"
|
||||
python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
|
||||
&& python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
|
||||
&& python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
|
||||
&& python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
|
||||
echo "--- Python dependencies installed ---"
|
||||
|
||||
export VLLM_XLA_CHECK_RECOMPILATION=1
|
||||
export VLLM_XLA_CACHE_PATH=
|
||||
|
||||
echo "--- Hardware Information ---"
|
||||
# tpu-info
|
||||
echo "--- Starting Tests ---"
|
||||
set +e
|
||||
overall_script_exit_code=0
|
||||
|
||||
# --- Test Definitions ---
|
||||
# If a test fails, this function will print logs and will not cause the main script to exit.
|
||||
run_test() {
|
||||
local test_num=$1
|
||||
local test_name=$2
|
||||
local test_command=$3
|
||||
local log_file="$RESULTS_DIR/test_${test_num}.log"
|
||||
local actual_exit_code
|
||||
|
||||
echo "--- TEST_$test_num: Running $test_name ---"
|
||||
|
||||
# Execute the test command.
|
||||
eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
|
||||
actual_exit_code=$?
|
||||
|
||||
echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
|
||||
echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
|
||||
|
||||
if [ "$actual_exit_code" -ne 0 ]; then
|
||||
echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
|
||||
echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
|
||||
if [ -f "$log_file" ]; then
|
||||
cat "$log_file" >&2
|
||||
else
|
||||
echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
|
||||
fi
|
||||
echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
|
||||
return "$actual_exit_code" # Return the failure code
|
||||
else
|
||||
echo "TEST_$test_num ($test_name) PASSED."
|
||||
return 0 # Return success
|
||||
fi
|
||||
}
|
||||
|
||||
# Helper function to call run_test and update the overall script exit code
|
||||
run_and_track_test() {
|
||||
local test_num_arg="$1"
|
||||
local test_name_arg="$2"
|
||||
local test_command_arg="$3"
|
||||
|
||||
# Run the test
|
||||
run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
|
||||
local test_specific_exit_code=$?
|
||||
|
||||
# If the test failed, set the overall script exit code to 1
|
||||
if [ "$test_specific_exit_code" -ne 0 ]; then
|
||||
# No need for extra echo here, run_test already logged the failure.
|
||||
overall_script_exit_code=1
|
||||
fi
|
||||
}
|
||||
|
||||
# --- Actual Test Execution ---
|
||||
run_and_track_test 0 "test_perf.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_perf.py"
|
||||
run_and_track_test 1 "test_compilation.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py"
|
||||
run_and_track_test 2 "test_basic.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
|
||||
run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
|
||||
run_and_track_test 4 "test_quantization_accuracy.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
|
||||
run_and_track_test 5 "examples/offline_inference/tpu.py" \
|
||||
"python3 /workspace/vllm/examples/offline_inference/tpu.py"
|
||||
run_and_track_test 6 "test_tpu_model_runner.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
|
||||
run_and_track_test 7 "test_sampler.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py"
|
||||
run_and_track_test 8 "test_topk_topp_sampler.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py"
|
||||
run_and_track_test 9 "test_multimodal.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
|
||||
run_and_track_test 10 "test_pallas.py" \
|
||||
"python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
|
||||
|
||||
# After all tests have been attempted, exit with the overall status.
|
||||
if [ "$overall_script_exit_code" -ne 0 ]; then
|
||||
echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
|
||||
else
|
||||
echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
|
||||
fi
|
||||
exit "$overall_script_exit_code"
|
||||
' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
|
||||
|
||||
# Capture the exit code of the docker run command
|
||||
DOCKER_RUN_EXIT_CODE=$?
|
||||
|
||||
# The trap will run for cleanup.
|
||||
# Exit the main script with the Docker run command's exit code.
|
||||
if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
|
||||
echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
|
||||
exit "$DOCKER_RUN_EXIT_CODE"
|
||||
else
|
||||
echo "Docker run command completed successfully."
|
||||
exit 0
|
||||
fi
|
||||
# TODO: This test fails because it uses RANDOM_SEED sampling
|
||||
# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
|
||||
@@ -1,51 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
|
||||
container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t ${image_name} -f docker/Dockerfile.xpu .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() {
|
||||
docker rm -f "${container_name}" || true;
|
||||
docker image rm -f "${image_name}" || true;
|
||||
docker system prune -f || true;
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Run the image and test offline inference/tensor parallel
|
||||
docker run \
|
||||
--device /dev/dri:/dev/dri \
|
||||
--net=host \
|
||||
--ipc=host \
|
||||
--privileged \
|
||||
-v /dev/dri/by-path:/dev/dri/by-path \
|
||||
--entrypoint="" \
|
||||
-e "HF_TOKEN=${HF_TOKEN}" \
|
||||
-e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
|
||||
--name "${container_name}" \
|
||||
"${image_name}" \
|
||||
bash -c '
|
||||
set -e
|
||||
echo $ZE_AFFINITY_MASK
|
||||
pip install tblib==3.1.0
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -O.cudagraph_mode=NONE
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
|
||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
|
||||
VLLM_ATTENTION_BACKEND=TRITON_ATTN python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
|
||||
cd tests
|
||||
pytest -v -s v1/core
|
||||
pytest -v -s v1/engine
|
||||
pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
|
||||
pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
|
||||
pytest -v -s v1/structured_output
|
||||
pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py
|
||||
pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py
|
||||
pytest -v -s v1/test_serial_utils.py
|
||||
'
|
||||
@@ -1,18 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Usage: ./rerun_test.sh path/to/test.py::test_name
|
||||
|
||||
# Check if argument is given
|
||||
if [ $# -lt 1 ]; then
|
||||
echo "Usage: $0 path/to/test.py::test_name"
|
||||
echo "Example: $0 tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
TEST=$1
|
||||
COUNT=1
|
||||
|
||||
while pytest -sv "$TEST"; do
|
||||
COUNT=$((COUNT + 1))
|
||||
echo "RUN NUMBER ${COUNT}"
|
||||
done
|
||||
@@ -1,80 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script is run by buildkite to run the benchmarks and upload the results to buildkite
|
||||
|
||||
set -ex
|
||||
set -o pipefail
|
||||
|
||||
# cd 2 levels into the working directory
|
||||
cd "$(dirname "${BASH_SOURCE[0]}")/../.."
|
||||
|
||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||
|
||||
# run python-based benchmarks and upload the result to buildkite
|
||||
vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
||||
bench_latency_exit_code=$?
|
||||
|
||||
vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
||||
bench_throughput_exit_code=$?
|
||||
|
||||
# run server-based benchmarks and upload the result to buildkite
|
||||
vllm serve meta-llama/Llama-2-7b-chat-hf &
|
||||
server_pid=$!
|
||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
|
||||
# wait for server to start, timeout after 600 seconds
|
||||
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||
vllm bench serve \
|
||||
--backend vllm \
|
||||
--dataset-name sharegpt \
|
||||
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
||||
--model meta-llama/Llama-2-7b-chat-hf \
|
||||
--num-prompts 20 \
|
||||
--endpoint /v1/completions \
|
||||
--tokenizer meta-llama/Llama-2-7b-chat-hf \
|
||||
--save-result \
|
||||
2>&1 | tee benchmark_serving.txt
|
||||
bench_serving_exit_code=$?
|
||||
kill $server_pid
|
||||
|
||||
# write the results into a markdown file
|
||||
echo "### Latency Benchmarks" >> benchmark_results.md
|
||||
sed -n '1p' benchmark_latency.txt >> benchmark_results.md # first line
|
||||
echo "" >> benchmark_results.md
|
||||
sed -n '$p' benchmark_latency.txt >> benchmark_results.md # last line
|
||||
|
||||
echo "### Throughput Benchmarks" >> benchmark_results.md
|
||||
sed -n '1p' benchmark_throughput.txt >> benchmark_results.md # first line
|
||||
echo "" >> benchmark_results.md
|
||||
sed -n '$p' benchmark_throughput.txt >> benchmark_results.md # last line
|
||||
|
||||
echo "### Serving Benchmarks" >> benchmark_results.md
|
||||
sed -n '1p' benchmark_serving.txt >> benchmark_results.md # first line
|
||||
echo "" >> benchmark_results.md
|
||||
echo '```' >> benchmark_results.md
|
||||
tail -n 24 benchmark_serving.txt >> benchmark_results.md # last 24 lines
|
||||
echo '```' >> benchmark_results.md
|
||||
|
||||
# if the agent binary is not found, skip uploading the results, exit 0
|
||||
if [ ! -f /usr/bin/buildkite-agent ]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# upload the results to buildkite
|
||||
buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
|
||||
|
||||
# exit with the exit code of the benchmarks
|
||||
if [ $bench_latency_exit_code -ne 0 ]; then
|
||||
exit $bench_latency_exit_code
|
||||
fi
|
||||
|
||||
if [ $bench_throughput_exit_code -ne 0 ]; then
|
||||
exit $bench_throughput_exit_code
|
||||
fi
|
||||
|
||||
if [ $bench_serving_exit_code -ne 0 ]; then
|
||||
exit $bench_serving_exit_code
|
||||
fi
|
||||
|
||||
rm ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
buildkite-agent artifact upload "*.json"
|
||||
@@ -1,108 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euox pipefail
|
||||
|
||||
if [[ $# -lt 4 ]]; then
|
||||
echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
WORKING_DIR=$1
|
||||
NUM_NODES=$2
|
||||
NUM_GPUS=$3
|
||||
DOCKER_IMAGE=$4
|
||||
|
||||
shift 4
|
||||
COMMANDS=("$@")
|
||||
if [ ${#COMMANDS[@]} -ne "$NUM_NODES" ]; then
|
||||
echo "The number of commands must be equal to the number of nodes."
|
||||
echo "Number of nodes: $NUM_NODES"
|
||||
echo "Number of commands: ${#COMMANDS[@]}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "List of commands"
|
||||
for command in "${COMMANDS[@]}"; do
|
||||
echo "$command"
|
||||
done
|
||||
|
||||
start_network() {
|
||||
docker network create --subnet=192.168.10.0/24 docker-net
|
||||
}
|
||||
|
||||
start_nodes() {
|
||||
for node in $(seq 0 $(($NUM_NODES-1))); do
|
||||
GPU_DEVICES='"device='
|
||||
for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
|
||||
DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
|
||||
GPU_DEVICES+=$(($DEVICE_NUM))
|
||||
if [ "$node_gpu" -lt $(($NUM_GPUS - 1)) ]; then
|
||||
GPU_DEVICES+=','
|
||||
fi
|
||||
done
|
||||
GPU_DEVICES+='"'
|
||||
|
||||
# start the container in detached mode
|
||||
# things to note:
|
||||
# 1. --shm-size=10.24gb is required. don't use --ipc=host
|
||||
# 2. pass HF_TOKEN to the container
|
||||
# 3. map the huggingface cache directory to the container
|
||||
# 3. assign ip addresses to the containers (head node: 192.168.10.10, worker nodes:
|
||||
# starting from 192.168.10.11)
|
||||
docker run -d --gpus "$GPU_DEVICES" --shm-size=10.24gb -e HF_TOKEN \
|
||||
-v ~/.cache/huggingface:/root/.cache/huggingface --name "node$node" \
|
||||
--network docker-net --ip 192.168.10.$((10 + $node)) --rm "$DOCKER_IMAGE" \
|
||||
/bin/bash -c "tail -f /dev/null"
|
||||
|
||||
# organize containers into a ray cluster
|
||||
if [ "$node" -eq 0 ]; then
|
||||
# start the ray head node
|
||||
docker exec -d "node$node" /bin/bash -c "ray start --head --port=6379 --block"
|
||||
# wait for the head node to be ready
|
||||
sleep 10
|
||||
else
|
||||
# start the ray worker nodes, and connect them to the head node
|
||||
docker exec -d "node$node" /bin/bash -c "ray start --address=192.168.10.10:6379 --block"
|
||||
fi
|
||||
done
|
||||
|
||||
# wait for the cluster to be ready
|
||||
sleep 10
|
||||
|
||||
# print the cluster status
|
||||
docker exec node0 /bin/bash -c "ray status"
|
||||
}
|
||||
|
||||
run_nodes() {
|
||||
# important: iterate in reverse order to start the head node last
|
||||
# we start the worker nodes first, in detached mode, and then start the head node
|
||||
# in the foreground, so that the output of the head node is visible in the buildkite logs
|
||||
for node in $(seq $(($NUM_NODES - 1)) -1 0); do
|
||||
GPU_DEVICES='"device='
|
||||
for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
|
||||
DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
|
||||
GPU_DEVICES+=$(($DEVICE_NUM))
|
||||
if [ "$node_gpu" -lt $(($NUM_GPUS - 1)) ]; then
|
||||
GPU_DEVICES+=','
|
||||
fi
|
||||
done
|
||||
GPU_DEVICES+='"'
|
||||
echo "Running node$node with GPU devices: $GPU_DEVICES"
|
||||
if [ "$node" -ne 0 ]; then
|
||||
docker exec -d "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
|
||||
else
|
||||
docker exec "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
|
||||
fi
|
||||
done
|
||||
}
|
||||
cleanup() {
|
||||
for node in $(seq 0 $(($NUM_NODES-1))); do
|
||||
docker stop "node$node"
|
||||
done
|
||||
docker network rm docker-net
|
||||
}
|
||||
trap cleanup EXIT
|
||||
start_network
|
||||
start_nodes
|
||||
run_nodes
|
||||
|
||||
@@ -1,59 +0,0 @@
|
||||
#!/bin/bash
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Setup script for Prime-RL integration tests
|
||||
# This script prepares the environment for running Prime-RL tests with nightly vLLM
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
|
||||
PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
|
||||
PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
|
||||
|
||||
echo "Setting up Prime-RL integration test environment..."
|
||||
|
||||
# Clean up any existing Prime-RL directory
|
||||
if [ -d "${PRIME_RL_DIR}" ]; then
|
||||
echo "Removing existing Prime-RL directory..."
|
||||
rm -rf "${PRIME_RL_DIR}"
|
||||
fi
|
||||
|
||||
# Install UV if not available
|
||||
if ! command -v uv &> /dev/null; then
|
||||
echo "Installing UV package manager..."
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
source $HOME/.local/bin/env
|
||||
fi
|
||||
|
||||
# Clone Prime-RL repository at specific branch for reproducible tests
|
||||
PRIME_RL_BRANCH="integ-vllm-main"
|
||||
echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
|
||||
git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
|
||||
cd "${PRIME_RL_DIR}"
|
||||
|
||||
echo "Setting up UV project environment..."
|
||||
export UV_PROJECT_ENVIRONMENT=/usr/local
|
||||
ln -s /usr/bin/python3 /usr/local/bin/python
|
||||
|
||||
# Remove vllm pin from pyproject.toml
|
||||
echo "Removing vllm pin from pyproject.toml..."
|
||||
sed -i '/vllm==/d' pyproject.toml
|
||||
|
||||
# Sync Prime-RL dependencies
|
||||
echo "Installing Prime-RL dependencies..."
|
||||
uv sync --inexact && uv sync --inexact --all-extras
|
||||
|
||||
# Verify installation
|
||||
echo "Verifying installations..."
|
||||
uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
|
||||
uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
|
||||
|
||||
echo "Prime-RL integration test environment setup complete!"
|
||||
|
||||
echo "Running Prime-RL integration tests..."
|
||||
export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
|
||||
uv run pytest -vs tests/integration/test_rl.py -m gpu
|
||||
|
||||
echo "Prime-RL integration tests completed!"
|
||||
@@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
||||
if [ -z "$docker_root" ]; then
|
||||
echo "Failed to determine Docker root directory."
|
||||
exit 1
|
||||
fi
|
||||
echo "Docker root directory: $docker_root"
|
||||
# Check disk usage of the filesystem where Docker's root directory is located
|
||||
disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
|
||||
# Define the threshold
|
||||
threshold=70
|
||||
if [ "$disk_usage" -gt "$threshold" ]; then
|
||||
echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
|
||||
# Remove dangling images (those that are not tagged and not used by any container)
|
||||
docker image prune -f
|
||||
# Remove unused volumes / force the system prune for old images as well.
|
||||
docker volume prune -f && docker system prune --force --filter "until=24h" --all
|
||||
echo "Docker images and volumes cleanup completed."
|
||||
else
|
||||
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||
fi
|
||||
@@ -1,14 +0,0 @@
|
||||
# Environment config
|
||||
TEST_NAME=llama8b
|
||||
CONTAINER_NAME=tpu-test
|
||||
|
||||
# vllm config
|
||||
MODEL=meta-llama/Llama-3.1-8B-Instruct
|
||||
MAX_NUM_SEQS=256
|
||||
MAX_NUM_BATCHED_TOKENS=1024
|
||||
TENSOR_PARALLEL_SIZE=1
|
||||
MAX_MODEL_LEN=2048
|
||||
DOWNLOAD_DIR=/mnt/disks/persist
|
||||
EXPECTED_THROUGHPUT=8.0
|
||||
INPUT_LEN=1800
|
||||
OUTPUT_LEN=128
|
||||
@@ -1,90 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
if [ ! -f "$1" ]; then
|
||||
echo "Error: The env file '$1' does not exist."
|
||||
exit 1 # Exit the script with a non-zero status to indicate an error
|
||||
fi
|
||||
|
||||
ENV_FILE=$1
|
||||
|
||||
# For testing on local vm, use `set -a` to export all variables
|
||||
source /etc/environment
|
||||
source $ENV_FILE
|
||||
|
||||
remove_docker_container() {
|
||||
docker rm -f $CONTAINER_NAME || true;
|
||||
}
|
||||
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Remove the container that might not be cleaned up in the previous run.
|
||||
remove_docker_container
|
||||
|
||||
LOG_ROOT=$(mktemp -d)
|
||||
# If mktemp fails, set -e will cause the script to exit.
|
||||
echo "Results will be stored in: $LOG_ROOT"
|
||||
|
||||
if [ -z "$HF_TOKEN" ]; then
|
||||
echo "Error: HF_TOKEN is not set or is empty."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Make sure mounted disk or dir exists
|
||||
if [ ! -d "$DOWNLOAD_DIR" ]; then
|
||||
echo "Error: Folder $DOWNLOAD_DIR does not exist. This is useually a mounted drive. If no mounted drive, just create a folder."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "Run model $MODEL"
|
||||
echo
|
||||
|
||||
echo "starting docker...$CONTAINER_NAME"
|
||||
echo
|
||||
docker run \
|
||||
-v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
|
||||
--env-file $ENV_FILE \
|
||||
-e HF_TOKEN="$HF_TOKEN" \
|
||||
-e TARGET_COMMIT=$BUILDKITE_COMMIT \
|
||||
-e MODEL=$MODEL \
|
||||
-e WORKSPACE=/workspace \
|
||||
--name $CONTAINER_NAME \
|
||||
-d \
|
||||
--privileged \
|
||||
--network host \
|
||||
-v /dev/shm:/dev/shm \
|
||||
vllm/vllm-tpu-bm tail -f /dev/null
|
||||
|
||||
echo "run script..."
|
||||
echo
|
||||
docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/tpu/run_bm.sh"
|
||||
|
||||
echo "copy result back..."
|
||||
VLLM_LOG="$LOG_ROOT/$TEST_NAME"_vllm_log.txt
|
||||
BM_LOG="$LOG_ROOT/$TEST_NAME"_bm_log.txt
|
||||
docker cp "$CONTAINER_NAME:/workspace/vllm_log.txt" "$VLLM_LOG"
|
||||
docker cp "$CONTAINER_NAME:/workspace/bm_log.txt" "$BM_LOG"
|
||||
|
||||
throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
|
||||
echo "throughput for $TEST_NAME at $BUILDKITE_COMMIT: $throughput"
|
||||
|
||||
if [ "$BUILDKITE" = "true" ]; then
|
||||
echo "Running inside Buildkite"
|
||||
buildkite-agent artifact upload "$VLLM_LOG"
|
||||
buildkite-agent artifact upload "$BM_LOG"
|
||||
else
|
||||
echo "Not running inside Buildkite"
|
||||
fi
|
||||
|
||||
#
|
||||
# compare the throughput with EXPECTED_THROUGHPUT
|
||||
# and assert meeting the expectation
|
||||
#
|
||||
if [[ -z "$throughput" || ! "$throughput" =~ ^[0-9]+([.][0-9]+)?$ ]]; then
|
||||
echo "Failed to get the throughput"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if (( $(echo "$throughput < $EXPECTED_THROUGHPUT" | bc -l) )); then
|
||||
echo "Error: throughput($throughput) is less than expected($EXPECTED_THROUGHPUT)"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,14 +0,0 @@
|
||||
# Environment config
|
||||
TEST_NAME=llama8bw8a8
|
||||
CONTAINER_NAME=tpu-test
|
||||
|
||||
# vllm config
|
||||
MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
|
||||
MAX_NUM_SEQS=128
|
||||
MAX_NUM_BATCHED_TOKENS=1024
|
||||
TENSOR_PARALLEL_SIZE=1
|
||||
MAX_MODEL_LEN=2048
|
||||
DOWNLOAD_DIR=/mnt/disks/persist
|
||||
EXPECTED_THROUGHPUT=8.7
|
||||
INPUT_LEN=1800
|
||||
OUTPUT_LEN=128
|
||||
@@ -1,93 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
VLLM_LOG="$WORKSPACE/vllm_log.txt"
|
||||
BM_LOG="$WORKSPACE/bm_log.txt"
|
||||
|
||||
if [ -n "$TARGET_COMMIT" ]; then
|
||||
head_hash=$(git rev-parse HEAD)
|
||||
if [ "$TARGET_COMMIT" != "$head_hash" ]; then
|
||||
echo "Error: target commit $TARGET_COMMIT does not match HEAD: $head_hash"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
echo "model: $MODEL"
|
||||
echo
|
||||
|
||||
#
|
||||
# create a log folder
|
||||
#
|
||||
mkdir "$WORKSPACE/log"
|
||||
|
||||
# TODO: Move to image building.
|
||||
pip install pandas
|
||||
pip install datasets
|
||||
|
||||
#
|
||||
# create sonnet_4x
|
||||
#
|
||||
echo "Create sonnet_4x.txt"
|
||||
echo "" > benchmarks/sonnet_4x.txt
|
||||
for _ in {1..4}
|
||||
do
|
||||
cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
|
||||
done
|
||||
|
||||
#
|
||||
# start vllm service in backend
|
||||
#
|
||||
echo "lanching vllm..."
|
||||
echo "logging to $VLLM_LOG"
|
||||
echo
|
||||
|
||||
vllm serve $MODEL \
|
||||
--seed 42 \
|
||||
--max-num-seqs $MAX_NUM_SEQS \
|
||||
--max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
|
||||
--tensor-parallel-size $TENSOR_PARALLEL_SIZE \
|
||||
--no-enable-prefix-caching \
|
||||
--download_dir $DOWNLOAD_DIR \
|
||||
--max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
|
||||
|
||||
|
||||
echo "wait for 20 minutes.."
|
||||
echo
|
||||
# sleep 1200
|
||||
# wait for 10 minutes...
|
||||
for i in {1..120}; do
|
||||
# TODO: detect other type of errors.
|
||||
if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
|
||||
echo "Detected RuntimeError, exiting."
|
||||
exit 1
|
||||
elif grep -Fq "Application startup complete" "$VLLM_LOG"; then
|
||||
echo "Application started"
|
||||
break
|
||||
else
|
||||
echo "wait for 10 seconds..."
|
||||
sleep 10
|
||||
fi
|
||||
done
|
||||
|
||||
#
|
||||
# run test
|
||||
#
|
||||
echo "run benchmark test..."
|
||||
echo "logging to $BM_LOG"
|
||||
echo
|
||||
vllm bench serve \
|
||||
--backend vllm \
|
||||
--model $MODEL \
|
||||
--dataset-name sonnet \
|
||||
--dataset-path benchmarks/sonnet_4x.txt \
|
||||
--sonnet-input-len $INPUT_LEN \
|
||||
--sonnet-output-len $OUTPUT_LEN \
|
||||
--ignore-eos > "$BM_LOG"
|
||||
|
||||
echo "completed..."
|
||||
echo
|
||||
|
||||
throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
|
||||
echo "throughput: $throughput"
|
||||
echo
|
||||
@@ -1,83 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
# Assume wheels are in artifacts/dist/*.whl
|
||||
wheel_files=(artifacts/dist/*.whl)
|
||||
|
||||
# Check that exactly one wheel is found
|
||||
if [[ ${#wheel_files[@]} -ne 1 ]]; then
|
||||
echo "Error: Expected exactly one wheel file in artifacts/dist/, but found ${#wheel_files[@]}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Get the single wheel file
|
||||
wheel="${wheel_files[0]}"
|
||||
|
||||
# Detect architecture and rename 'linux' to appropriate manylinux version
|
||||
arch=$(uname -m)
|
||||
if [[ $arch == "x86_64" ]]; then
|
||||
manylinux_version="manylinux1"
|
||||
elif [[ $arch == "aarch64" ]]; then
|
||||
manylinux_version="manylinux2014"
|
||||
else
|
||||
echo "Warning: Unknown architecture $arch, using manylinux1 as default"
|
||||
manylinux_version="manylinux1"
|
||||
fi
|
||||
|
||||
# Rename 'linux' to the appropriate manylinux version in the wheel filename
|
||||
new_wheel="${wheel/linux/$manylinux_version}"
|
||||
mv -- "$wheel" "$new_wheel"
|
||||
wheel="$new_wheel"
|
||||
|
||||
# Extract the version from the wheel
|
||||
version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
|
||||
echo "Version: $version"
|
||||
|
||||
normal_wheel="$wheel" # Save the original wheel filename
|
||||
|
||||
# If the version contains "dev", rename it to v1.0.0.dev for consistency
|
||||
if [[ $version == *dev* ]]; then
|
||||
suffix="${version##*.}"
|
||||
if [[ $suffix == cu* ]]; then
|
||||
new_version="1.0.0.dev+${suffix}"
|
||||
else
|
||||
new_version="1.0.0.dev"
|
||||
fi
|
||||
new_wheel="${wheel/$version/$new_version}"
|
||||
# use cp to keep both files in the artifacts directory
|
||||
cp -- "$wheel" "$new_wheel"
|
||||
wheel="$new_wheel"
|
||||
version="$new_version"
|
||||
fi
|
||||
|
||||
# Upload the wheel to S3
|
||||
python3 .buildkite/generate_index.py --wheel "$normal_wheel"
|
||||
|
||||
# generate index for this commit
|
||||
aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
||||
aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
||||
|
||||
if [[ $normal_wheel == *"cu129"* ]]; then
|
||||
# only upload index.html for cu129 wheels (default wheels) as it
|
||||
# is available on both x86 and arm64
|
||||
aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
|
||||
aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
|
||||
else
|
||||
echo "Skipping index files for non-cu129 wheels"
|
||||
fi
|
||||
|
||||
# generate index for nightly
|
||||
aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
|
||||
aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
|
||||
|
||||
if [[ $normal_wheel == *"cu129"* ]]; then
|
||||
# only upload index.html for cu129 wheels (default wheels) as it
|
||||
# is available on both x86 and arm64
|
||||
aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
|
||||
else
|
||||
echo "Skipping index files for non-cu129 wheels"
|
||||
fi
|
||||
|
||||
aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
|
||||
aws s3 cp index.html "s3://vllm-wheels/$version/vllm/index.html"
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,26 +0,0 @@
|
||||
BasedOnStyle: Google
|
||||
UseTab: Never
|
||||
IndentWidth: 2
|
||||
ColumnLimit: 80
|
||||
|
||||
# Force pointers to the type for C++.
|
||||
DerivePointerAlignment: false
|
||||
PointerAlignment: Left
|
||||
|
||||
# Reordering #include statements can (and currently will) introduce errors
|
||||
SortIncludes: false
|
||||
|
||||
# Style choices
|
||||
AlignConsecutiveAssignments: false
|
||||
AlignConsecutiveDeclarations: false
|
||||
IndentPPDirectives: BeforeHash
|
||||
|
||||
IncludeCategories:
|
||||
- Regex: '^<'
|
||||
Priority: 4
|
||||
- Regex: '^"(llvm|llvm-c|clang|clang-c|mlir|mlir-c)/'
|
||||
Priority: 3
|
||||
- Regex: '^"(qoda|\.\.)/'
|
||||
Priority: 2
|
||||
- Regex: '.*'
|
||||
Priority: 1
|
||||
47
.coveragerc
47
.coveragerc
@@ -1,47 +0,0 @@
|
||||
[run]
|
||||
# Track the installed vllm package (this is what actually gets imported during tests)
|
||||
# Use wildcard pattern to match the installed location
|
||||
source =
|
||||
vllm
|
||||
*/dist-packages/vllm
|
||||
*/site-packages/vllm
|
||||
omit =
|
||||
*/tests/*
|
||||
*/test_*
|
||||
*/__pycache__/*
|
||||
*/build/*
|
||||
*/dist/*
|
||||
*/vllm.egg-info/*
|
||||
*/third_party/*
|
||||
*/examples/*
|
||||
*/benchmarks/*
|
||||
*/docs/*
|
||||
|
||||
[paths]
|
||||
# Map all possible vllm locations to a canonical "vllm" path
|
||||
# This ensures coverage.combine properly merges data from different test runs
|
||||
source =
|
||||
vllm
|
||||
/vllm-workspace/src/vllm
|
||||
/vllm-workspace/vllm
|
||||
*/site-packages/vllm
|
||||
*/dist-packages/vllm
|
||||
|
||||
[report]
|
||||
exclude_lines =
|
||||
pragma: no cover
|
||||
def __repr__
|
||||
if self.debug:
|
||||
if settings.DEBUG
|
||||
raise AssertionError
|
||||
raise NotImplementedError
|
||||
if 0:
|
||||
if __name__ == .__main__.:
|
||||
class .*\bProtocol\):
|
||||
@(abc\.)?abstractmethod
|
||||
|
||||
[html]
|
||||
directory = htmlcov
|
||||
|
||||
[xml]
|
||||
output = coverage.xml
|
||||
@@ -1,33 +0,0 @@
|
||||
/.venv
|
||||
/build
|
||||
dist
|
||||
vllm/*.so
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
.mypy_cache
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
/build/
|
||||
cmake-build-*/
|
||||
CMakeUserPresets.json
|
||||
develop-eggs/
|
||||
/dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
@@ -1,6 +0,0 @@
|
||||
# https://developers.google.com/gemini-code-assist/docs/customize-gemini-behavior-github
|
||||
have_fun: false # Just review the code
|
||||
code_review:
|
||||
comment_severity_threshold: HIGH # Reduce quantity of comments
|
||||
pull_request_opened:
|
||||
summary: false # Don't summarize the PR in a separate comment
|
||||
@@ -1,4 +0,0 @@
|
||||
# Migrate from `yapf` & `isort` to `ruff`
|
||||
d6953beb91da4e9c99be4c0a1304a2d24189535c
|
||||
# Convert `Optional[x]` to `x | None` and `Union[x, y]` to `x | y`
|
||||
8fcaaf6a165e661f63fc51be906bc05b0767332f
|
||||
24
.github/.bc-linter.yml
vendored
24
.github/.bc-linter.yml
vendored
@@ -1,24 +0,0 @@
|
||||
# doc: https://github.com/pytorch/test-infra/blob/main/tools/stronghold/docs/bc_linter_config.md
|
||||
version: 1
|
||||
paths:
|
||||
# We temporarily disable globally, and will only enable with `annotations.include`
|
||||
# include:
|
||||
# - "vllm/v1/attetion/*.py"
|
||||
# - "vllm/v1/core/*.py"
|
||||
exclude:
|
||||
- "**/*.py"
|
||||
|
||||
scan:
|
||||
functions: true # check free functions and methods
|
||||
classes: true # check classes/dataclasses
|
||||
public_only: true # ignore names starting with "_" at any level
|
||||
|
||||
annotations:
|
||||
include: # decorators that force‑include a symbol
|
||||
- name: "bc_linter_include" # matched by simple name or dotted suffix
|
||||
propagate_to_members: false # for classes, include methods/inner classes
|
||||
exclude: # decorators that force‑exclude a symbol
|
||||
- name: "bc_linter_skip" # matched by simple name or dotted suffix
|
||||
propagate_to_members: true # for classes, exclude methods/inner classes
|
||||
|
||||
excluded_violations: [] # e.g. ["ParameterRenamed", "FieldTypeChanged"]
|
||||
129
.github/CODEOWNERS
vendored
129
.github/CODEOWNERS
vendored
@@ -1,129 +0,0 @@
|
||||
# See https://help.github.com/articles/about-codeowners/
|
||||
# for more info about CODEOWNERS file
|
||||
|
||||
# This lists cover the "core" components of vLLM that require careful review
|
||||
/vllm/attention @LucasWilkinson
|
||||
/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||
/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
|
||||
/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
|
||||
/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
|
||||
/vllm/model_executor/layers/mamba @tdoublep
|
||||
/vllm/model_executor/model_loader @22quinn
|
||||
/vllm/multimodal @DarkLight1337 @ywang96 @NickLucche
|
||||
/vllm/vllm_flash_attn @LucasWilkinson
|
||||
/vllm/lora @jeejeelee
|
||||
/vllm/reasoning @aarnphm @chaunceyjiang
|
||||
/vllm/entrypoints @aarnphm @chaunceyjiang
|
||||
/vllm/compilation @zou3519 @youkaichao @ProExpertProg
|
||||
/vllm/distributed/kv_transfer @NickLucche @ApostaC
|
||||
CMakeLists.txt @tlrmchlsmth @LucasWilkinson
|
||||
|
||||
# Any change to the VllmConfig changes can have a large user-facing impact,
|
||||
# so spam a lot of people
|
||||
/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
|
||||
/vllm/config/cache.py @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg @heheda12345
|
||||
|
||||
# vLLM V1
|
||||
/vllm/v1/attention @LucasWilkinson
|
||||
/vllm/v1/attention/backends/mla @pavanimajety
|
||||
/vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
|
||||
/vllm/v1/attention/backends/triton_attn.py @tdoublep
|
||||
/vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC
|
||||
/vllm/v1/sample @22quinn @houseroad @njhill
|
||||
/vllm/v1/spec_decode @benchislett @luccafong
|
||||
/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
|
||||
/vllm/v1/kv_cache_interface.py @heheda12345
|
||||
/vllm/v1/offloading @ApostaC
|
||||
|
||||
# Test ownership
|
||||
/.buildkite/lm-eval-harness @mgoin @simon-mo
|
||||
/tests/distributed/test_multi_node_assignment.py @youkaichao
|
||||
/tests/distributed/test_pipeline_parallel.py @youkaichao
|
||||
/tests/distributed/test_same_node.py @youkaichao
|
||||
/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm @NickLucche
|
||||
/tests/evals @mgoin
|
||||
/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
|
||||
/tests/models @DarkLight1337 @ywang96
|
||||
/tests/multimodal @DarkLight1337 @ywang96 @NickLucche
|
||||
/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
|
||||
/tests/test_inputs.py @DarkLight1337 @ywang96
|
||||
/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
|
||||
/tests/v1/structured_output @mgoin @russellb @aarnphm
|
||||
/tests/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC
|
||||
/tests/weight_loading @mgoin @youkaichao @yewentao256
|
||||
/tests/lora @jeejeelee
|
||||
/tests/models/language/generation/test_hybrid.py @tdoublep
|
||||
/tests/v1/kv_connector/nixl_integration @NickLucche
|
||||
/tests/v1/kv_connector @ApostaC
|
||||
/tests/v1/offloading @ApostaC
|
||||
|
||||
# Transformers backend
|
||||
/vllm/model_executor/models/transformers @hmellor
|
||||
/tests/models/test_transformers.py @hmellor
|
||||
|
||||
# Docs
|
||||
/docs/mkdocs @hmellor
|
||||
/docs/**/*.yml @hmellor
|
||||
/requirements/docs.txt @hmellor
|
||||
.readthedocs.yaml @hmellor
|
||||
mkdocs.yaml @hmellor
|
||||
|
||||
# Linting
|
||||
.markdownlint.yaml @hmellor
|
||||
.pre-commit-config.yaml @hmellor
|
||||
/tools/pre_commit @hmellor
|
||||
|
||||
# CPU
|
||||
/vllm/v1/worker/cpu* @bigPYJ1151
|
||||
/csrc/cpu @bigPYJ1151
|
||||
/vllm/platforms/cpu.py @bigPYJ1151
|
||||
/cmake/cpu_extension.cmake @bigPYJ1151
|
||||
/docker/Dockerfile.cpu @bigPYJ1151
|
||||
|
||||
# Intel GPU
|
||||
/vllm/v1/worker/xpu* @jikunshang
|
||||
/vllm/platforms/xpu.py @jikunshang
|
||||
/docker/Dockerfile.xpu @jikunshang
|
||||
|
||||
# Qwen-specific files
|
||||
/vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
|
||||
/vllm/model_executor/models/qwen* @sighingnow
|
||||
|
||||
# MTP-specific files
|
||||
/vllm/model_executor/models/deepseek_mtp.py @luccafong
|
||||
|
||||
# Mistral-specific files
|
||||
/vllm/model_executor/models/mistral*.py @patrickvonplaten
|
||||
/vllm/model_executor/models/mixtral*.py @patrickvonplaten
|
||||
/vllm/model_executor/models/voxtral*.py @patrickvonplaten
|
||||
/vllm/model_executor/models/pixtral*.py @patrickvonplaten
|
||||
/vllm/transformers_utils/configs/mistral.py @patrickvonplaten
|
||||
/vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
|
||||
|
||||
# Kernels
|
||||
/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
|
||||
/vllm/attention/ops/triton_unified_attention.py @tdoublep
|
||||
|
||||
# ROCm related: specify owner with write access to notify AMD folks for careful code review
|
||||
/docker/Dockerfile.rocm* @gshtras
|
||||
/vllm/v1/attention/backends/rocm*.py @gshtras
|
||||
/vllm/v1/attention/backends/mla/rocm*.py @gshtras
|
||||
/vllm/attention/ops/rocm*.py @gshtras
|
||||
/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras
|
||||
|
||||
# TPU
|
||||
/vllm/v1/worker/tpu* @NickLucche
|
||||
/vllm/platforms/tpu.py @NickLucche
|
||||
/vllm/v1/sample/tpu @NickLucche
|
||||
/vllm/tests/v1/tpu @NickLucche
|
||||
|
||||
# KVConnector installation files
|
||||
/requirements/kv_connectors.txt @NickLucche
|
||||
|
||||
# Pooling models
|
||||
/examples/*/pooling/ @noooop
|
||||
/tests/models/*/pooling* @noooop
|
||||
/tests/entrypoints/pooling @noooop
|
||||
/vllm/config/pooler.py @noooop
|
||||
/vllm/pooling_params.py @noooop
|
||||
/vllm/model_executor/layers/pooler.py @noooop
|
||||
2
.github/FUNDING.yml
vendored
2
.github/FUNDING.yml
vendored
@@ -1,2 +0,0 @@
|
||||
github: [vllm-project]
|
||||
open_collective: vllm
|
||||
29
.github/ISSUE_TEMPLATE/100-documentation.yml
vendored
29
.github/ISSUE_TEMPLATE/100-documentation.yml
vendored
@@ -1,29 +0,0 @@
|
||||
name: 📚 Documentation
|
||||
description: Report an issue related to https://docs.vllm.ai/
|
||||
title: "[Doc]: "
|
||||
labels: ["documentation"]
|
||||
|
||||
body:
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 📚 The doc issue
|
||||
description: >
|
||||
A clear and concise description of what content in https://docs.vllm.ai/ is an issue.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Suggest a potential alternative/fix
|
||||
description: >
|
||||
Tell us how we could improve the documentation in this regard.
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
47
.github/ISSUE_TEMPLATE/200-installation.yml
vendored
47
.github/ISSUE_TEMPLATE/200-installation.yml
vendored
@@ -1,47 +0,0 @@
|
||||
name: 🛠️ Installation
|
||||
description: Report an issue here when you hit errors during installation.
|
||||
title: "[Installation]: "
|
||||
labels: ["installation"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Your current environment
|
||||
description: |
|
||||
Please run the following and paste the output below.
|
||||
```sh
|
||||
wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
|
||||
# For security purposes, please feel free to check the contents of collect_env.py before running it.
|
||||
python collect_env.py
|
||||
```
|
||||
It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
|
||||
value: |
|
||||
```text
|
||||
The output of `python collect_env.py`
|
||||
```
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: How you are installing vllm
|
||||
description: |
|
||||
Paste the full command you are trying to execute.
|
||||
value: |
|
||||
```sh
|
||||
pip install -vvv vllm
|
||||
```
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
45
.github/ISSUE_TEMPLATE/300-usage.yml
vendored
45
.github/ISSUE_TEMPLATE/300-usage.yml
vendored
@@ -1,45 +0,0 @@
|
||||
name: 💻 Usage
|
||||
description: Raise an issue here if you don't know how to use vllm.
|
||||
title: "[Usage]: "
|
||||
labels: ["usage"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Your current environment
|
||||
description: |
|
||||
Please run the following and paste the output below.
|
||||
```sh
|
||||
wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
|
||||
# For security purposes, please feel free to check the contents of collect_env.py before running it.
|
||||
python collect_env.py
|
||||
```
|
||||
It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
|
||||
value: |
|
||||
```text
|
||||
The output of `python collect_env.py`
|
||||
```
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: How would you like to use vllm
|
||||
description: |
|
||||
A detailed description of how you want to use vllm.
|
||||
value: |
|
||||
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
108
.github/ISSUE_TEMPLATE/400-bug-report.yml
vendored
108
.github/ISSUE_TEMPLATE/400-bug-report.yml
vendored
@@ -1,108 +0,0 @@
|
||||
name: 🐛 Bug report
|
||||
description: Raise an issue here if you find a bug.
|
||||
title: "[Bug]: "
|
||||
labels: ["bug"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
⚠️ **SECURITY WARNING:** Please review any text you paste to ensure it does not contain sensitive information such as:
|
||||
- API tokens or keys (e.g., Hugging Face tokens, OpenAI API keys)
|
||||
- Passwords or authentication credentials
|
||||
- Private URLs or endpoints
|
||||
- Personal or confidential data
|
||||
|
||||
Consider redacting or replacing sensitive values with placeholders like `<YOUR_TOKEN_HERE>` when sharing configuration or code examples.
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Your current environment
|
||||
description: |
|
||||
Please run the following and paste the output below.
|
||||
```sh
|
||||
wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
|
||||
# For security purposes, please feel free to check the contents of collect_env.py before running it.
|
||||
python collect_env.py
|
||||
```
|
||||
It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
|
||||
value: |
|
||||
<details>
|
||||
<summary>The output of <code>python collect_env.py</code></summary>
|
||||
|
||||
```text
|
||||
Your output of `python collect_env.py` here
|
||||
```
|
||||
|
||||
</details>
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 🐛 Describe the bug
|
||||
description: |
|
||||
Please provide a clear and concise description of what the bug is.
|
||||
|
||||
If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for the snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
|
||||
|
||||
```python
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
prompts = [
|
||||
"Hello, my name is",
|
||||
"The president of the United States is",
|
||||
"The capital of France is",
|
||||
"The future of AI is",
|
||||
]
|
||||
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
|
||||
|
||||
llm = LLM(model="facebook/opt-125m")
|
||||
|
||||
outputs = llm.generate(prompts, sampling_params)
|
||||
|
||||
# Print the outputs.
|
||||
for output in outputs:
|
||||
prompt = output.prompt
|
||||
generated_text = output.outputs[0].text
|
||||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||||
```
|
||||
|
||||
If the code is too long (hopefully, it isn't), feel free to put it in a public gist and link it in the issue: https://gist.github.com.
|
||||
|
||||
Please also paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full** traceback of the exception. It may be relevant to wrap error messages in ```` ```triple quotes blocks``` ````.
|
||||
|
||||
Please set the environment variable `export VLLM_LOGGING_LEVEL=DEBUG` to turn on more logging to help debugging potential issues.
|
||||
|
||||
If you experienced crashes or hangs, it would be helpful to run vllm with `export VLLM_TRACE_FUNCTION=1` . All the function calls in vllm will be recorded. Inspect these log files, and tell which function crashes or hangs.
|
||||
placeholder: |
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
```python
|
||||
# Sample code to reproduce the problem
|
||||
```
|
||||
|
||||
```
|
||||
The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
|
||||
```
|
||||
validations:
|
||||
required: true
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the model's output:
|
||||
|
||||
- Try the counterpart of `transformers` first. If the error appears, please go to [their issues](https://github.com/huggingface/transformers/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc).
|
||||
|
||||
- If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.
|
||||
|
||||
Thanks for reporting 🙏!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
69
.github/ISSUE_TEMPLATE/450-ci-failure.yml
vendored
69
.github/ISSUE_TEMPLATE/450-ci-failure.yml
vendored
@@ -1,69 +0,0 @@
|
||||
name: 🧪 CI failure report
|
||||
description: Report a failing test.
|
||||
title: "[CI Failure]: "
|
||||
labels: ["ci-failure"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Include the name of the failing Buildkite step and test file in the title.
|
||||
- type: input
|
||||
attributes:
|
||||
label: Name of failing test
|
||||
description: |
|
||||
Paste in the fully-qualified name of the failing test from the logs.
|
||||
placeholder: |
|
||||
`path/to/test_file.py::test_name[params]`
|
||||
validations:
|
||||
required: true
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Basic information
|
||||
description: Select all items that apply to the failing test.
|
||||
options:
|
||||
- label: Flaky test
|
||||
- label: Can reproduce locally
|
||||
- label: Caused by external libraries (e.g. bug in `transformers`)
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 🧪 Describe the failing test
|
||||
description: |
|
||||
Please provide a clear and concise description of the failing test.
|
||||
placeholder: |
|
||||
A clear and concise description of the failing test.
|
||||
|
||||
```
|
||||
The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
|
||||
```
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 📝 History of failing test
|
||||
description: |
|
||||
Since when did the test start to fail?
|
||||
You can look up its history via [Buildkite Test Suites](https://buildkite.com/organizations/vllm/analytics/suites/ci-1/tests?branch=main).
|
||||
|
||||
If you have time, identify the PR that caused the test to fail on main. You can do so via the following methods:
|
||||
|
||||
- Use Buildkite Test Suites to find the PR where the test failure first occurred, and reproduce the failure locally.
|
||||
|
||||
- Run [`git bisect`](https://git-scm.com/docs/git-bisect) locally.
|
||||
|
||||
- Manually unblock Buildkite steps for suspected PRs on main and check the results. (authorized users only)
|
||||
placeholder: |
|
||||
Approximate timeline and/or problematic PRs
|
||||
|
||||
A link to the Buildkite analytics of the failing test (if available)
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: CC List.
|
||||
description: >
|
||||
The list of people you want to CC. Usually, this includes those who worked on the PR that failed the test.
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for reporting 🙏!
|
||||
38
.github/ISSUE_TEMPLATE/500-feature-request.yml
vendored
38
.github/ISSUE_TEMPLATE/500-feature-request.yml
vendored
@@ -1,38 +0,0 @@
|
||||
name: 🚀 Feature request
|
||||
description: Submit a proposal/request for a new vllm feature
|
||||
title: "[Feature]: "
|
||||
labels: ["feature request"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 🚀 The feature, motivation and pitch
|
||||
description: >
|
||||
A clear and concise description of the feature proposal. Please outline the motivation for the proposal. Is your feature request related to a specific problem? e.g., *"I'm working on X and would like Y to be possible"*. If this is related to another GitHub issue, please link here too.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Alternatives
|
||||
description: >
|
||||
A description of any alternative solutions or features you've considered, if any.
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: >
|
||||
Add any other context or screenshots about the feature request.
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
40
.github/ISSUE_TEMPLATE/600-new-model.yml
vendored
40
.github/ISSUE_TEMPLATE/600-new-model.yml
vendored
@@ -1,40 +0,0 @@
|
||||
name: 🤗 Support request for a new model from huggingface
|
||||
description: Submit a proposal/request for a new model from huggingface
|
||||
title: "[New Model]: "
|
||||
labels: ["new model"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||
|
||||
#### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/index.html first to understand how to add a new model.
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: The model to consider.
|
||||
description: >
|
||||
A huggingface url, pointing to the model, e.g. https://huggingface.co/openai-community/gpt2 .
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: The closest model vllm already supports.
|
||||
description: >
|
||||
Here is the list of models already supported by vllm: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models . Which model is the most similar to the model you want to add support for?
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: What's your difficulty of supporting the model you want?
|
||||
description: >
|
||||
For example, any new operators or new architecture?
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
- type: checkboxes
|
||||
id: askllm
|
||||
attributes:
|
||||
label: Before submitting a new issue...
|
||||
options:
|
||||
- label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
||||
required: true
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user