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[pre-commit.ci] auto fixes from pre-commit.com hooks

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for more information, see https://pre-commit.ci
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pre-commit-ci[bot] 2026-04-21 04:58:24 +00:00
commit 1b2bd65e38
2 changed files with 14 additions and 13 deletions
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2
scripts/benchmarks/flex_paged_attention.py
View file

@ -17,7 +17,7 @@ from torch.nn.attention.flex_attention import (
create_block_mask,
)
create_block_mask = torch.compile(create_block_mask, dynamic=True)
create_block_mask = torch.compile(create_block_mask, dynamic = True)
def _cdiv(x: int | float | torch.Tensor, multiple: int | float | torch.Tensor):

25
scripts/benchmarks/qwen3_flex_inference.py
View file

@ -308,8 +308,8 @@ def refresh_lora_merge_from_pristine(base_model, peft_model):
pristine_w,
B.to(W.dtype),
A.to(W.dtype),
alpha=module.scaling[adapter0],
out=W,
alpha = module.scaling[adapter0],
out = W,
)
for adapter in active[1:]:
A = module.lora_A[adapter].weight.data
@ -318,8 +318,8 @@ def refresh_lora_merge_from_pristine(base_model, peft_model):
W,
B.to(W.dtype),
A.to(W.dtype),
alpha=module.scaling[adapter],
out=W,
alpha = module.scaling[adapter],
out = W,
)
module.merged_adapters = list(active)
n_refreshed += 1
@ -343,8 +343,9 @@ def _hash_state_dict(model) -> str:
return h.hexdigest()
def run_drift_verification(base_model, peft_model, n_iters: int = 10,
noise_scale: float = 0.01):
def run_drift_verification(
base_model, peft_model, n_iters: int = 10, noise_scale: float = 0.01
):
"""Simulate N GRPO iterations: perturb LoRA weights with random noise,
call `refresh_lora_merge_from_pristine`, repeat. Assert the pristine
`base_model`'s parameters are bit-identical before and after.
@ -363,12 +364,12 @@ def run_drift_verification(base_model, peft_model, n_iters: int = 10,
if not isinstance(module, LoraLayer):
continue
for adapter_name in list(module.lora_A.keys()):
initial_lora[(name, "A", adapter_name)] = (
module.lora_A[adapter_name].weight.data.clone()
)
initial_lora[(name, "B", adapter_name)] = (
module.lora_B[adapter_name].weight.data.clone()
)
initial_lora[(name, "A", adapter_name)] = module.lora_A[
adapter_name
].weight.data.clone()
initial_lora[(name, "B", adapter_name)] = module.lora_B[
adapter_name
].weight.data.clone()
base_hash_before = _hash_state_dict(base_model)