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fix-hashin
| Author | SHA1 | Date | |
|---|---|---|---|
| 1936d7bab0 | |||
| 996cf2de5c |
@ -1,53 +1,36 @@
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|||||||
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import os
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import sys
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import zipfile
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import zipfile
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# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 500 MiB
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MAX_SIZE_MB = 200
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# Note that we have 800 MiB quota, please use it wisely.
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# See https://github.com/pypi/support/issues/6326 .
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# Please also sync the value with the one in Dockerfile.
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VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 500))
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def print_top_10_largest_files(zip_file):
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def print_top_10_largest_files(zip_file):
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"""Print the top 10 largest files in the given zip file."""
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with zipfile.ZipFile(zip_file, 'r') as z:
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with zipfile.ZipFile(zip_file, "r") as z:
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file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
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file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
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file_sizes.sort(key=lambda x: x[1], reverse=True)
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file_sizes.sort(key=lambda x: x[1], reverse=True)
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for f, size in file_sizes[:10]:
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for f, size in file_sizes[:10]:
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print(f"{f}: {size / (1024 * 1024):.2f} MBs uncompressed.")
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print(f"{f}: {size/(1024*1024)} MBs uncompressed.")
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def check_wheel_size(directory):
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def check_wheel_size(directory):
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"""Check the size of .whl files in the given directory."""
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for root, _, files in os.walk(directory):
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for root, _, files in os.walk(directory):
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for file_name in files:
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for f in files:
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if file_name.endswith(".whl"):
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if f.endswith(".whl"):
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wheel_path = os.path.join(root, file_name)
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wheel_path = os.path.join(root, f)
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wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
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wheel_size = os.path.getsize(wheel_path)
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if wheel_size_mb > VLLM_MAX_SIZE_MB:
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wheel_size_mb = wheel_size / (1024 * 1024)
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if wheel_size_mb > MAX_SIZE_MB:
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print(
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print(
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f"Not allowed: Wheel {wheel_path} is larger "
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f"Wheel {wheel_path} is too large ({wheel_size_mb} MB) "
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f"({wheel_size_mb:.2f} MB) than the limit "
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f"compare to the allowed size ({MAX_SIZE_MB} MB).")
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f"({VLLM_MAX_SIZE_MB} MB)."
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)
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print_top_10_largest_files(wheel_path)
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print_top_10_largest_files(wheel_path)
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return 1
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return 1
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else:
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else:
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print(
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print(f"Wheel {wheel_path} is within the allowed size "
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f"Wheel {wheel_path} is within the allowed size "
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f"({wheel_size_mb} MB).")
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f"({wheel_size_mb:.2f} MB)."
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)
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return 0
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return 0
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if __name__ == "__main__":
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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import sys
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print("Usage: python check-wheel-size.py <directory>")
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sys.exit(check_wheel_size(sys.argv[1]))
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sys.exit(1)
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directory = sys.argv[1]
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sys.exit(check_wheel_size(directory))
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18
.buildkite/download-images.sh
Normal file
18
.buildkite/download-images.sh
Normal file
@ -0,0 +1,18 @@
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#!/bin/bash
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set -ex
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set -o pipefail
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(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
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# aws s3 sync s3://air-example-data-2/vllm_opensource_llava/ images/
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mkdir -p images
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cd images
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign_pixel_values.pt
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign_image_features.pt
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom_pixel_values.pt
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom_image_features.pt
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign.jpg
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wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom.jpg
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cd -
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@ -1,46 +0,0 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import os
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template = """<!DOCTYPE html>
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<html>
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<body>
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<h1>Links for vLLM</h1/>
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<a href="../{x86_wheel_html_escaped}">{x86_wheel}</a><br/>
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<a href="../{arm_wheel_html_escaped}">{arm_wheel}</a><br/>
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</body>
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</html>
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument("--wheel", help="The wheel path.", required=True)
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args = parser.parse_args()
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filename = os.path.basename(args.wheel)
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with open("index.html", "w") as f:
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print(f"Generated index.html for {args.wheel}")
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# sync the abi tag with .buildkite/scripts/upload-wheels.sh
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if "x86_64" in filename:
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x86_wheel = filename
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arm_wheel = filename.replace("x86_64", "aarch64").replace(
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"manylinux1", "manylinux2014"
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)
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elif "aarch64" in filename:
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x86_wheel = filename.replace("aarch64", "x86_64").replace(
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"manylinux2014", "manylinux1"
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)
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arm_wheel = filename
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else:
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raise ValueError(f"Unsupported wheel: {filename}")
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# cloudfront requires escaping the '+' character
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f.write(
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template.format(
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x86_wheel=x86_wheel,
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x86_wheel_html_escaped=x86_wheel.replace("+", "%2B"),
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arm_wheel=arm_wheel,
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arm_wheel_html_escaped=arm_wheel.replace("+", "%2B"),
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)
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)
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@ -1,13 +0,0 @@
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# For vllm script, with -t option (tensor parallel size).
|
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# bash ./run-lm-eval-gsm-vllm-baseline.sh -m deepseek-ai/DeepSeek-V2-Lite-Chat -b "auto" -l 1000 -f 5 -t 2
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model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
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|
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tasks:
|
|
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- name: "gsm8k"
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metrics:
|
|
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- name: "exact_match,strict-match"
|
|
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value: 0.671
|
|
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- name: "exact_match,flexible-extract"
|
|
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value: 0.664
|
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limit: 1000
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|
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num_fewshot: 5
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trust_remote_code: True
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@ -1,12 +0,0 @@
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# For hf script, without -t option (tensor parallel size).
|
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# 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
|
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model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
|
|
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tasks:
|
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- name: "gsm8k"
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metrics:
|
|
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- name: "exact_match,strict-match"
|
|
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value: 0.905
|
|
||||||
- name: "exact_match,flexible-extract"
|
|
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value: 0.905
|
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limit: 1000
|
|
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num_fewshot: 5
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@ -1,12 +0,0 @@
|
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# For hf script, without -t option (tensor parallel size).
|
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# 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
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model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
|
|
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tasks:
|
|
||||||
- name: "gsm8k"
|
|
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metrics:
|
|
||||||
- name: "exact_match,strict-match"
|
|
||||||
value: 0.892
|
|
||||||
- name: "exact_match,flexible-extract"
|
|
||||||
value: 0.892
|
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limit: 250
|
|
||||||
num_fewshot: 5
|
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@ -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
|
|
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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
|
|
||||||
@ -1,134 +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, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators 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.
|
|
||||||
For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.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/HPU 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/HPU 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/HPU 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 [[ "$CURRENT_LLM_SERVING_ENGINE" == *"vllm"* ]]; then
|
|
||||||
launch_vllm_server
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
main
|
|
||||||
@ -1,504 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
# This script should be run inside the CI process
|
|
||||||
# This script assumes that we are already inside the vllm/ directory
|
|
||||||
# Benchmarking results will be available inside vllm/benchmarks/results/
|
|
||||||
|
|
||||||
# Do not set -e, as the mixtral 8x22B model tends to crash occasionally
|
|
||||||
# and we still want to see other benchmarking results even when mixtral crashes.
|
|
||||||
set -x
|
|
||||||
set -o pipefail
|
|
||||||
|
|
||||||
check_gpus() {
|
|
||||||
if command -v nvidia-smi; then
|
|
||||||
# check the number of GPUs and GPU type.
|
|
||||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
|
||||||
elif command -v amd-smi; then
|
|
||||||
declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
|
|
||||||
elif command -v hl-smi; then
|
|
||||||
declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [[ $gpu_count -gt 0 ]]; then
|
|
||||||
echo "GPU found."
|
|
||||||
else
|
|
||||||
echo "Need at least 1 GPU to run benchmarking."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
declare -g arch_suffix=''
|
|
||||||
|
|
||||||
if command -v nvidia-smi; then
|
|
||||||
declare -g gpu_type=$(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 $2}')
|
|
||||||
elif command -v hl-smi; then
|
|
||||||
declare -g gpu_type=$(hl-smi -q | grep "Product Name" | head -n 1 | awk -F ':' '{print $2}' | sed 's/^ *//')
|
|
||||||
arch_suffix='-hpu'
|
|
||||||
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 $2}')" -ge 1000 ]; do
|
|
||||||
sleep 1
|
|
||||||
done
|
|
||||||
elif command -v hl-smi; then
|
|
||||||
while [ "$(hl-smi -q | grep "Used" | head -n 1 | awk '{print $3}')" -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
|
|
||||||
ARCH="$arch_suffix"
|
|
||||||
fi
|
|
||||||
check_hf_token
|
|
||||||
|
|
||||||
# dependencies
|
|
||||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
|
||||||
(which jq) || (apt-get update && apt-get -y install jq)
|
|
||||||
(which lsof) || (apt-get update && apt-get install -y lsof)
|
|
||||||
|
|
||||||
# get the current IP address, required by `vllm bench serve` command
|
|
||||||
export VLLM_HOST_IP=$(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
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,55 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"test_name": "latency_llama8B_tp1",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
|
||||||
"tensor_parallel_size": 1,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"num-iters-warmup": 5,
|
|
||||||
"num-iters": 15,
|
|
||||||
"max-model-len": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "latency_llama70B_tp4",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
|
||||||
"tensor_parallel_size": 4,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"num-iters-warmup": 5,
|
|
||||||
"num-iters": 15,
|
|
||||||
"max-model-len": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "latency_mixtral8x7B_tp2",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"num-iters-warmup": 5,
|
|
||||||
"num-iters": 15,
|
|
||||||
"max-model-len": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -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,
|
|
||||||
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|
|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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|
|
||||||
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|
|
||||||
{
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
|
||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
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|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"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,
|
|
||||||
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|
|
||||||
"dataset_path": "./sonnet_4x.txt",
|
|
||||||
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|
|
||||||
"port": 8000,
|
|
||||||
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|
|
||||||
"sonnet_output_len": 16,
|
|
||||||
"sonnet_prefix_len": 50,
|
|
||||||
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|
|
||||||
},
|
|
||||||
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|
|
||||||
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|
|
||||||
},
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
||||||
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||||||
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|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"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,
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
},
|
|
||||||
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|
|
||||||
"dtype": "bfloat16"
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -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": {
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"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,
|
|
||||||
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|
|
||||||
"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],
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"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": "",
|
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|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,276 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
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|
|
||||||
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|
|
||||||
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||||||
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|
|
||||||
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||||||
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|
|
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|
|
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||||||
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|
|
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|
|
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|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
||||||
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|
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||||||
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||||||
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||||||
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||||||
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|
||||||
"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
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,82 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_tp1_sharegpt",
|
|
||||||
"qps_list": [1, 4, 16, "inf"],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
|
||||||
"tensor_parallel_size": 1,
|
|
||||||
"swap_space": 16,
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"load_format": "dummy",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
},
|
|
||||||
"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_environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
|
||||||
"tensor_parallel_size": 4,
|
|
||||||
"swap_space": 16,
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"load_format": "dummy",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
},
|
|
||||||
"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_environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"swap_space": 16,
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"load_format": "dummy",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 256,
|
|
||||||
"async-scheduling": ""
|
|
||||||
},
|
|
||||||
"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
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -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"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,61 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"test_name": "throughput_llama8B_tp1",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
|
||||||
"tensor_parallel_size": 1,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
||||||
"num_prompts": 1000,
|
|
||||||
"backend": "vllm",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 512,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "throughput_llama70B_tp4",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
|
||||||
"tensor_parallel_size": 4,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
||||||
"num_prompts": 1000,
|
|
||||||
"backend": "vllm",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 512,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "throughput_mixtral8x7B_tp2",
|
|
||||||
"environment_variables": {
|
|
||||||
"PT_HPU_LAZY_MODE": 1,
|
|
||||||
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
|
|
||||||
"VLLM_CONTIGUOUS_PA": 1,
|
|
||||||
"VLLM_DEFRAG": 1
|
|
||||||
},
|
|
||||||
"parameters": {
|
|
||||||
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"load_format": "dummy",
|
|
||||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
||||||
"num_prompts": 1000,
|
|
||||||
"backend": "vllm",
|
|
||||||
"max-model-len": 2048,
|
|
||||||
"max-num-seqs": 512,
|
|
||||||
"async-scheduling": ""
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -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-$BUILDKITE_COMMIT"
|
|
||||||
# Clean up old nightly builds (keep only last 14)
|
|
||||||
- "bash .buildkite/scripts/cleanup-nightly-builds.sh"
|
|
||||||
plugins:
|
|
||||||
- docker-login#v3.0.0:
|
|
||||||
username: vllmbot
|
|
||||||
password-env: DOCKERHUB_TOKEN
|
|
||||||
env:
|
|
||||||
DOCKER_BUILDKIT: "1"
|
|
||||||
DOCKERHUB_USERNAME: "vllmbot"
|
|
||||||
73
.buildkite/run-amd-test.sh
Normal file
73
.buildkite/run-amd-test.sh
Normal file
@ -0,0 +1,73 @@
|
|||||||
|
# This script runs test inside the corresponding ROCm docker container.
|
||||||
|
set -ex
|
||||||
|
|
||||||
|
# Print ROCm version
|
||||||
|
echo "--- ROCm info"
|
||||||
|
rocminfo
|
||||||
|
|
||||||
|
# cleanup older docker images
|
||||||
|
cleanup_docker() {
|
||||||
|
# Get Docker's root directory
|
||||||
|
docker_root=$(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
|
||||||
|
docker volume prune -f
|
||||||
|
echo "Docker images and volumes cleanup completed."
|
||||||
|
else
|
||||||
|
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
# Call the cleanup docker function
|
||||||
|
cleanup_docker
|
||||||
|
|
||||||
|
echo "--- Resetting GPUs"
|
||||||
|
|
||||||
|
echo "reset" > /opt/amdgpu/etc/gpu_state
|
||||||
|
|
||||||
|
while true; do
|
||||||
|
sleep 3
|
||||||
|
if grep -q clean /opt/amdgpu/etc/gpu_state; then
|
||||||
|
echo "GPUs state is \"clean\""
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
done
|
||||||
|
|
||||||
|
echo "--- Building container"
|
||||||
|
sha=$(git rev-parse --short HEAD)
|
||||||
|
image_name=rocm_${sha}
|
||||||
|
container_name=rocm_${sha}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)
|
||||||
|
docker build \
|
||||||
|
-t ${image_name} \
|
||||||
|
-f Dockerfile.rocm \
|
||||||
|
--progress plain \
|
||||||
|
.
|
||||||
|
|
||||||
|
remove_docker_container() {
|
||||||
|
docker rm -f ${container_name} || docker image rm -f ${image_name} || true
|
||||||
|
}
|
||||||
|
trap remove_docker_container EXIT
|
||||||
|
|
||||||
|
echo "--- Running container"
|
||||||
|
|
||||||
|
docker run \
|
||||||
|
--device /dev/kfd --device /dev/dri \
|
||||||
|
--network host \
|
||||||
|
--rm \
|
||||||
|
-e HF_TOKEN \
|
||||||
|
--name ${container_name} \
|
||||||
|
${image_name} \
|
||||||
|
/bin/bash -c "${@}"
|
||||||
|
|
||||||
@ -1,30 +1,28 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
# This script is run by buildkite to run the benchmarks and upload the results to buildkite
|
# This script is run by buildkite to run the benchmarks and upload the results to buildkite
|
||||||
|
|
||||||
set -ex
|
set -ex
|
||||||
set -o pipefail
|
set -o pipefail
|
||||||
|
|
||||||
# cd 2 levels into the working directory
|
# cd into parent directory of this file
|
||||||
cd "$(dirname "${BASH_SOURCE[0]}")/../.."
|
cd "$(dirname "${BASH_SOURCE[0]}")/.."
|
||||||
|
|
||||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||||
|
|
||||||
# run python-based benchmarks and upload the result to buildkite
|
# run python-based benchmarks and upload the result to buildkite
|
||||||
vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
||||||
bench_latency_exit_code=$?
|
bench_latency_exit_code=$?
|
||||||
|
|
||||||
vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
||||||
bench_throughput_exit_code=$?
|
bench_throughput_exit_code=$?
|
||||||
|
|
||||||
# run server-based benchmarks and upload the result to buildkite
|
# run server-based benchmarks and upload the result to buildkite
|
||||||
vllm serve meta-llama/Llama-2-7b-chat-hf &
|
python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf &
|
||||||
server_pid=$!
|
server_pid=$!
|
||||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
|
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
|
# 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
|
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||||
vllm bench serve \
|
python3 benchmarks/benchmark_serving.py \
|
||||||
--backend vllm \
|
--backend vllm \
|
||||||
--dataset-name sharegpt \
|
--dataset-name sharegpt \
|
||||||
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
||||||
@ -52,16 +50,16 @@ echo "### Serving Benchmarks" >> benchmark_results.md
|
|||||||
sed -n '1p' benchmark_serving.txt >> benchmark_results.md # first line
|
sed -n '1p' benchmark_serving.txt >> benchmark_results.md # first line
|
||||||
echo "" >> benchmark_results.md
|
echo "" >> benchmark_results.md
|
||||||
echo '```' >> benchmark_results.md
|
echo '```' >> benchmark_results.md
|
||||||
tail -n 24 benchmark_serving.txt >> benchmark_results.md # last 24 lines
|
tail -n 20 benchmark_serving.txt >> benchmark_results.md # last 20 lines
|
||||||
echo '```' >> benchmark_results.md
|
echo '```' >> benchmark_results.md
|
||||||
|
|
||||||
# if the agent binary is not found, skip uploading the results, exit 0
|
# if the agent binary is not found, skip uploading the results, exit 0
|
||||||
if [ ! -f /usr/bin/buildkite-agent ]; then
|
if [ ! -f /workspace/buildkite-agent ]; then
|
||||||
exit 0
|
exit 0
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# upload the results to buildkite
|
# upload the results to buildkite
|
||||||
buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
|
/workspace/buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
|
||||||
|
|
||||||
# exit with the exit code of the benchmarks
|
# exit with the exit code of the benchmarks
|
||||||
if [ $bench_latency_exit_code -ne 0 ]; then
|
if [ $bench_latency_exit_code -ne 0 ]; then
|
||||||
@ -77,4 +75,4 @@ if [ $bench_serving_exit_code -ne 0 ]; then
|
|||||||
fi
|
fi
|
||||||
|
|
||||||
rm ShareGPT_V3_unfiltered_cleaned_split.json
|
rm ShareGPT_V3_unfiltered_cleaned_split.json
|
||||||
buildkite-agent artifact upload "*.json"
|
/workspace/buildkite-agent artifact upload "*.json"
|
||||||
14
.buildkite/run-cpu-test.sh
Normal file
14
.buildkite/run-cpu-test.sh
Normal file
@ -0,0 +1,14 @@
|
|||||||
|
# This script build the CPU docker image and run the offline inference inside the container.
|
||||||
|
# It serves a sanity check for compilation and basic model usage.
|
||||||
|
set -ex
|
||||||
|
|
||||||
|
# Try building the docker image
|
||||||
|
docker build -t cpu-test -f Dockerfile.cpu .
|
||||||
|
|
||||||
|
# Setup cleanup
|
||||||
|
remove_docker_container() { docker rm -f cpu-test || true; }
|
||||||
|
trap remove_docker_container EXIT
|
||||||
|
remove_docker_container
|
||||||
|
|
||||||
|
# Run the image and launch offline inference
|
||||||
|
docker run --network host --env VLLM_CPU_KVCACHE_SPACE=1 --name cpu-test cpu-test python3 vllm/examples/offline_inference.py
|
||||||
51
.buildkite/run-neuron-test.sh
Normal file
51
.buildkite/run-neuron-test.sh
Normal file
@ -0,0 +1,51 @@
|
|||||||
|
# This script build the Neuron docker image and run the API server inside the container.
|
||||||
|
# It serves a sanity check for compilation and basic model usage.
|
||||||
|
set -e
|
||||||
|
|
||||||
|
# Try building the docker image
|
||||||
|
aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-west-2.amazonaws.com
|
||||||
|
|
||||||
|
# prune old image and containers to save disk space, and only once a day
|
||||||
|
# by using a timestamp file in tmp.
|
||||||
|
if [ -f /tmp/neuron-docker-build-timestamp ]; then
|
||||||
|
last_build=$(cat /tmp/neuron-docker-build-timestamp)
|
||||||
|
current_time=$(date +%s)
|
||||||
|
if [ $((current_time - last_build)) -gt 86400 ]; then
|
||||||
|
docker system prune -f
|
||||||
|
echo $current_time > /tmp/neuron-docker-build-timestamp
|
||||||
|
fi
|
||||||
|
else
|
||||||
|
echo $(date +%s) > /tmp/neuron-docker-build-timestamp
|
||||||
|
fi
|
||||||
|
|
||||||
|
docker build -t neuron -f Dockerfile.neuron .
|
||||||
|
|
||||||
|
# Setup cleanup
|
||||||
|
remove_docker_container() { docker rm -f neuron || true; }
|
||||||
|
trap remove_docker_container EXIT
|
||||||
|
remove_docker_container
|
||||||
|
|
||||||
|
# Run the image
|
||||||
|
docker run --device=/dev/neuron0 --device=/dev/neuron1 --network host --name neuron neuron python3 -m vllm.entrypoints.api_server \
|
||||||
|
--model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --max-num-seqs 8 --max-model-len 128 --block-size 128 --device neuron --tensor-parallel-size 2 &
|
||||||
|
|
||||||
|
# Wait for the server to start
|
||||||
|
wait_for_server_to_start() {
|
||||||
|
timeout=300
|
||||||
|
counter=0
|
||||||
|
|
||||||
|
while [ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:8000/health)" != "200" ]; do
|
||||||
|
sleep 1
|
||||||
|
counter=$((counter + 1))
|
||||||
|
if [ $counter -ge $timeout ]; then
|
||||||
|
echo "Timeout after $timeout seconds"
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
done
|
||||||
|
}
|
||||||
|
wait_for_server_to_start
|
||||||
|
|
||||||
|
# Test a simple prompt
|
||||||
|
curl -X POST -H "Content-Type: application/json" \
|
||||||
|
localhost:8000/generate \
|
||||||
|
-d '{"prompt": "San Francisco is a"}'
|
||||||
@ -1,46 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -ex
|
|
||||||
|
|
||||||
# Get release version and strip leading 'v' if present
|
|
||||||
RELEASE_VERSION=$(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
|
|
||||||
@ -1,108 +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!"
|
|
||||||
@ -1,62 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -euxo pipefail
|
|
||||||
|
|
||||||
# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
|
|
||||||
THRESHOLD=${1:-0.25}
|
|
||||||
NUM_Q=${2:-1319}
|
|
||||||
PORT=${3:-8010}
|
|
||||||
OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
|
|
||||||
mkdir -p "${OUT_DIR}"
|
|
||||||
|
|
||||||
wait_for_server() {
|
|
||||||
local port=$1
|
|
||||||
timeout 600 bash -c '
|
|
||||||
until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
|
|
||||||
sleep 1
|
|
||||||
done'
|
|
||||||
}
|
|
||||||
|
|
||||||
MODEL="deepseek-ai/DeepSeek-V2-lite"
|
|
||||||
BACKENDS=("deepep_high_throughput" "deepep_low_latency")
|
|
||||||
|
|
||||||
cleanup() {
|
|
||||||
if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
|
|
||||||
kill "${SERVER_PID}" 2>/dev/null || true
|
|
||||||
for _ in {1..20}; do
|
|
||||||
kill -0 "${SERVER_PID}" 2>/dev/null || break
|
|
||||||
sleep 0.5
|
|
||||||
done
|
|
||||||
kill -9 "${SERVER_PID}" 2>/dev/null || true
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
trap cleanup EXIT
|
|
||||||
|
|
||||||
for BACK in "${BACKENDS[@]}"; do
|
|
||||||
VLLM_DEEP_GEMM_WARMUP=skip \
|
|
||||||
VLLM_ALL2ALL_BACKEND=$BACK \
|
|
||||||
vllm serve "$MODEL" \
|
|
||||||
--enforce-eager \
|
|
||||||
--tensor-parallel-size 2 \
|
|
||||||
--data-parallel-size 2 \
|
|
||||||
--enable-expert-parallel \
|
|
||||||
--enable-eplb \
|
|
||||||
--trust-remote-code \
|
|
||||||
--max-model-len 2048 \
|
|
||||||
--port $PORT &
|
|
||||||
SERVER_PID=$!
|
|
||||||
wait_for_server $PORT
|
|
||||||
|
|
||||||
TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
|
|
||||||
OUT="${OUT_DIR}/${TAG}_${BACK}.json"
|
|
||||||
python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
|
|
||||||
python3 - <<PY
|
|
||||||
import json; acc=json.load(open('${OUT}'))['accuracy']
|
|
||||||
print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
|
|
||||||
assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
|
|
||||||
PY
|
|
||||||
|
|
||||||
cleanup
|
|
||||||
SERVER_PID=
|
|
||||||
sleep 1
|
|
||||||
PORT=$((PORT+1))
|
|
||||||
done
|
|
||||||
@ -1,61 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -euxo pipefail
|
|
||||||
|
|
||||||
# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
|
|
||||||
THRESHOLD=${1:-0.8}
|
|
||||||
NUM_Q=${2:-1319}
|
|
||||||
PORT=${3:-8020}
|
|
||||||
OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
|
|
||||||
mkdir -p "${OUT_DIR}"
|
|
||||||
|
|
||||||
wait_for_server() {
|
|
||||||
local port=$1
|
|
||||||
timeout 600 bash -c '
|
|
||||||
until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
|
|
||||||
sleep 1
|
|
||||||
done'
|
|
||||||
}
|
|
||||||
|
|
||||||
MODEL="QWen/Qwen3-30B-A3B-FP8"
|
|
||||||
BACKENDS=("deepep_high_throughput" "deepep_low_latency")
|
|
||||||
|
|
||||||
cleanup() {
|
|
||||||
if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
|
|
||||||
kill "${SERVER_PID}" 2>/dev/null || true
|
|
||||||
for _ in {1..20}; do
|
|
||||||
kill -0 "${SERVER_PID}" 2>/dev/null || break
|
|
||||||
sleep 0.5
|
|
||||||
done
|
|
||||||
kill -9 "${SERVER_PID}" 2>/dev/null || true
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
trap cleanup EXIT
|
|
||||||
|
|
||||||
for BACK in "${BACKENDS[@]}"; do
|
|
||||||
VLLM_DEEP_GEMM_WARMUP=skip \
|
|
||||||
VLLM_ALL2ALL_BACKEND=$BACK \
|
|
||||||
vllm serve "$MODEL" \
|
|
||||||
--enforce-eager \
|
|
||||||
--tensor-parallel-size 2 \
|
|
||||||
--data-parallel-size 2 \
|
|
||||||
--enable-expert-parallel \
|
|
||||||
--trust-remote-code \
|
|
||||||
--max-model-len 2048 \
|
|
||||||
--port $PORT &
|
|
||||||
SERVER_PID=$!
|
|
||||||
wait_for_server $PORT
|
|
||||||
|
|
||||||
TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
|
|
||||||
OUT="${OUT_DIR}/${TAG}_${BACK}.json"
|
|
||||||
python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
|
|
||||||
python3 - <<PY
|
|
||||||
import json; acc=json.load(open('${OUT}'))['accuracy']
|
|
||||||
print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
|
|
||||||
assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
|
|
||||||
PY
|
|
||||||
|
|
||||||
cleanup
|
|
||||||
SERVER_PID=
|
|
||||||
sleep 1
|
|
||||||
PORT=$((PORT+1))
|
|
||||||
done
|
|
||||||
@ -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
93
.buildkite/test-template.j2
Normal file
93
.buildkite/test-template.j2
Normal file
@ -0,0 +1,93 @@
|
|||||||
|
{% set docker_image = "us-central1-docker.pkg.dev/vllm-405802/vllm-ci-test-repo/vllm-test:$BUILDKITE_COMMIT" %}
|
||||||
|
{% set default_num_gpu = 1 %}
|
||||||
|
{% set default_working_dir = "/vllm-workspace/tests" %}
|
||||||
|
|
||||||
|
steps:
|
||||||
|
- label: ":docker: build image"
|
||||||
|
commands:
|
||||||
|
- "docker build --build-arg max_jobs=16 --tag {{ docker_image }} --target test --progress plain ."
|
||||||
|
- "docker push {{ docker_image }}"
|
||||||
|
env:
|
||||||
|
DOCKER_BUILDKIT: "1"
|
||||||
|
retry:
|
||||||
|
automatic:
|
||||||
|
- exit_status: -1 # Agent was lost
|
||||||
|
limit: 5
|
||||||
|
- exit_status: -10 # Agent was lost
|
||||||
|
limit: 5
|
||||||
|
- wait
|
||||||
|
|
||||||
|
- group: "AMD Tests"
|
||||||
|
depends_on: ~
|
||||||
|
steps:
|
||||||
|
{% for step in steps %}
|
||||||
|
{% if step.mirror_hardwares and "amd" in step.mirror_hardwares %}
|
||||||
|
- label: "AMD: {{ step.label }}"
|
||||||
|
agents:
|
||||||
|
queue: amd
|
||||||
|
command: bash .buildkite/run-amd-test.sh "cd {{ (step.working_dir or default_working_dir) | safe }} ; {{ step.command or (step.commands | join(" ; ")) | safe }}"
|
||||||
|
env:
|
||||||
|
DOCKER_BUILDKIT: "1"
|
||||||
|
{% endif %}
|
||||||
|
{% endfor %}
|
||||||
|
|
||||||
|
- label: "Neuron Test"
|
||||||
|
depends_on: ~
|
||||||
|
agents:
|
||||||
|
queue: neuron
|
||||||
|
command: bash .buildkite/run-neuron-test.sh
|
||||||
|
soft_fail: true
|
||||||
|
|
||||||
|
- label: "Intel Test"
|
||||||
|
depends_on: ~
|
||||||
|
command: bash .buildkite/run-cpu-test.sh
|
||||||
|
|
||||||
|
{% for step in steps %}
|
||||||
|
- label: "{{ step.label }}"
|
||||||
|
agents:
|
||||||
|
queue: kubernetes
|
||||||
|
soft_fail: {{ step.soft_fail or false }}
|
||||||
|
{% if step.parallelism %}
|
||||||
|
parallelism: {{ step.parallelism }}
|
||||||
|
{% endif %}
|
||||||
|
retry:
|
||||||
|
automatic:
|
||||||
|
- exit_status: -1 # Agent was lost
|
||||||
|
limit: 5
|
||||||
|
- exit_status: -10 # Agent was lost
|
||||||
|
limit: 5
|
||||||
|
plugins:
|
||||||
|
- kubernetes:
|
||||||
|
podSpec:
|
||||||
|
{% if step.num_gpus %}
|
||||||
|
priorityClassName: gpu-priority-cls-{{ step.num_gpus }}
|
||||||
|
{% endif %}
|
||||||
|
volumes:
|
||||||
|
- name: dshm
|
||||||
|
emptyDir:
|
||||||
|
medium: Memory
|
||||||
|
containers:
|
||||||
|
- image: "{{ docker_image }}"
|
||||||
|
command: ["bash"]
|
||||||
|
args:
|
||||||
|
- '-c'
|
||||||
|
- "'cd {{ (step.working_dir or default_working_dir) | safe }} && {{ step.command or (step.commands | join(' && ')) | safe }}'"
|
||||||
|
{% if not step.no_gpu %}
|
||||||
|
resources:
|
||||||
|
requests:
|
||||||
|
nvidia.com/gpu: "{{ step.num_gpus or default_num_gpu }}"
|
||||||
|
limits:
|
||||||
|
nvidia.com/gpu: "{{ step.num_gpus or default_num_gpu }}"
|
||||||
|
{% endif %}
|
||||||
|
env:
|
||||||
|
- name: VLLM_USAGE_SOURCE
|
||||||
|
value: ci-test
|
||||||
|
- name: HF_TOKEN
|
||||||
|
valueFrom:
|
||||||
|
secretKeyRef:
|
||||||
|
name: hf-token-secret
|
||||||
|
key: token
|
||||||
|
volumeMounts:
|
||||||
|
- mountPath: /dev/shm
|
||||||
|
name: dshm
|
||||||
|
{% endfor %}
|
||||||
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 +1 @@
|
|||||||
/.venv
|
|
||||||
/build
|
|
||||||
dist
|
|
||||||
vllm/*.so
|
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"]
|
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user