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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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183
tests/quantization/test_cpu_offload.py
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@@ -1,77 +1,108 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Expanded quantized model tests for CPU offloading
# Base tests: tests/basic_correctness/test_cpu_offload.py
import pytest
from tests.quantization.utils import is_quant_method_supported
from ..utils import compare_two_settings
@pytest.mark.skipif(not is_quant_method_supported("fp8"),
reason="fp8 is not supported on this GPU type.")
def test_cpu_offload_fp8():
# Test quantization of an unquantized checkpoint
compare_two_settings("meta-llama/Llama-3.2-1B-Instruct",
["--quantization", "fp8"],
["--quantization", "fp8", "--cpu-offload-gb", "1"],
max_wait_seconds=480)
# Test loading a quantized checkpoint
compare_two_settings("neuralmagic/Qwen2-1.5B-Instruct-FP8", [],
["--cpu-offload-gb", "1"],
max_wait_seconds=480)
@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),
reason="gptq_marlin is not supported on this GPU type.")
def test_cpu_offload_gptq(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv('VLLM_TEST_FORCE_LOAD_FORMAT', 'auto')
# Test GPTQ Marlin
compare_two_settings("Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4", [],
["--cpu-offload-gb", "1"],
max_wait_seconds=480)
# Test GPTQ
compare_two_settings("Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4",
["--quantization", "gptq"],
["--quantization", "gptq", "--cpu-offload-gb", "1"],
max_wait_seconds=480)
@pytest.mark.skipif(not is_quant_method_supported("awq_marlin"),
reason="awq_marlin is not supported on this GPU type.")
def test_cpu_offload_awq(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv('VLLM_TEST_FORCE_LOAD_FORMAT', 'auto')
# Test AWQ Marlin
compare_two_settings("Qwen/Qwen2-1.5B-Instruct-AWQ", [],
["--cpu-offload-gb", "1"],
max_wait_seconds=480)
# Test AWQ
compare_two_settings("Qwen/Qwen2-1.5B-Instruct-AWQ",
["--quantization", "awq"],
["--quantization", "awq", "--cpu-offload-gb", "1"],
max_wait_seconds=480)
@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),
reason="gptq_marlin is not supported on this GPU type.")
def test_cpu_offload_compressed_tensors(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv('VLLM_TEST_FORCE_LOAD_FORMAT', 'auto')
# Test wNa16
compare_two_settings("nm-testing/tinyllama-oneshot-w4a16-channel-v2", [],
["--cpu-offload-gb", "1"],
max_wait_seconds=480)
# Test w4a16_marlin24
compare_two_settings("nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t",
[], ["--cpu-offload-gb", "1"],
max_wait_seconds=480)
# Test w8a8
compare_two_settings(
"nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", [],
["--cpu-offload-gb", "1"],
max_wait_seconds=480)
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Expanded quantized model tests for CPU offloading
# Base tests: tests/basic_correctness/test_cpu_offload.py
import pytest
from tests.quantization.utils import is_quant_method_supported
from ..utils import compare_two_settings
@pytest.mark.skipif(
not is_quant_method_supported("fp8"),
reason="fp8 is not supported on this GPU type.",
)
def test_cpu_offload_fp8():
# Test quantization of an unquantized checkpoint
compare_two_settings(
"meta-llama/Llama-3.2-1B-Instruct",
["--quantization", "fp8"],
["--quantization", "fp8", "--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
# Test loading a quantized checkpoint
compare_two_settings(
"neuralmagic/Qwen2-1.5B-Instruct-FP8",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
@pytest.mark.skipif(
not is_quant_method_supported("gptq_marlin"),
reason="gptq_marlin is not supported on this GPU type.",
)
def test_cpu_offload_gptq(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv("VLLM_TEST_FORCE_LOAD_FORMAT", "auto")
# Test GPTQ Marlin
compare_two_settings(
"Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
# Test GPTQ
compare_two_settings(
"Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4",
["--quantization", "gptq"],
["--quantization", "gptq", "--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
@pytest.mark.skipif(
not is_quant_method_supported("awq_marlin"),
reason="awq_marlin is not supported on this GPU type.",
)
def test_cpu_offload_awq(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv("VLLM_TEST_FORCE_LOAD_FORMAT", "auto")
# Test AWQ Marlin
compare_two_settings(
"Qwen/Qwen2-1.5B-Instruct-AWQ",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
# Test AWQ
compare_two_settings(
"Qwen/Qwen2-1.5B-Instruct-AWQ",
["--quantization", "awq"],
["--quantization", "awq", "--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
@pytest.mark.skipif(
not is_quant_method_supported("gptq_marlin"),
reason="gptq_marlin is not supported on this GPU type.",
)
def test_cpu_offload_compressed_tensors(monkeypatch):
# This quant method is sensitive to dummy weights, so we force real weights
monkeypatch.setenv("VLLM_TEST_FORCE_LOAD_FORMAT", "auto")
# Test wNa16
compare_two_settings(
"nm-testing/tinyllama-oneshot-w4a16-channel-v2",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
# Test w4a16_marlin24
compare_two_settings(
"nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)
# Test w8a8
compare_two_settings(
"nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change",
[],
["--cpu-offload-gb", "1"],
max_wait_seconds=480,
)