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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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44
tests/quantization/test_modelopt.py
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@@ -19,21 +19,26 @@ def enable_pickle(monkeypatch):
monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
@pytest.mark.skipif(not is_quant_method_supported("modelopt"),
reason="ModelOpt FP8 is not supported on this GPU type.")
@pytest.mark.skipif(
not is_quant_method_supported("modelopt"),
reason="ModelOpt FP8 is not supported on this GPU type.",
)
def test_modelopt_fp8_checkpoint_setup(vllm_runner):
"""Test ModelOpt FP8 checkpoint loading and structure validation."""
# TODO: provide a small publicly available test checkpoint
model_path = ("/home/scratch.omniml_data_1/zhiyu/ckpts/test_ckpts/"
"TinyLlama-1.1B-Chat-v1.0-fp8-0710")
model_path = (
"/home/scratch.omniml_data_1/zhiyu/ckpts/test_ckpts/"
"TinyLlama-1.1B-Chat-v1.0-fp8-0710"
)
# Skip test if checkpoint doesn't exist
if not os.path.exists(model_path):
pytest.skip(f"Test checkpoint not found at {model_path}. "
"This test requires a local ModelOpt FP8 checkpoint.")
pytest.skip(
f"Test checkpoint not found at {model_path}. "
"This test requires a local ModelOpt FP8 checkpoint."
)
with vllm_runner(model_path, quantization="modelopt",
enforce_eager=True) as llm:
with vllm_runner(model_path, quantization="modelopt", enforce_eager=True) as llm:
def check_model(model):
layer = model.model.layers[0]
@@ -45,11 +50,12 @@ def test_modelopt_fp8_checkpoint_setup(vllm_runner):
# Check that ModelOpt quantization method is properly applied
from vllm.model_executor.layers.quantization.modelopt import (
ModelOptFp8LinearMethod)
ModelOptFp8LinearMethod,
)
assert isinstance(qkv_proj.quant_method, ModelOptFp8LinearMethod)
assert isinstance(o_proj.quant_method, ModelOptFp8LinearMethod)
assert isinstance(gate_up_proj.quant_method,
ModelOptFp8LinearMethod)
assert isinstance(gate_up_proj.quant_method, ModelOptFp8LinearMethod)
assert isinstance(down_proj.quant_method, ModelOptFp8LinearMethod)
# Check weight dtype is FP8
@@ -59,23 +65,23 @@ def test_modelopt_fp8_checkpoint_setup(vllm_runner):
assert down_proj.weight.dtype == torch.float8_e4m3fn
# Check scales are present and have correct dtype
assert hasattr(qkv_proj, 'weight_scale')
assert hasattr(qkv_proj, 'input_scale')
assert hasattr(qkv_proj, "weight_scale")
assert hasattr(qkv_proj, "input_scale")
assert qkv_proj.weight_scale.dtype == torch.float32
assert qkv_proj.input_scale.dtype == torch.float32
assert hasattr(o_proj, 'weight_scale')
assert hasattr(o_proj, 'input_scale')
assert hasattr(o_proj, "weight_scale")
assert hasattr(o_proj, "input_scale")
assert o_proj.weight_scale.dtype == torch.float32
assert o_proj.input_scale.dtype == torch.float32
assert hasattr(gate_up_proj, 'weight_scale')
assert hasattr(gate_up_proj, 'input_scale')
assert hasattr(gate_up_proj, "weight_scale")
assert hasattr(gate_up_proj, "input_scale")
assert gate_up_proj.weight_scale.dtype == torch.float32
assert gate_up_proj.input_scale.dtype == torch.float32
assert hasattr(down_proj, 'weight_scale')
assert hasattr(down_proj, 'input_scale')
assert hasattr(down_proj, "weight_scale")
assert hasattr(down_proj, "input_scale")
assert down_proj.weight_scale.dtype == torch.float32
assert down_proj.input_scale.dtype == torch.float32