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pd_schedul
| Author | SHA1 | Date | |
|---|---|---|---|
| 161010c384 |
@ -1,20 +1,19 @@
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|||||||
# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import os
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import sys
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import sys
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import zipfile
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import zipfile
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# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 450 MiB
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# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 400 MiB
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# Note that we have 800 MiB quota, please use it wisely.
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# Note that we have 400 MiB quota, please use it wisely.
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# See https://github.com/pypi/support/issues/6326 .
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# See https://github.com/pypi/support/issues/3792 .
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# Please also sync the value with the one in Dockerfile.
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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", 450))
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VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 400))
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def print_top_10_largest_files(zip_file):
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def print_top_10_largest_files(zip_file):
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"""Print the top 10 largest files in the given zip file."""
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"""Print the top 10 largest files in the given zip file."""
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with zipfile.ZipFile(zip_file, "r") as z:
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with zipfile.ZipFile(zip_file, 'r') as z:
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file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
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file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
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file_sizes.sort(key=lambda x: x[1], reverse=True)
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file_sizes.sort(key=lambda x: x[1], reverse=True)
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for f, size in file_sizes[:10]:
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for f, size in file_sizes[:10]:
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@ -29,18 +28,14 @@ def check_wheel_size(directory):
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wheel_path = os.path.join(root, file_name)
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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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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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if wheel_size_mb > VLLM_MAX_SIZE_MB:
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print(
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print(f"Not allowed: Wheel {wheel_path} is larger "
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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"({wheel_size_mb:.2f} MB) than the limit "
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f"({VLLM_MAX_SIZE_MB} MB).")
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f"({VLLM_MAX_SIZE_MB} MB)."
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)
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print_top_10_largest_files(wheel_path)
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print_top_10_largest_files(wheel_path)
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return 1
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return 1
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else:
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else:
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print(
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print(f"Wheel {wheel_path} is within the allowed size "
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f"Wheel {wheel_path} is within the allowed size "
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f"({wheel_size_mb:.2f} MB).")
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f"({wheel_size_mb:.2f} MB)."
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)
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return 0
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return 0
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@ -50,4 +45,4 @@ if __name__ == "__main__":
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sys.exit(1)
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sys.exit(1)
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directory = sys.argv[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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@ -1,5 +1,4 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import argparse
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import os
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import os
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@ -8,8 +7,7 @@ template = """<!DOCTYPE html>
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<html>
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<html>
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<body>
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<body>
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<h1>Links for vLLM</h1/>
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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="../{wheel_html_escaped}">{wheel}</a><br/>
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<a href="../{arm_wheel_html_escaped}">{arm_wheel}</a><br/>
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</body>
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</body>
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</html>
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</html>
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"""
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"""
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@ -22,25 +20,7 @@ filename = os.path.basename(args.wheel)
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with open("index.html", "w") as f:
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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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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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# cloudfront requires escaping the '+' character
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f.write(
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f.write(
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template.format(
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template.format(wheel=filename,
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x86_wheel=x86_wheel,
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wheel_html_escaped=filename.replace("+", "%2B")))
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x86_wheel_html_escaped=x86_wheel.replace("+", "%2B"),
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arm_wheel=arm_wheel,
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arm_wheel_html_escaped=arm_wheel.replace("+", "%2B"),
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)
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)
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@ -1,4 +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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# 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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model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
|
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tasks:
|
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
|
# 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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model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -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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# 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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model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
|
||||||
tasks:
|
tasks:
|
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|
|||||||
@ -1,4 +1,3 @@
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|||||||
# For vllm script, with -t option (tensor parallel size).
|
|
||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors -b auto -l 1000 -f 5 -t 1
|
# 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"
|
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5 -t 1
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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
|
||||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
|
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
|
||||||
model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
|
model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
|
||||||
model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
|
model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
||||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
|
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
|
||||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
|
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
|
||||||
tasks:
|
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/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
|
||||||
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
|
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# For hf script, without -t option (tensor parallel size).
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5 -t 1
|
||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5
|
|
||||||
model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
|
model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
|
||||||
tasks:
|
tasks:
|
||||||
- name: "gsm8k"
|
- name: "gsm8k"
|
||||||
|
|||||||
@ -1,11 +1,11 @@
|
|||||||
# 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
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
|
||||||
model_name: "Qwen/Qwen2.5-1.5B-Instruct"
|
model_name: "HandH1998/QQQ-Llama-3-8b-g128"
|
||||||
tasks:
|
tasks:
|
||||||
- name: "gsm8k"
|
- name: "gsm8k"
|
||||||
metrics:
|
metrics:
|
||||||
- name: "exact_match,strict-match"
|
- name: "exact_match,strict-match"
|
||||||
value: 0.54
|
value: 0.419
|
||||||
- name: "exact_match,flexible-extract"
|
- name: "exact_match,flexible-extract"
|
||||||
value: 0.59
|
value: 0.416
|
||||||
limit: 1319
|
limit: 1000
|
||||||
num_fewshot: 5
|
num_fewshot: 5
|
||||||
@ -1,11 +0,0 @@
|
|||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Llama-3.2-1B-Instruct-FP8 -b "auto" -l 1319 -f 5 -t 1
|
|
||||||
model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
|
|
||||||
tasks:
|
|
||||||
- name: "gsm8k"
|
|
||||||
metrics:
|
|
||||||
- name: "exact_match,strict-match"
|
|
||||||
value: 0.335
|
|
||||||
- name: "exact_match,flexible-extract"
|
|
||||||
value: 0.323
|
|
||||||
limit: 1319
|
|
||||||
num_fewshot: 5
|
|
||||||
@ -1,4 +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/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
|
||||||
model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
|
model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
|
||||||
tasks:
|
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 mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
|
||||||
model_name: "mgoin/Minitron-4B-Base-FP8"
|
model_name: "mgoin/Minitron-4B-Base-FP8"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -1,4 +1,3 @@
|
|||||||
# For vllm script, with -t option (tensor parallel size).
|
|
||||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
|
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
|
||||||
model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
|
model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -1,4 +1,3 @@
|
|||||||
# For vllm script, with -t option (tensor parallel size).
|
|
||||||
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
|
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
|
||||||
model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
|
model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# For hf script, without -t option (tensor parallel size).
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5 -t 4
|
||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5
|
|
||||||
model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
|
model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
|
||||||
tasks:
|
tasks:
|
||||||
- name: "gsm8k"
|
- name: "gsm8k"
|
||||||
|
|||||||
@ -1,12 +1,11 @@
|
|||||||
# For vllm script, with -t option (tensor parallel size).
|
|
||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
|
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
|
||||||
model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
|
model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
|
||||||
tasks:
|
tasks:
|
||||||
- name: "gsm8k"
|
- name: "gsm8k"
|
||||||
metrics:
|
metrics:
|
||||||
- name: "exact_match,strict-match"
|
- name: "exact_match,strict-match"
|
||||||
value: 0.30
|
value: 0.31
|
||||||
- name: "exact_match,flexible-extract"
|
- name: "exact_match,flexible-extract"
|
||||||
value: 0.465
|
value: 0.47
|
||||||
limit: 1319
|
limit: 1319
|
||||||
num_fewshot: 5
|
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
|
# 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"
|
model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
|
||||||
tasks:
|
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
|
# 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"
|
model_name: "neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8"
|
||||||
tasks:
|
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
|
# 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"
|
model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
|
||||||
tasks:
|
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
|
# 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"
|
model_name: "Qwen/Qwen2-57B-A14B-Instruct"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -1,11 +0,0 @@
|
|||||||
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -b auto -l 1319 -f 5 -t 1
|
|
||||||
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
|
|
||||||
@ -1,4 +1,3 @@
|
|||||||
# 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
|
# 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"
|
model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
|
||||||
tasks:
|
tasks:
|
||||||
|
|||||||
@ -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-3.2-1B-Instruct-INT8-compressed-tensors.yaml
|
||||||
Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
|
Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
|
||||||
Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
|
Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
|
||||||
Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
|
Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
|
||||||
Qwen1.5-MoE-W4A16-compressed-tensors.yaml
|
Qwen1.5-MoE-W4A16-compressed-tensors.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)
|
|
||||||
@ -2,7 +2,7 @@
|
|||||||
# We can use this script to compute baseline accuracy on GSM for transformers.
|
# We can use this script to compute baseline accuracy on GSM for transformers.
|
||||||
#
|
#
|
||||||
# Make sure you have lm-eval-harness installed:
|
# 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() {
|
usage() {
|
||||||
echo``
|
echo``
|
||||||
|
|||||||
@ -3,7 +3,7 @@
|
|||||||
# We use this for fp8, which HF does not support.
|
# We use this for fp8, which HF does not support.
|
||||||
#
|
#
|
||||||
# Make sure you have lm-eval-harness installed:
|
# 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() {
|
usage() {
|
||||||
echo``
|
echo``
|
||||||
@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
|
|||||||
done
|
done
|
||||||
|
|
||||||
lm_eval --model vllm \
|
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" \
|
--tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
|
||||||
--batch_size "$BATCH_SIZE"
|
--batch_size "$BATCH_SIZE"
|
||||||
|
|||||||
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 [ "${SUCCESS}" -eq "0" ]; then
|
||||||
|
exit 0
|
||||||
|
else
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
@ -1,57 +1,69 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
"""
|
"""
|
||||||
LM eval harness on model to compare vs HF baseline computed offline.
|
LM eval harness on model to compare vs HF baseline computed offline.
|
||||||
Configs are found in configs/$MODEL.yaml
|
Configs are found in configs/$MODEL.yaml
|
||||||
|
|
||||||
pytest -s -v test_lm_eval_correctness.py \
|
* export LM_EVAL_TEST_DATA_FILE=configs/Meta-Llama-3-70B-Instruct.yaml
|
||||||
--config-list-file=configs/models-small.txt \
|
* export LM_EVAL_TP_SIZE=4
|
||||||
--tp-size=1
|
* pytest -s test_lm_eval_correctness.py
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
import lm_eval
|
import lm_eval
|
||||||
import numpy as np
|
import numpy
|
||||||
|
import pytest
|
||||||
import yaml
|
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):
|
def launch_lm_eval(eval_config):
|
||||||
trust_remote_code = eval_config.get("trust_remote_code", False)
|
trust_remote_code = eval_config.get('trust_remote_code', False)
|
||||||
max_model_len = eval_config.get("max_model_len", 4096)
|
|
||||||
model_args = (
|
model_args = f"pretrained={eval_config['model_name']}," \
|
||||||
f"pretrained={eval_config['model_name']},"
|
f"tensor_parallel_size={TP_SIZE}," \
|
||||||
f"tensor_parallel_size={tp_size},"
|
f"add_bos_token=true," \
|
||||||
f"enforce_eager=true,"
|
f"trust_remote_code={trust_remote_code}"
|
||||||
f"add_bos_token=true,"
|
|
||||||
f"trust_remote_code={trust_remote_code},"
|
|
||||||
f"max_model_len={max_model_len}"
|
|
||||||
)
|
|
||||||
results = lm_eval.simple_evaluate(
|
results = lm_eval.simple_evaluate(
|
||||||
model="vllm",
|
model="vllm",
|
||||||
model_args=model_args,
|
model_args=model_args,
|
||||||
tasks=[task["name"] for task in eval_config["tasks"]],
|
tasks=[task["name"] for task in eval_config["tasks"]],
|
||||||
num_fewshot=eval_config["num_fewshot"],
|
num_fewshot=eval_config["num_fewshot"],
|
||||||
limit=eval_config["limit"],
|
limit=eval_config["limit"],
|
||||||
batch_size="auto",
|
batch_size="auto")
|
||||||
)
|
|
||||||
return results
|
return results
|
||||||
|
|
||||||
|
|
||||||
def test_lm_eval_correctness_param(config_filename, tp_size):
|
def test_lm_eval_correctness():
|
||||||
eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
|
eval_config = yaml.safe_load(
|
||||||
|
Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
|
||||||
|
|
||||||
results = launch_lm_eval(eval_config, tp_size)
|
if eval_config[
|
||||||
|
"model_name"] == "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform": #noqa: E501
|
||||||
|
pytest.skip("FBGEMM is currently failing on main.")
|
||||||
|
|
||||||
|
# Launch eval requests.
|
||||||
|
results = launch_lm_eval(eval_config)
|
||||||
|
|
||||||
|
# Confirm scores match ground truth.
|
||||||
success = True
|
success = True
|
||||||
for task in eval_config["tasks"]:
|
for task in eval_config["tasks"]:
|
||||||
for metric in task["metrics"]:
|
for metric in task["metrics"]:
|
||||||
ground_truth = metric["value"]
|
ground_truth = metric["value"]
|
||||||
measured_value = results["results"][task["name"]][metric["name"]]
|
measured_value = results["results"][task["name"]][metric["name"]]
|
||||||
print(
|
print(f'{task["name"]} | {metric["name"]}: '
|
||||||
f"{task['name']} | {metric['name']}: "
|
f'ground_truth={ground_truth} | measured={measured_value}')
|
||||||
f"ground_truth={ground_truth} | measured={measured_value}"
|
success = success and numpy.isclose(
|
||||||
)
|
ground_truth, measured_value, rtol=RTOL)
|
||||||
success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
|
|
||||||
|
|
||||||
|
# Assert at the end, print all scores even on failure for debugging.
|
||||||
assert success
|
assert success
|
||||||
|
|||||||
@ -7,11 +7,11 @@ 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
|
- 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.
|
- 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
|
## 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.
|
**Benchmarking Duration**: about 1hr.
|
||||||
|
|
||||||
@ -28,34 +28,16 @@ See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName
|
|||||||
## Trigger the benchmark
|
## Trigger the benchmark
|
||||||
|
|
||||||
Performance benchmark will be triggered when:
|
Performance benchmark will be triggered when:
|
||||||
|
|
||||||
- A PR being merged into vllm.
|
- A PR being merged into vllm.
|
||||||
- Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
|
- 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:
|
Nightly benchmark will be triggered when:
|
||||||
|
|
||||||
- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
|
- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
|
||||||
|
|
||||||
## Performance benchmark details
|
## 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.
|
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`:
|
Here is an example of one test inside `latency-tests.json`:
|
||||||
@ -78,7 +60,7 @@ Here is an example of one test inside `latency-tests.json`:
|
|||||||
In this example:
|
In this example:
|
||||||
|
|
||||||
- The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
|
- 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 `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.
|
Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
|
||||||
|
|
||||||
@ -86,13 +68,13 @@ WARNING: The benchmarking script will save json results by itself, so please do
|
|||||||
|
|
||||||
### Throughput test
|
### Throughput test
|
||||||
|
|
||||||
The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
|
The 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.
|
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 `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:
|
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:
|
||||||
|
|
||||||
```json
|
```json
|
||||||
[
|
[
|
||||||
@ -104,6 +86,7 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
|
|||||||
"tensor_parallel_size": 1,
|
"tensor_parallel_size": 1,
|
||||||
"swap_space": 16,
|
"swap_space": 16,
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"load_format": "dummy"
|
"load_format": "dummy"
|
||||||
},
|
},
|
||||||
"client_parameters": {
|
"client_parameters": {
|
||||||
@ -121,8 +104,8 @@ Inside this example:
|
|||||||
|
|
||||||
- The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
|
- 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 `server-parameters` includes the command line arguments for vLLM server.
|
||||||
- The `client-parameters` includes the command line arguments for `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 `vllm bench serve`
|
- 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.
|
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.
|
||||||
|
|
||||||
@ -130,29 +113,12 @@ WARNING: The benchmarking script will save json results by itself, so please do
|
|||||||
|
|
||||||
### Visualizing the 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](performance-benchmarks-descriptions.md) with real benchmarking 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.
|
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.
|
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 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 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
|
## 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.
|
See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
|
||||||
@ -160,9 +126,9 @@ See [nightly-descriptions.md](nightly-descriptions.md) for the detailed descript
|
|||||||
### Workflow
|
### Workflow
|
||||||
|
|
||||||
- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
|
- 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.
|
- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.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`.
|
- 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/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
|
- 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
|
### Nightly tests
|
||||||
|
|
||||||
@ -172,6 +138,6 @@ In [nightly-tests.json](tests/nightly-tests.json), we include the command line a
|
|||||||
|
|
||||||
The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
|
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).
|
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).
|
||||||
|
|||||||
@ -1,4 +1,3 @@
|
|||||||
# Nightly benchmark annotation
|
|
||||||
|
|
||||||
## Description
|
## Description
|
||||||
|
|
||||||
@ -14,15 +13,15 @@ Please download the visualization scripts in the post
|
|||||||
|
|
||||||
- Find the docker we use in `benchmarking pipeline`
|
- Find the docker we use in `benchmarking pipeline`
|
||||||
- Deploy the docker, and inside the docker:
|
- Deploy the docker, and inside the docker:
|
||||||
- Download `nightly-benchmarks.zip`.
|
- Download `nightly-benchmarks.zip`.
|
||||||
- In the same folder, run the following code:
|
- In the same folder, run the following code:
|
||||||
|
|
||||||
```bash
|
```console
|
||||||
export HF_TOKEN=<your HF token>
|
export HF_TOKEN=<your HF token>
|
||||||
apt update
|
apt update
|
||||||
apt install -y git
|
apt install -y git
|
||||||
unzip nightly-benchmarks.zip
|
unzip nightly-benchmarks.zip
|
||||||
VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
|
VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
|
||||||
```
|
```
|
||||||
|
|
||||||
And the results will be inside `./benchmarks/results`.
|
And the results will be inside `./benchmarks/results`.
|
||||||
|
|||||||
@ -13,25 +13,25 @@ Latest reproduction guilde: [github issue link](https://github.com/vllm-project/
|
|||||||
## Setup
|
## Setup
|
||||||
|
|
||||||
- Docker images:
|
- Docker images:
|
||||||
- vLLM: `vllm/vllm-openai:v0.6.2`
|
- vLLM: `vllm/vllm-openai:v0.6.2`
|
||||||
- SGLang: `lmsysorg/sglang:v0.3.2-cu121`
|
- SGLang: `lmsysorg/sglang:v0.3.2-cu121`
|
||||||
- LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
|
- LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
|
||||||
- TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
|
- 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.*
|
- *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.
|
- Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
|
||||||
- Hardware
|
- Hardware
|
||||||
- 8x Nvidia A100 GPUs
|
- 8x Nvidia A100 GPUs
|
||||||
- Workload:
|
- Workload:
|
||||||
- Dataset
|
- Dataset
|
||||||
- ShareGPT dataset
|
- ShareGPT dataset
|
||||||
- Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
|
- Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
|
||||||
- Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
|
- 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.
|
- Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
|
||||||
- Models: llama-3 8B, llama-3 70B.
|
- 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)).
|
- 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.
|
- 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.
|
- 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).
|
- 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
|
||||||
|
|
||||||
|
|||||||
@ -1,12 +1,10 @@
|
|||||||
# Performance benchmarks descriptions
|
|
||||||
|
|
||||||
## Latency tests
|
## Latency tests
|
||||||
|
|
||||||
- Input length: 32 tokens.
|
- Input length: 32 tokens.
|
||||||
- Output length: 128 tokens.
|
- Output length: 128 tokens.
|
||||||
- Batch size: fixed (8).
|
- Batch size: fixed (8).
|
||||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||||
- CPU Models: llama-3.1 8B.
|
|
||||||
- Evaluation metrics: end-to-end latency (mean, median, p99).
|
- Evaluation metrics: end-to-end latency (mean, median, p99).
|
||||||
|
|
||||||
{latency_tests_markdown_table}
|
{latency_tests_markdown_table}
|
||||||
@ -16,8 +14,7 @@
|
|||||||
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
|
||||||
- Output length: the corresponding output length of these 200 prompts.
|
- Output length: the corresponding output length of these 200 prompts.
|
||||||
- Batch size: dynamically determined by vllm to achieve maximum throughput.
|
- Batch size: dynamically determined by vllm to achieve maximum throughput.
|
||||||
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
|
||||||
- CPU Models: llama-3.1 8B.
|
|
||||||
- Evaluation metrics: throughput.
|
- Evaluation metrics: throughput.
|
||||||
|
|
||||||
{throughput_tests_markdown_table}
|
{throughput_tests_markdown_table}
|
||||||
@ -28,18 +25,12 @@
|
|||||||
- Output length: the corresponding output length of these 200 prompts.
|
- Output length: the corresponding output length of these 200 prompts.
|
||||||
- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
|
- 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).
|
- **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.
|
- 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
|
- We also added a speculative decoding test for llama-3 70B, under QPS 2
|
||||||
- CPU Models: llama-3.1 8B.
|
|
||||||
- Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
|
- 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}
|
{serving_tests_markdown_table}
|
||||||
|
|
||||||
## Platform Information
|
|
||||||
|
|
||||||
{platform_markdown_table}
|
|
||||||
|
|
||||||
## json version of the benchmarking tables
|
## 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.
|
||||||
|
|||||||
@ -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 +1,14 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
|
|
||||||
import argparse
|
|
||||||
import json
|
import json
|
||||||
import os
|
import os
|
||||||
import shlex
|
|
||||||
from importlib import util
|
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import psutil
|
|
||||||
import regex as re
|
|
||||||
from tabulate import tabulate
|
from tabulate import tabulate
|
||||||
|
|
||||||
|
results_folder = Path("results/")
|
||||||
|
|
||||||
# latency results and the keys that will be printed into markdown
|
# latency results and the keys that will be printed into markdown
|
||||||
latency_results = []
|
latency_results = []
|
||||||
latency_column_mapping = {
|
latency_column_mapping = {
|
||||||
@ -33,39 +28,28 @@ throughput_results = []
|
|||||||
throughput_results_column_mapping = {
|
throughput_results_column_mapping = {
|
||||||
"test_name": "Test name",
|
"test_name": "Test name",
|
||||||
"gpu_type": "GPU",
|
"gpu_type": "GPU",
|
||||||
"num_requests": "# of req.",
|
# "num_requests": "# of req.",
|
||||||
"total_num_tokens": "Total # of tokens",
|
# "total_num_tokens": "Total # of tokens",
|
||||||
"elapsed_time": "Elapsed time (s)",
|
# "elapsed_time": "Elapsed time (s)",
|
||||||
"requests_per_second": "Tput (req/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 and the keys that will be printed into markdown
|
||||||
serving_results = []
|
serving_results = []
|
||||||
serving_column_mapping = {
|
serving_column_mapping = {
|
||||||
"test_name": "Test name",
|
"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",
|
"gpu_type": "GPU",
|
||||||
"completed": "# of req.",
|
# "completed": "# of req.",
|
||||||
"qps": "qps",
|
|
||||||
"max_concurrency": "# of max concurrency.",
|
|
||||||
"request_throughput": "Tput (req/s)",
|
"request_throughput": "Tput (req/s)",
|
||||||
"total_token_throughput": "Total Token Tput (tok/s)",
|
# "input_throughput": "Input Tput (tok/s)",
|
||||||
"output_throughput": "Output Tput (tok/s)",
|
# "output_throughput": "Output Tput (tok/s)",
|
||||||
# "total_input_tokens": "Total input tokens",
|
|
||||||
# "total_output_tokens": "Total output tokens",
|
|
||||||
"mean_ttft_ms": "Mean TTFT (ms)",
|
"mean_ttft_ms": "Mean TTFT (ms)",
|
||||||
"median_ttft_ms": "Median TTFT (ms)",
|
"median_ttft_ms": "Median TTFT (ms)",
|
||||||
"p99_ttft_ms": "P99 TTFT (ms)",
|
"p99_ttft_ms": "P99 TTFT (ms)",
|
||||||
"mean_tpot_ms": "Mean TPOT (ms)",
|
# "mean_tpot_ms": "Mean TPOT (ms)",
|
||||||
"median_tpot_ms": "Median",
|
# "median_tpot_ms": "Median",
|
||||||
"p99_tpot_ms": "P99",
|
# "p99_tpot_ms": "P99",
|
||||||
"mean_itl_ms": "Mean ITL (ms)",
|
"mean_itl_ms": "Mean ITL (ms)",
|
||||||
"median_itl_ms": "Median ITL (ms)",
|
"median_itl_ms": "Median ITL (ms)",
|
||||||
"p99_itl_ms": "P99 ITL (ms)",
|
"p99_itl_ms": "P99 ITL (ms)",
|
||||||
@ -81,134 +65,24 @@ def read_markdown(file):
|
|||||||
|
|
||||||
|
|
||||||
def results_to_json(latency, throughput, serving):
|
def results_to_json(latency, throughput, serving):
|
||||||
return json.dumps(
|
return json.dumps({
|
||||||
{
|
'latency': latency.to_dict(),
|
||||||
"latency": latency.to_dict(),
|
'throughput': throughput.to_dict(),
|
||||||
"throughput": throughput.to_dict(),
|
'serving': serving.to_dict()
|
||||||
"serving": serving.to_dict(),
|
})
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def get_size_with_unit(bytes, suffix="B"):
|
|
||||||
"""
|
|
||||||
Scale bytes to its proper format
|
|
||||||
e.g:
|
|
||||||
1253656 => '1.20MB'
|
|
||||||
1253656678 => '1.17GB'
|
|
||||||
"""
|
|
||||||
factor = 1024
|
|
||||||
for unit in ["", "K", "M", "G", "T", "P"]:
|
|
||||||
if bytes < factor:
|
|
||||||
return f"{bytes:.2f}{unit}{suffix}"
|
|
||||||
bytes /= factor
|
|
||||||
|
|
||||||
|
|
||||||
def _coerce(val: str) -> Any:
|
|
||||||
"""Best-effort type coercion from string to Python types."""
|
|
||||||
low = val.lower()
|
|
||||||
if low == "null":
|
|
||||||
return None
|
|
||||||
if low == "true":
|
|
||||||
return True
|
|
||||||
if low == "false":
|
|
||||||
return False
|
|
||||||
# integers
|
|
||||||
if re.fullmatch(r"[+-]?\d+", val):
|
|
||||||
try:
|
|
||||||
return int(val)
|
|
||||||
except ValueError:
|
|
||||||
pass
|
|
||||||
# floats (keep 'inf'/'-inf'/'nan' as strings)
|
|
||||||
if re.fullmatch(r"[+-]?\d*\.\d+", val):
|
|
||||||
try:
|
|
||||||
return float(val)
|
|
||||||
except ValueError:
|
|
||||||
pass
|
|
||||||
return val
|
|
||||||
|
|
||||||
|
|
||||||
def parse_client_command(cmd: str) -> dict[str, Any]:
|
|
||||||
"""Parse the client_command shell string into {executable, script, args}."""
|
|
||||||
toks = shlex.split(cmd)
|
|
||||||
if len(toks) < 2:
|
|
||||||
raise ValueError("client_command must include an executable and a script")
|
|
||||||
executable, script = toks[0], toks[1]
|
|
||||||
args: dict[str, Any] = {}
|
|
||||||
|
|
||||||
i = 2
|
|
||||||
while i < len(toks):
|
|
||||||
t = toks[i]
|
|
||||||
if t.startswith("--"):
|
|
||||||
# --key=value or --key (value) or boolean flag
|
|
||||||
if "=" in t:
|
|
||||||
key, val = t.split("=", 1)
|
|
||||||
if key == "--metadata":
|
|
||||||
md = {}
|
|
||||||
if val:
|
|
||||||
if "=" in val:
|
|
||||||
k, v = val.split("=", 1)
|
|
||||||
md[k] = _coerce(v)
|
|
||||||
else:
|
|
||||||
md[val] = True
|
|
||||||
args[key] = md
|
|
||||||
else:
|
|
||||||
args[key] = _coerce(val)
|
|
||||||
i += 1
|
|
||||||
continue
|
|
||||||
|
|
||||||
key = t
|
|
||||||
|
|
||||||
# Special: consume metadata k=v pairs until next --flag
|
|
||||||
if key == "--metadata":
|
|
||||||
i += 1
|
|
||||||
md = {}
|
|
||||||
while i < len(toks) and not toks[i].startswith("--"):
|
|
||||||
pair = toks[i]
|
|
||||||
if "=" in pair:
|
|
||||||
k, v = pair.split("=", 1)
|
|
||||||
md[k] = _coerce(v)
|
|
||||||
else:
|
|
||||||
md[pair] = True
|
|
||||||
i += 1
|
|
||||||
args[key] = md
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Standard: check if next token is a value (not a flag)
|
|
||||||
if i + 1 < len(toks) and not toks[i + 1].startswith("--"):
|
|
||||||
args[key] = _coerce(toks[i + 1])
|
|
||||||
i += 2
|
|
||||||
else:
|
|
||||||
# lone flag -> True
|
|
||||||
args[key] = True
|
|
||||||
i += 1
|
|
||||||
else:
|
|
||||||
# unexpected positional; skip
|
|
||||||
i += 1
|
|
||||||
|
|
||||||
return {"executable": executable, "script": script, "args": args}
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
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
|
# collect results
|
||||||
for test_file in results_folder.glob("*.json"):
|
for test_file in results_folder.glob("*.json"):
|
||||||
|
|
||||||
with open(test_file) as f:
|
with open(test_file) as f:
|
||||||
raw_result = json.loads(f.read())
|
raw_result = json.loads(f.read())
|
||||||
|
|
||||||
if "serving" in str(test_file):
|
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
|
# attach the benchmarking command to raw_result
|
||||||
try:
|
try:
|
||||||
with open(test_file.with_suffix(".commands")) as f:
|
with open(test_file.with_suffix(".commands")) as f:
|
||||||
@ -216,50 +90,18 @@ if __name__ == "__main__":
|
|||||||
except OSError as e:
|
except OSError as e:
|
||||||
print(e)
|
print(e)
|
||||||
continue
|
continue
|
||||||
# Parse Server Command Arg
|
|
||||||
out: dict[str, Any] = {
|
|
||||||
"server_command": parse_client_command(command["server_command"])
|
|
||||||
}
|
|
||||||
parse_args = [
|
|
||||||
"--tensor-parallel-size",
|
|
||||||
"--pipeline-parallel-size",
|
|
||||||
"--dtype",
|
|
||||||
]
|
|
||||||
col_mapping = ["tp_size", "pp_size", "dtype"]
|
|
||||||
for index, arg in enumerate(parse_args):
|
|
||||||
if arg in out["server_command"]["args"]:
|
|
||||||
raw_result.update(
|
|
||||||
{col_mapping[index]: out["server_command"]["args"][arg]}
|
|
||||||
)
|
|
||||||
|
|
||||||
# Parse Client Command Arg
|
|
||||||
out: dict[str, Any] = {
|
|
||||||
"client_command": parse_client_command(command["client_command"])
|
|
||||||
}
|
|
||||||
parse_args = [
|
|
||||||
"--dataset-name",
|
|
||||||
"--random-input-len",
|
|
||||||
"--random-output-len",
|
|
||||||
"--request-rate",
|
|
||||||
]
|
|
||||||
col_mapping = ["dataset_name", "input_len", "output_len", "qps"]
|
|
||||||
|
|
||||||
for index, arg in enumerate(parse_args):
|
|
||||||
if arg in out["client_command"]["args"]:
|
|
||||||
raw_result.update(
|
|
||||||
{col_mapping[index]: out["client_command"]["args"][arg]}
|
|
||||||
)
|
|
||||||
# Add Server, Client command
|
|
||||||
raw_result.update(command)
|
raw_result.update(command)
|
||||||
|
|
||||||
# update the test name of this result
|
# update the test name of this result
|
||||||
raw_result.update({"test_name": test_file.stem})
|
raw_result.update({"test_name": test_file.stem})
|
||||||
|
|
||||||
# add the result to raw_result
|
# add the result to raw_result
|
||||||
serving_results.append(raw_result)
|
serving_results.append(raw_result)
|
||||||
continue
|
continue
|
||||||
|
|
||||||
elif "latency" in f.name:
|
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
|
# attach the benchmarking command to raw_result
|
||||||
try:
|
try:
|
||||||
@ -278,8 +120,7 @@ if __name__ == "__main__":
|
|||||||
for perc in [10, 25, 50, 75, 90, 99]:
|
for perc in [10, 25, 50, 75, 90, 99]:
|
||||||
# Multiply 1000 to convert the time unit from s to ms
|
# Multiply 1000 to convert the time unit from s to ms
|
||||||
raw_result.update(
|
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
|
raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
|
||||||
|
|
||||||
# add the result to raw_result
|
# add the result to raw_result
|
||||||
@ -287,7 +128,7 @@ if __name__ == "__main__":
|
|||||||
continue
|
continue
|
||||||
|
|
||||||
elif "throughput" in f.name:
|
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
|
# attach the benchmarking command to raw_result
|
||||||
try:
|
try:
|
||||||
@ -312,51 +153,26 @@ if __name__ == "__main__":
|
|||||||
serving_results = pd.DataFrame.from_dict(serving_results)
|
serving_results = pd.DataFrame.from_dict(serving_results)
|
||||||
throughput_results = pd.DataFrame.from_dict(throughput_results)
|
throughput_results = pd.DataFrame.from_dict(throughput_results)
|
||||||
|
|
||||||
svmem = psutil.virtual_memory()
|
raw_results_json = results_to_json(latency_results, throughput_results,
|
||||||
platform_data = {
|
serving_results)
|
||||||
"Physical cores": [psutil.cpu_count(logical=False)],
|
|
||||||
"Total cores": [psutil.cpu_count(logical=True)],
|
|
||||||
"Total Memory": [get_size_with_unit(svmem.total)],
|
|
||||||
}
|
|
||||||
|
|
||||||
if util.find_spec("numa") is not None:
|
|
||||||
from numa import info
|
|
||||||
|
|
||||||
platform_data["Total NUMA nodes"] = [info.get_num_configured_nodes()]
|
|
||||||
|
|
||||||
if util.find_spec("cpuinfo") is not None:
|
|
||||||
from cpuinfo import get_cpu_info
|
|
||||||
|
|
||||||
platform_data["CPU Brand"] = [get_cpu_info()["brand_raw"]]
|
|
||||||
|
|
||||||
platform_results = pd.DataFrame.from_dict(
|
|
||||||
platform_data, orient="index", columns=["Platform Info"]
|
|
||||||
)
|
|
||||||
|
|
||||||
raw_results_json = results_to_json(
|
|
||||||
latency_results, throughput_results, serving_results
|
|
||||||
)
|
|
||||||
|
|
||||||
# remapping the key, for visualization purpose
|
# remapping the key, for visualization purpose
|
||||||
if not latency_results.empty:
|
if not latency_results.empty:
|
||||||
latency_results = latency_results[list(latency_column_mapping.keys())].rename(
|
latency_results = latency_results[list(
|
||||||
columns=latency_column_mapping
|
latency_column_mapping.keys())].rename(
|
||||||
)
|
columns=latency_column_mapping)
|
||||||
if not serving_results.empty:
|
if not serving_results.empty:
|
||||||
valid_columns = [
|
serving_results = serving_results[list(
|
||||||
col for col in serving_column_mapping if col in serving_results.columns
|
serving_column_mapping.keys())].rename(
|
||||||
]
|
columns=serving_column_mapping)
|
||||||
serving_results = serving_results[valid_columns].rename(
|
|
||||||
columns=serving_column_mapping
|
|
||||||
)
|
|
||||||
if not throughput_results.empty:
|
if not throughput_results.empty:
|
||||||
throughput_results = throughput_results[
|
throughput_results = throughput_results[list(
|
||||||
list(throughput_results_column_mapping.keys())
|
throughput_results_column_mapping.keys())].rename(
|
||||||
].rename(columns=throughput_results_column_mapping)
|
columns=throughput_results_column_mapping)
|
||||||
|
|
||||||
processed_results_json = results_to_json(
|
processed_results_json = results_to_json(latency_results,
|
||||||
latency_results, throughput_results, serving_results
|
throughput_results,
|
||||||
)
|
serving_results)
|
||||||
|
|
||||||
for df in [latency_results, serving_results, throughput_results]:
|
for df in [latency_results, serving_results, throughput_results]:
|
||||||
if df.empty:
|
if df.empty:
|
||||||
@ -368,45 +184,38 @@ if __name__ == "__main__":
|
|||||||
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
|
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
|
||||||
# we want to turn it into "8xGPUTYPE"
|
# we want to turn it into "8xGPUTYPE"
|
||||||
df["GPU"] = df["GPU"].apply(
|
df["GPU"] = df["GPU"].apply(
|
||||||
lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
|
lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")
|
||||||
)
|
|
||||||
|
|
||||||
# get markdown tables
|
# get markdown tables
|
||||||
latency_md_table = tabulate(
|
latency_md_table = tabulate(latency_results,
|
||||||
latency_results, headers="keys", tablefmt="pipe", showindex=False
|
headers='keys',
|
||||||
)
|
tablefmt='pipe',
|
||||||
serving_md_table = tabulate(
|
showindex=False)
|
||||||
serving_results, headers="keys", tablefmt="pipe", showindex=False
|
serving_md_table = tabulate(serving_results,
|
||||||
)
|
headers='keys',
|
||||||
throughput_md_table = tabulate(
|
tablefmt='pipe',
|
||||||
throughput_results, headers="keys", tablefmt="pipe", showindex=False
|
showindex=False)
|
||||||
)
|
throughput_md_table = tabulate(throughput_results,
|
||||||
platform_md_table = tabulate(
|
headers='keys',
|
||||||
platform_results, headers="keys", tablefmt="pipe", showindex=True
|
tablefmt='pipe',
|
||||||
)
|
showindex=False)
|
||||||
|
|
||||||
# document the result
|
# document the result
|
||||||
md_file = "benchmark_results.md"
|
with open(results_folder / "benchmark_results.md", "w") as f:
|
||||||
json_file = "benchmark_results.json"
|
|
||||||
with open(results_folder / md_file, "w") as f:
|
results = read_markdown("../.buildkite/nightly-benchmarks/" +
|
||||||
results = read_markdown(
|
"performance-benchmarks-descriptions.md")
|
||||||
"../.buildkite/nightly-benchmarks/"
|
|
||||||
+ "performance-benchmarks-descriptions.md"
|
|
||||||
)
|
|
||||||
results = results.format(
|
results = results.format(
|
||||||
latency_tests_markdown_table=latency_md_table,
|
latency_tests_markdown_table=latency_md_table,
|
||||||
throughput_tests_markdown_table=throughput_md_table,
|
throughput_tests_markdown_table=throughput_md_table,
|
||||||
serving_tests_markdown_table=serving_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)
|
f.write(results)
|
||||||
|
|
||||||
# document benchmarking results in json
|
# document benchmarking results in json
|
||||||
with open(results_folder / json_file, "w") as f:
|
with open(results_folder / "benchmark_results.json", "w") as f:
|
||||||
results = (
|
|
||||||
latency_results.to_dict(orient="records")
|
results = latency_results.to_dict(
|
||||||
+ throughput_results.to_dict(orient="records")
|
orient='records') + throughput_results.to_dict(
|
||||||
+ serving_results.to_dict(orient="records")
|
orient='records') + serving_results.to_dict(orient='records')
|
||||||
)
|
|
||||||
f.write(json.dumps(results))
|
f.write(json.dumps(results))
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
|
|
||||||
@ -15,12 +14,15 @@ def main(model, cachedir):
|
|||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
parser = argparse.ArgumentParser(
|
parser = argparse.ArgumentParser(
|
||||||
description="Download and save Hugging Face tokenizer"
|
description="Download and save Hugging Face tokenizer")
|
||||||
)
|
parser.add_argument("--model",
|
||||||
parser.add_argument("--model", type=str, required=True, help="Name of the model")
|
type=str,
|
||||||
parser.add_argument(
|
required=True,
|
||||||
"--cachedir", type=str, required=True, help="Directory to save the tokenizer"
|
help="Name of the model")
|
||||||
)
|
parser.add_argument("--cachedir",
|
||||||
|
type=str,
|
||||||
|
required=True,
|
||||||
|
help="Directory to save the tokenizer")
|
||||||
|
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
main(args.model, args.cachedir)
|
main(args.model, args.cachedir)
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
import json
|
import json
|
||||||
@ -12,33 +11,33 @@ from tabulate import tabulate
|
|||||||
|
|
||||||
def parse_arguments():
|
def parse_arguments():
|
||||||
parser = argparse.ArgumentParser(
|
parser = argparse.ArgumentParser(
|
||||||
description="Parse command line arguments for summary-nightly-results script."
|
description=
|
||||||
)
|
'Parse command line arguments for summary-nightly-results script.')
|
||||||
parser.add_argument(
|
parser.add_argument('--results-folder',
|
||||||
"--results-folder",
|
type=str,
|
||||||
type=str,
|
required=True,
|
||||||
required=True,
|
help='The folder where the results are stored.')
|
||||||
help="The folder where the results are stored.",
|
parser.add_argument('--description',
|
||||||
)
|
type=str,
|
||||||
parser.add_argument(
|
required=True,
|
||||||
"--description", type=str, required=True, help="Description of the results."
|
help='Description of the results.')
|
||||||
)
|
|
||||||
|
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
return args
|
return args
|
||||||
|
|
||||||
|
|
||||||
def get_perf(df, method, model, metric):
|
def get_perf(df, method, model, metric):
|
||||||
|
|
||||||
means = []
|
means = []
|
||||||
|
|
||||||
for qps in [2, 4, 8, 16, "inf"]:
|
for qps in [2, 4, 8, 16, "inf"]:
|
||||||
target = df["Test name"].str.contains(model)
|
target = df['Test name'].str.contains(model)
|
||||||
target = target & df["Engine"].str.contains(method)
|
target = target & df['Engine'].str.contains(method)
|
||||||
target = target & df["Test name"].str.contains("qps_" + str(qps))
|
target = target & df['Test name'].str.contains("qps_" + str(qps))
|
||||||
filtered_df = df[target]
|
filtered_df = df[target]
|
||||||
|
|
||||||
if filtered_df.empty:
|
if filtered_df.empty:
|
||||||
means.append(0.0)
|
means.append(0.)
|
||||||
else:
|
else:
|
||||||
means.append(filtered_df[metric].values[0])
|
means.append(filtered_df[metric].values[0])
|
||||||
|
|
||||||
@ -46,6 +45,7 @@ def get_perf(df, method, model, metric):
|
|||||||
|
|
||||||
|
|
||||||
def get_perf_w_std(df, method, model, metric):
|
def get_perf_w_std(df, method, model, metric):
|
||||||
|
|
||||||
if metric in ["TTFT", "ITL"]:
|
if metric in ["TTFT", "ITL"]:
|
||||||
mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
|
mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
|
||||||
mean = mean.tolist()
|
mean = mean.tolist()
|
||||||
@ -60,8 +60,7 @@ def get_perf_w_std(df, method, model, metric):
|
|||||||
else:
|
else:
|
||||||
assert metric == "Tput"
|
assert metric == "Tput"
|
||||||
mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
|
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()
|
mean = mean.tolist()
|
||||||
std = None
|
std = None
|
||||||
|
|
||||||
@ -81,17 +80,18 @@ def main(args):
|
|||||||
# generate markdown table
|
# generate markdown table
|
||||||
df = pd.DataFrame.from_dict(results)
|
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:
|
with open(args.description) as f:
|
||||||
description = f.read()
|
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:
|
with open("nightly_results.md", "w") as f:
|
||||||
f.write(description)
|
f.write(description)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == '__main__':
|
||||||
args = parse_arguments()
|
args = parse_arguments()
|
||||||
main(args)
|
main(args)
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
|
|
||||||
from lmdeploy.serve.openai.api_client import APIClient
|
from lmdeploy.serve.openai.api_client import APIClient
|
||||||
|
|
||||||
|
|||||||
@ -73,7 +73,7 @@ get_current_llm_serving_engine() {
|
|||||||
echo "Container: vllm"
|
echo "Container: vllm"
|
||||||
# move to a completely irrelevant directory, to avoid import vllm from current folder
|
# move to a completely irrelevant directory, to avoid import vllm from current folder
|
||||||
export CURRENT_LLM_SERVING_ENGINE=vllm
|
export CURRENT_LLM_SERVING_ENGINE=vllm
|
||||||
|
|
||||||
return
|
return
|
||||||
fi
|
fi
|
||||||
}
|
}
|
||||||
@ -95,14 +95,12 @@ json2args() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
kill_gpu_processes() {
|
kill_gpu_processes() {
|
||||||
pkill -f '[p]ython'
|
pkill -f python
|
||||||
pkill -f '[p]ython3'
|
pkill -f python3
|
||||||
pkill -f '[t]ritonserver'
|
pkill -f tritonserver
|
||||||
pkill -f '[p]t_main_thread'
|
pkill -f pt_main_thread
|
||||||
pkill -f '[t]ext-generation'
|
pkill -f text-generation
|
||||||
pkill -f '[l]mdeploy'
|
pkill -f lmdeploy
|
||||||
# vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
|
|
||||||
pkill -f '[V]LLM'
|
|
||||||
|
|
||||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||||
sleep 1
|
sleep 1
|
||||||
@ -127,7 +125,7 @@ ensure_installed() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
run_serving_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
|
# $1: a json file specifying serving test cases
|
||||||
|
|
||||||
local serving_test_file
|
local serving_test_file
|
||||||
@ -227,7 +225,7 @@ run_serving_tests() {
|
|||||||
|
|
||||||
if [[ "$dataset_name" = "sharegpt" ]]; then
|
if [[ "$dataset_name" = "sharegpt" ]]; then
|
||||||
|
|
||||||
client_command="vllm bench serve \
|
client_command="python3 benchmark_serving.py \
|
||||||
--backend $backend \
|
--backend $backend \
|
||||||
--tokenizer /tokenizer_cache \
|
--tokenizer /tokenizer_cache \
|
||||||
--model $model \
|
--model $model \
|
||||||
@ -248,7 +246,7 @@ run_serving_tests() {
|
|||||||
sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
|
sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
|
||||||
sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
|
sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
|
||||||
|
|
||||||
client_command="vllm bench serve \
|
client_command="python3 benchmark_serving.py \
|
||||||
--backend $backend \
|
--backend $backend \
|
||||||
--tokenizer /tokenizer_cache \
|
--tokenizer /tokenizer_cache \
|
||||||
--model $model \
|
--model $model \
|
||||||
@ -267,13 +265,13 @@ run_serving_tests() {
|
|||||||
$client_args"
|
$client_args"
|
||||||
|
|
||||||
else
|
else
|
||||||
|
|
||||||
echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
|
echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
|
||||||
exit 1
|
exit 1
|
||||||
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
echo "Running test case $test_name with qps $qps"
|
echo "Running test case $test_name with qps $qps"
|
||||||
echo "Client command: $client_command"
|
echo "Client command: $client_command"
|
||||||
@ -304,7 +302,7 @@ run_serving_tests() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
run_genai_perf_tests() {
|
run_genai_perf_tests() {
|
||||||
# run genai-perf tests
|
# run genai-perf tests
|
||||||
|
|
||||||
# $1: a json file specifying genai-perf test cases
|
# $1: a json file specifying genai-perf test cases
|
||||||
local genai_perf_test_file
|
local genai_perf_test_file
|
||||||
@ -313,14 +311,14 @@ run_genai_perf_tests() {
|
|||||||
# Iterate over genai-perf tests
|
# Iterate over genai-perf tests
|
||||||
jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
|
jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
|
||||||
# get the test name, and append the GPU type back to it.
|
# get the test name, and append the GPU type back to it.
|
||||||
test_name=$(echo "$params" | jq -r '.test_name')
|
test_name=$(echo "$params" | jq -r '.test_name')
|
||||||
|
|
||||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||||
echo "Skip test case $test_name."
|
echo "Skip test case $test_name."
|
||||||
continue
|
continue
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# prepend the current serving engine to the test name
|
# prepend the current serving engine to the test name
|
||||||
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
|
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
|
||||||
|
|
||||||
@ -371,10 +369,10 @@ run_genai_perf_tests() {
|
|||||||
qps=$num_prompts
|
qps=$num_prompts
|
||||||
echo "now qps is $qps"
|
echo "now qps is $qps"
|
||||||
fi
|
fi
|
||||||
|
|
||||||
new_test_name=$test_name"_qps_"$qps
|
new_test_name=$test_name"_qps_"$qps
|
||||||
backend=$CURRENT_LLM_SERVING_ENGINE
|
backend=$CURRENT_LLM_SERVING_ENGINE
|
||||||
|
|
||||||
if [[ "$backend" == *"vllm"* ]]; then
|
if [[ "$backend" == *"vllm"* ]]; then
|
||||||
backend="vllm"
|
backend="vllm"
|
||||||
fi
|
fi
|
||||||
@ -382,7 +380,7 @@ run_genai_perf_tests() {
|
|||||||
client_command="genai-perf profile \
|
client_command="genai-perf profile \
|
||||||
-m $model \
|
-m $model \
|
||||||
--service-kind openai \
|
--service-kind openai \
|
||||||
--backend "$backend" \
|
--backend vllm \
|
||||||
--endpoint-type chat \
|
--endpoint-type chat \
|
||||||
--streaming \
|
--streaming \
|
||||||
--url localhost:$port \
|
--url localhost:$port \
|
||||||
@ -415,7 +413,7 @@ prepare_dataset() {
|
|||||||
do
|
do
|
||||||
cat sonnet.txt >> sonnet_4x.txt
|
cat sonnet.txt >> sonnet_4x.txt
|
||||||
done
|
done
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
main() {
|
main() {
|
||||||
|
|||||||
@ -31,20 +31,6 @@ check_gpus() {
|
|||||||
echo "GPU type is $gpu_type"
|
echo "GPU type is $gpu_type"
|
||||||
}
|
}
|
||||||
|
|
||||||
check_cpus() {
|
|
||||||
# check the number of CPUs and NUMA Node and GPU type.
|
|
||||||
declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
|
|
||||||
if [[ $numa_count -gt 0 ]]; then
|
|
||||||
echo "NUMA found."
|
|
||||||
echo $numa_count
|
|
||||||
else
|
|
||||||
echo "Need at least 1 NUMA to run benchmarking."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
declare -g gpu_type="cpu"
|
|
||||||
echo "GPU type is $gpu_type"
|
|
||||||
}
|
|
||||||
|
|
||||||
check_hf_token() {
|
check_hf_token() {
|
||||||
# check if HF_TOKEN is available and valid
|
# check if HF_TOKEN is available and valid
|
||||||
if [[ -z "$HF_TOKEN" ]]; then
|
if [[ -z "$HF_TOKEN" ]]; then
|
||||||
@ -83,22 +69,6 @@ json2args() {
|
|||||||
echo "$args"
|
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_server() {
|
||||||
# wait for vllm server to start
|
# wait for vllm server to start
|
||||||
# return 1 if vllm server crashes
|
# return 1 if vllm server crashes
|
||||||
@ -126,8 +96,7 @@ kill_gpu_processes() {
|
|||||||
ps -aux
|
ps -aux
|
||||||
lsof -t -i:8000 | xargs -r kill -9
|
lsof -t -i:8000 | xargs -r kill -9
|
||||||
pgrep python3 | 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
|
# wait until GPU memory usage smaller than 1GB
|
||||||
if command -v nvidia-smi; then
|
if command -v nvidia-smi; then
|
||||||
@ -165,7 +134,7 @@ upload_to_buildkite() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
run_latency_tests() {
|
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
|
# $1: a json file specifying latency test cases
|
||||||
|
|
||||||
local latency_test_file
|
local latency_test_file
|
||||||
@ -189,26 +158,15 @@ run_latency_tests() {
|
|||||||
# get arguments
|
# get arguments
|
||||||
latency_params=$(echo "$params" | jq -r '.parameters')
|
latency_params=$(echo "$params" | jq -r '.parameters')
|
||||||
latency_args=$(json2args "$latency_params")
|
latency_args=$(json2args "$latency_params")
|
||||||
latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
|
|
||||||
latency_envs=$(json2envs "$latency_environment_variables")
|
|
||||||
|
|
||||||
# check if there is enough GPU to run the test
|
# check if there is enough GPU to run the test
|
||||||
tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
|
tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
|
||||||
if [ "$ON_CPU" == "1" ]; then
|
if [[ $gpu_count -lt $tp ]]; then
|
||||||
pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
|
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||||
world_size=$(($tp*$pp))
|
continue
|
||||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
|
||||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
if [[ $gpu_count -lt $tp ]]; then
|
|
||||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
latency_command=" $latency_envs vllm bench latency \
|
latency_command="python3 benchmark_latency.py \
|
||||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||||
$latency_args"
|
$latency_args"
|
||||||
|
|
||||||
@ -234,7 +192,7 @@ run_latency_tests() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
run_throughput_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
|
# $1: a json file specifying throughput test cases
|
||||||
|
|
||||||
local throughput_test_file
|
local throughput_test_file
|
||||||
@ -258,26 +216,15 @@ run_throughput_tests() {
|
|||||||
# get arguments
|
# get arguments
|
||||||
throughput_params=$(echo "$params" | jq -r '.parameters')
|
throughput_params=$(echo "$params" | jq -r '.parameters')
|
||||||
throughput_args=$(json2args "$throughput_params")
|
throughput_args=$(json2args "$throughput_params")
|
||||||
throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
|
|
||||||
throughput_envs=$(json2envs "$throughput_environment_variables")
|
|
||||||
|
|
||||||
# check if there is enough GPU to run the test
|
# check if there is enough GPU to run the test
|
||||||
tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
|
tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
|
||||||
if [ "$ON_CPU" == "1" ]; then
|
if [[ $gpu_count -lt $tp ]]; then
|
||||||
pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
|
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||||
world_size=$(($tp*$pp))
|
continue
|
||||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
|
||||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
if [[ $gpu_count -lt $tp ]]; then
|
|
||||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
throughput_command=" $throughput_envs vllm bench throughput \
|
throughput_command="python3 benchmark_throughput.py \
|
||||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||||
$throughput_args"
|
$throughput_args"
|
||||||
|
|
||||||
@ -302,7 +249,7 @@ run_throughput_tests() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
run_serving_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
|
# $1: a json file specifying serving test cases
|
||||||
|
|
||||||
local serving_test_file
|
local serving_test_file
|
||||||
@ -325,36 +272,18 @@ run_serving_tests() {
|
|||||||
|
|
||||||
# get client and server arguments
|
# get client and server arguments
|
||||||
server_params=$(echo "$params" | jq -r '.server_parameters')
|
server_params=$(echo "$params" | jq -r '.server_parameters')
|
||||||
server_envs=$(echo "$params" | jq -r '.server_environment_variables')
|
|
||||||
client_params=$(echo "$params" | jq -r '.client_parameters')
|
client_params=$(echo "$params" | jq -r '.client_parameters')
|
||||||
server_args=$(json2args "$server_params")
|
server_args=$(json2args "$server_params")
|
||||||
server_envs=$(json2envs "$server_envs")
|
|
||||||
client_args=$(json2args "$client_params")
|
client_args=$(json2args "$client_params")
|
||||||
qps_list=$(echo "$params" | jq -r '.qps_list')
|
qps_list=$(echo "$params" | jq -r '.qps_list')
|
||||||
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
||||||
echo "Running over qps list $qps_list"
|
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 "$server_params" | jq -r '.tensor_parallel_size')
|
tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
|
||||||
if [ "$ON_CPU" == "1" ]; then
|
if [[ $gpu_count -lt $tp ]]; then
|
||||||
pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
|
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||||
world_size=$(($tp*$pp))
|
continue
|
||||||
if [[ $numa_count -lt $world_size && -z "${REMOTE_HOST}" ]]; then
|
|
||||||
echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
if [[ $gpu_count -lt $tp ]]; then
|
|
||||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
|
||||||
continue
|
|
||||||
fi
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# check if server model and client model is aligned
|
# check if server model and client model is aligned
|
||||||
@ -365,33 +294,23 @@ run_serving_tests() {
|
|||||||
continue
|
continue
|
||||||
fi
|
fi
|
||||||
|
|
||||||
server_command="$server_envs python3 \
|
server_command="python3 \
|
||||||
-m vllm.entrypoints.openai.api_server \
|
-m vllm.entrypoints.openai.api_server \
|
||||||
$server_args"
|
$server_args"
|
||||||
|
|
||||||
# run the server
|
# run the server
|
||||||
echo "Running test case $test_name"
|
echo "Running test case $test_name"
|
||||||
echo "Server command: $server_command"
|
echo "Server command: $server_command"
|
||||||
# support remote vllm server
|
bash -c "$server_command" &
|
||||||
client_remote_args=""
|
server_pid=$!
|
||||||
if [[ -z "${REMOTE_HOST}" ]]; then
|
|
||||||
bash -c "$server_command" &
|
# wait until the server is alive
|
||||||
server_pid=$!
|
if wait_for_server; then
|
||||||
# wait until the server is alive
|
echo ""
|
||||||
if wait_for_server; then
|
echo "vllm server is up and running."
|
||||||
echo ""
|
|
||||||
echo "vLLM server is up and running."
|
|
||||||
else
|
|
||||||
echo ""
|
|
||||||
echo "vLLM failed to start within the timeout period."
|
|
||||||
fi
|
|
||||||
else
|
else
|
||||||
server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
|
echo ""
|
||||||
if [[ ${REMOTE_PORT} ]]; then
|
echo "vllm failed to start within the timeout period."
|
||||||
client_remote_args=" --host=$REMOTE_HOST --port=$REMOTE_PORT "
|
|
||||||
else
|
|
||||||
client_remote_args=" --host=$REMOTE_HOST "
|
|
||||||
fi
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# iterate over different QPS
|
# iterate over different QPS
|
||||||
@ -403,39 +322,35 @@ run_serving_tests() {
|
|||||||
echo "now qps is $qps"
|
echo "now qps is $qps"
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# iterate over different max_concurrency
|
new_test_name=$test_name"_qps_"$qps
|
||||||
for max_concurrency in $max_concurrency_list; do
|
|
||||||
new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
|
|
||||||
echo " new test name $new_test_name"
|
|
||||||
# pass the tensor parallel size to the client so that it can be displayed
|
|
||||||
# on the benchmark dashboard
|
|
||||||
client_command="vllm bench serve \
|
|
||||||
--save-result \
|
|
||||||
--result-dir $RESULTS_FOLDER \
|
|
||||||
--result-filename ${new_test_name}.json \
|
|
||||||
--request-rate $qps \
|
|
||||||
--max-concurrency $max_concurrency \
|
|
||||||
--metadata "tensor_parallel_size=$tp" \
|
|
||||||
$client_args $client_remote_args "
|
|
||||||
|
|
||||||
echo "Running test case $test_name with qps $qps"
|
# pass the tensor parallel size to the client so that it can be displayed
|
||||||
echo "Client command: $client_command"
|
# on the benchmark dashboard
|
||||||
|
client_command="python3 benchmark_serving.py \
|
||||||
|
--save-result \
|
||||||
|
--result-dir $RESULTS_FOLDER \
|
||||||
|
--result-filename ${new_test_name}.json \
|
||||||
|
--request-rate $qps \
|
||||||
|
--metadata "tensor_parallel_size=$tp" \
|
||||||
|
$client_args"
|
||||||
|
|
||||||
bash -c "$client_command"
|
echo "Running test case $test_name with qps $qps"
|
||||||
|
echo "Client command: $client_command"
|
||||||
|
|
||||||
# record the benchmarking commands
|
bash -c "$client_command"
|
||||||
jq_output=$(jq -n \
|
|
||||||
--arg server "$server_command" \
|
# record the benchmarking commands
|
||||||
--arg client "$client_command" \
|
jq_output=$(jq -n \
|
||||||
--arg gpu "$gpu_type" \
|
--arg server "$server_command" \
|
||||||
'{
|
--arg client "$client_command" \
|
||||||
server_command: $server,
|
--arg gpu "$gpu_type" \
|
||||||
client_command: $client,
|
'{
|
||||||
gpu_type: $gpu
|
server_command: $server,
|
||||||
}')
|
client_command: $client,
|
||||||
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
|
gpu_type: $gpu
|
||||||
|
}')
|
||||||
|
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
|
||||||
|
|
||||||
done
|
|
||||||
done
|
done
|
||||||
|
|
||||||
# clean up
|
# clean up
|
||||||
@ -445,14 +360,7 @@ run_serving_tests() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
main() {
|
main() {
|
||||||
local ARCH
|
check_gpus
|
||||||
ARCH=''
|
|
||||||
if [ "$ON_CPU" == "1" ];then
|
|
||||||
check_cpus
|
|
||||||
ARCH='-cpu'
|
|
||||||
else
|
|
||||||
check_gpus
|
|
||||||
fi
|
|
||||||
check_hf_token
|
check_hf_token
|
||||||
|
|
||||||
# Set to v1 to run v1 benchmark
|
# Set to v1 to run v1 benchmark
|
||||||
@ -465,7 +373,7 @@ main() {
|
|||||||
(which jq) || (apt-get update && apt-get -y install jq)
|
(which jq) || (apt-get update && apt-get -y install jq)
|
||||||
(which lsof) || (apt-get update && apt-get install -y lsof)
|
(which lsof) || (apt-get update && apt-get install -y lsof)
|
||||||
|
|
||||||
# get the current IP address, required by `vllm bench serve` command
|
# get the current IP address, required by benchmark_serving.py
|
||||||
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
||||||
# turn of the reporting of the status of each request, to clean up the terminal output
|
# turn of the reporting of the status of each request, to clean up the terminal output
|
||||||
export VLLM_LOGGING_LEVEL="WARNING"
|
export VLLM_LOGGING_LEVEL="WARNING"
|
||||||
@ -478,9 +386,9 @@ main() {
|
|||||||
QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
|
QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
|
||||||
|
|
||||||
# benchmarking
|
# benchmarking
|
||||||
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
|
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
|
||||||
run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
|
run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
|
||||||
run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
|
run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
|
||||||
|
|
||||||
# postprocess benchmarking results
|
# postprocess benchmarking results
|
||||||
pip install tabulate pandas
|
pip install tabulate pandas
|
||||||
|
|||||||
@ -1,5 +1,4 @@
|
|||||||
# SPDX-License-Identifier: Apache-2.0
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
||||||
|
|
||||||
import datetime
|
import datetime
|
||||||
import json
|
import json
|
||||||
@ -35,8 +34,10 @@ serving_column_mapping = {
|
|||||||
}
|
}
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|
||||||
# collect results
|
# collect results
|
||||||
for test_file in results_folder.glob("*.json"):
|
for test_file in results_folder.glob("*.json"):
|
||||||
|
|
||||||
with open(test_file) as f:
|
with open(test_file) as f:
|
||||||
raw_result = json.loads(f.read())
|
raw_result = json.loads(f.read())
|
||||||
|
|
||||||
@ -55,16 +56,17 @@ if __name__ == "__main__":
|
|||||||
serving_results = pd.DataFrame.from_dict(serving_results)
|
serving_results = pd.DataFrame.from_dict(serving_results)
|
||||||
|
|
||||||
if not serving_results.empty:
|
if not serving_results.empty:
|
||||||
serving_results = serving_results[list(serving_column_mapping.keys())].rename(
|
serving_results = serving_results[list(
|
||||||
columns=serving_column_mapping
|
serving_column_mapping.keys())].rename(
|
||||||
)
|
columns=serving_column_mapping)
|
||||||
|
|
||||||
serving_md_table_with_headers = tabulate(
|
serving_md_table_with_headers = tabulate(serving_results,
|
||||||
serving_results, headers="keys", tablefmt="pipe", showindex=False
|
headers='keys',
|
||||||
)
|
tablefmt='pipe',
|
||||||
|
showindex=False)
|
||||||
# remove the first line of header
|
# remove the first line of header
|
||||||
serving_md_table_lines = serving_md_table_with_headers.split("\n")
|
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_without_header = '\n'.join(serving_md_table_lines[2:])
|
||||||
|
|
||||||
prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||||
prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
|
prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
|
||||||
@ -74,9 +76,10 @@ if __name__ == "__main__":
|
|||||||
# document results with header.
|
# document results with header.
|
||||||
# for those who wants to reproduce our benchmark.
|
# for those who wants to reproduce our benchmark.
|
||||||
f.write(serving_md_table_with_headers)
|
f.write(serving_md_table_with_headers)
|
||||||
f.write("\n")
|
f.write('\n')
|
||||||
|
|
||||||
# document benchmarking results in json
|
# document benchmarking results in json
|
||||||
with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
|
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))
|
f.write(json.dumps(results))
|
||||||
|
|||||||
@ -11,7 +11,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
|
|||||||
@ -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
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -35,7 +35,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
@ -88,7 +90,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
@ -141,7 +145,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
@ -191,7 +197,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
@ -243,7 +251,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"dtype": "bfloat16"
|
||||||
},
|
},
|
||||||
@ -295,7 +305,9 @@
|
|||||||
},
|
},
|
||||||
"vllm_server_parameters": {
|
"vllm_server_parameters": {
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"gpu_memory_utilization": 0.9,
|
"gpu_memory_utilization": 0.9,
|
||||||
|
"num_scheduler_steps": 10,
|
||||||
"max_num_seqs": 512,
|
"max_num_seqs": 512,
|
||||||
"dtype": "bfloat16"
|
"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": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "random",
|
|
||||||
"random-input-len": 128,
|
|
||||||
"random-output-len": 128,
|
|
||||||
"ignore-eos": "",
|
|
||||||
"num_prompts": 1000
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_bf16_tp2_random_128_128",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
"VLLM_CPU_SGL_KERNEL": 1,
|
|
||||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"dtype": "bfloat16",
|
|
||||||
"distributed_executor_backend": "mp",
|
|
||||||
"block_size": 128,
|
|
||||||
"trust_remote_code": "",
|
|
||||||
"enable_chunked_prefill": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "random",
|
|
||||||
"random-input-len": 128,
|
|
||||||
"random-output-len": 128,
|
|
||||||
"ignore-eos": "",
|
|
||||||
"num_prompts": 1000
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_bf16_tp4_random_128_128",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
"VLLM_CPU_SGL_KERNEL": 1,
|
|
||||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
||||||
"tensor_parallel_size": 4,
|
|
||||||
"dtype": "bfloat16",
|
|
||||||
"distributed_executor_backend": "mp",
|
|
||||||
"block_size": 128,
|
|
||||||
"trust_remote_code": "",
|
|
||||||
"enable_chunked_prefill": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "random",
|
|
||||||
"random-input-len": 128,
|
|
||||||
"random-output-len": 128,
|
|
||||||
"num_prompts": 1000
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_int8_tp1_sharegpt",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
"VLLM_CPU_SGL_KERNEL": 1,
|
|
||||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
|
||||||
"tensor_parallel_size": 1,
|
|
||||||
"dtype": "bfloat16",
|
|
||||||
"distributed_executor_backend": "mp",
|
|
||||||
"block_size": 128,
|
|
||||||
"trust_remote_code": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "sharegpt",
|
|
||||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
||||||
"num_prompts": 200
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_int8_tp2_sharegpt",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
"VLLM_CPU_SGL_KERNEL": 1,
|
|
||||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"dtype": "bfloat16",
|
|
||||||
"distributed_executor_backend": "mp",
|
|
||||||
"block_size": 128,
|
|
||||||
"trust_remote_code": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "sharegpt",
|
|
||||||
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
||||||
"num_prompts": 200
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_int8_tp4_sharegpt",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
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]
|
|
||||||
@ -1,820 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
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|
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"trust_remote_code": "",
|
|
||||||
"enable_chunked_prefill": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "random",
|
|
||||||
"random-input-len": 128,
|
|
||||||
"random-output-len": 128,
|
|
||||||
"ignore-eos": "",
|
|
||||||
"num_prompts": 1000
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_int4_tp2_random_128_128",
|
|
||||||
"qps_list": ["inf"],
|
|
||||||
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
|
|
||||||
"server_environment_variables": {
|
|
||||||
"VLLM_RPC_TIMEOUT": 100000,
|
|
||||||
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
|
|
||||||
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
|
|
||||||
"VLLM_CPU_SGL_KERNEL": 1,
|
|
||||||
"VLLM_CPU_KVCACHE_SPACE": 40
|
|
||||||
},
|
|
||||||
"server_parameters": {
|
|
||||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
|
||||||
"quantization": "awq",
|
|
||||||
"tensor_parallel_size": 2,
|
|
||||||
"dtype": "bfloat16",
|
|
||||||
"distributed_executor_backend": "mp",
|
|
||||||
"block_size": 128,
|
|
||||||
"trust_remote_code": "",
|
|
||||||
"enable_chunked_prefill": "",
|
|
||||||
"disable_log_stats": "",
|
|
||||||
"enforce_eager": "",
|
|
||||||
"max_num_batched_tokens": 2048,
|
|
||||||
"max_num_seqs": 256,
|
|
||||||
"load_format": "dummy"
|
|
||||||
},
|
|
||||||
"client_parameters": {
|
|
||||||
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
|
||||||
"backend": "vllm",
|
|
||||||
"dataset_name": "random",
|
|
||||||
"random-input-len": 128,
|
|
||||||
"random-output-len": 128,
|
|
||||||
"ignore-eos": "",
|
|
||||||
"num_prompts": 1000
|
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"test_name": "serving_llama8B_int4_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,
|
"tensor_parallel_size": 1,
|
||||||
"swap_space": 16,
|
"swap_space": 16,
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"load_format": "dummy"
|
"load_format": "dummy"
|
||||||
},
|
},
|
||||||
"client_parameters": {
|
"client_parameters": {
|
||||||
@ -25,6 +26,7 @@
|
|||||||
"tensor_parallel_size": 4,
|
"tensor_parallel_size": 4,
|
||||||
"swap_space": 16,
|
"swap_space": 16,
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"load_format": "dummy"
|
"load_format": "dummy"
|
||||||
},
|
},
|
||||||
"client_parameters": {
|
"client_parameters": {
|
||||||
@ -43,6 +45,7 @@
|
|||||||
"tensor_parallel_size": 2,
|
"tensor_parallel_size": 2,
|
||||||
"swap_space": 16,
|
"swap_space": 16,
|
||||||
"disable_log_stats": "",
|
"disable_log_stats": "",
|
||||||
|
"disable_log_requests": "",
|
||||||
"load_format": "dummy"
|
"load_format": "dummy"
|
||||||
},
|
},
|
||||||
"client_parameters": {
|
"client_parameters": {
|
||||||
@ -57,7 +60,8 @@
|
|||||||
"test_name": "serving_llama70B_tp4_sharegpt_specdecode",
|
"test_name": "serving_llama70B_tp4_sharegpt_specdecode",
|
||||||
"qps_list": [2],
|
"qps_list": [2],
|
||||||
"server_parameters": {
|
"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,
|
"tensor_parallel_size": 4,
|
||||||
"swap_space": 16,
|
"swap_space": 16,
|
||||||
"speculative_config": {
|
"speculative_config": {
|
||||||
|
|||||||
@ -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"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,46 +0,0 @@
|
|||||||
# This local pyproject file is part of the migration from yapf to ruff format.
|
|
||||||
# It uses the same core rules as the main pyproject.toml file, but with the
|
|
||||||
# following differences:
|
|
||||||
# - ruff line length is overridden to 88
|
|
||||||
# - deprecated typing ignores (UP006, UP035) have been removed
|
|
||||||
|
|
||||||
[tool.ruff]
|
|
||||||
line-length = 88
|
|
||||||
|
|
||||||
[tool.ruff.lint.per-file-ignores]
|
|
||||||
"vllm/third_party/**" = ["ALL"]
|
|
||||||
"vllm/version.py" = ["F401"]
|
|
||||||
"vllm/_version.py" = ["ALL"]
|
|
||||||
|
|
||||||
[tool.ruff.lint]
|
|
||||||
select = [
|
|
||||||
# pycodestyle
|
|
||||||
"E",
|
|
||||||
# Pyflakes
|
|
||||||
"F",
|
|
||||||
# pyupgrade
|
|
||||||
"UP",
|
|
||||||
# flake8-bugbear
|
|
||||||
"B",
|
|
||||||
# flake8-simplify
|
|
||||||
"SIM",
|
|
||||||
# isort
|
|
||||||
"I",
|
|
||||||
# flake8-logging-format
|
|
||||||
"G",
|
|
||||||
]
|
|
||||||
ignore = [
|
|
||||||
# star imports
|
|
||||||
"F405", "F403",
|
|
||||||
# lambda expression assignment
|
|
||||||
"E731",
|
|
||||||
# Loop control variable not used within loop body
|
|
||||||
"B007",
|
|
||||||
# f-string format
|
|
||||||
"UP032",
|
|
||||||
# Can remove once 3.10+ is the minimum Python version
|
|
||||||
"UP007",
|
|
||||||
]
|
|
||||||
|
|
||||||
[tool.ruff.format]
|
|
||||||
docstring-code-format = true
|
|
||||||
@ -1,138 +1,76 @@
|
|||||||
steps:
|
steps:
|
||||||
# aarch64 + CUDA builds. PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
|
- label: "Build wheel - CUDA 12.4"
|
||||||
- label: "Build arm64 wheel - CUDA 12.9"
|
|
||||||
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 torch_cuda_arch_list='8.7 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"
|
|
||||||
|
|
||||||
- block: "Build CUDA 12.8 wheel"
|
|
||||||
key: block-build-cu128-wheel
|
|
||||||
|
|
||||||
- label: "Build wheel - CUDA 12.8"
|
|
||||||
depends_on: block-build-cu128-wheel
|
|
||||||
id: build-wheel-cuda-12-8
|
|
||||||
agents:
|
agents:
|
||||||
queue: cpu_queue_postmerge
|
queue: cpu_queue_postmerge
|
||||||
commands:
|
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=12.4.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||||
- "mkdir artifacts"
|
- "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'"
|
- "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/scripts/upload-wheels.sh"
|
||||||
env:
|
env:
|
||||||
DOCKER_BUILDKIT: "1"
|
DOCKER_BUILDKIT: "1"
|
||||||
|
|
||||||
- block: "Build CUDA 12.6 wheel"
|
- label: "Build wheel - CUDA 12.1"
|
||||||
key: block-build-cu126-wheel
|
|
||||||
depends_on: ~
|
|
||||||
|
|
||||||
- label: "Build wheel - CUDA 12.6"
|
|
||||||
depends_on: block-build-cu126-wheel
|
|
||||||
id: build-wheel-cuda-12-6
|
|
||||||
agents:
|
agents:
|
||||||
queue: cpu_queue_postmerge
|
queue: cpu_queue_postmerge
|
||||||
commands:
|
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=12.1.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
|
||||||
- "mkdir artifacts"
|
- "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'"
|
- "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/scripts/upload-wheels.sh"
|
||||||
env:
|
env:
|
||||||
DOCKER_BUILDKIT: "1"
|
DOCKER_BUILDKIT: "1"
|
||||||
|
|
||||||
# x86 + CUDA builds
|
# Note(simon): We can always build CUDA 11.8 wheel to ensure the build is working.
|
||||||
- label: "Build wheel - CUDA 12.9"
|
# However, this block can be uncommented to save some compute hours.
|
||||||
depends_on: ~
|
# - block: "Build CUDA 11.8 wheel"
|
||||||
id: build-wheel-cuda-12-9
|
# key: block-build-cu118-wheel
|
||||||
|
|
||||||
|
- label: "Build wheel - CUDA 11.8"
|
||||||
|
# depends_on: block-build-cu118-wheel
|
||||||
agents:
|
agents:
|
||||||
queue: cpu_queue_postmerge
|
queue: cpu_queue_postmerge
|
||||||
commands:
|
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 --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 -f docker/Dockerfile ."
|
||||||
- "mkdir artifacts"
|
- "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'"
|
- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
|
||||||
- "bash .buildkite/scripts/upload-wheels.sh"
|
- "bash .buildkite/scripts/upload-wheels.sh"
|
||||||
env:
|
env:
|
||||||
DOCKER_BUILDKIT: "1"
|
DOCKER_BUILDKIT: "1"
|
||||||
|
|
||||||
- label: "Build release image (x86)"
|
- block: "Build release image"
|
||||||
depends_on: ~
|
depends_on: ~
|
||||||
id: build-release-image-x86
|
key: block-release-image-build
|
||||||
|
|
||||||
|
- label: "Build release image"
|
||||||
|
depends_on: block-release-image-build
|
||||||
agents:
|
agents:
|
||||||
queue: cpu_queue_postmerge
|
queue: cpu_queue_postmerge
|
||||||
commands:
|
commands:
|
||||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
|
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
|
||||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
|
|
||||||
# re-tag to default image tag and push, just in case arm64 build fails
|
|
||||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
|
||||||
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
||||||
|
|
||||||
# PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
|
|
||||||
- label: "Build release image (arm64)"
|
|
||||||
depends_on: ~
|
|
||||||
id: build-release-image-arm64
|
|
||||||
agents:
|
|
||||||
queue: arm64_cpu_queue_postmerge
|
|
||||||
commands:
|
|
||||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
|
||||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$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:$BUILDKITE_COMMIT"
|
|
||||||
|
|
||||||
- label: "Annotate release workflow"
|
|
||||||
depends_on:
|
|
||||||
- create-multi-arch-manifest
|
|
||||||
- build-wheel-cuda-12-8
|
|
||||||
- build-wheel-cuda-12-6
|
|
||||||
- build-wheel-cuda-12-9
|
|
||||||
id: annotate-release-workflow
|
|
||||||
agents:
|
|
||||||
queue: cpu_queue_postmerge
|
|
||||||
commands:
|
|
||||||
- "bash .buildkite/scripts/annotate-release.sh"
|
|
||||||
|
|
||||||
- label: "Build and publish TPU release image"
|
- label: "Build and publish TPU release image"
|
||||||
depends_on: ~
|
depends_on: ~
|
||||||
if: build.env("NIGHTLY") == "1"
|
if: build.env("NIGHTLY") == "1"
|
||||||
agents:
|
agents:
|
||||||
queue: tpu_queue_postmerge
|
queue: tpu_queue_postmerge
|
||||||
commands:
|
commands:
|
||||||
- "yes | docker system prune -a"
|
|
||||||
- "git fetch --all"
|
|
||||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$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:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
|
||||||
- "docker push vllm/vllm-tpu:nightly"
|
- "docker push vllm/vllm-tpu:nightly"
|
||||||
- "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
|
- "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
|
||||||
plugins:
|
plugins:
|
||||||
- docker-login#v3.0.0:
|
- docker-login#v3.0.0:
|
||||||
username: vllmbot
|
username: vllm
|
||||||
password-env: DOCKERHUB_TOKEN
|
password-env: DOCKERHUB_TOKEN
|
||||||
env:
|
env:
|
||||||
DOCKER_BUILDKIT: "1"
|
DOCKER_BUILDKIT: "1"
|
||||||
|
|
||||||
- input: "Provide Release version here"
|
- input: "Provide Release version here"
|
||||||
id: input-release-version
|
|
||||||
fields:
|
fields:
|
||||||
- text: "What is the release version?"
|
- text: "What is the release version?"
|
||||||
key: release-version
|
key: "release-version"
|
||||||
|
|
||||||
- block: "Build CPU release image"
|
- block: "Build CPU release image"
|
||||||
key: block-cpu-release-image-build
|
key: block-cpu-release-image-build
|
||||||
@ -144,30 +82,7 @@ steps:
|
|||||||
queue: cpu_queue_postmerge
|
queue: cpu_queue_postmerge
|
||||||
commands:
|
commands:
|
||||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
||||||
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(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_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --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 push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
|
||||||
env:
|
env:
|
||||||
DOCKER_BUILDKIT: "1"
|
DOCKER_BUILDKIT: "1"
|
||||||
|
|
||||||
- label: "Build and publish nightly multi-arch image to DockerHub"
|
|
||||||
depends_on:
|
|
||||||
- create-multi-arch-manifest
|
|
||||||
if: build.env("NIGHTLY") == "1"
|
|
||||||
agents:
|
|
||||||
queue: cpu_queue_postmerge
|
|
||||||
commands:
|
|
||||||
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
|
|
||||||
- "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
|
|
||||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT vllm/vllm-openai:nightly"
|
|
||||||
- "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
|
|
||||||
- "docker push vllm/vllm-openai:nightly"
|
|
||||||
- "docker push vllm/vllm-openai:nightly-$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"
|
|
||||||
|
|||||||
@ -1,31 +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/${RELEASE_VERSION}+cu126/vllm-${RELEASE_VERSION}+cu126-cp38-abi3-manylinux1_x86_64.whl .
|
|
||||||
aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu118/vllm-${RELEASE_VERSION}+cu118-cp38-abi3-manylinux1_x86_64.whl .
|
|
||||||
\`\`\`
|
|
||||||
|
|
||||||
To download and upload the image:
|
|
||||||
|
|
||||||
\`\`\`
|
|
||||||
docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}
|
|
||||||
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT} vllm/vllm-openai
|
|
||||||
docker tag vllm/vllm-openai vllm/vllm-openai:latest
|
|
||||||
docker tag vllm/vllm-openai vllm/vllm-openai:v${RELEASE_VERSION}
|
|
||||||
docker push vllm/vllm-openai:latest
|
|
||||||
docker push vllm/vllm-openai:v${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' "$INPUT_FILE"
|
|
||||||
@ -1,97 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -ex
|
|
||||||
|
|
||||||
# Clean up old nightly builds from DockerHub, keeping only the last 14 builds
|
|
||||||
# This script uses DockerHub API to list and delete old tags with "nightly-" prefix
|
|
||||||
|
|
||||||
# DockerHub API endpoint for vllm/vllm-openai repository
|
|
||||||
REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
|
|
||||||
|
|
||||||
# Get DockerHub token from environment
|
|
||||||
if [ -z "$DOCKERHUB_TOKEN" ]; then
|
|
||||||
echo "Error: DOCKERHUB_TOKEN environment variable is not set"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
# Function to get all tags from DockerHub
|
|
||||||
get_all_tags() {
|
|
||||||
local page=1
|
|
||||||
local all_tags=""
|
|
||||||
|
|
||||||
while true; do
|
|
||||||
local response=$(curl -s -H "Authorization: Bearer $DOCKERHUB_TOKEN" \
|
|
||||||
"$REPO_API_URL?page=$page&page_size=100")
|
|
||||||
|
|
||||||
# Get both last_updated timestamp and tag name, separated by |
|
|
||||||
local tags=$(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"
|
|
||||||
local response=$(curl -s -X DELETE -H "Authorization: Bearer $DOCKERHUB_TOKEN" "$delete_url")
|
|
||||||
|
|
||||||
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"
|
|
||||||
@ -3,9 +3,6 @@
|
|||||||
# This script runs test inside the corresponding ROCm docker container.
|
# This script runs test inside the corresponding ROCm docker container.
|
||||||
set -o pipefail
|
set -o pipefail
|
||||||
|
|
||||||
# Export Python path
|
|
||||||
export PYTHONPATH=".."
|
|
||||||
|
|
||||||
# Print ROCm version
|
# Print ROCm version
|
||||||
echo "--- Confirming Clean Initial State"
|
echo "--- Confirming Clean Initial State"
|
||||||
while true; do
|
while true; do
|
||||||
@ -77,70 +74,31 @@ HF_MOUNT="/root/.cache/huggingface"
|
|||||||
|
|
||||||
commands=$@
|
commands=$@
|
||||||
echo "Commands:$commands"
|
echo "Commands:$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 [[ $commands == *"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"* ]]; then
|
|
||||||
commands=${commands//"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"/"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2 and not BambaForCausalLM and not Gemma2ForCausalLM and not Grok1ModelForCausalLM and not Zamba2ForCausalLM and not Gemma2Model and not GritLM'"}
|
|
||||||
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
|
#ignore certain kernels tests
|
||||||
if [[ $commands == *" kernels/core"* ]]; then
|
if [[ $commands == *" kernels "* ]]; then
|
||||||
commands="${commands} \
|
commands="${commands} \
|
||||||
--ignore=kernels/core/test_fused_quant_layernorm.py \
|
--ignore=kernels/test_attention_selector.py \
|
||||||
--ignore=kernels/core/test_permute_cols.py"
|
--ignore=kernels/test_blocksparse_attention.py \
|
||||||
fi
|
--ignore=kernels/test_causal_conv1d.py \
|
||||||
|
--ignore=kernels/test_cutlass.py \
|
||||||
if [[ $commands == *" kernels/attention"* ]]; then
|
--ignore=kernels/test_encoder_decoder_attn.py \
|
||||||
commands="${commands} \
|
--ignore=kernels/test_flash_attn.py \
|
||||||
--ignore=kernels/attention/test_attention_selector.py \
|
--ignore=kernels/test_flashinfer.py \
|
||||||
--ignore=kernels/attention/test_encoder_decoder_attn.py \
|
--ignore=kernels/test_int8_quant.py \
|
||||||
--ignore=kernels/attention/test_flash_attn.py \
|
--ignore=kernels/test_machete_gemm.py \
|
||||||
--ignore=kernels/attention/test_flashinfer.py \
|
--ignore=kernels/test_mamba_ssm.py \
|
||||||
--ignore=kernels/attention/test_prefix_prefill.py \
|
--ignore=kernels/test_marlin_gemm.py \
|
||||||
--ignore=kernels/attention/test_cascade_flash_attn.py \
|
--ignore=kernels/test_moe.py \
|
||||||
--ignore=kernels/attention/test_mha_attn.py \
|
--ignore=kernels/test_prefix_prefill.py \
|
||||||
--ignore=kernels/attention/test_lightning_attn.py \
|
--ignore=kernels/test_rand.py \
|
||||||
--ignore=kernels/attention/test_attention.py"
|
--ignore=kernels/test_sampler.py \
|
||||||
fi
|
--ignore=kernels/test_cascade_flash_attn.py \
|
||||||
|
--ignore=kernels/test_mamba_mixer2.py \
|
||||||
if [[ $commands == *" kernels/quantization"* ]]; then
|
--ignore=kernels/test_aqlm.py \
|
||||||
commands="${commands} \
|
--ignore=kernels/test_machete_mm.py \
|
||||||
--ignore=kernels/quantization/test_int8_quant.py \
|
--ignore=kernels/test_mha_attn.py \
|
||||||
--ignore=kernels/quantization/test_machete_mm.py \
|
--ignore=kernels/test_block_fp8.py \
|
||||||
--ignore=kernels/quantization/test_block_fp8.py \
|
--ignore=kernels/test_permute_cols.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
|
fi
|
||||||
|
|
||||||
#ignore certain Entrypoints/openai tests
|
#ignore certain Entrypoints/openai tests
|
||||||
@ -164,6 +122,7 @@ if [[ $commands == *" entrypoints/llm "* ]]; then
|
|||||||
--ignore=entrypoints/llm/test_chat.py \
|
--ignore=entrypoints/llm/test_chat.py \
|
||||||
--ignore=entrypoints/llm/test_accuracy.py \
|
--ignore=entrypoints/llm/test_accuracy.py \
|
||||||
--ignore=entrypoints/llm/test_init.py \
|
--ignore=entrypoints/llm/test_init.py \
|
||||||
|
--ignore=entrypoints/llm/test_generate_multiple_loras.py \
|
||||||
--ignore=entrypoints/llm/test_prompt_validation.py "}
|
--ignore=entrypoints/llm/test_prompt_validation.py "}
|
||||||
fi
|
fi
|
||||||
|
|
||||||
@ -181,8 +140,6 @@ fi
|
|||||||
|
|
||||||
|
|
||||||
PARALLEL_JOB_COUNT=8
|
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.
|
# 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
|
if [[ $commands == *"--shard-id="* ]]; then
|
||||||
# assign job count as the number of shards used
|
# assign job count as the number of shards used
|
||||||
@ -203,7 +160,6 @@ if [[ $commands == *"--shard-id="* ]]; then
|
|||||||
-e AWS_SECRET_ACCESS_KEY \
|
-e AWS_SECRET_ACCESS_KEY \
|
||||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||||
-e "HF_HOME=${HF_MOUNT}" \
|
-e "HF_HOME=${HF_MOUNT}" \
|
||||||
-e "PYTHONPATH=${MYPYTHONPATH}" \
|
|
||||||
--name "${container_name}_${GPU}" \
|
--name "${container_name}_${GPU}" \
|
||||||
"${image_name}" \
|
"${image_name}" \
|
||||||
/bin/bash -c "${commands_gpu}" \
|
/bin/bash -c "${commands_gpu}" \
|
||||||
@ -234,7 +190,6 @@ else
|
|||||||
-e AWS_SECRET_ACCESS_KEY \
|
-e AWS_SECRET_ACCESS_KEY \
|
||||||
-v "${HF_CACHE}:${HF_MOUNT}" \
|
-v "${HF_CACHE}:${HF_MOUNT}" \
|
||||||
-e "HF_HOME=${HF_MOUNT}" \
|
-e "HF_HOME=${HF_MOUNT}" \
|
||||||
-e "PYTHONPATH=${MYPYTHONPATH}" \
|
|
||||||
--name "${container_name}" \
|
--name "${container_name}" \
|
||||||
"${image_name}" \
|
"${image_name}" \
|
||||||
/bin/bash -c "${commands}"
|
/bin/bash -c "${commands}"
|
||||||
|
|||||||
@ -5,13 +5,7 @@
|
|||||||
set -ex
|
set -ex
|
||||||
|
|
||||||
# Setup cleanup
|
# Setup cleanup
|
||||||
remove_docker_container() {
|
remove_docker_container() { podman rm -f cpu-test-ubi9-ppc || true; podman system prune -f; }
|
||||||
if [[ -n "$container_id" ]]; then
|
|
||||||
podman stop --all -t0
|
|
||||||
podman rm -f "$container_id" || true
|
|
||||||
fi
|
|
||||||
podman system prune -f
|
|
||||||
}
|
|
||||||
trap remove_docker_container EXIT
|
trap remove_docker_container EXIT
|
||||||
remove_docker_container
|
remove_docker_container
|
||||||
|
|
||||||
@ -19,31 +13,26 @@ remove_docker_container
|
|||||||
podman build -t cpu-test-ubi9-ppc -f docker/Dockerfile.ppc64le .
|
podman build -t cpu-test-ubi9-ppc -f docker/Dockerfile.ppc64le .
|
||||||
|
|
||||||
# Run the image
|
# Run the image
|
||||||
container_id=$(podman run -itd --entrypoint /bin/bash -v /tmp/:/root/.cache/huggingface --privileged=true --network host -e HF_TOKEN cpu-test-ubi9-ppc)
|
podman run -itd --entrypoint /bin/bash -v /tmp/:/root/.cache/huggingface --privileged=true --network host -e HF_TOKEN --name cpu-test-ubi9-ppc cpu-test-ubi9-ppc
|
||||||
|
|
||||||
function cpu_tests() {
|
function cpu_tests() {
|
||||||
|
|
||||||
# offline inference
|
# offline inference
|
||||||
podman exec -it "$container_id" bash -c "
|
podman exec cpu-test-ubi9-ppc bash -c "
|
||||||
set -e
|
set -e
|
||||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
|
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
|
||||||
|
|
||||||
# Run basic model test
|
# Run basic model test
|
||||||
podman exec -it "$container_id" bash -c "
|
podman exec cpu-test-ubi9-ppc bash -c "
|
||||||
set -e
|
set -e
|
||||||
pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
|
pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
|
||||||
pip install sentence-transformers datamodel_code_generator
|
pip install sentence-transformers datamodel_code_generator
|
||||||
pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
|
pytest -v -s tests/models/embedding/language/test_cls_models.py::test_classification_models[float-jason9693/Qwen2.5-1.5B-apeach]
|
||||||
pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
|
pytest -v -s tests/models/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
|
||||||
pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
|
pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
|
||||||
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]
|
|
||||||
pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model"
|
|
||||||
}
|
}
|
||||||
|
|
||||||
# All of CPU tests are expected to be finished less than 40 mins.
|
# All of CPU tests are expected to be finished less than 40 mins.
|
||||||
|
|
||||||
export container_id
|
|
||||||
export -f cpu_tests
|
export -f cpu_tests
|
||||||
timeout 40m bash -c cpu_tests
|
timeout 40m bash -c cpu_tests
|
||||||
|
|
||||||
|
|||||||
@ -6,118 +6,89 @@ set -ex
|
|||||||
|
|
||||||
# allow to bind to different cores
|
# allow to bind to different cores
|
||||||
CORE_RANGE=${CORE_RANGE:-48-95}
|
CORE_RANGE=${CORE_RANGE:-48-95}
|
||||||
# used for TP/PP E2E test
|
|
||||||
OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
|
|
||||||
NUMA_NODE=${NUMA_NODE:-1}
|
NUMA_NODE=${NUMA_NODE:-1}
|
||||||
|
|
||||||
export CMAKE_BUILD_PARALLEL_LEVEL=32
|
|
||||||
|
|
||||||
# Setup cleanup
|
# Setup cleanup
|
||||||
remove_docker_container() {
|
remove_docker_container() {
|
||||||
set -e;
|
set -e;
|
||||||
docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
|
docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true;
|
||||||
|
docker image rm cpu-test-"$BUILDKITE_BUILD_NUMBER" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 || true;
|
||||||
}
|
}
|
||||||
trap remove_docker_container EXIT
|
trap remove_docker_container EXIT
|
||||||
remove_docker_container
|
remove_docker_container
|
||||||
|
|
||||||
# Try building the docker image
|
# Try building the docker image
|
||||||
numactl -C "$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 --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
|
||||||
numactl -C "$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 .
|
numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
|
||||||
|
|
||||||
# Run the image, setting --shm-size=4g for tensor parallel.
|
# Run the image, setting --shm-size=4g for tensor parallel.
|
||||||
docker run -itd --cpuset-cpus="$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 --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
|
||||||
docker run -itd --cpuset-cpus="$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
|
--cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$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 -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() {
|
function cpu_tests() {
|
||||||
set -e
|
set -e
|
||||||
export NUMA_NODE=$2
|
export NUMA_NODE=$2
|
||||||
|
export BUILDKITE_BUILD_NUMBER=$3
|
||||||
# list packages
|
|
||||||
docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
|
|
||||||
set -e
|
|
||||||
pip list"
|
|
||||||
|
|
||||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
|
||||||
set -e
|
|
||||||
pip list"
|
|
||||||
|
|
||||||
# offline inference
|
# offline inference
|
||||||
docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
|
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
|
||||||
set -e
|
set -e
|
||||||
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
|
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
|
||||||
|
|
||||||
# Run kernel tests
|
|
||||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
|
||||||
set -e
|
|
||||||
pytest -x -v -s tests/kernels/test_onednn.py"
|
|
||||||
|
|
||||||
# Run basic model test
|
# Run basic model test
|
||||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||||
set -e
|
set -e
|
||||||
# Note: disable until supports V1
|
pytest -v -s tests/kernels/test_cache.py -m cpu_model
|
||||||
# pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
|
pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model
|
||||||
# pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
|
pytest -v -s tests/models/decoder_only/language -m cpu_model
|
||||||
|
pytest -v -s tests/models/embedding/language -m cpu_model
|
||||||
# Note: disable Bart until supports V1
|
pytest -v -s tests/models/encoder_decoder/language -m cpu_model
|
||||||
pytest -x -v -s tests/models/language/generation -m cpu_model \
|
pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
|
||||||
--ignore=tests/models/language/generation/test_bart.py
|
pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
|
||||||
VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model \
|
|
||||||
--ignore=tests/models/language/generation/test_bart.py
|
|
||||||
|
|
||||||
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_mllama.py \
|
|
||||||
--ignore=tests/models/multimodal/generation/test_pixtral.py \
|
|
||||||
-m cpu_model"
|
|
||||||
|
|
||||||
# Run compressed-tensor test
|
# Run compressed-tensor test
|
||||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||||
set -e
|
set -e
|
||||||
pytest -x -s -v \
|
pytest -s -v \
|
||||||
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
|
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
|
||||||
|
tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
|
||||||
|
|
||||||
# Note: disable it until supports V1
|
|
||||||
# Run AWQ test
|
# Run AWQ test
|
||||||
# docker exec cpu-test-"$NUMA_NODE" bash -c "
|
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||||
# set -e
|
set -e
|
||||||
# VLLM_USE_V1=0 pytest -x -s -v \
|
pytest -s -v \
|
||||||
# tests/quantization/test_ipex_quant.py"
|
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 serving
|
||||||
|
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=$1
|
||||||
|
python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half &
|
||||||
|
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||||
|
python3 benchmarks/benchmark_serving.py \
|
||||||
|
--backend vllm \
|
||||||
|
--dataset-name random \
|
||||||
|
--model facebook/opt-125m \
|
||||||
|
--num-prompts 20 \
|
||||||
|
--endpoint /v1/completions \
|
||||||
|
--tokenizer facebook/opt-125m"
|
||||||
|
|
||||||
# Run multi-lora tests
|
# Run multi-lora tests
|
||||||
docker exec cpu-test-"$NUMA_NODE" bash -c "
|
docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
|
||||||
set -e
|
set -e
|
||||||
pytest -x -s -v \
|
pytest -s -v \
|
||||||
tests/lora/test_qwen2vl.py"
|
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.
|
# All of CPU tests are expected to be finished less than 40 mins.
|
||||||
export -f cpu_tests
|
export -f cpu_tests
|
||||||
timeout 2h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
|
timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE $BUILDKITE_BUILD_NUMBER"
|
||||||
|
|||||||
@ -16,7 +16,8 @@ DOCKER_BUILDKIT=1 docker build . \
|
|||||||
--build-arg max_jobs=66 \
|
--build-arg max_jobs=66 \
|
||||||
--build-arg nvcc_threads=2 \
|
--build-arg nvcc_threads=2 \
|
||||||
--build-arg RUN_WHEEL_CHECK=false \
|
--build-arg RUN_WHEEL_CHECK=false \
|
||||||
--build-arg torch_cuda_arch_list="9.0+PTX"
|
--build-arg torch_cuda_arch_list="9.0+PTX" \
|
||||||
|
--build-arg vllm_fa_cmake_gpu_arches="90-real"
|
||||||
|
|
||||||
# Setup cleanup
|
# Setup cleanup
|
||||||
remove_docker_container() { docker rm -f gh200-test || true; }
|
remove_docker_container() { docker rm -f gh200-test || true; }
|
||||||
|
|||||||
@ -2,55 +2,23 @@
|
|||||||
|
|
||||||
# This script build the CPU docker image and run the offline inference inside the container.
|
# 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.
|
# It serves a sanity check for compilation and basic model usage.
|
||||||
set -exuo pipefail
|
set -ex
|
||||||
|
|
||||||
# Try building the docker image
|
# Try building the docker image
|
||||||
cat <<EOF | docker build -t hpu-plugin-v1-test-env -f - .
|
docker build -t hpu-test-env -f docker/Dockerfile.hpu .
|
||||||
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
|
# Setup cleanup
|
||||||
# certain versions of HPU software stack have a bug that can
|
# certain versions of HPU software stack have a bug that can
|
||||||
# override the exit code of the script, so we need to use
|
# override the exit code of the script, so we need to use
|
||||||
# separate remove_docker_containers and remove_docker_containers_and_exit
|
# separate remove_docker_container and remove_docker_container_and_exit
|
||||||
# functions, while other platforms only need one remove_docker_container
|
# functions, while other platforms only need one remove_docker_container
|
||||||
# function.
|
# function.
|
||||||
EXITCODE=1
|
EXITCODE=1
|
||||||
remove_docker_containers() { docker rm -f hpu-plugin-v1-test || true; }
|
remove_docker_container() { docker rm -f hpu-test || true; }
|
||||||
trap 'remove_docker_containers; exit $EXITCODE;' EXIT
|
remove_docker_container_and_exit() { remove_docker_container; exit $EXITCODE; }
|
||||||
remove_docker_containers
|
trap remove_docker_container_and_exit EXIT
|
||||||
|
remove_docker_container
|
||||||
echo "Running HPU plugin v1 test"
|
|
||||||
docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
|
|
||||||
-e HABANA_VISIBLE_DEVICES=all \
|
|
||||||
hpu-plugin-v1-test-env \
|
|
||||||
/bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
|
|
||||||
|
|
||||||
|
# Run the image and launch offline inference
|
||||||
|
docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
|
||||||
EXITCODE=$?
|
EXITCODE=$?
|
||||||
if [ $EXITCODE -eq 0 ]; then
|
|
||||||
echo "Test with basic model passed"
|
|
||||||
else
|
|
||||||
echo "Test with basic model FAILED with exit code: $EXITCODE" >&2
|
|
||||||
fi
|
|
||||||
|
|
||||||
# The trap will handle the container removal and final exit.
|
|
||||||
54
.buildkite/scripts/hardware_ci/run-neuron-test.sh
Normal file
54
.buildkite/scripts/hardware_ci/run-neuron-test.sh
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
# This script build the Neuron docker image and run the API server inside the container.
|
||||||
|
# It serves a sanity check for compilation and basic model usage.
|
||||||
|
set -e
|
||||||
|
set -v
|
||||||
|
|
||||||
|
image_name="neuron/vllm-ci"
|
||||||
|
container_name="neuron_$(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 [ $((current_time - last_build)) -gt 86400 ]; then
|
||||||
|
# Remove dangling images (those that are not tagged and not used by any container)
|
||||||
|
docker image prune -f
|
||||||
|
# Remove unused volumes / force the system prune for old images as well.
|
||||||
|
docker volume prune -f && docker system prune -f
|
||||||
|
echo "$current_time" > /tmp/neuron-docker-build-timestamp
|
||||||
|
fi
|
||||||
|
else
|
||||||
|
date "+%s" > /tmp/neuron-docker-build-timestamp
|
||||||
|
fi
|
||||||
|
|
||||||
|
docker build -t "${image_name}" -f docker/Dockerfile.neuron .
|
||||||
|
|
||||||
|
# Setup cleanup
|
||||||
|
remove_docker_container() {
|
||||||
|
docker image rm -f "${image_name}" || true;
|
||||||
|
}
|
||||||
|
trap remove_docker_container EXIT
|
||||||
|
|
||||||
|
# Run the image
|
||||||
|
docker run --rm -it --device=/dev/neuron0 --network bridge \
|
||||||
|
-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}" \
|
||||||
|
${image_name} \
|
||||||
|
/bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys && python3 -m pytest /workspace/vllm/tests/neuron/2_core/ -v --capture=tee-sys"
|
||||||
@ -1,167 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -xu
|
|
||||||
|
|
||||||
|
|
||||||
remove_docker_container() {
|
|
||||||
docker rm -f tpu-test || true;
|
|
||||||
}
|
|
||||||
|
|
||||||
trap remove_docker_container EXIT
|
|
||||||
|
|
||||||
# Remove the container that might not be cleaned up in the previous run.
|
|
||||||
remove_docker_container
|
|
||||||
|
|
||||||
# Build the docker image.
|
|
||||||
docker build -f docker/Dockerfile.tpu -t vllm-tpu .
|
|
||||||
|
|
||||||
# Set up cleanup.
|
|
||||||
cleanup_docker() {
|
|
||||||
# Get Docker's root directory
|
|
||||||
docker_root=$(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
|
|
||||||
echo "--- Python dependencies installed ---"
|
|
||||||
export VLLM_USE_V1=1
|
|
||||||
export VLLM_XLA_CHECK_RECOMPILATION=1
|
|
||||||
export VLLM_XLA_CACHE_PATH=
|
|
||||||
echo "Using VLLM V1"
|
|
||||||
|
|
||||||
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 \
|
|
||||||
@ -1,175 +1,49 @@
|
|||||||
#!/bin/bash
|
#!/bin/bash
|
||||||
|
|
||||||
set -xu
|
set -xue
|
||||||
|
|
||||||
|
|
||||||
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.
|
# Build the docker image.
|
||||||
docker build -f docker/Dockerfile.tpu -t vllm-tpu .
|
docker build -f docker/Dockerfile.tpu -t vllm-tpu .
|
||||||
|
|
||||||
# Set up cleanup.
|
# Set up cleanup.
|
||||||
cleanup_docker() {
|
remove_docker_container() { docker rm -f tpu-test || true; }
|
||||||
# Get Docker's root directory
|
trap remove_docker_container EXIT
|
||||||
docker_root=$(docker info -f '{{.DockerRootDir}}')
|
# Remove the container that might not be cleaned up in the previous run.
|
||||||
if [ -z "$docker_root" ]; then
|
remove_docker_container
|
||||||
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 $threshold%. No cleanup needed."
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
cleanup_docker
|
|
||||||
|
|
||||||
# For HF_TOKEN.
|
# For HF_TOKEN.
|
||||||
source /etc/environment
|
source /etc/environment
|
||||||
|
# Run a simple end-to-end example.
|
||||||
docker run --privileged --net host --shm-size=16G -it \
|
docker run --privileged --net host --shm-size=16G -it \
|
||||||
-e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
|
-e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
|
||||||
vllm-tpu /bin/bash -c '
|
vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
|
||||||
set -e # Exit immediately if a command exits with a non-zero status.
|
&& python3 -m pip install pytest tpu-info \
|
||||||
set -u # Treat unset variables as an error.
|
&& python3 -m pip install lm_eval[api]==0.4.4 \
|
||||||
|
&& export VLLM_USE_V1=1 \
|
||||||
|
&& export VLLM_XLA_CHECK_RECOMPILATION=1 \
|
||||||
|
&& echo HARDWARE \
|
||||||
|
&& tpu-info \
|
||||||
|
&& echo TEST_0 \
|
||||||
|
&& pytest -v -s /workspace/vllm/tests/v1/tpu/test_perf.py \
|
||||||
|
&& echo TEST_1 \
|
||||||
|
&& pytest -v -s /workspace/vllm/tests/tpu/test_compilation.py \
|
||||||
|
&& echo TEST_2 \
|
||||||
|
&& pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
|
||||||
|
&& echo TEST_3 \
|
||||||
|
&& pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
|
||||||
|
&& echo TEST_4 \
|
||||||
|
&& pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
|
||||||
|
&& echo TEST_5 \
|
||||||
|
&& python3 /workspace/vllm/examples/offline_inference/tpu.py \
|
||||||
|
&& echo TEST_6 \
|
||||||
|
&& pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py \
|
||||||
|
&& echo TEST_7 \
|
||||||
|
&& pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py \
|
||||||
|
&& echo TEST_8 \
|
||||||
|
&& pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py \
|
||||||
|
&& echo TEST_9 \
|
||||||
|
&& pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py" \
|
||||||
|
|
||||||
echo "--- Starting script inside Docker container ---"
|
|
||||||
|
|
||||||
# Create results directory
|
|
||||||
RESULTS_DIR=$(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
|
|
||||||
echo "--- Python dependencies installed ---"
|
|
||||||
export VLLM_USE_V1=1
|
|
||||||
export VLLM_XLA_CHECK_RECOMPILATION=1
|
|
||||||
export VLLM_XLA_CACHE_PATH=
|
|
||||||
echo "Using VLLM V1"
|
|
||||||
|
|
||||||
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
|
# TODO: This test fails because it uses RANDOM_SEED sampling
|
||||||
# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
|
# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
|
||||||
|
|||||||
@ -11,8 +11,8 @@ container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head
|
|||||||
docker build -t ${image_name} -f docker/Dockerfile.xpu .
|
docker build -t ${image_name} -f docker/Dockerfile.xpu .
|
||||||
|
|
||||||
# Setup cleanup
|
# Setup cleanup
|
||||||
remove_docker_container() {
|
remove_docker_container() {
|
||||||
docker rm -f "${container_name}" || true;
|
docker rm -f "${container_name}" || true;
|
||||||
docker image rm -f "${image_name}" || true;
|
docker image rm -f "${image_name}" || true;
|
||||||
docker system prune -f || true;
|
docker system prune -f || true;
|
||||||
}
|
}
|
||||||
@ -23,27 +23,9 @@ docker run \
|
|||||||
--device /dev/dri \
|
--device /dev/dri \
|
||||||
-v /dev/dri/by-path:/dev/dri/by-path \
|
-v /dev/dri/by-path:/dev/dri/by-path \
|
||||||
--entrypoint="" \
|
--entrypoint="" \
|
||||||
-e "HF_TOKEN=${HF_TOKEN}" \
|
|
||||||
-e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
|
|
||||||
--name "${container_name}" \
|
--name "${container_name}" \
|
||||||
"${image_name}" \
|
"${image_name}" \
|
||||||
bash -c '
|
sh -c '
|
||||||
set -e
|
VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
|
||||||
echo $ZE_AFFINITY_MASK
|
VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
|
||||||
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_VLLM_V1 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_eagle.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
|
|
||||||
pytest -v -s v1/test_utils.py
|
|
||||||
pytest -v -s v1/test_metrics_reader.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 ${COUNT}"
|
|
||||||
done
|
|
||||||
@ -11,10 +11,10 @@ cd "$(dirname "${BASH_SOURCE[0]}")/../.."
|
|||||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||||
|
|
||||||
# run python-based benchmarks and upload the result to buildkite
|
# run python-based benchmarks and upload the result to buildkite
|
||||||
vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
|
||||||
bench_latency_exit_code=$?
|
bench_latency_exit_code=$?
|
||||||
|
|
||||||
vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
|
||||||
bench_throughput_exit_code=$?
|
bench_throughput_exit_code=$?
|
||||||
|
|
||||||
# run server-based benchmarks and upload the result to buildkite
|
# run server-based benchmarks and upload the result to buildkite
|
||||||
@ -24,7 +24,7 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
|
|||||||
|
|
||||||
# wait for server to start, timeout after 600 seconds
|
# wait for server to start, timeout after 600 seconds
|
||||||
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
|
||||||
vllm bench serve \
|
python3 benchmarks/benchmark_serving.py \
|
||||||
--backend vllm \
|
--backend vllm \
|
||||||
--dataset-name sharegpt \
|
--dataset-name sharegpt \
|
||||||
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
--dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
|
||||||
|
|||||||
@ -1,24 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -euo pipefail
|
|
||||||
|
|
||||||
docker_root=$(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($EXPECTED_THROUGHPUT)"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
@ -1,14 +0,0 @@
|
|||||||
# Environment config
|
|
||||||
TEST_NAME=llama8bw8a8
|
|
||||||
CONTAINER_NAME=tpu-test
|
|
||||||
|
|
||||||
# vllm config
|
|
||||||
MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
|
|
||||||
MAX_NUM_SEQS=128
|
|
||||||
MAX_NUM_BATCHED_TOKENS=1024
|
|
||||||
TENSOR_PARALLEL_SIZE=1
|
|
||||||
MAX_MODEL_LEN=2048
|
|
||||||
DOWNLOAD_DIR=/mnt/disks/persist
|
|
||||||
EXPECTED_THROUGHPUT=10.0
|
|
||||||
INPUT_LEN=1800
|
|
||||||
OUTPUT_LEN=128
|
|
||||||
@ -1,93 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -euo pipefail
|
|
||||||
|
|
||||||
VLLM_LOG="$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_USE_V1=1 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: $throughput"
|
|
||||||
echo
|
|
||||||
@ -14,19 +14,8 @@ fi
|
|||||||
# Get the single wheel file
|
# Get the single wheel file
|
||||||
wheel="${wheel_files[0]}"
|
wheel="${wheel_files[0]}"
|
||||||
|
|
||||||
# Detect architecture and rename 'linux' to appropriate manylinux version
|
# Rename 'linux' to 'manylinux1' in the wheel filename
|
||||||
arch=$(uname -m)
|
new_wheel="${wheel/linux/manylinux1}"
|
||||||
if [[ $arch == "x86_64" ]]; then
|
|
||||||
manylinux_version="manylinux1"
|
|
||||||
elif [[ $arch == "aarch64" ]]; then
|
|
||||||
manylinux_version="manylinux2014"
|
|
||||||
else
|
|
||||||
echo "Warning: Unknown architecture $arch, using manylinux1 as default"
|
|
||||||
manylinux_version="manylinux1"
|
|
||||||
fi
|
|
||||||
|
|
||||||
# Rename 'linux' to the appropriate manylinux version in the wheel filename
|
|
||||||
new_wheel="${wheel/linux/$manylinux_version}"
|
|
||||||
mv -- "$wheel" "$new_wheel"
|
mv -- "$wheel" "$new_wheel"
|
||||||
wheel="$new_wheel"
|
wheel="$new_wheel"
|
||||||
|
|
||||||
@ -58,15 +47,14 @@ python3 .buildkite/generate_index.py --wheel "$normal_wheel"
|
|||||||
aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
||||||
aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
|
||||||
|
|
||||||
if [[ $normal_wheel == *"cu126"* ]]; then
|
if [[ $normal_wheel == *"cu118"* ]]; then
|
||||||
# if $normal_wheel matches cu126, do not upload the index.html
|
# if $normal_wheel matches cu118, do not upload the index.html
|
||||||
echo "Skipping index files for cu126 wheels"
|
echo "Skipping index files for cu118 wheels"
|
||||||
elif [[ $normal_wheel == *"cu128"* ]]; then
|
elif [[ $normal_wheel == *"cu121"* ]]; then
|
||||||
# if $normal_wheel matches cu128, do not upload the index.html
|
# if $normal_wheel matches cu121, do not upload the index.html
|
||||||
echo "Skipping index files for cu128 wheels"
|
echo "Skipping index files for cu121 wheels"
|
||||||
else
|
else
|
||||||
# only upload index.html for cu129 wheels (default wheels) as it
|
# only upload index.html for cu124 wheels (default wheels)
|
||||||
# is available on both x86 and arm64
|
|
||||||
aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
|
aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
|
||||||
aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
|
aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
|
||||||
fi
|
fi
|
||||||
@ -75,17 +63,15 @@ fi
|
|||||||
aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
|
aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
|
||||||
aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
|
aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
|
||||||
|
|
||||||
if [[ $normal_wheel == *"cu126"* ]]; then
|
if [[ $normal_wheel == *"cu118"* ]]; then
|
||||||
# if $normal_wheel matches cu126, do not upload the index.html
|
# if $normal_wheel matches cu118, do not upload the index.html
|
||||||
echo "Skipping index files for cu126 wheels"
|
echo "Skipping index files for cu118 wheels"
|
||||||
elif [[ $normal_wheel == *"cu128"* ]]; then
|
elif [[ $normal_wheel == *"cu121"* ]]; then
|
||||||
# if $normal_wheel matches cu128, do not upload the index.html
|
# if $normal_wheel matches cu121, do not upload the index.html
|
||||||
echo "Skipping index files for cu128 wheels"
|
echo "Skipping index files for cu121 wheels"
|
||||||
else
|
else
|
||||||
# only upload index.html for cu129 wheels (default wheels) as it
|
# only upload index.html for cu124 wheels (default wheels)
|
||||||
# is available on both x86 and arm64
|
|
||||||
aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
|
aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
|
||||||
fi
|
fi
|
||||||
|
|
||||||
aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
|
aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
|
||||||
aws s3 cp index.html "s3://vllm-wheels/$version/vllm/index.html"
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,6 +0,0 @@
|
|||||||
# https://developers.google.com/gemini-code-assist/docs/customize-gemini-behavior-github
|
|
||||||
have_fun: false # Just review the code
|
|
||||||
code_review:
|
|
||||||
comment_severity_threshold: HIGH # Reduce quantity of comments
|
|
||||||
pull_request_opened:
|
|
||||||
summary: false # Don't summarize the PR in a separate comment
|
|
||||||
86
.github/CODEOWNERS
vendored
86
.github/CODEOWNERS
vendored
@ -5,89 +5,37 @@
|
|||||||
/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/core @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
/vllm/core @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/engine/llm_engine.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
/vllm/engine/llm_engine.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
|
/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
|
/vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
/vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
/vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
|
||||||
/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
|
/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
|
||||||
/vllm/model_executor/layers/mamba @tdoublep
|
/vllm/model_executor/guided_decoding @mgoin @russellb
|
||||||
/vllm/model_executor/model_loader @22quinn
|
|
||||||
/vllm/multimodal @DarkLight1337 @ywang96
|
/vllm/multimodal @DarkLight1337 @ywang96
|
||||||
/vllm/v1/sample @22quinn @houseroad
|
CMakeLists.txt @tlrmchlsmth
|
||||||
/vllm/vllm_flash_attn @LucasWilkinson
|
|
||||||
/vllm/lora @jeejeelee
|
|
||||||
/vllm/reasoning @aarnphm
|
|
||||||
/vllm/entrypoints @aarnphm
|
|
||||||
/vllm/compilation @zou3519 @youkaichao @ProExpertProg
|
|
||||||
CMakeLists.txt @tlrmchlsmth @LucasWilkinson
|
|
||||||
|
|
||||||
# Any change to the VllmConfig changes can have a large user-facing impact,
|
|
||||||
# so spam a lot of people
|
|
||||||
/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
|
|
||||||
|
|
||||||
# vLLM V1
|
# vLLM V1
|
||||||
/vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
|
/vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
|
||||||
/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
|
/vllm/v1/structured_output @mgoin @russellb
|
||||||
/vllm/v1/spec_decode @benchislett @luccafong
|
|
||||||
/vllm/v1/attention/backends/triton_attn.py @tdoublep
|
|
||||||
|
|
||||||
# Test ownership
|
# Test ownership
|
||||||
/.buildkite/lm-eval-harness @mgoin @simon-mo
|
/.buildkite/lm-eval-harness @mgoin @simon-mo
|
||||||
/tests/async_engine @njhill @robertgshaw2-redhat @simon-mo
|
/tests/async_engine @njhill @robertgshaw2-redhat @simon-mo
|
||||||
|
/tests/basic_correctness/test_chunked_prefill @rkooo567 @comaniac
|
||||||
/tests/distributed/test_multi_node_assignment.py @youkaichao
|
/tests/distributed/test_multi_node_assignment.py @youkaichao
|
||||||
/tests/distributed/test_pipeline_parallel.py @youkaichao
|
/tests/distributed/test_pipeline_parallel.py @youkaichao
|
||||||
/tests/distributed/test_same_node.py @youkaichao
|
/tests/distributed/test_same_node.py @youkaichao
|
||||||
/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
|
/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo
|
||||||
/tests/kernels @tlrmchlsmth @WoosukKwon @yewentao256
|
/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb
|
||||||
|
/tests/kernels @tlrmchlsmth @WoosukKwon
|
||||||
|
/tests/model_executor/test_guided_processors.py @mgoin @russellb
|
||||||
/tests/models @DarkLight1337 @ywang96
|
/tests/models @DarkLight1337 @ywang96
|
||||||
|
/tests/multi_step @alexm-redhat @comaniac
|
||||||
/tests/multimodal @DarkLight1337 @ywang96
|
/tests/multimodal @DarkLight1337 @ywang96
|
||||||
/tests/prefix_caching @comaniac @KuntaiDu
|
/tests/prefix_caching @comaniac @KuntaiDu
|
||||||
/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
|
/tests/quantization @mgoin @robertgshaw2-redhat
|
||||||
|
/tests/spec_decode @njhill @LiuXiaoxuanPKU
|
||||||
/tests/test_inputs.py @DarkLight1337 @ywang96
|
/tests/test_inputs.py @DarkLight1337 @ywang96
|
||||||
/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
|
/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb
|
||||||
/tests/v1/structured_output @mgoin @russellb @aarnphm
|
/tests/v1/structured_output @mgoin @russellb
|
||||||
/tests/weight_loading @mgoin @youkaichao @yewentao256
|
/tests/weight_loading @mgoin @youkaichao
|
||||||
/tests/lora @jeejeelee
|
|
||||||
/tests/models/language/generation/test_hybrid.py @tdoublep
|
|
||||||
|
|
||||||
# Docs
|
|
||||||
/docs @hmellor
|
|
||||||
mkdocs.yaml @hmellor
|
|
||||||
|
|
||||||
# CPU
|
|
||||||
/vllm/v1/worker/^cpu @bigPYJ1151
|
|
||||||
/csrc/cpu @bigPYJ1151
|
|
||||||
/vllm/platforms/cpu.py @bigPYJ1151
|
|
||||||
/cmake/cpu_extension.cmake @bigPYJ1151
|
|
||||||
/docker/Dockerfile.cpu @bigPYJ1151
|
|
||||||
|
|
||||||
# Intel GPU
|
|
||||||
/vllm/v1/worker/^xpu @jikunshang
|
|
||||||
/vllm/platforms/xpu.py @jikunshang
|
|
||||||
/docker/Dockerfile.xpu @jikunshang
|
|
||||||
|
|
||||||
# Qwen-specific files
|
|
||||||
/vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
|
|
||||||
/vllm/model_executor/models/qwen* @sighingnow
|
|
||||||
|
|
||||||
# MTP-specific files
|
|
||||||
/vllm/model_executor/models/deepseek_mtp.py @luccafong
|
|
||||||
|
|
||||||
# Mistral-specific files
|
|
||||||
/vllm/model_executor/models/mistral*.py @patrickvonplaten
|
|
||||||
/vllm/model_executor/models/mixtral*.py @patrickvonplaten
|
|
||||||
/vllm/model_executor/models/voxtral*.py @patrickvonplaten
|
|
||||||
/vllm/model_executor/models/pixtral*.py @patrickvonplaten
|
|
||||||
/vllm/transformers_utils/configs/mistral.py @patrickvonplaten
|
|
||||||
/vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
|
|
||||||
|
|
||||||
# Kernels
|
|
||||||
/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
|
|
||||||
/vllm/attention/ops/triton_unified_attention.py @tdoublep
|
|
||||||
|
|
||||||
# ROCm related: specify owner with write access to notify AMD folks for careful code review
|
|
||||||
/docker/Dockerfile.rocm* @gshtras
|
|
||||||
/vllm/v1/attention/backends/rocm*.py @gshtras
|
|
||||||
/vllm/v1/attention/backends/mla/rocm*.py @gshtras
|
|
||||||
/vllm/attention/ops/rocm*.py @gshtras
|
|
||||||
/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras
|
|
||||||
|
|||||||
22
.github/ISSUE_TEMPLATE/400-bug-report.yml
vendored
22
.github/ISSUE_TEMPLATE/400-bug-report.yml
vendored
@ -8,16 +8,6 @@ body:
|
|||||||
attributes:
|
attributes:
|
||||||
value: >
|
value: >
|
||||||
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
|
||||||
- type: markdown
|
|
||||||
attributes:
|
|
||||||
value: |
|
|
||||||
⚠️ **SECURITY WARNING:** Please review any text you paste to ensure it does not contain sensitive information such as:
|
|
||||||
- API tokens or keys (e.g., Hugging Face tokens, OpenAI API keys)
|
|
||||||
- Passwords or authentication credentials
|
|
||||||
- Private URLs or endpoints
|
|
||||||
- Personal or confidential data
|
|
||||||
|
|
||||||
Consider redacting or replacing sensitive values with placeholders like `<YOUR_TOKEN_HERE>` when sharing configuration or code examples.
|
|
||||||
- type: textarea
|
- type: textarea
|
||||||
attributes:
|
attributes:
|
||||||
label: Your current environment
|
label: Your current environment
|
||||||
@ -31,12 +21,12 @@ body:
|
|||||||
It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
|
It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
|
||||||
value: |
|
value: |
|
||||||
<details>
|
<details>
|
||||||
<summary>The output of <code>python collect_env.py</code></summary>
|
<summary>The output of `python collect_env.py`</summary>
|
||||||
|
|
||||||
```text
|
```text
|
||||||
Your output of `python collect_env.py` here
|
Your output of `python collect_env.py` here
|
||||||
```
|
```
|
||||||
|
|
||||||
</details>
|
</details>
|
||||||
validations:
|
validations:
|
||||||
required: true
|
required: true
|
||||||
@ -85,20 +75,20 @@ body:
|
|||||||
```
|
```
|
||||||
|
|
||||||
```
|
```
|
||||||
The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
|
The error message you got, with the full traceback.
|
||||||
```
|
```
|
||||||
validations:
|
validations:
|
||||||
required: true
|
required: true
|
||||||
- type: markdown
|
- type: markdown
|
||||||
attributes:
|
attributes:
|
||||||
value: |
|
value: >
|
||||||
⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the model's output:
|
⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the models' output:
|
||||||
|
|
||||||
- Try the counterpart of `transformers` first. If the error appears, please go to [their issues](https://github.com/huggingface/transformers/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc).
|
- Try the counterpart of `transformers` first. If the error appears, please go to [their issues](https://github.com/huggingface/transformers/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc).
|
||||||
|
|
||||||
- If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.
|
- If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.
|
||||||
|
|
||||||
Thanks for reporting 🙏!
|
Thanks for contributing 🎉!
|
||||||
- type: checkboxes
|
- type: checkboxes
|
||||||
id: askllm
|
id: askllm
|
||||||
attributes:
|
attributes:
|
||||||
|
|||||||
69
.github/ISSUE_TEMPLATE/450-ci-failure.yml
vendored
69
.github/ISSUE_TEMPLATE/450-ci-failure.yml
vendored
@ -1,69 +0,0 @@
|
|||||||
name: 🧪 CI failure report
|
|
||||||
description: Report a failing test.
|
|
||||||
title: "[CI Failure]: "
|
|
||||||
labels: ["ci-failure"]
|
|
||||||
|
|
||||||
body:
|
|
||||||
- type: markdown
|
|
||||||
attributes:
|
|
||||||
value: >
|
|
||||||
#### Include the name of the failing Buildkite step and test file in the title.
|
|
||||||
- type: input
|
|
||||||
attributes:
|
|
||||||
label: Name of failing test
|
|
||||||
description: |
|
|
||||||
Paste in the fully-qualified name of the failing test from the logs.
|
|
||||||
placeholder: |
|
|
||||||
`path/to/test_file.py::test_name[params]`
|
|
||||||
validations:
|
|
||||||
required: true
|
|
||||||
- type: checkboxes
|
|
||||||
attributes:
|
|
||||||
label: Basic information
|
|
||||||
description: Select all items that apply to the failing test.
|
|
||||||
options:
|
|
||||||
- label: Flaky test
|
|
||||||
- label: Can reproduce locally
|
|
||||||
- label: Caused by external libraries (e.g. bug in `transformers`)
|
|
||||||
- type: textarea
|
|
||||||
attributes:
|
|
||||||
label: 🧪 Describe the failing test
|
|
||||||
description: |
|
|
||||||
Please provide a clear and concise description of the failing test.
|
|
||||||
placeholder: |
|
|
||||||
A clear and concise description of the failing test.
|
|
||||||
|
|
||||||
```
|
|
||||||
The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
|
|
||||||
```
|
|
||||||
validations:
|
|
||||||
required: true
|
|
||||||
- type: textarea
|
|
||||||
attributes:
|
|
||||||
label: 📝 History of failing test
|
|
||||||
description: |
|
|
||||||
Since when did the test start to fail?
|
|
||||||
You can look up its history via [Buildkite Test Suites](https://buildkite.com/organizations/vllm/analytics/suites/ci-1/tests?branch=main).
|
|
||||||
|
|
||||||
If you have time, identify the PR that caused the test to fail on main. You can do so via the following methods:
|
|
||||||
|
|
||||||
- Use Buildkite Test Suites to find the PR where the test failure first occurred, and reproduce the failure locally.
|
|
||||||
|
|
||||||
- Run [`git bisect`](https://git-scm.com/docs/git-bisect) locally.
|
|
||||||
|
|
||||||
- Manually unblock Buildkite steps for suspected PRs on main and check the results. (authorized users only)
|
|
||||||
placeholder: |
|
|
||||||
Approximate timeline and/or problematic PRs
|
|
||||||
|
|
||||||
A link to the Buildkite analytics of the failing test (if available)
|
|
||||||
validations:
|
|
||||||
required: true
|
|
||||||
- type: textarea
|
|
||||||
attributes:
|
|
||||||
label: CC List.
|
|
||||||
description: >
|
|
||||||
The list of people you want to CC. Usually, this includes those who worked on the PR that failed the test.
|
|
||||||
- type: markdown
|
|
||||||
attributes:
|
|
||||||
value: >
|
|
||||||
Thanks for reporting 🙏!
|
|
||||||
2
.github/ISSUE_TEMPLATE/750-RFC.yml
vendored
2
.github/ISSUE_TEMPLATE/750-RFC.yml
vendored
@ -46,7 +46,7 @@ body:
|
|||||||
- type: markdown
|
- type: markdown
|
||||||
attributes:
|
attributes:
|
||||||
value: >
|
value: >
|
||||||
Thanks for contributing 🎉! The vLLM core team hosts a biweekly RFC review session at 9:30AM Pacific Time, while most RFCs can be discussed online, you can optionally sign up for a slot to discuss your RFC online [here](https://docs.google.com/document/d/1CiLVBZeIVfR7_PNAKVSusxpceywkoOOB78qoWqHvSZc/edit).
|
Thanks for contributing 🎉!
|
||||||
- type: checkboxes
|
- type: checkboxes
|
||||||
id: askllm
|
id: askllm
|
||||||
attributes:
|
attributes:
|
||||||
|
|||||||
23
.github/PULL_REQUEST_TEMPLATE.md
vendored
23
.github/PULL_REQUEST_TEMPLATE.md
vendored
@ -1,21 +1,6 @@
|
|||||||
<!-- markdownlint-disable -->
|
FILL IN THE PR DESCRIPTION HERE
|
||||||
PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.
|
|
||||||
|
|
||||||
## Purpose
|
FIX #xxxx (*link existing issues this PR will resolve*)
|
||||||
|
|
||||||
## Test Plan
|
<!--- pyml disable-next-line no-emphasis-as-heading -->
|
||||||
|
**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)
|
||||||
## Test Result
|
|
||||||
|
|
||||||
---
|
|
||||||
<details>
|
|
||||||
<summary> Essential Elements of an Effective PR Description Checklist </summary>
|
|
||||||
|
|
||||||
- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
|
|
||||||
- [ ] The test plan, such as providing test command.
|
|
||||||
- [ ] The test results, such as pasting the results comparison before and after, or e2e results
|
|
||||||
- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
|
|
||||||
- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft in the [Google Doc](https://docs.google.com/document/d/1YyVqrgX4gHTtrstbq8oWUImOyPCKSGnJ7xtTpmXzlRs/edit?tab=t.0).
|
|
||||||
</details>
|
|
||||||
|
|
||||||
**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)
|
|
||||||
|
|||||||
174
.github/mergify.yml
vendored
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vendored
@ -27,22 +27,6 @@ pull_request_rules:
|
|||||||
add:
|
add:
|
||||||
- ci/build
|
- ci/build
|
||||||
|
|
||||||
- name: label-deepseek
|
|
||||||
description: Automatically apply deepseek label
|
|
||||||
conditions:
|
|
||||||
- or:
|
|
||||||
- files~=^examples/.*deepseek.*\.py
|
|
||||||
- files~=^tests/.*deepseek.*\.py
|
|
||||||
- files~=^vllm/entrypoints/openai/tool_parsers/.*deepseek.*\.py
|
|
||||||
- files~=^vllm/model_executor/models/.*deepseek.*\.py
|
|
||||||
- files~=^vllm/reasoning/.*deepseek.*\.py
|
|
||||||
- files~=^vllm/transformers_utils/.*deepseek.*\.py
|
|
||||||
- title~=(?i)DeepSeek
|
|
||||||
actions:
|
|
||||||
label:
|
|
||||||
add:
|
|
||||||
- deepseek
|
|
||||||
|
|
||||||
- name: label-frontend
|
- name: label-frontend
|
||||||
description: Automatically apply frontend label
|
description: Automatically apply frontend label
|
||||||
conditions:
|
conditions:
|
||||||
@ -52,21 +36,6 @@ pull_request_rules:
|
|||||||
add:
|
add:
|
||||||
- frontend
|
- frontend
|
||||||
|
|
||||||
- name: label-llama
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|
||||||
description: Automatically apply llama label
|
|
||||||
conditions:
|
|
||||||
- or:
|
|
||||||
- files~=^examples/.*llama.*\.py
|
|
||||||
- files~=^tests/.*llama.*\.py
|
|
||||||
- files~=^vllm/entrypoints/openai/tool_parsers/llama.*\.py
|
|
||||||
- files~=^vllm/model_executor/models/.*llama.*\.py
|
|
||||||
- files~=^vllm/transformers_utils/configs/.*llama.*\.py
|
|
||||||
- title~=(?i)llama
|
|
||||||
actions:
|
|
||||||
label:
|
|
||||||
add:
|
|
||||||
- llama
|
|
||||||
|
|
||||||
- name: label-multi-modality
|
- name: label-multi-modality
|
||||||
description: Automatically apply multi-modality label
|
description: Automatically apply multi-modality label
|
||||||
conditions:
|
conditions:
|
||||||
@ -74,98 +43,23 @@ pull_request_rules:
|
|||||||
- files~=^vllm/multimodal/
|
- files~=^vllm/multimodal/
|
||||||
- files~=^tests/multimodal/
|
- files~=^tests/multimodal/
|
||||||
- files~=^tests/models/multimodal/
|
- files~=^tests/models/multimodal/
|
||||||
|
- files~=^tests/models/*/audio_language/
|
||||||
|
- files~=^tests/models/*/vision_language/
|
||||||
- files=tests/models/test_vision.py
|
- files=tests/models/test_vision.py
|
||||||
actions:
|
actions:
|
||||||
label:
|
label:
|
||||||
add:
|
add:
|
||||||
- multi-modality
|
- multi-modality
|
||||||
|
|
||||||
- name: label-new-model
|
|
||||||
description: Automatically apply new-model label
|
|
||||||
conditions:
|
|
||||||
- and:
|
|
||||||
- files~=^vllm/model_executor/models/
|
|
||||||
- files=vllm/model_executor/models/registry.py
|
|
||||||
actions:
|
|
||||||
label:
|
|
||||||
add:
|
|
||||||
- new-model
|
|
||||||
|
|
||||||
- name: label-performance
|
|
||||||
description: Automatically apply performance label
|
|
||||||
conditions:
|
|
||||||
- or:
|
|
||||||
- files~=^benchmarks/
|
|
||||||
- files~=^vllm/benchmarks/
|
|
||||||
- files~=^tests/benchmarks/
|
|
||||||
- files~=^\.buildkite/nightly-benchmarks/
|
|
||||||
actions:
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|
||||||
label:
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|
||||||
add:
|
|
||||||
- performance
|
|
||||||
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||||||
- name: label-qwen
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||||||
description: Automatically apply qwen label
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|
||||||
conditions:
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||||||
- or:
|
|
||||||
- files~=^examples/.*qwen.*\.py
|
|
||||||
- files~=^tests/.*qwen.*\.py
|
|
||||||
- files~=^vllm/model_executor/models/.*qwen.*\.py
|
|
||||||
- files~=^vllm/reasoning/.*qwen.*\.py
|
|
||||||
- title~=(?i)Qwen
|
|
||||||
actions:
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|
||||||
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|
||||||
- qwen
|
|
||||||
|
|
||||||
- name: label-gpt-oss
|
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||||||
description: Automatically apply gpt-oss label
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||||||
conditions:
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||||||
- or:
|
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||||||
- files~=^examples/.*gpt[-_]?oss.*\.py
|
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- files~=^tests/.*gpt[-_]?oss.*\.py
|
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- files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
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||||||
- files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
|
|
||||||
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|
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||||||
actions:
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|
||||||
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|
|
||||||
- gpt-oss
|
|
||||||
|
|
||||||
- name: label-rocm
|
|
||||||
description: Automatically apply rocm label
|
|
||||||
conditions:
|
|
||||||
- or:
|
|
||||||
- files~=^csrc/rocm/
|
|
||||||
- files~=^docker/Dockerfile.rocm
|
|
||||||
- files~=^requirements/rocm.*\.txt
|
|
||||||
- files~=^vllm/attention/backends/rocm.*\.py
|
|
||||||
- files~=^vllm/attention/ops/rocm.*\.py
|
|
||||||
- files~=^vllm/model_executor/layers/fused_moe/rocm.*\.py
|
|
||||||
- files~=^vllm/v1/attention/backends/mla/rocm.*\.py
|
|
||||||
- files~=^tests/kernels/.*_rocm.*\.py
|
|
||||||
- files=vllm/platforms/rocm.py
|
|
||||||
- title~=(?i)AMD
|
|
||||||
- title~=(?i)ROCm
|
|
||||||
actions:
|
|
||||||
label:
|
|
||||||
add:
|
|
||||||
- rocm
|
|
||||||
|
|
||||||
- name: label-structured-output
|
- name: label-structured-output
|
||||||
description: Automatically apply structured-output label
|
description: Automatically apply structured-output label
|
||||||
conditions:
|
conditions:
|
||||||
- or:
|
- or:
|
||||||
- files~=^benchmarks/structured_schemas/
|
- files~=^vllm/model_executor/guided_decoding/
|
||||||
- files=benchmarks/benchmark_serving_structured_output.py
|
- files=tests/model_executor/test_guided_processors.py
|
||||||
- files=benchmarks/run_structured_output_benchmark.sh
|
- files=tests/entrypoints/llm/test_guided_generate.py
|
||||||
- files=docs/features/structured_outputs.md
|
- files=benchmarks/benchmark_serving_guided.py
|
||||||
- files=examples/offline_inference/structured_outputs.py
|
- files=benchmarks/benchmark_guided.py
|
||||||
- files=examples/online_serving/openai_chat_completion_structured_outputs.py
|
|
||||||
- files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
|
|
||||||
- files~=^tests/v1/structured_output/
|
|
||||||
- files=tests/v1/entrypoints/llm/test_guided_generate.py
|
|
||||||
- files~=^vllm/v1/structured_output/
|
|
||||||
actions:
|
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|
||||||
label:
|
label:
|
||||||
add:
|
add:
|
||||||
@ -175,12 +69,9 @@ pull_request_rules:
|
|||||||
description: Automatically apply speculative-decoding label
|
description: Automatically apply speculative-decoding label
|
||||||
conditions:
|
conditions:
|
||||||
- or:
|
- or:
|
||||||
- files~=^vllm/v1/spec_decode/
|
- files~=^vllm/spec_decode/
|
||||||
- files~=^tests/v1/spec_decode/
|
- files=vllm/model_executor/layers/spec_decode_base_sampler.py
|
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- files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
|
- files~=^tests/spec_decode/
|
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- files~=^vllm/model_executor/models/.*eagle.*\.py
|
|
||||||
- files=vllm/model_executor/models/mlp_speculator.py
|
|
||||||
- files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
|
|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
@ -227,26 +118,6 @@ pull_request_rules:
|
|||||||
remove:
|
remove:
|
||||||
- tpu
|
- tpu
|
||||||
|
|
||||||
- name: label-tool-calling
|
|
||||||
description: Automatically add tool-calling label
|
|
||||||
conditions:
|
|
||||||
- or:
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|
||||||
- files~=^tests/tool_use/
|
|
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- files~=^tests/mistral_tool_use/
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- files~=^tests/entrypoints/openai/tool_parsers/
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- files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py
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|
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- files=docs/features/tool_calling.md
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|
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- files~=^examples/tool_chat_*
|
|
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- files=examples/offline_inference/chat_with_tools.py
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|
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- files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
|
|
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- files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
|
|
||||||
- files=examples/online_serving/openai_chat_completion_client_with_tools.py
|
|
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actions:
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|
||||||
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add:
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|
||||||
- tool-calling
|
|
||||||
|
|
||||||
- name: ping author on conflicts and add 'needs-rebase' label
|
- name: ping author on conflicts and add 'needs-rebase' label
|
||||||
conditions:
|
conditions:
|
||||||
- conflict
|
- conflict
|
||||||
@ -262,31 +133,6 @@ pull_request_rules:
|
|||||||
|
|
||||||
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
|
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
|
||||||
|
|
||||||
- name: assign reviewer for tensorizer changes
|
|
||||||
conditions:
|
|
||||||
- files~=^vllm/model_executor/model_loader/tensorizer.py
|
|
||||||
- files~=^vllm/model_executor/model_loader/tensorizer_loader.py
|
|
||||||
- files~=^tests/entrypoints/openai/test_tensorizer_entrypoint.py
|
|
||||||
- files~=^tests/tensorizer_loader/
|
|
||||||
actions:
|
|
||||||
assign:
|
|
||||||
users:
|
|
||||||
- "sangstar"
|
|
||||||
|
|
||||||
- name: assign reviewer for modelopt changes
|
|
||||||
conditions:
|
|
||||||
- or:
|
|
||||||
- files~=^vllm/model_executor/layers/quantization/modelopt\.py$
|
|
||||||
- files~=^vllm/model_executor/layers/quantization/__init__\.py$
|
|
||||||
- files~=^tests/models/quantization/test_modelopt\.py$
|
|
||||||
- files~=^tests/quantization/test_modelopt\.py$
|
|
||||||
- files~=^tests/models/quantization/test_nvfp4\.py$
|
|
||||||
- files~=^docs/features/quantization/modelopt\.md$
|
|
||||||
actions:
|
|
||||||
assign:
|
|
||||||
users:
|
|
||||||
- "Edwardf0t1"
|
|
||||||
|
|
||||||
- name: remove 'needs-rebase' label when conflict is resolved
|
- name: remove 'needs-rebase' label when conflict is resolved
|
||||||
conditions:
|
conditions:
|
||||||
- -conflict
|
- -conflict
|
||||||
|
|||||||
21
.github/scale-config.yml
vendored
21
.github/scale-config.yml
vendored
@ -1,21 +0,0 @@
|
|||||||
# scale-config.yml:
|
|
||||||
# Powers what instance types are available for GHA auto-scaled
|
|
||||||
# runners. Runners listed here will be available as self hosted
|
|
||||||
# runners, configuration is directly pulled from the main branch.
|
|
||||||
# runner_types:
|
|
||||||
# runner_label:
|
|
||||||
# instance_type: m4.large
|
|
||||||
# os: linux
|
|
||||||
# # min_available defaults to the global cfg in the ALI Terraform
|
|
||||||
# min_available: undefined
|
|
||||||
# # when max_available value is not defined, no max runners is enforced
|
|
||||||
# max_available: undefined
|
|
||||||
# disk_size: 50
|
|
||||||
# is_ephemeral: true
|
|
||||||
|
|
||||||
runner_types:
|
|
||||||
linux.2xlarge:
|
|
||||||
disk_size: 150
|
|
||||||
instance_type: c5.2xlarge
|
|
||||||
is_ephemeral: true
|
|
||||||
os: linux
|
|
||||||
10
.github/scripts/cleanup_pr_body.sh
vendored
10
.github/scripts/cleanup_pr_body.sh
vendored
@ -15,18 +15,18 @@ NEW=/tmp/new_pr_body.txt
|
|||||||
gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
|
gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
|
||||||
cp "${OLD}" "${NEW}"
|
cp "${OLD}" "${NEW}"
|
||||||
|
|
||||||
# Remove markdown comments (like the <!-- markdownlint-disable --> at the start)
|
# Remove "FIX #xxxx (*link existing issues this PR will resolve*)"
|
||||||
sed -i '/<!--.*-->$/d' "${NEW}"
|
sed -i '/FIX #xxxx.*$/d' "${NEW}"
|
||||||
|
|
||||||
# Remove "PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED."
|
# Remove "FILL IN THE PR DESCRIPTION HERE"
|
||||||
sed -i '/PLEASE FILL IN THE PR DESCRIPTION HERE.*$/d' "${NEW}"
|
sed -i '/FILL IN THE PR DESCRIPTION HERE/d' "${NEW}"
|
||||||
|
|
||||||
# Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
|
# Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
|
||||||
sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"
|
sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"
|
||||||
|
|
||||||
# Remove HTML <details> section that includes <summary> text of "PR Checklist (Click to Expand)"
|
# Remove HTML <details> section that includes <summary> text of "PR Checklist (Click to Expand)"
|
||||||
python3 - <<EOF
|
python3 - <<EOF
|
||||||
import regex as re
|
import re
|
||||||
|
|
||||||
with open("${NEW}", "r") as file:
|
with open("${NEW}", "r") as file:
|
||||||
content = file.read()
|
content = file.read()
|
||||||
|
|||||||
2
.github/workflows/add_label_automerge.yml
vendored
2
.github/workflows/add_label_automerge.yml
vendored
@ -1,6 +1,4 @@
|
|||||||
name: Add label on auto-merge enabled
|
name: Add label on auto-merge enabled
|
||||||
permissions:
|
|
||||||
pull-requests: write
|
|
||||||
on:
|
on:
|
||||||
pull_request_target:
|
pull_request_target:
|
||||||
types:
|
types:
|
||||||
|
|||||||
9
.github/workflows/cleanup_pr_body.yml
vendored
9
.github/workflows/cleanup_pr_body.yml
vendored
@ -16,16 +16,11 @@ jobs:
|
|||||||
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||||
|
|
||||||
- name: Set up Python
|
- name: Set up Python
|
||||||
uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
|
uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
|
||||||
with:
|
with:
|
||||||
python-version: '3.12'
|
python-version: '3.12'
|
||||||
|
|
||||||
- name: Install Python dependencies
|
|
||||||
run: |
|
|
||||||
python3 -m pip install --upgrade pip
|
|
||||||
python3 -m pip install regex
|
|
||||||
|
|
||||||
- name: Update PR description
|
- name: Update PR description
|
||||||
env:
|
env:
|
||||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||||
run: bash .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
|
run: .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
|
||||||
|
|||||||
309
.github/workflows/issue_autolabel.yml
vendored
309
.github/workflows/issue_autolabel.yml
vendored
@ -1,309 +0,0 @@
|
|||||||
name: Label issues based on keywords
|
|
||||||
on:
|
|
||||||
issues:
|
|
||||||
types: [opened, edited, reopened]
|
|
||||||
permissions:
|
|
||||||
issues: write # needed so the workflow can add labels
|
|
||||||
contents: read
|
|
||||||
concurrency:
|
|
||||||
group: issue-labeler-${{ github.event.issue.number }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
jobs:
|
|
||||||
add-labels:
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
steps:
|
|
||||||
- name: Label issues based on keywords
|
|
||||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
|
||||||
with:
|
|
||||||
script: |
|
|
||||||
// Configuration: Add new labels and keywords here
|
|
||||||
const labelConfig = {
|
|
||||||
rocm: {
|
|
||||||
// Keyword search - matches whole words only (with word boundaries)
|
|
||||||
keywords: [
|
|
||||||
{
|
|
||||||
term: "composable kernel",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "rccl",
|
|
||||||
searchIn: "body" // only search in body
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "migraphx",
|
|
||||||
searchIn: "title" // only search in title
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "hipgraph",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "ROCm System Management Interface",
|
|
||||||
searchIn: "body"
|
|
||||||
},
|
|
||||||
],
|
|
||||||
|
|
||||||
// Substring search - matches anywhere in text (partial matches)
|
|
||||||
substrings: [
|
|
||||||
{
|
|
||||||
term: "VLLM_ROCM_",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "aiter",
|
|
||||||
searchIn: "title"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "rocm",
|
|
||||||
searchIn: "title"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "amd",
|
|
||||||
searchIn: "title"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "hip-",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "gfx",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "cdna",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "rdna",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "torch_hip",
|
|
||||||
searchIn: "body" // only in body
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "_hip",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
term: "hip_",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
|
|
||||||
// ROCm tools and libraries
|
|
||||||
{
|
|
||||||
term: "hipify",
|
|
||||||
searchIn: "both"
|
|
||||||
},
|
|
||||||
],
|
|
||||||
|
|
||||||
// Regex patterns - for complex pattern matching
|
|
||||||
regexPatterns: [
|
|
||||||
{
|
|
||||||
pattern: "\\bmi\\d{3}[a-z]*\\b",
|
|
||||||
description: "AMD GPU names (mi + 3 digits + optional letters)",
|
|
||||||
flags: "gi",
|
|
||||||
searchIn: "both" // "title", "body", or "both"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
},
|
|
||||||
};
|
|
||||||
|
|
||||||
// Helper function to create regex based on search type
|
|
||||||
function createSearchRegex(term, type) {
|
|
||||||
// Escape special regex characters in the term
|
|
||||||
const escapedTerm = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
|
|
||||||
|
|
||||||
switch (type) {
|
|
||||||
case 'keyword':
|
|
||||||
// Word boundary search - matches whole words only
|
|
||||||
return new RegExp(`\\b${escapedTerm}\\b`, "gi");
|
|
||||||
case 'substring':
|
|
||||||
// Substring search - matches anywhere in the text
|
|
||||||
return new RegExp(escapedTerm, "gi");
|
|
||||||
default:
|
|
||||||
throw new Error(`Unknown search type: ${type}`);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Helper function to find matching terms in text with line information
|
|
||||||
function findMatchingTermsWithLines(text, searchTerms = [], searchType = 'keyword', searchLocation = '') {
|
|
||||||
const matches = [];
|
|
||||||
const lines = text.split('\n');
|
|
||||||
|
|
||||||
for (const termConfig of searchTerms) {
|
|
||||||
let regex;
|
|
||||||
let term, searchIn, pattern, description, flags;
|
|
||||||
|
|
||||||
// Handle different input formats (string or object)
|
|
||||||
if (typeof termConfig === 'string') {
|
|
||||||
term = termConfig;
|
|
||||||
searchIn = 'both'; // default
|
|
||||||
} else {
|
|
||||||
term = termConfig.term;
|
|
||||||
searchIn = termConfig.searchIn || 'both';
|
|
||||||
pattern = termConfig.pattern;
|
|
||||||
description = termConfig.description;
|
|
||||||
flags = termConfig.flags;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Skip if this term shouldn't be searched in the current location
|
|
||||||
if (searchIn !== 'both' && searchIn !== searchLocation) {
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Create appropriate regex
|
|
||||||
if (searchType === 'regex') {
|
|
||||||
regex = new RegExp(pattern, flags || "gi");
|
|
||||||
} else {
|
|
||||||
regex = createSearchRegex(term, searchType);
|
|
||||||
}
|
|
||||||
|
|
||||||
const termMatches = [];
|
|
||||||
|
|
||||||
// Check each line for matches
|
|
||||||
lines.forEach((line, lineIndex) => {
|
|
||||||
const lineMatches = line.match(regex);
|
|
||||||
if (lineMatches) {
|
|
||||||
lineMatches.forEach(match => {
|
|
||||||
termMatches.push({
|
|
||||||
match: match,
|
|
||||||
lineNumber: lineIndex + 1,
|
|
||||||
lineContent: line.trim(),
|
|
||||||
searchType: searchType,
|
|
||||||
searchLocation: searchLocation,
|
|
||||||
originalTerm: term || pattern,
|
|
||||||
description: description,
|
|
||||||
// Show context around the match in the line
|
|
||||||
context: line.length > 100 ?
|
|
||||||
line.substring(Math.max(0, line.toLowerCase().indexOf(match.toLowerCase()) - 30),
|
|
||||||
line.toLowerCase().indexOf(match.toLowerCase()) + match.length + 30) + '...'
|
|
||||||
: line.trim()
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
|
||||||
if (termMatches.length > 0) {
|
|
||||||
matches.push({
|
|
||||||
term: term || (description || pattern),
|
|
||||||
searchType: searchType,
|
|
||||||
searchLocation: searchLocation,
|
|
||||||
searchIn: searchIn,
|
|
||||||
pattern: pattern,
|
|
||||||
matches: termMatches,
|
|
||||||
count: termMatches.length
|
|
||||||
});
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
return matches;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Helper function to check if label should be added
|
|
||||||
async function processLabel(labelName, config) {
|
|
||||||
const body = context.payload.issue.body || "";
|
|
||||||
const title = context.payload.issue.title || "";
|
|
||||||
|
|
||||||
core.notice(`Processing label: ${labelName}`);
|
|
||||||
core.notice(`Issue Title: "${title}"`);
|
|
||||||
core.notice(`Issue Body length: ${body.length} characters`);
|
|
||||||
|
|
||||||
let shouldAddLabel = false;
|
|
||||||
let allMatches = [];
|
|
||||||
let reason = '';
|
|
||||||
|
|
||||||
const keywords = config.keywords || [];
|
|
||||||
const substrings = config.substrings || [];
|
|
||||||
const regexPatterns = config.regexPatterns || [];
|
|
||||||
|
|
||||||
core.notice(`Searching with ${keywords.length} keywords, ${substrings.length} substrings, and ${regexPatterns.length} regex patterns`);
|
|
||||||
|
|
||||||
// Search in title
|
|
||||||
if (title.trim()) {
|
|
||||||
core.notice(`Searching in title: "${title}"`);
|
|
||||||
|
|
||||||
const titleKeywordMatches = findMatchingTermsWithLines(title, keywords, 'keyword', 'title');
|
|
||||||
const titleSubstringMatches = findMatchingTermsWithLines(title, substrings, 'substring', 'title');
|
|
||||||
const titleRegexMatches = findMatchingTermsWithLines(title, regexPatterns, 'regex', 'title');
|
|
||||||
|
|
||||||
allMatches.push(...titleKeywordMatches, ...titleSubstringMatches, ...titleRegexMatches);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Search in body
|
|
||||||
if (body.trim()) {
|
|
||||||
core.notice(`Searching in body (${body.length} characters)`);
|
|
||||||
|
|
||||||
const bodyKeywordMatches = findMatchingTermsWithLines(body, keywords, 'keyword', 'body');
|
|
||||||
const bodySubstringMatches = findMatchingTermsWithLines(body, substrings, 'substring', 'body');
|
|
||||||
const bodyRegexMatches = findMatchingTermsWithLines(body, regexPatterns, 'regex', 'body');
|
|
||||||
|
|
||||||
allMatches.push(...bodyKeywordMatches, ...bodySubstringMatches, ...bodyRegexMatches);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (allMatches.length > 0) {
|
|
||||||
core.notice(`Found ${allMatches.length} matching term(s):`);
|
|
||||||
|
|
||||||
for (const termMatch of allMatches) {
|
|
||||||
const locationText = termMatch.searchLocation === 'title' ? 'title' : 'body';
|
|
||||||
const searchInText = termMatch.searchIn === 'both' ? 'both' : termMatch.searchIn;
|
|
||||||
|
|
||||||
if (termMatch.searchType === 'regex') {
|
|
||||||
core.notice(` 📍 Regex: "${termMatch.term}" (pattern: ${termMatch.pattern}) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
|
|
||||||
} else {
|
|
||||||
core.notice(` 📍 Term: "${termMatch.term}" (${termMatch.searchType} search) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Show details for each match
|
|
||||||
termMatch.matches.forEach((match, index) => {
|
|
||||||
core.notice(` ${index + 1}. Line ${match.lineNumber} in ${match.searchLocation}: "${match.match}" [${match.searchType}]`);
|
|
||||||
if (match.description) {
|
|
||||||
core.notice(` Description: ${match.description}`);
|
|
||||||
}
|
|
||||||
core.notice(` Context: ${match.context}`);
|
|
||||||
if (match.lineContent !== match.context) {
|
|
||||||
core.notice(` Full line: ${match.lineContent}`);
|
|
||||||
}
|
|
||||||
});
|
|
||||||
}
|
|
||||||
|
|
||||||
shouldAddLabel = true;
|
|
||||||
const totalMatches = allMatches.reduce((sum, t) => sum + t.count, 0);
|
|
||||||
const titleMatches = allMatches.filter(t => t.searchLocation === 'title').reduce((sum, t) => sum + t.count, 0);
|
|
||||||
const bodyMatches = allMatches.filter(t => t.searchLocation === 'body').reduce((sum, t) => sum + t.count, 0);
|
|
||||||
const keywordMatches = allMatches.filter(t => t.searchType === 'keyword').reduce((sum, t) => sum + t.count, 0);
|
|
||||||
const substringMatches = allMatches.filter(t => t.searchType === 'substring').reduce((sum, t) => sum + t.count, 0);
|
|
||||||
const regexMatches = allMatches.filter(t => t.searchType === 'regex').reduce((sum, t) => sum + t.count, 0);
|
|
||||||
|
|
||||||
reason = `Found ${totalMatches} total matches (${titleMatches} in title, ${bodyMatches} in body) - ${keywordMatches} keyword matches, ${substringMatches} substring matches, ${regexMatches} regex matches`;
|
|
||||||
}
|
|
||||||
|
|
||||||
core.notice(`Final decision: ${shouldAddLabel ? 'ADD LABEL' : 'DO NOT ADD LABEL'}`);
|
|
||||||
core.notice(`Reason: ${reason || 'No matching terms found'}`);
|
|
||||||
|
|
||||||
if (shouldAddLabel) {
|
|
||||||
const existingLabels = context.payload.issue.labels.map(l => l.name);
|
|
||||||
if (!existingLabels.includes(labelName)) {
|
|
||||||
await github.rest.issues.addLabels({
|
|
||||||
owner: context.repo.owner,
|
|
||||||
repo: context.repo.repo,
|
|
||||||
issue_number: context.issue.number,
|
|
||||||
labels: [labelName],
|
|
||||||
});
|
|
||||||
core.notice(`Label "${labelName}" added. ${reason}`);
|
|
||||||
return true;
|
|
||||||
}
|
|
||||||
core.notice(`Label "${labelName}" already present.`);
|
|
||||||
return false;
|
|
||||||
}
|
|
||||||
|
|
||||||
core.notice(`No matching terms found for label "${labelName}".`);
|
|
||||||
return false;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Process all configured labels
|
|
||||||
const processLabels = Object.entries(labelConfig)
|
|
||||||
.map(([labelName, config]) => processLabel(labelName, config));
|
|
||||||
const labelsAdded = await Promise.all(processLabels);
|
|
||||||
const numLabelsAdded = labelsAdded.reduce((x, y) => x + y, 0);
|
|
||||||
core.notice(`Processing complete. ${numLabelsAdded} label(s) added.`);
|
|
||||||
82
.github/workflows/lint-and-deploy.yaml
vendored
Normal file
82
.github/workflows/lint-and-deploy.yaml
vendored
Normal file
@ -0,0 +1,82 @@
|
|||||||
|
name: Lint and Deploy Charts
|
||||||
|
|
||||||
|
on: pull_request
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
lint-and-deploy:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
steps:
|
||||||
|
- name: Checkout
|
||||||
|
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||||
|
with:
|
||||||
|
fetch-depth: 0
|
||||||
|
|
||||||
|
- name: Set up Helm
|
||||||
|
uses: azure/setup-helm@b9e51907a09c216f16ebe8536097933489208112 # v4.3.0
|
||||||
|
with:
|
||||||
|
version: v3.14.4
|
||||||
|
|
||||||
|
#Python is required because ct lint runs Yamale and yamllint which require Python.
|
||||||
|
- uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
|
||||||
|
with:
|
||||||
|
python-version: '3.13'
|
||||||
|
|
||||||
|
- name: Set up chart-testing
|
||||||
|
uses: helm/chart-testing-action@0d28d3144d3a25ea2cc349d6e59901c4ff469b3b # v2.7.0
|
||||||
|
with:
|
||||||
|
version: v3.10.1
|
||||||
|
|
||||||
|
- name: Run chart-testing (lint)
|
||||||
|
run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/online_serving/chart-helm --charts examples/online_serving/chart-helm
|
||||||
|
|
||||||
|
- name: Setup minio
|
||||||
|
run: |
|
||||||
|
docker network create vllm-net
|
||||||
|
docker run -d -p 9000:9000 --name minio --net vllm-net \
|
||||||
|
-e "MINIO_ACCESS_KEY=minioadmin" \
|
||||||
|
-e "MINIO_SECRET_KEY=minioadmin" \
|
||||||
|
-v /tmp/data:/data \
|
||||||
|
-v /tmp/config:/root/.minio \
|
||||||
|
minio/minio server /data
|
||||||
|
export AWS_ACCESS_KEY_ID=minioadmin
|
||||||
|
export AWS_SECRET_ACCESS_KEY=minioadmin
|
||||||
|
export AWS_EC2_METADATA_DISABLED=true
|
||||||
|
mkdir opt-125m
|
||||||
|
cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd ..
|
||||||
|
aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket
|
||||||
|
aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive
|
||||||
|
|
||||||
|
- name: Create kind cluster
|
||||||
|
uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
|
||||||
|
|
||||||
|
- name: Build the Docker image vllm cpu
|
||||||
|
run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
|
||||||
|
|
||||||
|
- name: Configuration of docker images, network and namespace for the kind cluster
|
||||||
|
run: |
|
||||||
|
docker pull amazon/aws-cli:2.6.4
|
||||||
|
kind load docker-image amazon/aws-cli:2.6.4 --name chart-testing
|
||||||
|
kind load docker-image vllm-cpu-env:latest --name chart-testing
|
||||||
|
docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")"
|
||||||
|
kubectl create ns ns-vllm
|
||||||
|
|
||||||
|
- name: Run chart-testing (install)
|
||||||
|
run: |
|
||||||
|
export AWS_ACCESS_KEY_ID=minioadmin
|
||||||
|
export AWS_SECRET_ACCESS_KEY=minioadmin
|
||||||
|
sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
|
||||||
|
helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
|
||||||
|
|
||||||
|
- name: curl test
|
||||||
|
run: |
|
||||||
|
kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 &
|
||||||
|
sleep 10
|
||||||
|
CODE="$(curl -v -f --location http://localhost:8001/v1/completions \
|
||||||
|
--header "Content-Type: application/json" \
|
||||||
|
--data '{
|
||||||
|
"model": "opt-125m",
|
||||||
|
"prompt": "San Francisco is a",
|
||||||
|
"max_tokens": 7,
|
||||||
|
"temperature": 0
|
||||||
|
}'):$CODE"
|
||||||
|
echo "$CODE"
|
||||||
17
.github/workflows/matchers/markdownlint.json
vendored
17
.github/workflows/matchers/markdownlint.json
vendored
@ -1,17 +0,0 @@
|
|||||||
{
|
|
||||||
"problemMatcher": [
|
|
||||||
{
|
|
||||||
"owner": "markdownlint",
|
|
||||||
"pattern": [
|
|
||||||
{
|
|
||||||
"regexp": "^([^:]*):(\\d+):?(\\d+)?\\s([\\w-\\/]*)\\s(.*)$",
|
|
||||||
"file": 1,
|
|
||||||
"line": 2,
|
|
||||||
"column": 3,
|
|
||||||
"code": 4,
|
|
||||||
"message": 5
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
10
.github/workflows/pre-commit.yml
vendored
10
.github/workflows/pre-commit.yml
vendored
@ -5,23 +5,15 @@ on:
|
|||||||
push:
|
push:
|
||||||
branches: [main]
|
branches: [main]
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: ${{ github.workflow }}-${{ github.ref }}
|
|
||||||
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
|
|
||||||
|
|
||||||
permissions:
|
|
||||||
contents: read
|
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
pre-commit:
|
pre-commit:
|
||||||
runs-on: ubuntu-latest
|
runs-on: ubuntu-latest
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||||
- uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
|
- uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
|
||||||
with:
|
with:
|
||||||
python-version: "3.12"
|
python-version: "3.12"
|
||||||
- run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
|
- run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
|
||||||
- run: echo "::add-matcher::.github/workflows/matchers/markdownlint.json"
|
|
||||||
- run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
|
- run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
|
||||||
- uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
|
- uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
|
||||||
with:
|
with:
|
||||||
|
|||||||
111
.github/workflows/publish.yml
vendored
Normal file
111
.github/workflows/publish.yml
vendored
Normal file
@ -0,0 +1,111 @@
|
|||||||
|
# This workflow will upload a Python Package to Release asset
|
||||||
|
# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions
|
||||||
|
|
||||||
|
name: Create Release
|
||||||
|
|
||||||
|
on:
|
||||||
|
push:
|
||||||
|
tags:
|
||||||
|
- v*
|
||||||
|
|
||||||
|
# Needed to create release and upload assets
|
||||||
|
permissions:
|
||||||
|
contents: write
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
release:
|
||||||
|
# Retrieve tag and create release
|
||||||
|
name: Create Release
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
outputs:
|
||||||
|
upload_url: ${{ steps.create_release.outputs.upload_url }}
|
||||||
|
steps:
|
||||||
|
- name: Checkout
|
||||||
|
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||||
|
|
||||||
|
- name: Extract branch info
|
||||||
|
shell: bash
|
||||||
|
run: |
|
||||||
|
echo "release_tag=${GITHUB_REF#refs/*/}" >> "$GITHUB_ENV"
|
||||||
|
|
||||||
|
- name: Create Release
|
||||||
|
id: create_release
|
||||||
|
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||||
|
env:
|
||||||
|
RELEASE_TAG: ${{ env.release_tag }}
|
||||||
|
with:
|
||||||
|
github-token: "${{ secrets.GITHUB_TOKEN }}"
|
||||||
|
script: |
|
||||||
|
const script = require('.github/workflows/scripts/create_release.js')
|
||||||
|
await script(github, context, core)
|
||||||
|
|
||||||
|
# NOTE(simon): No longer build wheel using GitHub Actions. See buildkite's release workflow.
|
||||||
|
# wheel:
|
||||||
|
# name: Build Wheel
|
||||||
|
# runs-on: ${{ matrix.os }}
|
||||||
|
# needs: release
|
||||||
|
|
||||||
|
# strategy:
|
||||||
|
# fail-fast: false
|
||||||
|
# matrix:
|
||||||
|
# os: ['ubuntu-20.04']
|
||||||
|
# python-version: ['3.9', '3.10', '3.11', '3.12']
|
||||||
|
# pytorch-version: ['2.4.0'] # Must be the most recent version that meets requirements/cuda.txt.
|
||||||
|
# cuda-version: ['11.8', '12.1']
|
||||||
|
|
||||||
|
# steps:
|
||||||
|
# - name: Checkout
|
||||||
|
# uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||||
|
|
||||||
|
# - name: Setup ccache
|
||||||
|
# uses: hendrikmuhs/ccache-action@ed74d11c0b343532753ecead8a951bb09bb34bc9 # v1.2.14
|
||||||
|
# with:
|
||||||
|
# create-symlink: true
|
||||||
|
# key: ${{ github.job }}-${{ matrix.python-version }}-${{ matrix.cuda-version }}
|
||||||
|
|
||||||
|
# - name: Set up Linux Env
|
||||||
|
# if: ${{ runner.os == 'Linux' }}
|
||||||
|
# run: |
|
||||||
|
# bash -x .github/workflows/scripts/env.sh
|
||||||
|
|
||||||
|
# - name: Set up Python
|
||||||
|
# uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
|
||||||
|
# with:
|
||||||
|
# python-version: ${{ matrix.python-version }}
|
||||||
|
|
||||||
|
# - name: Install CUDA ${{ matrix.cuda-version }}
|
||||||
|
# run: |
|
||||||
|
# bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
|
||||||
|
|
||||||
|
# - name: Install PyTorch ${{ matrix.pytorch-version }} with CUDA ${{ matrix.cuda-version }}
|
||||||
|
# run: |
|
||||||
|
# bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.pytorch-version }} ${{ matrix.cuda-version }}
|
||||||
|
|
||||||
|
# - name: Build wheel
|
||||||
|
# shell: bash
|
||||||
|
# env:
|
||||||
|
# CMAKE_BUILD_TYPE: Release # do not compile with debug symbol to reduce wheel size
|
||||||
|
# run: |
|
||||||
|
# bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
|
||||||
|
# wheel_name=$(find dist -name "*whl" -print0 | xargs -0 -n 1 basename)
|
||||||
|
# asset_name=${wheel_name//"linux"/"manylinux1"}
|
||||||
|
# echo "wheel_name=${wheel_name}" >> "$GITHUB_ENV"
|
||||||
|
# echo "asset_name=${asset_name}" >> "$GITHUB_ENV"
|
||||||
|
|
||||||
|
# - name: Upload Release Asset
|
||||||
|
# uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5 # v1.0.2
|
||||||
|
# env:
|
||||||
|
# GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||||
|
# with:
|
||||||
|
# upload_url: ${{ needs.release.outputs.upload_url }}
|
||||||
|
# asset_path: ./dist/${{ env.wheel_name }}
|
||||||
|
# asset_name: ${{ env.asset_name }}
|
||||||
|
# asset_content_type: application/*
|
||||||
|
|
||||||
|
# (Danielkinz): This last step will publish the .whl to pypi. Warning: untested
|
||||||
|
# - name: Publish package
|
||||||
|
# uses: pypa/gh-action-pypi-publish@release/v1.8
|
||||||
|
# with:
|
||||||
|
# repository-url: https://test.pypi.org/legacy/
|
||||||
|
# password: ${{ secrets.PYPI_API_TOKEN }}
|
||||||
|
# skip-existing: true
|
||||||
51
.github/workflows/reminder_comment.yml
vendored
51
.github/workflows/reminder_comment.yml
vendored
@ -1,6 +1,4 @@
|
|||||||
name: PR Reminder Comment Bot
|
name: PR Reminder Comment Bot
|
||||||
permissions:
|
|
||||||
pull-requests: write
|
|
||||||
on:
|
on:
|
||||||
pull_request_target:
|
pull_request_target:
|
||||||
types: [opened]
|
types: [opened]
|
||||||
@ -12,43 +10,16 @@ jobs:
|
|||||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||||
with:
|
with:
|
||||||
script: |
|
script: |
|
||||||
try {
|
github.rest.issues.createComment({
|
||||||
// Get the PR author
|
owner: context.repo.owner,
|
||||||
const prAuthor = context.payload.pull_request.user.login;
|
repo: context.repo.repo,
|
||||||
|
issue_number: context.issue.number,
|
||||||
// Check if this is the author's first PR in this repository
|
body: '👋 Hi! Thank you for contributing to the vLLM project.\n\n' +
|
||||||
// Use GitHub's search API to find all PRs by this author
|
'💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.\n\n' +
|
||||||
const { data: searchResults } = await github.rest.search.issuesAndPullRequests({
|
'Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your `fastcheck` build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping `simon-mo` or `khluu` to add you in our Buildkite org.\n\n' +
|
||||||
q: `repo:${context.repo.owner}/${context.repo.repo} type:pr author:${prAuthor}`,
|
'Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n' +
|
||||||
per_page: 100
|
'To run CI, PR reviewers can either: Add `ready` label to the PR or enable auto-merge.\n\n' +
|
||||||
});
|
'🚀'
|
||||||
|
})
|
||||||
const authorPRCount = searchResults.total_count;
|
|
||||||
|
|
||||||
console.log(`Found ${authorPRCount} PRs by ${prAuthor}`);
|
|
||||||
|
|
||||||
// Only post comment if this is the first PR (only one PR by this author)
|
|
||||||
if (authorPRCount === 1) {
|
|
||||||
console.log(`Posting welcome comment for first-time contributor: ${prAuthor}`);
|
|
||||||
await github.rest.issues.createComment({
|
|
||||||
owner: context.repo.owner,
|
|
||||||
repo: context.repo.repo,
|
|
||||||
issue_number: context.issue.number,
|
|
||||||
body: '👋 Hi! Thank you for contributing to the vLLM project.\n\n' +
|
|
||||||
'💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.\n\n' +
|
|
||||||
'Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. \n\n' +
|
|
||||||
'You ask your reviewers to trigger select CI tests on top of `fastcheck` CI. \n\n' +
|
|
||||||
'Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n' +
|
|
||||||
'To run CI, PR reviewers can either: Add `ready` label to the PR or enable auto-merge.\n\n' +
|
|
||||||
'If you have any questions, please reach out to us on Slack at https://slack.vllm.ai.\n\n' +
|
|
||||||
'🚀'
|
|
||||||
});
|
|
||||||
} else {
|
|
||||||
console.log(`Skipping comment for ${prAuthor} - not their first PR (${authorPRCount} PRs found)`);
|
|
||||||
}
|
|
||||||
} catch (error) {
|
|
||||||
console.error('Error checking PR history or posting comment:', error);
|
|
||||||
// Don't fail the workflow, just log the error
|
|
||||||
}
|
|
||||||
env:
|
env:
|
||||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||||
|
|||||||
1
.github/workflows/scripts/build.sh
vendored
1
.github/workflows/scripts/build.sh
vendored
@ -15,6 +15,7 @@ $python_executable -m pip install -r requirements/build.txt -r requirements/cuda
|
|||||||
export MAX_JOBS=1
|
export MAX_JOBS=1
|
||||||
# Make sure release wheels are built for the following architectures
|
# Make sure release wheels are built for the following architectures
|
||||||
export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
|
export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
|
||||||
|
export VLLM_FA_CMAKE_GPU_ARCHES="80-real;90-real"
|
||||||
|
|
||||||
bash tools/check_repo.sh
|
bash tools/check_repo.sh
|
||||||
|
|
||||||
|
|||||||
16
.gitignore
vendored
16
.gitignore
vendored
@ -3,9 +3,7 @@
|
|||||||
|
|
||||||
# vllm-flash-attn built from source
|
# vllm-flash-attn built from source
|
||||||
vllm/vllm_flash_attn/*
|
vllm/vllm_flash_attn/*
|
||||||
|
!vllm/vllm_flash_attn/fa_utils.py
|
||||||
# triton jit
|
|
||||||
.triton
|
|
||||||
|
|
||||||
# Byte-compiled / optimized / DLL files
|
# Byte-compiled / optimized / DLL files
|
||||||
__pycache__/
|
__pycache__/
|
||||||
@ -80,6 +78,10 @@ instance/
|
|||||||
# Scrapy stuff:
|
# Scrapy stuff:
|
||||||
.scrapy
|
.scrapy
|
||||||
|
|
||||||
|
# Sphinx documentation
|
||||||
|
docs/_build/
|
||||||
|
docs/source/getting_started/examples/
|
||||||
|
|
||||||
# PyBuilder
|
# PyBuilder
|
||||||
.pybuilder/
|
.pybuilder/
|
||||||
target/
|
target/
|
||||||
@ -149,9 +151,6 @@ venv.bak/
|
|||||||
|
|
||||||
# mkdocs documentation
|
# mkdocs documentation
|
||||||
/site
|
/site
|
||||||
docs/argparse
|
|
||||||
docs/examples/*
|
|
||||||
!docs/examples/README.md
|
|
||||||
|
|
||||||
# mypy
|
# mypy
|
||||||
.mypy_cache/
|
.mypy_cache/
|
||||||
@ -205,8 +204,5 @@ benchmarks/**/*.json
|
|||||||
actionlint
|
actionlint
|
||||||
shellcheck*/
|
shellcheck*/
|
||||||
|
|
||||||
# Ignore moe/marlin_moe gen code
|
# Ingore moe/marlin_moe gen code
|
||||||
csrc/moe/marlin_moe_wna16/kernel_*
|
csrc/moe/marlin_moe_wna16/kernel_*
|
||||||
|
|
||||||
# Ignore ep_kernels_workspace folder
|
|
||||||
ep_kernels_workspace/
|
|
||||||
@ -1,13 +0,0 @@
|
|||||||
MD007:
|
|
||||||
indent: 4
|
|
||||||
MD013: false
|
|
||||||
MD024:
|
|
||||||
siblings_only: true
|
|
||||||
MD033: false
|
|
||||||
MD042: false
|
|
||||||
MD045: false
|
|
||||||
MD046: false
|
|
||||||
MD051: false
|
|
||||||
MD052: false
|
|
||||||
MD053: false
|
|
||||||
MD059: false
|
|
||||||
@ -11,59 +11,51 @@ repos:
|
|||||||
hooks:
|
hooks:
|
||||||
- id: yapf
|
- id: yapf
|
||||||
args: [--in-place, --verbose]
|
args: [--in-place, --verbose]
|
||||||
# Keep the same list from yapfignore here to avoid yapf failing without any inputs
|
|
||||||
exclude: '(.buildkite|benchmarks|build|examples)/.*'
|
|
||||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||||
rev: v0.11.7
|
rev: v0.9.3
|
||||||
hooks:
|
hooks:
|
||||||
- id: ruff
|
- id: ruff
|
||||||
args: [--output-format, github, --fix]
|
args: [--output-format, github, --fix]
|
||||||
- id: ruff-format
|
- repo: https://github.com/codespell-project/codespell
|
||||||
files: ^(.buildkite|benchmarks|examples)/.*
|
rev: v2.4.0
|
||||||
- repo: https://github.com/crate-ci/typos
|
|
||||||
rev: v1.35.5
|
|
||||||
hooks:
|
hooks:
|
||||||
- id: typos
|
- id: codespell
|
||||||
|
additional_dependencies: ['tomli']
|
||||||
|
args: ['--toml', 'pyproject.toml']
|
||||||
- repo: https://github.com/PyCQA/isort
|
- repo: https://github.com/PyCQA/isort
|
||||||
rev: 6.0.1
|
rev: 0a0b7a830386ba6a31c2ec8316849ae4d1b8240d # 6.0.0
|
||||||
hooks:
|
hooks:
|
||||||
- id: isort
|
- id: isort
|
||||||
- repo: https://github.com/pre-commit/mirrors-clang-format
|
- repo: https://github.com/pre-commit/mirrors-clang-format
|
||||||
rev: v20.1.3
|
rev: v19.1.7
|
||||||
hooks:
|
hooks:
|
||||||
- id: clang-format
|
- id: clang-format
|
||||||
exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
|
exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
|
||||||
types_or: [c++, cuda]
|
types_or: [c++, cuda]
|
||||||
args: [--style=file, --verbose]
|
args: [--style=file, --verbose]
|
||||||
- repo: https://github.com/igorshubovych/markdownlint-cli
|
- repo: https://github.com/jackdewinter/pymarkdown
|
||||||
rev: v0.45.0
|
rev: v0.9.27
|
||||||
hooks:
|
hooks:
|
||||||
- id: markdownlint
|
- id: pymarkdown
|
||||||
exclude: '.*\.inc\.md'
|
args: [fix]
|
||||||
stages: [manual] # Only run in CI
|
|
||||||
- repo: https://github.com/rhysd/actionlint
|
- repo: https://github.com/rhysd/actionlint
|
||||||
rev: v1.7.7
|
rev: v1.7.7
|
||||||
hooks:
|
hooks:
|
||||||
- id: actionlint
|
- id: actionlint
|
||||||
- repo: https://github.com/astral-sh/uv-pre-commit
|
- repo: https://github.com/astral-sh/uv-pre-commit
|
||||||
rev: 0.6.17
|
rev: 0.6.2
|
||||||
hooks:
|
hooks:
|
||||||
- id: pip-compile
|
- id: pip-compile
|
||||||
args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128]
|
args: [requirements/test.in, -o, requirements/test.txt]
|
||||||
files: ^requirements/test\.(in|txt)$
|
files: ^requirements/test\.(in|txt)$
|
||||||
- repo: local
|
- repo: local
|
||||||
hooks:
|
hooks:
|
||||||
- id: format-torch-nightly-test
|
|
||||||
name: reformat nightly_torch_test.txt to be in sync with test.in
|
|
||||||
language: python
|
|
||||||
entry: python tools/generate_nightly_torch_test.py
|
|
||||||
files: ^requirements/test\.(in|txt)$
|
|
||||||
- id: mypy-local
|
- id: mypy-local
|
||||||
name: Run mypy for local Python installation
|
name: Run mypy for local Python installation
|
||||||
entry: tools/mypy.sh 0 "local"
|
entry: tools/mypy.sh 0 "local"
|
||||||
language: python
|
language: python
|
||||||
types: [python]
|
types: [python]
|
||||||
additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
|
additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
|
||||||
stages: [pre-commit] # Don't run in CI
|
stages: [pre-commit] # Don't run in CI
|
||||||
- id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
|
- id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
|
||||||
name: Run mypy for Python 3.9
|
name: Run mypy for Python 3.9
|
||||||
@ -109,8 +101,8 @@ repos:
|
|||||||
args:
|
args:
|
||||||
- -c
|
- -c
|
||||||
- |
|
- |
|
||||||
if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" "$(git rev-parse --git-path COMMIT_EDITMSG)"; then
|
if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" .git/COMMIT_EDITMSG; then
|
||||||
printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> "$(git rev-parse --git-path COMMIT_EDITMSG)"
|
printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> .git/COMMIT_EDITMSG
|
||||||
fi
|
fi
|
||||||
language: system
|
language: system
|
||||||
verbose: true
|
verbose: true
|
||||||
@ -120,11 +112,6 @@ repos:
|
|||||||
entry: python tools/check_spdx_header.py
|
entry: python tools/check_spdx_header.py
|
||||||
language: python
|
language: python
|
||||||
types: [python]
|
types: [python]
|
||||||
- id: check-root-lazy-imports
|
|
||||||
name: Check root lazy imports
|
|
||||||
entry: python tools/check_init_lazy_imports.py
|
|
||||||
language: python
|
|
||||||
types: [python]
|
|
||||||
- id: check-filenames
|
- id: check-filenames
|
||||||
name: Check for spaces in all filenames
|
name: Check for spaces in all filenames
|
||||||
entry: bash
|
entry: bash
|
||||||
@ -138,39 +125,12 @@ repos:
|
|||||||
name: Update Dockerfile dependency graph
|
name: Update Dockerfile dependency graph
|
||||||
entry: tools/update-dockerfile-graph.sh
|
entry: tools/update-dockerfile-graph.sh
|
||||||
language: script
|
language: script
|
||||||
- id: enforce-import-regex-instead-of-re
|
files: ^docker/Dockerfile$
|
||||||
name: Enforce import regex as re
|
|
||||||
entry: python tools/enforce_regex_import.py
|
|
||||||
language: python
|
|
||||||
types: [python]
|
|
||||||
pass_filenames: false
|
pass_filenames: false
|
||||||
additional_dependencies: [regex]
|
|
||||||
# forbid directly import triton
|
|
||||||
- id: forbid-direct-triton-import
|
|
||||||
name: "Forbid direct 'import triton'"
|
|
||||||
entry: python tools/check_triton_import.py
|
|
||||||
language: python
|
|
||||||
types: [python]
|
|
||||||
pass_filenames: false
|
|
||||||
additional_dependencies: [regex]
|
|
||||||
- id: check-pickle-imports
|
|
||||||
name: Prevent new pickle/cloudpickle imports
|
|
||||||
entry: python tools/check_pickle_imports.py
|
|
||||||
language: python
|
|
||||||
types: [python]
|
|
||||||
pass_filenames: false
|
|
||||||
additional_dependencies: [pathspec, regex]
|
|
||||||
- id: validate-config
|
|
||||||
name: Validate configuration has default values and that each field has a docstring
|
|
||||||
entry: python tools/validate_config.py
|
|
||||||
language: python
|
|
||||||
types: [python]
|
|
||||||
pass_filenames: true
|
|
||||||
files: vllm/config.py|tests/test_config.py|vllm/entrypoints/openai/cli_args.py
|
|
||||||
# Keep `suggestion` last
|
# Keep `suggestion` last
|
||||||
- id: suggestion
|
- id: suggestion
|
||||||
name: Suggestion
|
name: Suggestion
|
||||||
entry: bash -c 'echo "To bypass all the pre-commit hooks, add --no-verify to git commit. To skip a specific hook, prefix the commit command with SKIP=<hook-id>."'
|
entry: bash -c 'echo "To bypass pre-commit hooks, add --no-verify to git commit."'
|
||||||
language: system
|
language: system
|
||||||
verbose: true
|
verbose: true
|
||||||
pass_filenames: false
|
pass_filenames: false
|
||||||
|
|||||||
@ -7,12 +7,13 @@ build:
|
|||||||
os: ubuntu-22.04
|
os: ubuntu-22.04
|
||||||
tools:
|
tools:
|
||||||
python: "3.12"
|
python: "3.12"
|
||||||
jobs:
|
|
||||||
post_checkout:
|
|
||||||
- git fetch --unshallow || true
|
|
||||||
|
|
||||||
mkdocs:
|
sphinx:
|
||||||
configuration: mkdocs.yaml
|
configuration: docs/source/conf.py
|
||||||
|
fail_on_warning: true
|
||||||
|
|
||||||
|
# If using Sphinx, optionally build your docs in additional formats such as PDF
|
||||||
|
formats: []
|
||||||
|
|
||||||
# Optionally declare the Python requirements required to build your docs
|
# Optionally declare the Python requirements required to build your docs
|
||||||
python:
|
python:
|
||||||
|
|||||||
346
CMakeLists.txt
346
CMakeLists.txt
@ -15,6 +15,7 @@ project(vllm_extensions LANGUAGES CXX)
|
|||||||
|
|
||||||
# CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
|
# CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
|
||||||
set(VLLM_TARGET_DEVICE "cuda" CACHE STRING "Target device backend for vLLM")
|
set(VLLM_TARGET_DEVICE "cuda" CACHE STRING "Target device backend for vLLM")
|
||||||
|
|
||||||
message(STATUS "Build type: ${CMAKE_BUILD_TYPE}")
|
message(STATUS "Build type: ${CMAKE_BUILD_TYPE}")
|
||||||
message(STATUS "Target device: ${VLLM_TARGET_DEVICE}")
|
message(STATUS "Target device: ${VLLM_TARGET_DEVICE}")
|
||||||
|
|
||||||
@ -23,14 +24,14 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
|
|||||||
# Suppress potential warnings about unused manually-specified variables
|
# Suppress potential warnings about unused manually-specified variables
|
||||||
set(ignoreMe "${VLLM_PYTHON_PATH}")
|
set(ignoreMe "${VLLM_PYTHON_PATH}")
|
||||||
|
|
||||||
# Prevent installation of dependencies (cutlass) by default.
|
|
||||||
install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
|
|
||||||
|
|
||||||
#
|
#
|
||||||
# Supported python versions. These versions will be searched in order, the
|
# Supported python versions. These versions will be searched in order, the
|
||||||
# first match will be selected. These should be kept in sync with setup.py.
|
# first match will be selected. These should be kept in sync with setup.py.
|
||||||
#
|
#
|
||||||
set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12" "3.13")
|
set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
|
||||||
|
|
||||||
|
# Supported NVIDIA architectures.
|
||||||
|
set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
|
||||||
|
|
||||||
# Supported AMD GPU architectures.
|
# Supported AMD GPU architectures.
|
||||||
set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
|
set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
|
||||||
@ -45,8 +46,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
|
|||||||
# requirements.txt files and should be kept consistent. The ROCm torch
|
# requirements.txt files and should be kept consistent. The ROCm torch
|
||||||
# versions are derived from docker/Dockerfile.rocm
|
# versions are derived from docker/Dockerfile.rocm
|
||||||
#
|
#
|
||||||
set(TORCH_SUPPORTED_VERSION_CUDA "2.8.0")
|
set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
|
||||||
set(TORCH_SUPPORTED_VERSION_ROCM "2.8.0")
|
set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
|
||||||
|
|
||||||
#
|
#
|
||||||
# Try to find python package with an executable that exactly matches
|
# Try to find python package with an executable that exactly matches
|
||||||
@ -79,15 +80,6 @@ endif()
|
|||||||
#
|
#
|
||||||
find_package(Torch REQUIRED)
|
find_package(Torch REQUIRED)
|
||||||
|
|
||||||
# Supported NVIDIA architectures.
|
|
||||||
# This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
|
|
||||||
if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
|
|
||||||
CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
|
|
||||||
set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
|
|
||||||
else()
|
|
||||||
set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
#
|
#
|
||||||
# Forward the non-CUDA device extensions to external CMake scripts.
|
# Forward the non-CUDA device extensions to external CMake scripts.
|
||||||
#
|
#
|
||||||
@ -171,6 +163,7 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
|
list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
|
|
||||||
#
|
#
|
||||||
# Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process.
|
# Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process.
|
||||||
# setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache.
|
# setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache.
|
||||||
@ -181,6 +174,9 @@ include(FetchContent)
|
|||||||
file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists
|
file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists
|
||||||
message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}")
|
message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}")
|
||||||
|
|
||||||
|
#
|
||||||
|
# Set rocm version dev int.
|
||||||
|
#
|
||||||
if(VLLM_GPU_LANG STREQUAL "HIP")
|
if(VLLM_GPU_LANG STREQUAL "HIP")
|
||||||
#
|
#
|
||||||
# Overriding the default -O set up by cmake, adding ggdb3 for the most verbose devug info
|
# Overriding the default -O set up by cmake, adding ggdb3 for the most verbose devug info
|
||||||
@ -188,6 +184,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
|
|||||||
set(CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG "${CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG} -O0 -ggdb3")
|
set(CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG "${CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG} -O0 -ggdb3")
|
||||||
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -ggdb3")
|
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -ggdb3")
|
||||||
|
|
||||||
|
|
||||||
#
|
#
|
||||||
# Certain HIP functions are marked as [[nodiscard]], yet vllm ignores the result which generates
|
# Certain HIP functions are marked as [[nodiscard]], yet vllm ignores the result which generates
|
||||||
# a lot of warnings that always mask real issues. Suppressing until this is properly addressed.
|
# a lot of warnings that always mask real issues. Suppressing until this is properly addressed.
|
||||||
@ -230,33 +227,31 @@ endif()
|
|||||||
#
|
#
|
||||||
|
|
||||||
set(VLLM_EXT_SRC
|
set(VLLM_EXT_SRC
|
||||||
"csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
|
|
||||||
"csrc/cache_kernels.cu"
|
"csrc/cache_kernels.cu"
|
||||||
"csrc/attention/paged_attention_v1.cu"
|
"csrc/attention/paged_attention_v1.cu"
|
||||||
"csrc/attention/paged_attention_v2.cu"
|
"csrc/attention/paged_attention_v2.cu"
|
||||||
"csrc/attention/merge_attn_states.cu"
|
"csrc/attention/merge_attn_states.cu"
|
||||||
"csrc/attention/vertical_slash_index.cu"
|
|
||||||
"csrc/pos_encoding_kernels.cu"
|
"csrc/pos_encoding_kernels.cu"
|
||||||
"csrc/activation_kernels.cu"
|
"csrc/activation_kernels.cu"
|
||||||
"csrc/layernorm_kernels.cu"
|
"csrc/layernorm_kernels.cu"
|
||||||
"csrc/layernorm_quant_kernels.cu"
|
"csrc/layernorm_quant_kernels.cu"
|
||||||
"csrc/sampler.cu"
|
|
||||||
"csrc/cuda_view.cu"
|
"csrc/cuda_view.cu"
|
||||||
"csrc/quantization/gptq/q_gemm.cu"
|
"csrc/quantization/gptq/q_gemm.cu"
|
||||||
"csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
|
"csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
|
||||||
"csrc/quantization/fp8/common.cu"
|
"csrc/quantization/fp8/common.cu"
|
||||||
"csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
|
"csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
|
||||||
"csrc/quantization/gguf/gguf_kernel.cu"
|
"csrc/quantization/gguf/gguf_kernel.cu"
|
||||||
"csrc/quantization/activation_kernels.cu"
|
|
||||||
"csrc/cuda_utils_kernels.cu"
|
"csrc/cuda_utils_kernels.cu"
|
||||||
|
"csrc/prepare_inputs/advance_step.cu"
|
||||||
"csrc/custom_all_reduce.cu"
|
"csrc/custom_all_reduce.cu"
|
||||||
"csrc/torch_bindings.cpp")
|
"csrc/torch_bindings.cpp")
|
||||||
|
|
||||||
if(VLLM_GPU_LANG STREQUAL "CUDA")
|
if(VLLM_GPU_LANG STREQUAL "CUDA")
|
||||||
SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
|
SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
|
||||||
|
|
||||||
# Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
|
# Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
|
||||||
set(CUTLASS_REVISION "v4.0.0" CACHE STRING "CUTLASS revision to use")
|
# Please keep this in sync with FetchContent_Declare line below.
|
||||||
|
set(CUTLASS_REVISION "v3.8.0" CACHE STRING "CUTLASS revision to use")
|
||||||
|
|
||||||
# Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
|
# Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
|
||||||
if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
|
if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
|
||||||
@ -274,7 +269,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
cutlass
|
cutlass
|
||||||
GIT_REPOSITORY https://github.com/nvidia/cutlass.git
|
GIT_REPOSITORY https://github.com/nvidia/cutlass.git
|
||||||
# Please keep this in sync with CUTLASS_REVISION line above.
|
# Please keep this in sync with CUTLASS_REVISION line above.
|
||||||
GIT_TAG ${CUTLASS_REVISION}
|
GIT_TAG v3.8.0
|
||||||
GIT_PROGRESS TRUE
|
GIT_PROGRESS TRUE
|
||||||
|
|
||||||
# Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
|
# Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
|
||||||
@ -286,16 +281,16 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
FetchContent_MakeAvailable(cutlass)
|
FetchContent_MakeAvailable(cutlass)
|
||||||
|
|
||||||
list(APPEND VLLM_EXT_SRC
|
list(APPEND VLLM_EXT_SRC
|
||||||
|
"csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
|
||||||
|
"csrc/mamba/causal_conv1d/causal_conv1d.cu"
|
||||||
|
"csrc/quantization/aqlm/gemm_kernels.cu"
|
||||||
"csrc/quantization/awq/gemm_kernels.cu"
|
"csrc/quantization/awq/gemm_kernels.cu"
|
||||||
"csrc/permute_cols.cu"
|
"csrc/permute_cols.cu"
|
||||||
"csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
|
"csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
|
||||||
"csrc/quantization/fp4/nvfp4_quant_entry.cu"
|
"csrc/quantization/fp4/nvfp4_quant_entry.cu"
|
||||||
"csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
|
"csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
|
||||||
"csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
|
|
||||||
"csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
|
"csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
|
||||||
"csrc/cutlass_extensions/common.cpp"
|
"csrc/cutlass_extensions/common.cpp")
|
||||||
"csrc/attention/mla/cutlass_mla_entry.cu"
|
|
||||||
"csrc/quantization/fp8/per_token_group_quant.cu")
|
|
||||||
|
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${VLLM_EXT_SRC}"
|
SRCS "${VLLM_EXT_SRC}"
|
||||||
@ -304,72 +299,20 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
# Only build Marlin kernels if we are building for at least some compatible archs.
|
# Only build Marlin kernels if we are building for at least some compatible archs.
|
||||||
# Keep building Marlin for 9.0 as there are some group sizes and shapes that
|
# Keep building Marlin for 9.0 as there are some group sizes and shapes that
|
||||||
# are not supported by Machete yet.
|
# are not supported by Machete yet.
|
||||||
# 9.0 for latest bf16 atomicAdd PTX
|
cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
|
||||||
cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
|
|
||||||
if (MARLIN_ARCHS)
|
if (MARLIN_ARCHS)
|
||||||
|
|
||||||
#
|
|
||||||
# For the Marlin kernels we automatically generate sources for various
|
|
||||||
# preselected input type pairs and schedules.
|
|
||||||
# Generate sources:
|
|
||||||
set(MARLIN_GEN_SCRIPT
|
|
||||||
${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/gptq_marlin/generate_kernels.py)
|
|
||||||
file(MD5 ${MARLIN_GEN_SCRIPT} MARLIN_GEN_SCRIPT_HASH)
|
|
||||||
|
|
||||||
message(STATUS "Marlin generation script hash: ${MARLIN_GEN_SCRIPT_HASH}")
|
|
||||||
message(STATUS "Last run Marlin generate script hash: $CACHE{MARLIN_GEN_SCRIPT_HASH}")
|
|
||||||
|
|
||||||
if (NOT DEFINED CACHE{MARLIN_GEN_SCRIPT_HASH}
|
|
||||||
OR NOT $CACHE{MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MARLIN_GEN_SCRIPT_HASH})
|
|
||||||
execute_process(
|
|
||||||
COMMAND ${CMAKE_COMMAND} -E env
|
|
||||||
PYTHONPATH=$PYTHONPATH
|
|
||||||
${Python_EXECUTABLE} ${MARLIN_GEN_SCRIPT}
|
|
||||||
RESULT_VARIABLE marlin_generation_result
|
|
||||||
OUTPUT_VARIABLE marlin_generation_result
|
|
||||||
OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
|
|
||||||
ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
|
|
||||||
)
|
|
||||||
|
|
||||||
if (NOT marlin_generation_result EQUAL 0)
|
|
||||||
message(FATAL_ERROR "Marlin generation failed."
|
|
||||||
" Result: \"${marlin_generation_result}\""
|
|
||||||
"\nCheck the log for details: "
|
|
||||||
"${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log")
|
|
||||||
else()
|
|
||||||
set(MARLIN_GEN_SCRIPT_HASH ${MARLIN_GEN_SCRIPT_HASH}
|
|
||||||
CACHE STRING "Last run Marlin generate script hash" FORCE)
|
|
||||||
message(STATUS "Marlin generation completed successfully.")
|
|
||||||
endif()
|
|
||||||
else()
|
|
||||||
message(STATUS "Marlin generation script has not changed, skipping generation.")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/kernel_*.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
|
|
||||||
CUDA_ARCHS "${MARLIN_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
|
|
||||||
set_source_files_properties(${MARLIN_TEMPLATE_KERNEL_SRC}
|
|
||||||
PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
|
|
||||||
|
|
||||||
set(MARLIN_SRCS
|
set(MARLIN_SRCS
|
||||||
|
"csrc/quantization/fp8/fp8_marlin.cu"
|
||||||
|
"csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
|
||||||
"csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
|
"csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
|
||||||
|
"csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
|
||||||
"csrc/quantization/gptq_marlin/gptq_marlin.cu"
|
"csrc/quantization/gptq_marlin/gptq_marlin.cu"
|
||||||
"csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
|
"csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
|
||||||
"csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
|
"csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${MARLIN_SRCS}"
|
SRCS "${MARLIN_SRCS}"
|
||||||
CUDA_ARCHS "${MARLIN_ARCHS}")
|
CUDA_ARCHS "${MARLIN_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
|
|
||||||
set_source_files_properties("csrc/quantization/gptq_marlin/gptq_marlin.cu"
|
|
||||||
PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
|
|
||||||
endif()
|
|
||||||
list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")
|
list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")
|
||||||
|
|
||||||
message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
|
message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
|
||||||
else()
|
else()
|
||||||
message(STATUS "Not building Marlin kernels as no compatible archs found"
|
message(STATUS "Not building Marlin kernels as no compatible archs found"
|
||||||
@ -397,7 +340,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
# The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
|
# The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
|
||||||
# CUDA 12.0 or later
|
# CUDA 12.0 or later
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
|
||||||
set(SRCS
|
set(SRCS
|
||||||
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
|
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
|
||||||
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
|
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
|
||||||
@ -413,7 +356,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
|
list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
|
||||||
message(STATUS "Building scaled_mm_c3x_sm90 for archs: ${SCALED_MM_ARCHS}")
|
message(STATUS "Building scaled_mm_c3x_sm90 for archs: ${SCALED_MM_ARCHS}")
|
||||||
else()
|
else()
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
|
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
|
||||||
message(STATUS "Not building scaled_mm_c3x_sm90 as CUDA Compiler version is "
|
message(STATUS "Not building scaled_mm_c3x_sm90 as CUDA Compiler version is "
|
||||||
"not >= 12.0, we recommend upgrading to CUDA 12.0 or "
|
"not >= 12.0, we recommend upgrading to CUDA 12.0 or "
|
||||||
"later if you intend on running FP8 quantized models on "
|
"later if you intend on running FP8 quantized models on "
|
||||||
@ -424,45 +367,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
endif()
|
endif()
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
|
# The cutlass_scaled_mm kernels for Blackwell (c3x, i.e. CUTLASS 3.x) require
|
||||||
# The cutlass_scaled_mm kernels for Geforce Blackwell SM120 (c3x, i.e. CUTLASS 3.x) require
|
|
||||||
# CUDA 12.8 or later
|
# CUDA 12.8 or later
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;12.0a" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
|
||||||
set(SRCS
|
|
||||||
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
|
|
||||||
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
|
|
||||||
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
|
|
||||||
)
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${SCALED_MM_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_SM120=1")
|
|
||||||
# Let scaled_mm_c2x know it doesn't need to build these arches
|
|
||||||
list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
|
|
||||||
message(STATUS "Building scaled_mm_c3x_sm120 for archs: ${SCALED_MM_ARCHS}")
|
|
||||||
else()
|
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
message(STATUS "Not building scaled_mm_c3x_sm120 as CUDA Compiler version is "
|
|
||||||
"not >= 12.8, we recommend upgrading to CUDA 12.8 or "
|
|
||||||
"later if you intend on running FP8 quantized models on "
|
|
||||||
"Blackwell.")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building scaled_mm_c3x_120 as no compatible archs found "
|
|
||||||
"in CUDA target architectures")
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
|
|
||||||
# The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x)
|
|
||||||
# require CUDA 12.8 or later
|
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
set(SRCS
|
set(SRCS
|
||||||
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
|
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
|
||||||
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
|
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
|
||||||
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8.cu"
|
|
||||||
)
|
)
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${SRCS}"
|
SRCS "${SRCS}"
|
||||||
@ -473,7 +384,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
|
list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
|
||||||
message(STATUS "Building scaled_mm_c3x_sm100 for archs: ${SCALED_MM_ARCHS}")
|
message(STATUS "Building scaled_mm_c3x_sm100 for archs: ${SCALED_MM_ARCHS}")
|
||||||
else()
|
else()
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
|
||||||
message(STATUS "Not building scaled_mm_c3x_sm100 as CUDA Compiler version is "
|
message(STATUS "Not building scaled_mm_c3x_sm100 as CUDA Compiler version is "
|
||||||
"not >= 12.8, we recommend upgrading to CUDA 12.8 or "
|
"not >= 12.8, we recommend upgrading to CUDA 12.8 or "
|
||||||
"later if you intend on running FP8 quantized models on "
|
"later if you intend on running FP8 quantized models on "
|
||||||
@ -487,9 +398,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
#
|
#
|
||||||
# For the cutlass_scaled_mm kernels we want to build the c2x (CUTLASS 2.x)
|
# For the cutlass_scaled_mm kernels we want to build the c2x (CUTLASS 2.x)
|
||||||
# kernels for the remaining archs that are not already built for 3x.
|
# kernels for the remaining archs that are not already built for 3x.
|
||||||
# (Build 8.9 for FP8)
|
|
||||||
cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
|
cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
|
||||||
"7.5;8.0;8.7;8.9+PTX" "${CUDA_ARCHS}")
|
"7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
|
||||||
# subtract out the archs that are already built for 3x
|
# subtract out the archs that are already built for 3x
|
||||||
list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
|
list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
|
||||||
if (SCALED_MM_2X_ARCHS)
|
if (SCALED_MM_2X_ARCHS)
|
||||||
@ -516,7 +426,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
# The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
|
# The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
|
||||||
# require CUDA 12.2 or later (and only work on Hopper).
|
# require CUDA 12.2 or later (and only work on Hopper).
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
|
||||||
set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
|
set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${SRCS}"
|
SRCS "${SRCS}"
|
||||||
@ -525,7 +435,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
|
list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
|
||||||
message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_ARCHS}")
|
message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_ARCHS}")
|
||||||
else()
|
else()
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
|
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
|
||||||
message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
|
message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
|
||||||
"not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
|
"not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
|
||||||
"if you intend on running FP8 sparse quantized models on Hopper.")
|
"if you intend on running FP8 sparse quantized models on Hopper.")
|
||||||
@ -535,41 +445,17 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
endif()
|
endif()
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
# The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require
|
|
||||||
# CUDA 12.8 or later
|
|
||||||
cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
|
|
||||||
set(SRCS
|
|
||||||
"csrc/quantization/fp4/nvfp4_quant_kernels.cu"
|
|
||||||
"csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
|
|
||||||
"csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${FP4_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
|
|
||||||
message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building NVFP4 as no compatible archs were found.")
|
|
||||||
# clear FP4_ARCHS
|
|
||||||
set(FP4_ARCHS)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
# FP4 Archs and flags
|
# FP4 Archs and flags
|
||||||
cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
|
||||||
set(SRCS
|
set(SRCS
|
||||||
"csrc/quantization/fp4/nvfp4_quant_kernels.cu"
|
"csrc/quantization/fp4/nvfp4_quant_kernels.cu"
|
||||||
"csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
|
"csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu")
|
||||||
"csrc/quantization/fp4/nvfp4_experts_quant.cu"
|
|
||||||
"csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
|
|
||||||
"csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${SRCS}"
|
SRCS "${SRCS}"
|
||||||
CUDA_ARCHS "${FP4_ARCHS}")
|
CUDA_ARCHS "${FP4_ARCHS}")
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
|
list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4=1")
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
|
|
||||||
message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
|
message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
|
||||||
else()
|
else()
|
||||||
message(STATUS "Not building NVFP4 as no compatible archs were found.")
|
message(STATUS "Not building NVFP4 as no compatible archs were found.")
|
||||||
@ -577,35 +463,16 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
set(FP4_ARCHS)
|
set(FP4_ARCHS)
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
# CUTLASS MLA Archs and flags
|
#
|
||||||
cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
|
|
||||||
set(SRCS
|
|
||||||
"csrc/attention/mla/cutlass_mla_kernels.cu"
|
|
||||||
"csrc/attention/mla/sm100_cutlass_mla_kernel.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${MLA_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MLA=1")
|
|
||||||
# Add MLA-specific include directories only to MLA source files
|
|
||||||
set_source_files_properties(${SRCS}
|
|
||||||
PROPERTIES INCLUDE_DIRECTORIES "${CUTLASS_DIR}/examples/77_blackwell_fmha;${CUTLASS_DIR}/examples/common")
|
|
||||||
message(STATUS "Building CUTLASS MLA for archs: ${MLA_ARCHS}")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building CUTLASS MLA as no compatible archs were found.")
|
|
||||||
# clear MLA_ARCHS
|
|
||||||
set(MLA_ARCHS)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
# CUTLASS MoE kernels
|
# CUTLASS MoE kernels
|
||||||
|
|
||||||
# The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and ONLY works
|
# The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
|
||||||
# on Hopper). get_cutlass_(pplx_)moe_mm_data should only be compiled
|
# on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
|
||||||
# if it's possible to compile MoE kernels that use its output.
|
# to compile MoE kernels that use its output.
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
|
||||||
set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu")
|
set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
|
||||||
|
"csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${SRCS}"
|
SRCS "${SRCS}"
|
||||||
CUDA_ARCHS "${SCALED_MM_ARCHS}")
|
CUDA_ARCHS "${SCALED_MM_ARCHS}")
|
||||||
@ -619,66 +486,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
"if you intend on running FP8 quantized MoE models on Hopper.")
|
"if you intend on running FP8 quantized MoE models on Hopper.")
|
||||||
else()
|
else()
|
||||||
message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
|
message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
|
||||||
"in CUDA target architectures.")
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${SCALED_MM_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
|
|
||||||
message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
|
|
||||||
else()
|
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
|
|
||||||
"not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
|
|
||||||
"if you intend on running FP8 quantized MoE models on Blackwell.")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
|
|
||||||
"in CUDA target architectures.")
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
# moe_data.cu is used by all CUTLASS MoE kernels.
|
|
||||||
cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
|
|
||||||
set(SRCS "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${CUTLASS_MOE_DATA_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
message(STATUS "Building moe_data for archs: ${CUTLASS_MOE_DATA_ARCHS}")
|
|
||||||
else()
|
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
|
|
||||||
message(STATUS "Not building moe_data as CUDA Compiler version is "
|
|
||||||
"not >= 12.3, we recommend upgrading to CUDA 12.3 or later "
|
|
||||||
"if you intend on running FP8 quantized MoE models on Hopper or Blackwell.")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building moe_data as no compatible archs found "
|
|
||||||
"in CUDA target architectures.")
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${SCALED_MM_ARCHS}")
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
|
|
||||||
message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
|
|
||||||
else()
|
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
|
|
||||||
message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
|
|
||||||
"not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
|
|
||||||
"if you intend on running FP8 quantized MoE models on Blackwell.")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
|
|
||||||
"in CUDA target architectures")
|
"in CUDA target architectures")
|
||||||
endif()
|
endif()
|
||||||
endif()
|
endif()
|
||||||
@ -689,7 +496,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
# The machete kernels only work on hopper and require CUDA 12.0 or later.
|
# The machete kernels only work on hopper and require CUDA 12.0 or later.
|
||||||
# Only build Machete kernels if we are building for something compatible with sm90a
|
# Only build Machete kernels if we are building for something compatible with sm90a
|
||||||
cuda_archs_loose_intersection(MACHETE_ARCHS "9.0a" "${CUDA_ARCHS}")
|
cuda_archs_loose_intersection(MACHETE_ARCHS "9.0a" "${CUDA_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND MACHETE_ARCHS)
|
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND MACHETE_ARCHS)
|
||||||
#
|
#
|
||||||
# For the Machete kernels we automatically generate sources for various
|
# For the Machete kernels we automatically generate sources for various
|
||||||
# preselected input type pairs and schedules.
|
# preselected input type pairs and schedules.
|
||||||
@ -741,7 +548,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
|
|
||||||
message(STATUS "Building Machete kernels for archs: ${MACHETE_ARCHS}")
|
message(STATUS "Building Machete kernels for archs: ${MACHETE_ARCHS}")
|
||||||
else()
|
else()
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0
|
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0
|
||||||
AND MACHETE_ARCHS)
|
AND MACHETE_ARCHS)
|
||||||
message(STATUS "Not building Machete kernels as CUDA Compiler version is "
|
message(STATUS "Not building Machete kernels as CUDA Compiler version is "
|
||||||
"not >= 12.0, we recommend upgrading to CUDA 12.0 or "
|
"not >= 12.0, we recommend upgrading to CUDA 12.0 or "
|
||||||
@ -752,44 +559,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
"found in CUDA target architectures")
|
"found in CUDA target architectures")
|
||||||
endif()
|
endif()
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
# Only build W4A8 kernels if we are building for something compatible with sm90a
|
|
||||||
cuda_archs_loose_intersection(W4A8_ARCHS "9.0a" "${CUDA_ARCHS}")
|
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND W4A8_ARCHS)
|
|
||||||
set(SRCS
|
|
||||||
"csrc/quantization/cutlass_w4a8/w4a8_mm_entry.cu")
|
|
||||||
|
|
||||||
set_gencode_flags_for_srcs(
|
|
||||||
SRCS "${SRCS}"
|
|
||||||
CUDA_ARCHS "${W4A8_ARCHS}")
|
|
||||||
|
|
||||||
list(APPEND VLLM_EXT_SRC "${SRCS}")
|
|
||||||
|
|
||||||
message(STATUS "Building W4A8 kernels for archs: ${W4A8_ARCHS}")
|
|
||||||
else()
|
|
||||||
if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0
|
|
||||||
AND W4A8_ARCHS)
|
|
||||||
message(STATUS "Not building W4A8 kernels as CUDA Compiler version is "
|
|
||||||
"not >= 12.0, we recommend upgrading to CUDA 12.0 or "
|
|
||||||
"later if you intend on running w4a16 quantized models on "
|
|
||||||
"Hopper.")
|
|
||||||
else()
|
|
||||||
message(STATUS "Not building W4A8 kernels as no compatible archs "
|
|
||||||
"found in CUDA target architectures")
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
# if CUDA endif
|
# if CUDA endif
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
if (VLLM_GPU_LANG STREQUAL "HIP")
|
|
||||||
# Add QuickReduce kernels
|
|
||||||
list(APPEND VLLM_EXT_SRC
|
|
||||||
"csrc/custom_quickreduce.cu"
|
|
||||||
)
|
|
||||||
# if ROCM endif
|
|
||||||
endif()
|
|
||||||
|
|
||||||
message(STATUS "Enabling C extension.")
|
message(STATUS "Enabling C extension.")
|
||||||
define_gpu_extension_target(
|
define_gpu_extension_target(
|
||||||
_C
|
_C
|
||||||
@ -819,17 +591,7 @@ set(VLLM_MOE_EXT_SRC
|
|||||||
"csrc/moe/topk_softmax_kernels.cu")
|
"csrc/moe/topk_softmax_kernels.cu")
|
||||||
|
|
||||||
if(VLLM_GPU_LANG STREQUAL "CUDA")
|
if(VLLM_GPU_LANG STREQUAL "CUDA")
|
||||||
list(APPEND VLLM_MOE_EXT_SRC
|
list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
|
||||||
"csrc/moe/moe_wna16.cu"
|
|
||||||
"csrc/moe/grouped_topk_kernels.cu")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|
||||||
set(MOE_PERMUTE_SRC
|
|
||||||
"csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
|
|
||||||
"csrc/moe/moe_permute_unpermute_op.cu")
|
|
||||||
|
|
||||||
list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
|
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
@ -845,8 +607,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
CUDA_ARCHS "${CUDA_ARCHS}")
|
CUDA_ARCHS "${CUDA_ARCHS}")
|
||||||
|
|
||||||
list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
|
list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
|
||||||
# 9.0 for latest bf16 atomicAdd PTX
|
cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
|
||||||
cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
|
|
||||||
if (MARLIN_MOE_ARCHS)
|
if (MARLIN_MOE_ARCHS)
|
||||||
|
|
||||||
#
|
#
|
||||||
@ -864,7 +625,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
|
OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
|
||||||
execute_process(
|
execute_process(
|
||||||
COMMAND ${CMAKE_COMMAND} -E env
|
COMMAND ${CMAKE_COMMAND} -E env
|
||||||
PYTHONPATH=$PYTHONPATH
|
PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH
|
||||||
${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
|
${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
|
||||||
RESULT_VARIABLE moe_marlin_generation_result
|
RESULT_VARIABLE moe_marlin_generation_result
|
||||||
OUTPUT_VARIABLE moe_marlin_generation_output
|
OUTPUT_VARIABLE moe_marlin_generation_output
|
||||||
@ -890,10 +651,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
|
|||||||
set_gencode_flags_for_srcs(
|
set_gencode_flags_for_srcs(
|
||||||
SRCS "${MOE_WNAA16_MARLIN_SRC}"
|
SRCS "${MOE_WNAA16_MARLIN_SRC}"
|
||||||
CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
|
CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
|
||||||
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
|
|
||||||
set_source_files_properties(${MOE_WNAA16_MARLIN_SRC}
|
|
||||||
PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
|
list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
|
||||||
|
|
||||||
@ -912,8 +669,6 @@ define_gpu_extension_target(
|
|||||||
SOURCES ${VLLM_MOE_EXT_SRC}
|
SOURCES ${VLLM_MOE_EXT_SRC}
|
||||||
COMPILE_FLAGS ${VLLM_GPU_FLAGS}
|
COMPILE_FLAGS ${VLLM_GPU_FLAGS}
|
||||||
ARCHITECTURES ${VLLM_GPU_ARCHES}
|
ARCHITECTURES ${VLLM_GPU_ARCHES}
|
||||||
INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
|
|
||||||
INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
|
|
||||||
USE_SABI 3
|
USE_SABI 3
|
||||||
WITH_SOABI)
|
WITH_SOABI)
|
||||||
|
|
||||||
@ -923,7 +678,6 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
|
|||||||
#
|
#
|
||||||
set(VLLM_ROCM_EXT_SRC
|
set(VLLM_ROCM_EXT_SRC
|
||||||
"csrc/rocm/torch_bindings.cpp"
|
"csrc/rocm/torch_bindings.cpp"
|
||||||
"csrc/rocm/skinny_gemms.cu"
|
|
||||||
"csrc/rocm/attention.cu")
|
"csrc/rocm/attention.cu")
|
||||||
|
|
||||||
define_gpu_extension_target(
|
define_gpu_extension_target(
|
||||||
@ -940,7 +694,5 @@ endif()
|
|||||||
# For CUDA we also build and ship some external projects.
|
# For CUDA we also build and ship some external projects.
|
||||||
if (VLLM_GPU_LANG STREQUAL "CUDA")
|
if (VLLM_GPU_LANG STREQUAL "CUDA")
|
||||||
include(cmake/external_projects/flashmla.cmake)
|
include(cmake/external_projects/flashmla.cmake)
|
||||||
|
|
||||||
# vllm-flash-attn should be last as it overwrites some CMake functions
|
|
||||||
include(cmake/external_projects/vllm_flash_attn.cmake)
|
include(cmake/external_projects/vllm_flash_attn.cmake)
|
||||||
endif ()
|
endif ()
|
||||||
|
|||||||
@ -1,3 +1,3 @@
|
|||||||
# Contributing to vLLM
|
# Contributing to vLLM
|
||||||
|
|
||||||
You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing).
|
You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing/overview.html).
|
||||||
|
|||||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user