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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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30
tests/compile/test_noop_elimination.py
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@@ -6,14 +6,12 @@ import torch
import vllm
from vllm.compilation.noop_elimination import NoOpEliminationPass
from vllm.config import (CompilationConfig, CompilationLevel, PassConfig,
VllmConfig)
from vllm.config import CompilationConfig, CompilationLevel, PassConfig, VllmConfig
from .backend import TestBackend
@pytest.mark.parametrize("dtype",
[torch.float16, torch.bfloat16, torch.float32])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
@pytest.mark.parametrize("num_tokens", [256, 1024])
@pytest.mark.parametrize("hidden_size", [64, 4096])
def test_noop_elimination(dtype, num_tokens, hidden_size):
@@ -22,7 +20,6 @@ def test_noop_elimination(dtype, num_tokens, hidden_size):
torch.manual_seed(1)
class Model(torch.nn.Module):
def forward(self, x):
# Chain of reshapes
y = x.reshape(-1, 128, 32)
@@ -32,7 +29,7 @@ def test_noop_elimination(dtype, num_tokens, hidden_size):
# Final reshape that should remain
b = a.reshape(-1, 128, 32)
# No-op slice
c = b[0:b.shape[0]]
c = b[0 : b.shape[0]]
# The pass should replace the result of this op with `c`.
d = torch.slice_scatter(
torch.ones_like(c), # Dummy tensor to be scattered into
@@ -43,10 +40,12 @@ def test_noop_elimination(dtype, num_tokens, hidden_size):
)
return d
vllm_config = VllmConfig(compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
pass_config=PassConfig(enable_noop=True),
))
vllm_config = VllmConfig(
compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
pass_config=PassConfig(enable_noop=True),
)
)
with vllm.config.set_current_vllm_config(vllm_config):
noop_pass = NoOpEliminationPass(vllm_config)
@@ -82,17 +81,18 @@ def test_non_noop_slice_preserved():
x = torch.randn(16, 16)
class SliceModel(torch.nn.Module):
def forward(self, x):
base = x.clone()
src = torch.ones(15, 16)
y = torch.slice_scatter(base, src, dim=0, start=0, end=-1)
return x[0:-1, :], y
vllm_config = VllmConfig(compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
pass_config=PassConfig(enable_noop=True),
))
vllm_config = VllmConfig(
compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
pass_config=PassConfig(enable_noop=True),
)
)
with vllm.config.set_current_vllm_config(vllm_config):
noop_pass = NoOpEliminationPass(vllm_config)
backend = TestBackend(noop_pass)