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correct-do
| Author | SHA1 | Date | |
|---|---|---|---|
| c1d1875ba3 |
@ -1,20 +1,14 @@
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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 sys
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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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# 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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# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 250 MB
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VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 250))
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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.sort(key=lambda x: x[1], reverse=True)
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for f, size in file_sizes[:10]:
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@ -29,18 +23,14 @@ def check_wheel_size(directory):
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wheel_path = os.path.join(root, file_name)
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wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
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if wheel_size_mb > VLLM_MAX_SIZE_MB:
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print(
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f"Not allowed: Wheel {wheel_path} is larger "
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f"({wheel_size_mb:.2f} MB) than the limit "
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f"({VLLM_MAX_SIZE_MB} MB)."
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)
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print(f"Not allowed: Wheel {wheel_path} is larger "
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f"({wheel_size_mb:.2f} MB) than the limit "
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f"({VLLM_MAX_SIZE_MB} MB).")
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print_top_10_largest_files(wheel_path)
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return 1
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else:
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print(
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f"Wheel {wheel_path} is within the allowed size "
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f"({wheel_size_mb:.2f} MB)."
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)
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print(f"Wheel {wheel_path} is within the allowed size "
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f"({wheel_size_mb:.2f} MB).")
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return 0
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@ -50,4 +40,4 @@ if __name__ == "__main__":
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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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sys.exit(check_wheel_size(directory))
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@ -1,6 +1,3 @@
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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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@ -8,8 +5,7 @@ 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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<a href="../{wheel_html_escaped}">{wheel}</a><br/>
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</body>
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</html>
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"""
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@ -22,25 +18,7 @@ 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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template.format(wheel=filename,
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wheel_html_escaped=filename.replace("+", "%2B")))
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@ -1,4 +1,3 @@
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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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tasks:
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@ -1,4 +1,3 @@
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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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@ -1,4 +1,3 @@
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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:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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"
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tasks:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
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tasks:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
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tasks:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
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tasks:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
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tasks:
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|
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
|
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# 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
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model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
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tasks:
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
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tasks:
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@ -1,5 +1,4 @@
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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-3-8B-Instruct -b 32 -l 250 -f 5
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# 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 -t 1
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model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
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tasks:
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- name: "gsm8k"
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@ -1,4 +1,3 @@
|
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "HandH1998/QQQ-Llama-3-8b-g128"
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tasks:
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|
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@ -1,11 +0,0 @@
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# 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
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model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
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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: 0.335
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- name: "exact_match,flexible-extract"
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value: 0.323
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limit: 1319
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num_fewshot: 5
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@ -1,4 +1,3 @@
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# For vllm script, with -t option (tensor parallel size).
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# 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
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model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
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tasks:
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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-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
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model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
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backend: "vllm-vlm"
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tasks:
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- name: "chartqa"
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metrics:
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- name: "relaxed_accuracy,none"
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# TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
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value: 0.80
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limit: 100
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num_fewshot: 0
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@ -1,10 +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-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
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model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
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tasks:
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- name: "mmlu_pro"
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metrics:
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- name: "exact_match,custom-extract"
|
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value: 0.80
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limit: 250 # will run on 250 * 14 subjects = 3500 samples
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num_fewshot: 5
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@ -1,12 +1,11 @@
|
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# For vllm script, with -t option (tensor parallel size).
|
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# 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
|
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model_name: "mgoin/Minitron-4B-Base-FP8"
|
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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: 0.231
|
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value: 0.233
|
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- name: "exact_match,flexible-extract"
|
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value: 0.22
|
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value: 0.236
|
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limit: 1000
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num_fewshot: 5
|
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|
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@ -1,4 +1,3 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
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# 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
|
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model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
|
||||
tasks:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
# For vllm script, with -t option (tensor parallel size).
|
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# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
|
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model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
|
||||
tasks:
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
# 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 neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5
|
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# 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 -t 4
|
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model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
|
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tasks:
|
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- name: "gsm8k"
|
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|
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@ -1,12 +0,0 @@
|
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# For vllm script, with -t option (tensor parallel size).
|
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# 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
|
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model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
|
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tasks:
|
||||
- name: "gsm8k"
|
||||
metrics:
|
||||
- name: "exact_match,strict-match"
|
||||
value: 0.30
|
||||
- name: "exact_match,flexible-extract"
|
||||
value: 0.465
|
||||
limit: 1319
|
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num_fewshot: 5
|
||||
@ -1,4 +1,3 @@
|
||||
# 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:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
# 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:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
# 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:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
# 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:
|
||||
|
||||
@ -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
|
||||
@ -1,12 +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 @@
|
||||
Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.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 +1,10 @@
|
||||
Qwen2.5-1.5B-Instruct.yaml
|
||||
Meta-Llama-3-8B-Instruct.yaml
|
||||
Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.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
|
||||
Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
|
||||
Minitron-4B-Base-FP8.yaml
|
||||
Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
|
||||
Qwen2-1.5B-Instruct-FP8W8.yaml
|
||||
Meta-Llama-3-8B-QQQ.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 $LIMIT
|
||||
2
.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
Executable file → Normal file
2
.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
Executable file → Normal file
@ -2,7 +2,7 @@
|
||||
# We can use this script to compute baseline accuracy on GSM for transformers.
|
||||
#
|
||||
# Make sure you have lm-eval-harness installed:
|
||||
# pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
|
||||
# pip install lm-eval==0.4.4
|
||||
|
||||
usage() {
|
||||
echo``
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
# We use this for fp8, which HF does not support.
|
||||
#
|
||||
# Make sure you have lm-eval-harness installed:
|
||||
# pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
|
||||
# pip install lm-eval==0.4.4
|
||||
|
||||
usage() {
|
||||
echo``
|
||||
@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
|
||||
done
|
||||
|
||||
lm_eval --model vllm \
|
||||
--model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true,trust_remote_code=true,max_model_len=4096" \
|
||||
--model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
|
||||
--tasks gsm8k --num_fewshot "$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
|
||||
59
.buildkite/lm-eval-harness/run-tests.sh
Normal file
59
.buildkite/lm-eval-harness/run-tests.sh
Normal file
@ -0,0 +1,59 @@
|
||||
#!/bin/bash
|
||||
|
||||
usage() {
|
||||
echo``
|
||||
echo "Runs lm eval harness on GSM8k using vllm and compares to "
|
||||
echo "precomputed baseline (measured by HF transformers.)"
|
||||
echo
|
||||
echo "usage: ${0} <options>"
|
||||
echo
|
||||
echo " -c - path to the test data config (e.g. configs/small-models.txt)"
|
||||
echo " -t - tensor parallel size"
|
||||
echo
|
||||
}
|
||||
|
||||
SUCCESS=0
|
||||
|
||||
while getopts "c:t:" OPT; do
|
||||
case ${OPT} in
|
||||
c )
|
||||
CONFIG="$OPTARG"
|
||||
;;
|
||||
t )
|
||||
TP_SIZE="$OPTARG"
|
||||
;;
|
||||
\? )
|
||||
usage
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
# Parse list of configs.
|
||||
IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < "$CONFIG"
|
||||
|
||||
for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
|
||||
do
|
||||
LOCAL_SUCCESS=0
|
||||
|
||||
echo "=== RUNNING MODEL: $MODEL_CONFIG WITH TP SIZE: $TP_SIZE==="
|
||||
|
||||
export LM_EVAL_TEST_DATA_FILE=$PWD/configs/${MODEL_CONFIG}
|
||||
export LM_EVAL_TP_SIZE=$TP_SIZE
|
||||
pytest -s test_lm_eval_correctness.py || LOCAL_SUCCESS=$?
|
||||
|
||||
if [[ $LOCAL_SUCCESS == 0 ]]; then
|
||||
echo "=== PASSED MODEL: ${MODEL_CONFIG} ==="
|
||||
else
|
||||
echo "=== FAILED MODEL: ${MODEL_CONFIG} ==="
|
||||
fi
|
||||
|
||||
SUCCESS=$((SUCCESS + LOCAL_SUCCESS))
|
||||
|
||||
done
|
||||
|
||||
if [ "${SUCCESS}" -eq "0" ]; then
|
||||
exit 0
|
||||
else
|
||||
exit 1
|
||||
fi
|
||||
@ -1,63 +1,63 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
"""
|
||||
LM eval harness on model to compare vs HF baseline computed offline.
|
||||
Configs are found in configs/$MODEL.yaml
|
||||
|
||||
pytest -s -v test_lm_eval_correctness.py \
|
||||
--config-list-file=configs/models-small.txt \
|
||||
--tp-size=1
|
||||
* export LM_EVAL_TEST_DATA_FILE=configs/Meta-Llama-3-70B-Instruct.yaml
|
||||
* export LM_EVAL_TP_SIZE=4
|
||||
* pytest -s test_lm_eval_correctness.py
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import lm_eval
|
||||
import numpy as np
|
||||
import numpy
|
||||
import yaml
|
||||
|
||||
RTOL = 0.08
|
||||
RTOL = 0.05
|
||||
TEST_DATA_FILE = os.environ.get(
|
||||
"LM_EVAL_TEST_DATA_FILE",
|
||||
".buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml")
|
||||
|
||||
TP_SIZE = os.environ.get("LM_EVAL_TP_SIZE", 1)
|
||||
|
||||
|
||||
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")
|
||||
model_args = (
|
||||
f"pretrained={eval_config['model_name']},"
|
||||
f"tensor_parallel_size={tp_size},"
|
||||
f"enforce_eager=true,"
|
||||
f"add_bos_token=true,"
|
||||
f"trust_remote_code={trust_remote_code},"
|
||||
f"max_model_len={max_model_len},"
|
||||
)
|
||||
def launch_lm_eval(eval_config):
|
||||
trust_remote_code = eval_config.get('trust_remote_code', False)
|
||||
|
||||
model_args = f"pretrained={eval_config['model_name']}," \
|
||||
f"tensor_parallel_size={TP_SIZE}," \
|
||||
f"add_bos_token=true," \
|
||||
f"trust_remote_code={trust_remote_code}"
|
||||
|
||||
results = lm_eval.simple_evaluate(
|
||||
model=backend,
|
||||
model="vllm",
|
||||
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.
|
||||
apply_chat_template=backend == "vllm-vlm",
|
||||
batch_size=batch_size,
|
||||
)
|
||||
batch_size="auto")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def test_lm_eval_correctness_param(config_filename, tp_size):
|
||||
eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
|
||||
def test_lm_eval_correctness():
|
||||
eval_config = yaml.safe_load(
|
||||
Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
|
||||
|
||||
results = launch_lm_eval(eval_config, tp_size)
|
||||
# Launch eval requests.
|
||||
results = launch_lm_eval(eval_config)
|
||||
|
||||
# Confirm scores match ground truth.
|
||||
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)
|
||||
print(f'{task["name"]} | {metric["name"]}: '
|
||||
f'ground_truth={ground_truth} | measured={measured_value}')
|
||||
success = success and numpy.isclose(
|
||||
ground_truth, measured_value, rtol=RTOL)
|
||||
|
||||
# Assert at the end, print all scores even on failure for debugging.
|
||||
assert success
|
||||
|
||||
@ -1,62 +1,54 @@
|
||||
# vLLM benchmark suite
|
||||
|
||||
|
||||
## Introduction
|
||||
|
||||
This directory contains two sets of benchmark for vllm.
|
||||
|
||||
- Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
|
||||
- Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
|
||||
|
||||
See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
|
||||
|
||||
See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
|
||||
|
||||
|
||||
## Performance benchmark quick overview
|
||||
|
||||
**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) and Intel® Xeon® Processors, with different models.
|
||||
**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), 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.
|
||||
|
||||
|
||||
## Nightly benchmark quick overview
|
||||
|
||||
**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.
|
||||
**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.
|
||||
|
||||
**Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
|
||||
|
||||
**Benchmarking Duration**: about 3.5hrs.
|
||||
|
||||
|
||||
|
||||
## Trigger the benchmark
|
||||
|
||||
Performance benchmark will be triggered when:
|
||||
|
||||
- A PR being merged into vllm.
|
||||
- Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
|
||||
|
||||
Manually Trigger the benchmark
|
||||
|
||||
```bash
|
||||
bash .buildkite/nightly-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.
|
||||
|
||||
Nightly benchmark will be triggered when:
|
||||
|
||||
- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
|
||||
|
||||
|
||||
|
||||
|
||||
## Performance benchmark details
|
||||
|
||||
|
||||
See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
|
||||
> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
|
||||
>
|
||||
### Latency test
|
||||
|
||||
|
||||
#### Latency test
|
||||
|
||||
Here is an example of one test inside `latency-tests.json`:
|
||||
|
||||
@ -76,25 +68,23 @@ Here is an example of one test inside `latency-tests.json`:
|
||||
```
|
||||
|
||||
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`
|
||||
- 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 `benchmark_latency.py`. 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 `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` 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`.
|
||||
#### 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 `benchmark_throughput.py`.
|
||||
|
||||
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
|
||||
#### Serving test
|
||||
We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
|
||||
|
||||
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",
|
||||
@ -104,6 +94,7 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
|
||||
"tensor_parallel_size": 1,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
@ -118,60 +109,45 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
|
||||
```
|
||||
|
||||
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 `client-parameters` includes the command line arguments for `benchmark_serving.py`.
|
||||
- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `benchmark_serving.py`
|
||||
|
||||
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.
|
||||
#### Visualizing the results
|
||||
The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/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" />
|
||||
|
||||
## Nightly test details
|
||||
|
||||
See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
|
||||
|
||||
### Workflow
|
||||
|
||||
- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
|
||||
- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
|
||||
- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
|
||||
- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
|
||||
#### Workflow
|
||||
|
||||
### Nightly tests
|
||||
- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
|
||||
- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
|
||||
- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
|
||||
- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
|
||||
|
||||
#### Nightly tests
|
||||
|
||||
In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
|
||||
|
||||
### Docker containers
|
||||
#### Docker containers
|
||||
|
||||
The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
|
||||
|
||||
WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
|
||||
WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
|
||||
|
||||
WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
|
||||
|
||||
|
||||
@ -10,18 +10,12 @@ steps:
|
||||
- image: badouralix/curl-jq
|
||||
command:
|
||||
- sh .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
|
||||
- label: "Cleanup H100"
|
||||
agents:
|
||||
queue: H100
|
||||
depends_on: ~
|
||||
command: docker system prune -a --volumes --force
|
||||
|
||||
|
||||
- label: "A100"
|
||||
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
|
||||
agents:
|
||||
queue: A100
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch == "main"
|
||||
plugins:
|
||||
- kubernetes:
|
||||
podSpec:
|
||||
@ -56,7 +50,6 @@ steps:
|
||||
agents:
|
||||
queue: H200
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch == "main"
|
||||
plugins:
|
||||
- docker#v5.12.0:
|
||||
image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
|
||||
@ -82,7 +75,6 @@ steps:
|
||||
agents:
|
||||
queue: H100
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch == "main"
|
||||
plugins:
|
||||
- docker#v5.12.0:
|
||||
image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
|
||||
@ -98,87 +90,3 @@ steps:
|
||||
environment:
|
||||
- VLLM_USAGE_SOURCE
|
||||
- HF_TOKEN
|
||||
|
||||
# Premerge benchmark
|
||||
- label: "A100"
|
||||
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
|
||||
agents:
|
||||
queue: A100
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch != "main"
|
||||
plugins:
|
||||
- kubernetes:
|
||||
podSpec:
|
||||
priorityClassName: perf-benchmark
|
||||
containers:
|
||||
- image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
|
||||
command:
|
||||
- bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
|
||||
resources:
|
||||
limits:
|
||||
nvidia.com/gpu: 8
|
||||
volumeMounts:
|
||||
- name: devshm
|
||||
mountPath: /dev/shm
|
||||
env:
|
||||
- name: VLLM_USAGE_SOURCE
|
||||
value: ci-test
|
||||
- name: HF_TOKEN
|
||||
valueFrom:
|
||||
secretKeyRef:
|
||||
name: hf-token-secret
|
||||
key: token
|
||||
nodeSelector:
|
||||
nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
|
||||
volumes:
|
||||
- name: devshm
|
||||
emptyDir:
|
||||
medium: Memory
|
||||
|
||||
- label: "H200"
|
||||
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
|
||||
agents:
|
||||
queue: H200
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch != "main"
|
||||
plugins:
|
||||
- docker#v5.12.0:
|
||||
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
|
||||
command:
|
||||
- bash
|
||||
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
|
||||
mount-buildkite-agent: true
|
||||
propagate-environment: true
|
||||
ipc: host
|
||||
gpus: 4,5,6,7
|
||||
volumes:
|
||||
- /data/benchmark-hf-cache:/root/.cache/huggingface
|
||||
environment:
|
||||
- VLLM_USAGE_SOURCE
|
||||
- HF_TOKEN
|
||||
|
||||
#- block: "Run H100 Benchmark"
|
||||
#key: block-h100
|
||||
#depends_on: ~
|
||||
|
||||
- label: "H100"
|
||||
# skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
|
||||
agents:
|
||||
queue: H100
|
||||
depends_on: wait-for-container-image
|
||||
if: build.branch != "main"
|
||||
plugins:
|
||||
- docker#v5.12.0:
|
||||
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
|
||||
command:
|
||||
- bash
|
||||
- .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
|
||||
mount-buildkite-agent: true
|
||||
propagate-environment: true
|
||||
ipc: host
|
||||
gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
|
||||
volumes:
|
||||
- /data/benchmark-hf-cache:/root/.cache/huggingface
|
||||
environment:
|
||||
- VLLM_USAGE_SOURCE
|
||||
- HF_TOKEN
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
# Nightly benchmark annotation
|
||||
|
||||
## Description
|
||||
|
||||
@ -10,19 +9,20 @@ This file contains the downloading link for benchmarking results.
|
||||
|
||||
Please download the visualization scripts in the post
|
||||
|
||||
|
||||
## Results reproduction
|
||||
|
||||
- Find the docker we use in `benchmarking pipeline`
|
||||
- Deploy the docker, and inside the docker:
|
||||
- Download `nightly-benchmarks.zip`.
|
||||
- In the same folder, run the following code:
|
||||
|
||||
```bash
|
||||
export HF_TOKEN=<your HF token>
|
||||
apt update
|
||||
apt install -y git
|
||||
unzip nightly-benchmarks.zip
|
||||
VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
|
||||
```
|
||||
- Download `nightly-benchmarks.zip`.
|
||||
- In the same folder, run the following code
|
||||
```
|
||||
export HF_TOKEN=<your HF token>
|
||||
apt update
|
||||
apt install -y git
|
||||
unzip nightly-benchmarks.zip
|
||||
VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
|
||||
```
|
||||
|
||||
And the results will be inside `./benchmarks/results`.
|
||||
|
||||
|
||||
@ -2,38 +2,38 @@
|
||||
# Nightly benchmark
|
||||
|
||||
This benchmark aims to:
|
||||
|
||||
- Provide performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and SGLang) leads in performance in what workload.
|
||||
- Be reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions.
|
||||
|
||||
Latest results: [results link](https://blog.vllm.ai/2024/09/05/perf-update.html), scroll to the end.
|
||||
|
||||
Latest reproduction guide: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
|
||||
Latest reproduction guilde: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
|
||||
|
||||
|
||||
## Setup
|
||||
|
||||
- Docker images:
|
||||
- vLLM: `vllm/vllm-openai:v0.6.2`
|
||||
- SGLang: `lmsysorg/sglang:v0.3.2-cu121`
|
||||
- LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
|
||||
- TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
|
||||
- *NOTE: we use r24.07 as the current implementation only works for this version. We are going to bump this up.*
|
||||
- Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
|
||||
- vLLM: `vllm/vllm-openai:v0.6.2`
|
||||
- SGLang: `lmsysorg/sglang:v0.3.2-cu121`
|
||||
- LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
|
||||
- TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
|
||||
- *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
|
||||
- Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
|
||||
- Hardware
|
||||
- 8x Nvidia A100 GPUs
|
||||
- 8x Nvidia A100 GPUs
|
||||
- Workload:
|
||||
- Dataset
|
||||
- ShareGPT dataset
|
||||
- Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
|
||||
- Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
|
||||
- Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
|
||||
- Models: llama-3 8B, llama-3 70B.
|
||||
- We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
|
||||
- Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
|
||||
- Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
|
||||
- Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
|
||||
- Dataset
|
||||
- ShareGPT dataset
|
||||
- Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
|
||||
- Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
|
||||
- Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
|
||||
- Models: llama-3 8B, llama-3 70B.
|
||||
- We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
|
||||
- Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
|
||||
- Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
|
||||
- Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
|
||||
|
||||
## Known issues
|
||||
# Known issues
|
||||
|
||||
- TRT-LLM crashes with Llama 3.1 8B [issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105).
|
||||
- TGI does not support `ignore-eos` flag.
|
||||
- TGI does not support `ignore-eos` flag.
|
||||
@ -1,48 +1,45 @@
|
||||
# Performance benchmarks descriptions
|
||||
|
||||
## Latency tests
|
||||
|
||||
- Input length: 32 tokens.
|
||||
- Output length: 128 tokens.
|
||||
- Batch size: fixed (8).
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- Evaluation metrics: end-to-end latency (mean, median, p99).
|
||||
|
||||
|
||||
{latency_tests_markdown_table}
|
||||
|
||||
|
||||
## Throughput tests
|
||||
|
||||
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
||||
- Output length: the corresponding output length of these 200 prompts.
|
||||
- Batch size: dynamically determined by vllm to achieve maximum throughput.
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- Evaluation metrics: throughput.
|
||||
|
||||
|
||||
{throughput_tests_markdown_table}
|
||||
|
||||
|
||||
## Serving tests
|
||||
|
||||
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
||||
- Output length: the corresponding output length of these 200 prompts.
|
||||
- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
|
||||
- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
|
||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
|
||||
- CPU Models: llama-3.1 8B.
|
||||
- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||
- We also added a speculative decoding test for llama-3 70B, under QPS 2
|
||||
- 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.
|
||||
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
|
||||
@ -57,9 +54,9 @@ 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.
|
||||
|
||||
|
||||
@ -1,307 +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
|
||||
|
||||
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]
|
||||
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
|
||||
|
||||
|
||||
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",
|
||||
)
|
||||
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",
|
||||
]
|
||||
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",
|
||||
]
|
||||
|
||||
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_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
|
||||
for name, group in output_groups:
|
||||
html = group.to_html()
|
||||
text_file.write(html_msgs_for_data_cols[i])
|
||||
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,
|
||||
)
|
||||
# Export to HTML
|
||||
text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))
|
||||
@ -1,19 +1,12 @@
|
||||
# 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
|
||||
|
||||
results_folder = Path("results/")
|
||||
|
||||
# latency results and the keys that will be printed into markdown
|
||||
latency_results = []
|
||||
latency_column_mapping = {
|
||||
@ -33,39 +26,28 @@ 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)",
|
||||
# "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)",
|
||||
# "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.",
|
||||
# "completed": "# of req.",
|
||||
"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",
|
||||
# "input_throughput": "Input Tput (tok/s)",
|
||||
# "output_throughput": "Output Tput (tok/s)",
|
||||
"mean_ttft_ms": "Mean TTFT (ms)",
|
||||
"median_ttft_ms": "Median TTFT (ms)",
|
||||
"p99_ttft_ms": "P99 TTFT (ms)",
|
||||
"mean_tpot_ms": "Mean TPOT (ms)",
|
||||
"median_tpot_ms": "Median",
|
||||
"p99_tpot_ms": "P99",
|
||||
# "mean_tpot_ms": "Mean TPOT (ms)",
|
||||
# "median_tpot_ms": "Median",
|
||||
# "p99_tpot_ms": "P99",
|
||||
"mean_itl_ms": "Mean ITL (ms)",
|
||||
"median_itl_ms": "Median ITL (ms)",
|
||||
"p99_itl_ms": "P99 ITL (ms)",
|
||||
@ -81,194 +63,42 @@ def read_markdown(file):
|
||||
|
||||
|
||||
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}
|
||||
return json.dumps({
|
||||
'latency': latency.to_dict(),
|
||||
'throughput': throughput.to_dict(),
|
||||
'serving': serving.to_dict()
|
||||
})
|
||||
|
||||
|
||||
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
|
||||
# this result is generated via `benchmark_serving.py`
|
||||
|
||||
# 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
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
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
|
||||
# this result is generated via `benchmark_latency.py`
|
||||
|
||||
# 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
|
||||
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
raw_result.update(command)
|
||||
|
||||
# update the test name of this result
|
||||
@ -278,8 +108,7 @@ if __name__ == "__main__":
|
||||
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)]}
|
||||
)
|
||||
{f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
|
||||
raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
|
||||
|
||||
# add the result to raw_result
|
||||
@ -287,16 +116,11 @@ if __name__ == "__main__":
|
||||
continue
|
||||
|
||||
elif "throughput" in f.name:
|
||||
# this result is generated via `vllm bench throughput` command
|
||||
# this result is generated via `benchmark_throughput.py`
|
||||
|
||||
# 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
|
||||
|
||||
with open(test_file.with_suffix(".commands")) as f:
|
||||
command = json.loads(f.read())
|
||||
raw_result.update(command)
|
||||
|
||||
# update the test name of this result
|
||||
@ -312,51 +136,26 @@ if __name__ == "__main__":
|
||||
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
|
||||
)
|
||||
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
|
||||
)
|
||||
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
|
||||
)
|
||||
serving_results = serving_results[list(
|
||||
serving_column_mapping.keys())].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)
|
||||
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
|
||||
)
|
||||
processed_results_json = results_to_json(latency_results,
|
||||
throughput_results,
|
||||
serving_results)
|
||||
|
||||
for df in [latency_results, serving_results, throughput_results]:
|
||||
if df.empty:
|
||||
@ -368,45 +167,38 @@ if __name__ == "__main__":
|
||||
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
|
||||
# we want to turn it into "8xGPUTYPE"
|
||||
df["GPU"] = df["GPU"].apply(
|
||||
lambda x: f"{len(x.splitlines())}x{x.splitlines()[0]}"
|
||||
)
|
||||
lambda x: f"{len(x.split('\n'))}x{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
|
||||
)
|
||||
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)
|
||||
|
||||
# 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/nightly-benchmarks/"
|
||||
+ "performance-benchmarks-descriptions.md"
|
||||
)
|
||||
with open(results_folder / "benchmark_results.md", "w") as f:
|
||||
|
||||
results = read_markdown("../.buildkite/nightly-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,
|
||||
)
|
||||
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")
|
||||
)
|
||||
with open(results_folder / "benchmark_results.json", "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))
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import argparse
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
@ -15,12 +12,15 @@ def main(model, cachedir):
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Download and save Hugging Face tokenizer"
|
||||
)
|
||||
parser.add_argument("--model", type=str, required=True, help="Name of the model")
|
||||
parser.add_argument(
|
||||
"--cachedir", type=str, required=True, help="Directory to save the tokenizer"
|
||||
)
|
||||
description="Download and save Hugging Face tokenizer")
|
||||
parser.add_argument("--model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Name of the model")
|
||||
parser.add_argument("--cachedir",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Directory to save the tokenizer")
|
||||
|
||||
args = parser.parse_args()
|
||||
main(args.model, args.cachedir)
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import argparse
|
||||
import json
|
||||
from pathlib import Path
|
||||
@ -12,33 +9,33 @@ from tabulate import tabulate
|
||||
|
||||
def parse_arguments():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Parse command line arguments for summary-nightly-results script."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--results-folder",
|
||||
type=str,
|
||||
required=True,
|
||||
help="The folder where the results are stored.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--description", type=str, required=True, help="Description of the results."
|
||||
)
|
||||
description=
|
||||
'Parse command line arguments for summary-nightly-results script.')
|
||||
parser.add_argument('--results-folder',
|
||||
type=str,
|
||||
required=True,
|
||||
help='The folder where the results are stored.')
|
||||
parser.add_argument('--description',
|
||||
type=str,
|
||||
required=True,
|
||||
help='Description of the results.')
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def get_perf(df, method, model, metric):
|
||||
|
||||
means = []
|
||||
|
||||
for qps in [2, 4, 8, 16, "inf"]:
|
||||
target = df["Test name"].str.contains(model)
|
||||
target = target & df["Engine"].str.contains(method)
|
||||
target = target & df["Test name"].str.contains("qps_" + str(qps))
|
||||
target = df['Test name'].str.contains(model)
|
||||
target = target & df['Engine'].str.contains(method)
|
||||
target = target & df['Test name'].str.contains("qps_" + str(qps))
|
||||
filtered_df = df[target]
|
||||
|
||||
if filtered_df.empty:
|
||||
means.append(0.0)
|
||||
means.append(0.)
|
||||
else:
|
||||
means.append(filtered_df[metric].values[0])
|
||||
|
||||
@ -46,6 +43,7 @@ def get_perf(df, method, model, metric):
|
||||
|
||||
|
||||
def get_perf_w_std(df, method, model, metric):
|
||||
|
||||
if metric in ["TTFT", "ITL"]:
|
||||
mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
|
||||
mean = mean.tolist()
|
||||
@ -60,8 +58,7 @@ def get_perf_w_std(df, method, model, metric):
|
||||
else:
|
||||
assert metric == "Tput"
|
||||
mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
|
||||
df, method, model, "Output Tput (tok/s)"
|
||||
)
|
||||
df, method, model, "Output Tput (tok/s)")
|
||||
mean = mean.tolist()
|
||||
std = None
|
||||
|
||||
@ -81,17 +78,18 @@ def main(args):
|
||||
# generate markdown table
|
||||
df = pd.DataFrame.from_dict(results)
|
||||
|
||||
md_table = tabulate(df, headers="keys", tablefmt="pipe", showindex=False)
|
||||
md_table = tabulate(df, headers='keys', tablefmt='pipe', showindex=False)
|
||||
|
||||
with open(args.description) as f:
|
||||
description = f.read()
|
||||
|
||||
description = description.format(nightly_results_benchmarking_table=md_table)
|
||||
description = description.format(
|
||||
nightly_results_benchmarking_table=md_table)
|
||||
|
||||
with open("nightly_results.md", "w") as f:
|
||||
f.write(description)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
args = parse_arguments()
|
||||
main(args)
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from lmdeploy.serve.openai.api_client import APIClient
|
||||
|
||||
api_client = APIClient("http://localhost:8000")
|
||||
|
||||
@ -181,14 +181,18 @@ launch_vllm_server() {
|
||||
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 \
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
-tp $tp \
|
||||
--model $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="vllm serve $model \
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
-tp $tp \
|
||||
--model $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
@ -43,7 +43,7 @@ main() {
|
||||
|
||||
|
||||
|
||||
# The figures should be generated by a separate process outside the CI/CD pipeline
|
||||
# The figures should be genereated by a separate process outside the CI/CD pipeline
|
||||
|
||||
# # generate figures
|
||||
# python3 -m pip install tabulate pandas matplotlib
|
||||
|
||||
@ -73,7 +73,7 @@ get_current_llm_serving_engine() {
|
||||
echo "Container: vllm"
|
||||
# move to a completely irrelevant directory, to avoid import vllm from current folder
|
||||
export CURRENT_LLM_SERVING_ENGINE=vllm
|
||||
|
||||
|
||||
return
|
||||
fi
|
||||
}
|
||||
@ -95,14 +95,12 @@ json2args() {
|
||||
}
|
||||
|
||||
kill_gpu_processes() {
|
||||
pkill -f '[p]ython'
|
||||
pkill -f '[p]ython3'
|
||||
pkill -f '[t]ritonserver'
|
||||
pkill -f '[p]t_main_thread'
|
||||
pkill -f '[t]ext-generation'
|
||||
pkill -f '[l]mdeploy'
|
||||
# vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
|
||||
pkill -f '[V]LLM'
|
||||
pkill -f python
|
||||
pkill -f python3
|
||||
pkill -f tritonserver
|
||||
pkill -f pt_main_thread
|
||||
pkill -f text-generation
|
||||
pkill -f lmdeploy
|
||||
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
@ -127,7 +125,7 @@ ensure_installed() {
|
||||
}
|
||||
|
||||
run_serving_tests() {
|
||||
# run serving tests using `vllm bench serve` command
|
||||
# run serving tests using `benchmark_serving.py`
|
||||
# $1: a json file specifying serving test cases
|
||||
|
||||
local serving_test_file
|
||||
@ -227,7 +225,7 @@ run_serving_tests() {
|
||||
|
||||
if [[ "$dataset_name" = "sharegpt" ]]; then
|
||||
|
||||
client_command="vllm bench serve \
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--backend $backend \
|
||||
--tokenizer /tokenizer_cache \
|
||||
--model $model \
|
||||
@ -248,7 +246,7 @@ run_serving_tests() {
|
||||
sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
|
||||
sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
|
||||
|
||||
client_command="vllm bench serve \
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--backend $backend \
|
||||
--tokenizer /tokenizer_cache \
|
||||
--model $model \
|
||||
@ -267,13 +265,13 @@ run_serving_tests() {
|
||||
$client_args"
|
||||
|
||||
else
|
||||
|
||||
|
||||
echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
|
||||
exit 1
|
||||
|
||||
fi
|
||||
|
||||
|
||||
|
||||
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
@ -303,104 +301,6 @@ run_serving_tests() {
|
||||
kill_gpu_processes
|
||||
}
|
||||
|
||||
run_genai_perf_tests() {
|
||||
# run genai-perf tests
|
||||
|
||||
# $1: a json file specifying genai-perf test cases
|
||||
local genai_perf_test_file
|
||||
genai_perf_test_file=$1
|
||||
|
||||
# Iterate over genai-perf tests
|
||||
jq -c '.[]' "$genai_perf_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_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 $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# prepend the current serving engine to the test name
|
||||
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
|
||||
|
||||
# get common parameters
|
||||
common_params=$(echo "$params" | jq -r '.common_parameters')
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
|
||||
dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
|
||||
reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
|
||||
|
||||
# get client and server arguments
|
||||
server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
|
||||
qps_list=$(echo "$params" | jq -r '.qps_list')
|
||||
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
||||
echo "Running over qps list $qps_list"
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
if [[ $reuse_server == "true" ]]; then
|
||||
echo "Reuse previous server for test case $test_name"
|
||||
else
|
||||
kill_gpu_processes
|
||||
bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
|
||||
"$server_params" "$common_params"
|
||||
fi
|
||||
|
||||
if wait_for_server; then
|
||||
echo ""
|
||||
echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
|
||||
else
|
||||
echo ""
|
||||
echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
|
||||
break
|
||||
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 $qps"
|
||||
qps=$num_prompts
|
||||
echo "now qps is $qps"
|
||||
fi
|
||||
|
||||
new_test_name=$test_name"_qps_"$qps
|
||||
backend=$CURRENT_LLM_SERVING_ENGINE
|
||||
|
||||
if [[ "$backend" == *"vllm"* ]]; then
|
||||
backend="vllm"
|
||||
fi
|
||||
#TODO: add output dir.
|
||||
client_command="genai-perf profile \
|
||||
-m $model \
|
||||
--service-kind openai \
|
||||
--backend "$backend" \
|
||||
--endpoint-type chat \
|
||||
--streaming \
|
||||
--url localhost:$port \
|
||||
--request-rate $qps \
|
||||
--num-prompts $num_prompts \
|
||||
"
|
||||
|
||||
echo "Client command: $client_command"
|
||||
|
||||
eval "$client_command"
|
||||
|
||||
#TODO: process/record outputs
|
||||
done
|
||||
done
|
||||
|
||||
kill_gpu_processes
|
||||
|
||||
}
|
||||
|
||||
prepare_dataset() {
|
||||
|
||||
@ -415,7 +315,7 @@ prepare_dataset() {
|
||||
do
|
||||
cat sonnet.txt >> sonnet_4x.txt
|
||||
done
|
||||
|
||||
|
||||
}
|
||||
|
||||
main() {
|
||||
@ -428,17 +328,12 @@ main() {
|
||||
|
||||
pip install -U transformers
|
||||
|
||||
pip install -r requirements/dev.txt
|
||||
which genai-perf
|
||||
|
||||
# check storage
|
||||
df -h
|
||||
|
||||
ensure_installed wget
|
||||
ensure_installed curl
|
||||
ensure_installed jq
|
||||
# genai-perf dependency
|
||||
ensure_installed libb64-0d
|
||||
|
||||
prepare_dataset
|
||||
|
||||
@ -450,10 +345,6 @@ main() {
|
||||
# run the test
|
||||
run_serving_tests "$BENCHMARK_ROOT/tests/nightly-tests.json"
|
||||
|
||||
# run genai-perf tests
|
||||
run_genai_perf_tests "$BENCHMARK_ROOT/tests/genai-perf-tests.json"
|
||||
mv artifacts/ $RESULTS_FOLDER/
|
||||
|
||||
# upload benchmark results to buildkite
|
||||
python3 -m pip install tabulate pandas
|
||||
python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
|
||||
|
||||
@ -10,38 +10,15 @@ set -x
|
||||
set -o pipefail
|
||||
|
||||
check_gpus() {
|
||||
if command -v nvidia-smi; then
|
||||
# check the number of GPUs and GPU type.
|
||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
||||
elif command -v amd-smi; then
|
||||
declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
|
||||
fi
|
||||
|
||||
# check the number of GPUs and GPU type.
|
||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
||||
if [[ $gpu_count -gt 0 ]]; then
|
||||
echo "GPU found."
|
||||
else
|
||||
echo "Need at least 1 GPU to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
if command -v nvidia-smi; then
|
||||
declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
|
||||
elif command -v amd-smi; then
|
||||
declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
|
||||
fi
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
check_cpus() {
|
||||
# check the number of CPUs and NUMA Node and GPU type.
|
||||
declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
|
||||
if [[ $numa_count -gt 0 ]]; then
|
||||
echo "NUMA found."
|
||||
echo $numa_count
|
||||
else
|
||||
echo "Need at least 1 NUMA to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
declare -g gpu_type="cpu"
|
||||
declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
@ -83,22 +60,6 @@ json2args() {
|
||||
echo "$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
|
||||
@ -126,19 +87,12 @@ kill_gpu_processes() {
|
||||
ps -aux
|
||||
lsof -t -i:8000 | xargs -r kill -9
|
||||
pgrep python3 | xargs -r kill -9
|
||||
# vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
|
||||
pgrep VLLM | xargs -r kill -9
|
||||
|
||||
|
||||
# wait until GPU memory usage smaller than 1GB
|
||||
if command -v nvidia-smi; then
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
elif command -v amd-smi; then
|
||||
while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
fi
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
|
||||
# remove vllm config file
|
||||
rm -rf ~/.config/vllm
|
||||
@ -165,7 +119,7 @@ upload_to_buildkite() {
|
||||
}
|
||||
|
||||
run_latency_tests() {
|
||||
# run latency tests using `vllm bench latency` command
|
||||
# run latency tests using `benchmark_latency.py`
|
||||
# $1: a json file specifying latency test cases
|
||||
|
||||
local latency_test_file
|
||||
@ -189,26 +143,15 @@ run_latency_tests() {
|
||||
# get arguments
|
||||
latency_params=$(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
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
latency_command=" $latency_envs vllm bench latency \
|
||||
latency_command="python3 benchmark_latency.py \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$latency_args"
|
||||
|
||||
@ -234,7 +177,7 @@ run_latency_tests() {
|
||||
}
|
||||
|
||||
run_throughput_tests() {
|
||||
# run throughput tests using `vllm bench throughput`
|
||||
# run throughput tests using `benchmark_throughput.py`
|
||||
# $1: a json file specifying throughput test cases
|
||||
|
||||
local throughput_test_file
|
||||
@ -258,26 +201,15 @@ run_throughput_tests() {
|
||||
# get arguments
|
||||
throughput_params=$(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
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
throughput_command=" $throughput_envs vllm bench throughput \
|
||||
throughput_command="python3 benchmark_throughput.py \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$throughput_args"
|
||||
|
||||
@ -302,7 +234,7 @@ run_throughput_tests() {
|
||||
}
|
||||
|
||||
run_serving_tests() {
|
||||
# run serving tests using `vllm bench serve` command
|
||||
# run serving tests using `benchmark_serving.py`
|
||||
# $1: a json file specifying serving test cases
|
||||
|
||||
local serving_test_file
|
||||
@ -325,36 +257,18 @@ run_serving_tests() {
|
||||
|
||||
# get client and server arguments
|
||||
server_params=$(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 $max_concurrency_list"
|
||||
|
||||
# check if there is enough resources to run the test
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(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
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# check if server model and client model is aligned
|
||||
@ -365,32 +279,23 @@ run_serving_tests() {
|
||||
continue
|
||||
fi
|
||||
|
||||
server_command="$server_envs vllm serve \
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
$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
|
||||
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
|
||||
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
|
||||
echo ""
|
||||
echo "vllm failed to start within the timeout period."
|
||||
fi
|
||||
|
||||
# iterate over different QPS
|
||||
@ -402,39 +307,32 @@ run_serving_tests() {
|
||||
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 $client_remote_args "
|
||||
new_test_name=$test_name"_qps_"$qps
|
||||
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--save-result \
|
||||
--result-dir $RESULTS_FOLDER \
|
||||
--result-filename ${new_test_name}.json \
|
||||
--request-rate $qps \
|
||||
$client_args"
|
||||
|
||||
bash -c "$client_command"
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
|
||||
# record the benchmarking commands
|
||||
jq_output=$(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"
|
||||
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
|
||||
@ -444,14 +342,7 @@ run_serving_tests() {
|
||||
}
|
||||
|
||||
main() {
|
||||
local ARCH
|
||||
ARCH=''
|
||||
if [ "$ON_CPU" == "1" ];then
|
||||
check_cpus
|
||||
ARCH='-cpu'
|
||||
else
|
||||
check_gpus
|
||||
fi
|
||||
check_gpus
|
||||
check_hf_token
|
||||
|
||||
# dependencies
|
||||
@ -459,10 +350,10 @@ main() {
|
||||
(which jq) || (apt-get update && apt-get -y install jq)
|
||||
(which lsof) || (apt-get update && apt-get install -y lsof)
|
||||
|
||||
# get the current IP address, required by `vllm bench serve` command
|
||||
# get the current IP address, required by benchmark_serving.py
|
||||
export VLLM_HOST_IP=$(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"
|
||||
export VLLM_LOG_LEVEL="WARNING"
|
||||
|
||||
# prepare for benchmarking
|
||||
cd benchmarks || exit 1
|
||||
@ -472,9 +363,9 @@ main() {
|
||||
QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
|
||||
|
||||
# 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}"
|
||||
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
|
||||
run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
|
||||
run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
|
||||
|
||||
# postprocess benchmarking results
|
||||
pip install tabulate pandas
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import datetime
|
||||
import json
|
||||
import os
|
||||
@ -35,8 +32,10 @@ serving_column_mapping = {
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# collect results
|
||||
for test_file in results_folder.glob("*.json"):
|
||||
|
||||
with open(test_file) as f:
|
||||
raw_result = json.loads(f.read())
|
||||
|
||||
@ -55,16 +54,17 @@ if __name__ == "__main__":
|
||||
serving_results = pd.DataFrame.from_dict(serving_results)
|
||||
|
||||
if not serving_results.empty:
|
||||
serving_results = serving_results[list(serving_column_mapping.keys())].rename(
|
||||
columns=serving_column_mapping
|
||||
)
|
||||
serving_results = serving_results[list(
|
||||
serving_column_mapping.keys())].rename(
|
||||
columns=serving_column_mapping)
|
||||
|
||||
serving_md_table_with_headers = tabulate(
|
||||
serving_results, headers="keys", tablefmt="pipe", showindex=False
|
||||
)
|
||||
serving_md_table_with_headers = tabulate(serving_results,
|
||||
headers='keys',
|
||||
tablefmt='pipe',
|
||||
showindex=False)
|
||||
# remove the first line of header
|
||||
serving_md_table_lines = serving_md_table_with_headers.split("\n")
|
||||
serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])
|
||||
serving_md_table_lines = serving_md_table_with_headers.split('\n')
|
||||
serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
|
||||
|
||||
prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
|
||||
@ -74,9 +74,10 @@ if __name__ == "__main__":
|
||||
# document results with header.
|
||||
# for those who wants to reproduce our benchmark.
|
||||
f.write(serving_md_table_with_headers)
|
||||
f.write("\n")
|
||||
f.write('\n')
|
||||
|
||||
# document benchmarking results in json
|
||||
with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
|
||||
results = serving_results.to_dict(orient="records")
|
||||
|
||||
results = serving_results.to_dict(orient='records')
|
||||
f.write(json.dumps(results))
|
||||
|
||||
@ -1,10 +1,6 @@
|
||||
#!/bin/sh
|
||||
TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-postmerge-repo:pull" | jq -r .token)
|
||||
if [[ "$BUILDKITE_BRANCH" == "main" ]]; then
|
||||
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
|
||||
else
|
||||
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
|
||||
fi
|
||||
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
|
||||
|
||||
TIMEOUT_SECONDS=10
|
||||
|
||||
|
||||
@ -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": {
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -1,30 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "latency_llama8B_tp1",
|
||||
"environment_variables": {
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"load_format": "dummy",
|
||||
"num_iters_warmup": 5,
|
||||
"num_iters": 15
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "latency_llama8B_tp4",
|
||||
"environment_variables": {
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"load_format": "dummy",
|
||||
"num_iters_warmup": 5,
|
||||
"num_iters": 15
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -29,4 +29,4 @@
|
||||
"num-iters": 15
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
@ -35,7 +35,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
@ -88,7 +90,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
@ -141,7 +145,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
@ -191,7 +197,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
@ -243,7 +251,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
@ -295,7 +305,9 @@
|
||||
},
|
||||
"vllm_server_parameters": {
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"gpu_memory_utilization": 0.9,
|
||||
"num_scheduler_steps": 10,
|
||||
"max_num_seqs": 512,
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
|
||||
@ -1,610 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp1_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp2_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp4_sharegpt",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_bf16_tp1_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
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@ -1,820 +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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||||
"test_name": "serving_llama8B_int4_pp3_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"pipeline_parallel_size": 3,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_int4_tp2pp3_random_128_128",
|
||||
"qps_list": ["inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"quantization": "awq",
|
||||
"tensor_parallel_size": 2,
|
||||
"pipeline_parallel_size": 3,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"enable_chunked_prefill": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "random",
|
||||
"random-input-len": 128,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 1000
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -1,168 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "serving_llama8B_tp1_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 1,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp2_sharegpt",
|
||||
"qps_list": [1, 4, 16, "inf"],
|
||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
||||
"server_environment_variables": {
|
||||
"VLLM_RPC_TIMEOUT": 100000,
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
||||
"VLLM_CPU_SGL_KERNEL": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 2,
|
||||
"dtype": "bfloat16",
|
||||
"distributed_executor_backend": "mp",
|
||||
"block_size": 128,
|
||||
"trust_remote_code": "",
|
||||
"disable_log_stats": "",
|
||||
"enforce_eager": "",
|
||||
"max_num_batched_tokens": 2048,
|
||||
"max_num_seqs": 256,
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"backend": "vllm",
|
||||
"dataset_name": "sharegpt",
|
||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_tp4_sharegpt",
|
||||
"qps_list": [1, 4, 16, "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_tp4_random_1024_128",
|
||||
"qps_list": [1, 4, 16, "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": "",
|
||||
"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": 1024,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 100
|
||||
}
|
||||
},
|
||||
{
|
||||
"test_name": "serving_llama8B_pp6_random_1024_128",
|
||||
"qps_list": [1, 4, 16, "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",
|
||||
"pipeline_parallel_size": 6,
|
||||
"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": 1024,
|
||||
"random-output-len": 128,
|
||||
"ignore-eos": "",
|
||||
"num_prompts": 100
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -7,6 +7,7 @@
|
||||
"tensor_parallel_size": 1,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
@ -25,6 +26,7 @@
|
||||
"tensor_parallel_size": 4,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
@ -43,6 +45,7 @@
|
||||
"tensor_parallel_size": 2,
|
||||
"swap_space": 16,
|
||||
"disable_log_stats": "",
|
||||
"disable_log_requests": "",
|
||||
"load_format": "dummy"
|
||||
},
|
||||
"client_parameters": {
|
||||
@ -57,14 +60,14 @@
|
||||
"test_name": "serving_llama70B_tp4_sharegpt_specdecode",
|
||||
"qps_list": [2],
|
||||
"server_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"disable_log_requests": "",
|
||||
"tensor_parallel_size": 4,
|
||||
"swap_space": 16,
|
||||
"speculative_config": {
|
||||
"model": "turboderp/Qwama-0.5B-Instruct",
|
||||
"num_speculative_tokens": 4,
|
||||
"draft_tensor_parallel_size": 1
|
||||
}
|
||||
"swap_space": 16,
|
||||
"speculative_model": "turboderp/Qwama-0.5B-Instruct",
|
||||
"num_speculative_tokens": 4,
|
||||
"speculative_draft_tensor_parallel_size": 1,
|
||||
"use_v2_block_manager": ""
|
||||
},
|
||||
"client_parameters": {
|
||||
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
|
||||
@ -1,32 +0,0 @@
|
||||
[
|
||||
{
|
||||
"test_name": "throughput_llama8B_tp1",
|
||||
"environment_variables": {
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/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_llama8B_tp4",
|
||||
"environment_variables": {
|
||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
||||
},
|
||||
"parameters": {
|
||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"tensor_parallel_size": 4,
|
||||
"load_format": "dummy",
|
||||
"dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
||||
"num_prompts": 200,
|
||||
"backend": "vllm"
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -32,4 +32,4 @@
|
||||
"backend": "vllm"
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
@ -1,145 +1,61 @@
|
||||
steps:
|
||||
# aarch64 + CUDA builds. PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
|
||||
- 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 USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm-ci:build-image --target 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"
|
||||
|
||||
- label: "Build wheel - CUDA 12.8"
|
||||
depends_on: ~
|
||||
id: build-wheel-cuda-12-8
|
||||
- label: "Build wheel - CUDA 12.1"
|
||||
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 ."
|
||||
- "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=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
|
||||
- "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"
|
||||
- "bash .buildkite/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- label: "Build wheel - CUDA 12.6"
|
||||
depends_on: ~
|
||||
id: build-wheel-cuda-12-6
|
||||
# Note(simon): We can always build CUDA 11.8 wheel to ensure the build is working.
|
||||
# However, this block can be uncommented to save some compute hours.
|
||||
# - block: "Build CUDA 11.8 wheel"
|
||||
# key: block-build-cu118-wheel
|
||||
|
||||
- label: "Build wheel - CUDA 11.8"
|
||||
# depends_on: block-build-cu118-wheel
|
||||
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=12.6.3 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||
- "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=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
|
||||
- "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"
|
||||
- "bash .buildkite/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
# x86 + CUDA builds
|
||||
- label: "Build wheel - CUDA 12.9"
|
||||
- block: "Build release image"
|
||||
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 $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
key: block-release-image-build
|
||||
|
||||
- label: "Build release image (x86)"
|
||||
depends_on: ~
|
||||
id: build-release-image-x86
|
||||
- label: "Build release image"
|
||||
depends_on: block-release-image-build
|
||||
agents:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$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_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
||||
|
||||
# PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
|
||||
- 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:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
|
||||
- "docker push vllm/vllm-tpu:nightly"
|
||||
- "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
|
||||
plugins:
|
||||
- docker-login#v3.0.0:
|
||||
username: vllmbot
|
||||
username: vllm
|
||||
password-env: DOCKERHUB_TOKEN
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- input: "Provide Release version here"
|
||||
id: input-release-version
|
||||
fields:
|
||||
- text: "What is the release version?"
|
||||
key: release-version
|
||||
|
||||
- block: "Build CPU release image"
|
||||
key: block-cpu-release-image-build
|
||||
depends_on: ~
|
||||
@ -150,52 +66,7 @@ steps:
|
||||
queue: cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(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:$(buildkite-agent meta-data get release-version)"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION --progress plain -f Dockerfile.cpu ."
|
||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION"
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
|
||||
- block: "Build arm64 CPU release image"
|
||||
key: block-arm64-cpu-release-image-build
|
||||
depends_on: ~
|
||||
|
||||
- label: "Build and publish arm64 CPU release image"
|
||||
depends_on: block-arm64-cpu-release-image-build
|
||||
agents:
|
||||
queue: arm64_cpu_queue_postmerge
|
||||
commands:
|
||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(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-$BUILDKITE_COMMIT"
|
||||
# Clean up old nightly builds (keep only last 14)
|
||||
- "bash .buildkite/scripts/cleanup-nightly-builds.sh"
|
||||
plugins:
|
||||
- docker-login#v3.0.0:
|
||||
username: vllmbot
|
||||
password-env: DOCKERHUB_TOKEN
|
||||
env:
|
||||
DOCKER_BUILDKIT: "1"
|
||||
DOCKERHUB_USERNAME: "vllmbot"
|
||||
|
||||
156
.buildkite/run-amd-test.sh
Executable file
156
.buildkite/run-amd-test.sh
Executable file
@ -0,0 +1,156 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script runs test inside the corresponding ROCm docker container.
|
||||
set -o pipefail
|
||||
|
||||
# Print ROCm version
|
||||
echo "--- Confirming Clean Initial State"
|
||||
while true; do
|
||||
sleep 3
|
||||
if grep -q clean /opt/amdgpu/etc/gpu_state; then
|
||||
echo "GPUs state is \"clean\""
|
||||
break
|
||||
fi
|
||||
done
|
||||
|
||||
echo "--- ROCm info"
|
||||
rocminfo
|
||||
|
||||
# cleanup older docker images
|
||||
cleanup_docker() {
|
||||
# Get Docker's root directory
|
||||
docker_root=$(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"
|
||||
#ignore certain kernels tests
|
||||
if [[ $commands == *" kernels "* ]]; then
|
||||
commands="${commands} \
|
||||
--ignore=kernels/test_attention.py \
|
||||
--ignore=kernels/test_attention_selector.py \
|
||||
--ignore=kernels/test_blocksparse_attention.py \
|
||||
--ignore=kernels/test_causal_conv1d.py \
|
||||
--ignore=kernels/test_cutlass.py \
|
||||
--ignore=kernels/test_encoder_decoder_attn.py \
|
||||
--ignore=kernels/test_flash_attn.py \
|
||||
--ignore=kernels/test_flashinfer.py \
|
||||
--ignore=kernels/test_int8_quant.py \
|
||||
--ignore=kernels/test_machete_gemm.py \
|
||||
--ignore=kernels/test_mamba_ssm.py \
|
||||
--ignore=kernels/test_marlin_gemm.py \
|
||||
--ignore=kernels/test_moe.py \
|
||||
--ignore=kernels/test_prefix_prefill.py \
|
||||
--ignore=kernels/test_rand.py \
|
||||
--ignore=kernels/test_sampler.py"
|
||||
fi
|
||||
|
||||
#ignore certain Entrypoints tests
|
||||
if [[ $commands == *" entrypoints/openai "* ]]; then
|
||||
commands=${commands//" entrypoints/openai "/" entrypoints/openai \
|
||||
--ignore=entrypoints/openai/test_accuracy.py \
|
||||
--ignore=entrypoints/openai/test_audio.py \
|
||||
--ignore=entrypoints/openai/test_encoder_decoder.py \
|
||||
--ignore=entrypoints/openai/test_embedding.py \
|
||||
--ignore=entrypoints/openai/test_oot_registration.py "}
|
||||
fi
|
||||
|
||||
PARALLEL_JOB_COUNT=8
|
||||
# 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"
|
||||
docker run \
|
||||
--device /dev/kfd --device /dev/dri \
|
||||
--network host \
|
||||
--shm-size=16gb \
|
||||
--rm \
|
||||
-e HIP_VISIBLE_DEVICES="${GPU}" \
|
||||
-e HF_TOKEN \
|
||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||
-e "HF_HOME=${HF_MOUNT}" \
|
||||
--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
|
||||
docker run \
|
||||
--device /dev/kfd --device /dev/dri \
|
||||
--network host \
|
||||
--shm-size=16gb \
|
||||
--rm \
|
||||
-e HIP_VISIBLE_DEVICES=0 \
|
||||
-e HF_TOKEN \
|
||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||
-e "HF_HOME=${HF_MOUNT}" \
|
||||
--name "${container_name}" \
|
||||
"${image_name}" \
|
||||
/bin/bash -c "${commands}"
|
||||
fi
|
||||
@ -5,26 +5,26 @@
|
||||
set -ex
|
||||
set -o pipefail
|
||||
|
||||
# cd 2 levels into the working directory
|
||||
cd "$(dirname "${BASH_SOURCE[0]}")/../.."
|
||||
# cd into parent directory of this file
|
||||
cd "$(dirname "${BASH_SOURCE[0]}")/.."
|
||||
|
||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||
|
||||
# run python-based benchmarks and upload the result to buildkite
|
||||
vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
||||
python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
||||
bench_latency_exit_code=$?
|
||||
|
||||
vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
||||
python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
||||
bench_throughput_exit_code=$?
|
||||
|
||||
# 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=$!
|
||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
|
||||
# wait for server to start, timeout after 600 seconds
|
||||
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||
vllm bench serve \
|
||||
python3 benchmarks/benchmark_serving.py \
|
||||
--backend vllm \
|
||||
--dataset-name sharegpt \
|
||||
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
||||
14
.buildkite/run-cpu-test-ppc64le.sh
Executable file
14
.buildkite/run-cpu-test-ppc64le.sh
Executable file
@ -0,0 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f cpu-test || true; docker system prune -f; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t cpu-test -f Dockerfile.ppc64le .
|
||||
|
||||
85
.buildkite/run-cpu-test.sh
Normal file
85
.buildkite/run-cpu-test.sh
Normal file
@ -0,0 +1,85 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the CPU docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# allow to bind to different cores
|
||||
CORE_RANGE=${CORE_RANGE:-48-95}
|
||||
NUMA_NODE=${NUMA_NODE:-1}
|
||||
|
||||
# Try building the docker image
|
||||
numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build -t cpu-test-"$BUILDKITE_BUILD_NUMBER" -f Dockerfile.cpu .
|
||||
numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 -f Dockerfile.cpu .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Run the image, setting --shm-size=4g for tensor parallel.
|
||||
docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
|
||||
--cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
|
||||
docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
|
||||
--cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2
|
||||
|
||||
function cpu_tests() {
|
||||
set -e
|
||||
export NUMA_NODE=$2
|
||||
|
||||
# offline inference
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
python3 examples/offline_inference.py"
|
||||
|
||||
# Run basic model test
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pip install pytest pytest-asyncio \
|
||||
decord einops librosa peft Pillow sentence-transformers soundfile \
|
||||
transformers_stream_generator matplotlib datamodel_code_generator
|
||||
pip install torchvision --index-url https://download.pytorch.org/whl/cpu
|
||||
pytest -v -s tests/models/decoder_only/language -m cpu_model
|
||||
pytest -v -s tests/models/embedding/language -m cpu_model
|
||||
pytest -v -s tests/models/encoder_decoder/language -m cpu_model
|
||||
pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
|
||||
pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
|
||||
|
||||
# Run compressed-tensor test
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -s -v \
|
||||
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
|
||||
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
|
||||
|
||||
# Run AWQ test
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -s -v \
|
||||
tests/quantization/test_ipex_quant.py"
|
||||
|
||||
# Run chunked-prefill and prefix-cache test
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
pytest -s -v -k cpu_model \
|
||||
tests/basic_correctness/test_chunked_prefill.py"
|
||||
|
||||
# online inference
|
||||
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||
set -e
|
||||
export VLLM_CPU_KVCACHE_SPACE=10
|
||||
export VLLM_CPU_OMP_THREADS_BIND=$1
|
||||
python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half &
|
||||
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||
python3 benchmarks/benchmark_serving.py \
|
||||
--backend vllm \
|
||||
--dataset-name random \
|
||||
--model facebook/opt-125m \
|
||||
--num-prompts 20 \
|
||||
--endpoint /v1/completions \
|
||||
--tokenizer facebook/opt-125m"
|
||||
}
|
||||
|
||||
# All of CPU tests are expected to be finished less than 25 mins.
|
||||
export -f cpu_tests
|
||||
timeout 30m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
|
||||
28
.buildkite/run-gh200-test.sh
Normal file
28
.buildkite/run-gh200-test.sh
Normal file
@ -0,0 +1,28 @@
|
||||
#!/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 . \
|
||||
--target vllm-openai \
|
||||
--platform "linux/arm64" \
|
||||
-t gh200-test \
|
||||
--build-arg max_jobs=66 \
|
||||
--build-arg nvcc_threads=2 \
|
||||
--build-arg torch_cuda_arch_list="9.0+PTX" \
|
||||
--build-arg vllm_fa_cmake_gpu_arches="90-real"
|
||||
|
||||
# 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 --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
|
||||
python3 examples/offline_inference.py
|
||||
'
|
||||
16
.buildkite/run-hpu-test.sh
Normal file
16
.buildkite/run-hpu-test.sh
Normal file
@ -0,0 +1,16 @@
|
||||
#!/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
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t hpu-test-env -f Dockerfile.hpu .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f hpu-test || true; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Run the image and launch offline inference
|
||||
docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference.py
|
||||
@ -3,7 +3,7 @@
|
||||
set -euox pipefail
|
||||
|
||||
if [[ $# -lt 4 ]]; then
|
||||
echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
|
||||
echo "Usage: .buildkite/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
54
.buildkite/run-neuron-test.sh
Normal file
54
.buildkite/run-neuron-test.sh
Normal file
@ -0,0 +1,54 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the Neuron docker image and run the API server inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -e
|
||||
set -v
|
||||
|
||||
image_name="neuron/vllm-ci"
|
||||
container_name="neuron_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
|
||||
|
||||
HF_CACHE="$(realpath ~)/huggingface"
|
||||
mkdir -p "${HF_CACHE}"
|
||||
HF_MOUNT="/root/.cache/huggingface"
|
||||
|
||||
NEURON_COMPILE_CACHE_URL="$(realpath ~)/neuron_compile_cache"
|
||||
mkdir -p "${NEURON_COMPILE_CACHE_URL}"
|
||||
NEURON_COMPILE_CACHE_MOUNT="/root/.cache/neuron_compile_cache"
|
||||
|
||||
# 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 [ $((current_time - last_build)) -gt 86400 ]; then
|
||||
docker image prune -f
|
||||
docker system prune -f
|
||||
rm -rf "${HF_MOUNT:?}/*"
|
||||
rm -rf "${NEURON_COMPILE_CACHE_MOUNT:?}/*"
|
||||
echo "$current_time" > /tmp/neuron-docker-build-timestamp
|
||||
fi
|
||||
else
|
||||
date "+%s" > /tmp/neuron-docker-build-timestamp
|
||||
fi
|
||||
|
||||
docker build -t "${image_name}" -f Dockerfile.neuron .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() {
|
||||
docker image rm -f "${image_name}" || true;
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Run the image
|
||||
docker run --rm -it --device=/dev/neuron0 --device=/dev/neuron1 --network host \
|
||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||
-e "HF_HOME=${HF_MOUNT}" \
|
||||
-v "${NEURON_COMPILE_CACHE_URL}:${NEURON_COMPILE_CACHE_MOUNT}" \
|
||||
-e "NEURON_COMPILE_CACHE_URL=${NEURON_COMPILE_CACHE_MOUNT}" \
|
||||
--name "${container_name}" \
|
||||
${image_name} \
|
||||
/bin/bash -c "python3 /workspace/vllm/examples/offline_inference_neuron.py"
|
||||
16
.buildkite/run-openvino-test.sh
Executable file
16
.buildkite/run-openvino-test.sh
Executable file
@ -0,0 +1,16 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script build the OpenVINO docker image and run the offline inference inside the container.
|
||||
# It serves a sanity check for compilation and basic model usage.
|
||||
set -ex
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t openvino-test -f Dockerfile.openvino .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f openvino-test || true; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Run the image and launch offline inference
|
||||
docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference.py
|
||||
17
.buildkite/run-tpu-test.sh
Normal file
17
.buildkite/run-tpu-test.sh
Normal file
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
|
||||
# Build the docker image.
|
||||
docker build -f Dockerfile.tpu -t vllm-tpu .
|
||||
|
||||
# Set up cleanup.
|
||||
remove_docker_container() { docker rm -f tpu-test || true; }
|
||||
trap remove_docker_container EXIT
|
||||
# Remove the container that might not be cleaned up in the previous run.
|
||||
remove_docker_container
|
||||
|
||||
# For HF_TOKEN.
|
||||
source /etc/environment
|
||||
# Run a simple end-to-end example.
|
||||
docker run --privileged --net host --shm-size=16G -it -e "HF_TOKEN=$HF_TOKEN" --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 -m pip install pytest && python3 -m pip install lm_eval[api]==0.4.4 && pytest -v -s /workspace/vllm/tests/entrypoints/openai/test_accuracy.py && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference_tpu.py"
|
||||
19
.buildkite/run-xpu-test.sh
Normal file
19
.buildkite/run-xpu-test.sh
Normal file
@ -0,0 +1,19 @@
|
||||
#!/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
|
||||
|
||||
# Try building the docker image
|
||||
docker build -t xpu-test -f Dockerfile.xpu .
|
||||
|
||||
# Setup cleanup
|
||||
remove_docker_container() { docker rm -f xpu-test || true; }
|
||||
trap remove_docker_container EXIT
|
||||
remove_docker_container
|
||||
|
||||
# Run the image and test offline inference/tensor parallel
|
||||
docker run --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path --entrypoint="" xpu-test sh -c '
|
||||
python3 examples/offline_inference.py
|
||||
python3 examples/offline_inference_cli.py -tp 2
|
||||
'
|
||||
@ -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 \
|
||||
@ -1,48 +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 "${image_name}" || true;
|
||||
docker system prune -f || true;
|
||||
}
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Run the image and test offline inference/tensor parallel
|
||||
docker run \
|
||||
--device /dev/dri \
|
||||
-v /dev/dri/by-path:/dev/dri/by-path \
|
||||
--entrypoint="" \
|
||||
-e "HF_TOKEN=${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
|
||||
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 ${COUNT}"
|
||||
done
|
||||
@ -1,59 +0,0 @@
|
||||
#!/bin/bash
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Setup script for Prime-RL integration tests
|
||||
# This script prepares the environment for running Prime-RL tests with nightly vLLM
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
|
||||
PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
|
||||
PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
|
||||
|
||||
echo "Setting up Prime-RL integration test environment..."
|
||||
|
||||
# Clean up any existing Prime-RL directory
|
||||
if [ -d "${PRIME_RL_DIR}" ]; then
|
||||
echo "Removing existing Prime-RL directory..."
|
||||
rm -rf "${PRIME_RL_DIR}"
|
||||
fi
|
||||
|
||||
# Install UV if not available
|
||||
if ! command -v uv &> /dev/null; then
|
||||
echo "Installing UV package manager..."
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
source $HOME/.local/bin/env
|
||||
fi
|
||||
|
||||
# Clone Prime-RL repository at specific branch for reproducible tests
|
||||
PRIME_RL_BRANCH="integ-vllm-main"
|
||||
echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
|
||||
git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
|
||||
cd "${PRIME_RL_DIR}"
|
||||
|
||||
echo "Setting up UV project environment..."
|
||||
export UV_PROJECT_ENVIRONMENT=/usr/local
|
||||
ln -s /usr/bin/python3 /usr/local/bin/python
|
||||
|
||||
# Remove vllm pin from pyproject.toml
|
||||
echo "Removing vllm pin from pyproject.toml..."
|
||||
sed -i '/vllm==/d' pyproject.toml
|
||||
|
||||
# Sync Prime-RL dependencies
|
||||
echo "Installing Prime-RL dependencies..."
|
||||
uv sync --inexact && uv sync --inexact --all-extras
|
||||
|
||||
# Verify installation
|
||||
echo "Verifying installations..."
|
||||
uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
|
||||
uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
|
||||
|
||||
echo "Prime-RL integration test environment setup complete!"
|
||||
|
||||
echo "Running Prime-RL integration tests..."
|
||||
export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
|
||||
uv run pytest -vs tests/integration/test_rl.py -m gpu
|
||||
|
||||
echo "Prime-RL integration tests completed!"
|
||||
@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
||||
if [ -z "$docker_root" ]; then
|
||||
echo "Failed to determine Docker root directory."
|
||||
exit 1
|
||||
fi
|
||||
echo "Docker root directory: $docker_root"
|
||||
# Check disk usage of the filesystem where Docker's root directory is located
|
||||
disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
|
||||
# Define the threshold
|
||||
threshold=70
|
||||
if [ "$disk_usage" -gt "$threshold" ]; then
|
||||
echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
|
||||
# Remove dangling images (those that are not tagged and not used by any container)
|
||||
docker image prune -f
|
||||
# Remove unused volumes / force the system prune for old images as well.
|
||||
docker volume prune -f && docker system prune --force --filter "until=24h" --all
|
||||
echo "Docker images and volumes cleanup completed."
|
||||
else
|
||||
echo "Disk usage is below $threshold%. No cleanup needed."
|
||||
fi
|
||||
@ -1,14 +0,0 @@
|
||||
# Environment config
|
||||
TEST_NAME=llama8b
|
||||
CONTAINER_NAME=tpu-test
|
||||
|
||||
# vllm config
|
||||
MODEL=meta-llama/Llama-3.1-8B-Instruct
|
||||
MAX_NUM_SEQS=256
|
||||
MAX_NUM_BATCHED_TOKENS=1024
|
||||
TENSOR_PARALLEL_SIZE=1
|
||||
MAX_MODEL_LEN=2048
|
||||
DOWNLOAD_DIR=/mnt/disks/persist
|
||||
EXPECTED_THROUGHPUT=8.0
|
||||
INPUT_LEN=1800
|
||||
OUTPUT_LEN=128
|
||||
@ -1,90 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
if [ ! -f "$1" ]; then
|
||||
echo "Error: The env file '$1' does not exist."
|
||||
exit 1 # Exit the script with a non-zero status to indicate an error
|
||||
fi
|
||||
|
||||
ENV_FILE=$1
|
||||
|
||||
# For testing on local vm, use `set -a` to export all variables
|
||||
source /etc/environment
|
||||
source $ENV_FILE
|
||||
|
||||
remove_docker_container() {
|
||||
docker rm -f $CONTAINER_NAME || true;
|
||||
}
|
||||
|
||||
trap remove_docker_container EXIT
|
||||
|
||||
# Remove the container that might not be cleaned up in the previous run.
|
||||
remove_docker_container
|
||||
|
||||
LOG_ROOT=$(mktemp -d)
|
||||
# If mktemp fails, set -e will cause the script to exit.
|
||||
echo "Results will be stored in: $LOG_ROOT"
|
||||
|
||||
if [ -z "$HF_TOKEN" ]; then
|
||||
echo "Error: HF_TOKEN is not set or is empty."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Make sure mounted disk or dir exists
|
||||
if [ ! -d "$DOWNLOAD_DIR" ]; then
|
||||
echo "Error: Folder $DOWNLOAD_DIR does not exist. This is useually a mounted drive. If no mounted drive, just create a folder."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "Run model $MODEL"
|
||||
echo
|
||||
|
||||
echo "starting docker...$CONTAINER_NAME"
|
||||
echo
|
||||
docker run \
|
||||
-v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
|
||||
--env-file $ENV_FILE \
|
||||
-e HF_TOKEN="$HF_TOKEN" \
|
||||
-e TARGET_COMMIT=$BUILDKITE_COMMIT \
|
||||
-e MODEL=$MODEL \
|
||||
-e WORKSPACE=/workspace \
|
||||
--name $CONTAINER_NAME \
|
||||
-d \
|
||||
--privileged \
|
||||
--network host \
|
||||
-v /dev/shm:/dev/shm \
|
||||
vllm/vllm-tpu-bm tail -f /dev/null
|
||||
|
||||
echo "run script..."
|
||||
echo
|
||||
docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/tpu/run_bm.sh"
|
||||
|
||||
echo "copy result back..."
|
||||
VLLM_LOG="$LOG_ROOT/$TEST_NAME"_vllm_log.txt
|
||||
BM_LOG="$LOG_ROOT/$TEST_NAME"_bm_log.txt
|
||||
docker cp "$CONTAINER_NAME:/workspace/vllm_log.txt" "$VLLM_LOG"
|
||||
docker cp "$CONTAINER_NAME:/workspace/bm_log.txt" "$BM_LOG"
|
||||
|
||||
throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
|
||||
echo "throughput for $TEST_NAME at $BUILDKITE_COMMIT: $throughput"
|
||||
|
||||
if [ "$BUILDKITE" = "true" ]; then
|
||||
echo "Running inside Buildkite"
|
||||
buildkite-agent artifact upload "$VLLM_LOG"
|
||||
buildkite-agent artifact upload "$BM_LOG"
|
||||
else
|
||||
echo "Not running inside Buildkite"
|
||||
fi
|
||||
|
||||
#
|
||||
# compare the throughput with EXPECTED_THROUGHPUT
|
||||
# and assert meeting the expectation
|
||||
#
|
||||
if [[ -z "$throughput" || ! "$throughput" =~ ^[0-9]+([.][0-9]+)?$ ]]; then
|
||||
echo "Failed to get the throughput"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if (( $(echo "$throughput < $EXPECTED_THROUGHPUT" | bc -l) )); then
|
||||
echo "Error: throughput($throughput) is less than expected($EXPECTED_THROUGHPUT)"
|
||||
exit 1
|
||||
fi
|
||||
@ -1,14 +0,0 @@
|
||||
# Environment config
|
||||
TEST_NAME=llama8bw8a8
|
||||
CONTAINER_NAME=tpu-test
|
||||
|
||||
# vllm config
|
||||
MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
|
||||
MAX_NUM_SEQS=128
|
||||
MAX_NUM_BATCHED_TOKENS=1024
|
||||
TENSOR_PARALLEL_SIZE=1
|
||||
MAX_MODEL_LEN=2048
|
||||
DOWNLOAD_DIR=/mnt/disks/persist
|
||||
EXPECTED_THROUGHPUT=8.7
|
||||
INPUT_LEN=1800
|
||||
OUTPUT_LEN=128
|
||||
@ -1,93 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
VLLM_LOG="$WORKSPACE/vllm_log.txt"
|
||||
BM_LOG="$WORKSPACE/bm_log.txt"
|
||||
|
||||
if [ -n "$TARGET_COMMIT" ]; then
|
||||
head_hash=$(git rev-parse HEAD)
|
||||
if [ "$TARGET_COMMIT" != "$head_hash" ]; then
|
||||
echo "Error: target commit $TARGET_COMMIT does not match HEAD: $head_hash"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
echo "model: $MODEL"
|
||||
echo
|
||||
|
||||
#
|
||||
# create a log folder
|
||||
#
|
||||
mkdir "$WORKSPACE/log"
|
||||
|
||||
# TODO: Move to image building.
|
||||
pip install pandas
|
||||
pip install datasets
|
||||
|
||||
#
|
||||
# create sonnet_4x
|
||||
#
|
||||
echo "Create sonnet_4x.txt"
|
||||
echo "" > benchmarks/sonnet_4x.txt
|
||||
for _ in {1..4}
|
||||
do
|
||||
cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
|
||||
done
|
||||
|
||||
#
|
||||
# start vllm service in backend
|
||||
#
|
||||
echo "lanching vllm..."
|
||||
echo "logging to $VLLM_LOG"
|
||||
echo
|
||||
|
||||
vllm serve $MODEL \
|
||||
--seed 42 \
|
||||
--max-num-seqs $MAX_NUM_SEQS \
|
||||
--max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
|
||||
--tensor-parallel-size $TENSOR_PARALLEL_SIZE \
|
||||
--no-enable-prefix-caching \
|
||||
--download_dir $DOWNLOAD_DIR \
|
||||
--max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
|
||||
|
||||
|
||||
echo "wait for 20 minutes.."
|
||||
echo
|
||||
# sleep 1200
|
||||
# wait for 10 minutes...
|
||||
for i in {1..120}; do
|
||||
# TODO: detect other type of errors.
|
||||
if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
|
||||
echo "Detected RuntimeError, exiting."
|
||||
exit 1
|
||||
elif grep -Fq "Application startup complete" "$VLLM_LOG"; then
|
||||
echo "Application started"
|
||||
break
|
||||
else
|
||||
echo "wait for 10 seconds..."
|
||||
sleep 10
|
||||
fi
|
||||
done
|
||||
|
||||
#
|
||||
# run test
|
||||
#
|
||||
echo "run benchmark test..."
|
||||
echo "logging to $BM_LOG"
|
||||
echo
|
||||
vllm bench serve \
|
||||
--backend vllm \
|
||||
--model $MODEL \
|
||||
--dataset-name sonnet \
|
||||
--dataset-path benchmarks/sonnet_4x.txt \
|
||||
--sonnet-input-len $INPUT_LEN \
|
||||
--sonnet-output-len $OUTPUT_LEN \
|
||||
--ignore-eos > "$BM_LOG"
|
||||
|
||||
echo "completed..."
|
||||
echo
|
||||
|
||||
throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
|
||||
echo "throughput: $throughput"
|
||||
echo
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user