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

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pre-commit-ci[bot] 2026-04-20 13:54:21 +00:00
commit c31533fc02
4 changed files with 471 additions and 326 deletions
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46
scripts/benchmarks/make_lora_adapter.py
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@ -20,16 +20,16 @@ from pathlib import Path
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--model_name", default="unsloth/Qwen3-4B-Base")
p.add_argument("--output", default="outputs/lora_rank32_fresh")
p.add_argument("--rank", type=int, default=32)
p.add_argument("--model_name", default = "unsloth/Qwen3-4B-Base")
p.add_argument("--output", default = "outputs/lora_rank32_fresh")
p.add_argument("--rank", type = int, default = 32)
return p.parse_args()
def main():
args = parse_args()
out_dir = Path(args.output).resolve()
out_dir.mkdir(parents=True, exist_ok=True)
out_dir.mkdir(parents = True, exist_ok = True)
# Use vanilla HF -- PEFT's save_pretrained yields the canonical
# adapter_config.json + adapter_model.safetensors that vLLM's LoRARequest
@ -45,16 +45,23 @@ def main():
# bf16 base; we only need structure + save. Keep on CPU to avoid a GPU load
# just for `save_pretrained`.
print(f"[make_lora_adapter] Loading {args.model_name} on CPU...")
model = AutoModelForCausalLM.from_pretrained(args.model_name, dtype=torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained(args.model_name, dtype = torch.bfloat16)
peft_cfg = LoraConfig(
r=args.rank,
lora_alpha=args.rank * 2,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM",
lora_dropout=0.0,
r = args.rank,
lora_alpha = args.rank * 2,
target_modules = [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
],
bias = "none",
task_type = "CAUSAL_LM",
lora_dropout = 0.0,
)
peft_model = get_peft_model(model, peft_cfg)
peft_model.print_trainable_parameters()
@ -66,26 +73,31 @@ def main():
with torch.no_grad():
for name, p in peft_model.named_parameters():
if "lora_B" in name:
p.normal_(mean=0.0, std=1e-4)
p.normal_(mean = 0.0, std = 1e-4)
n_reinit += 1
print(f"[make_lora_adapter] Reinitialized {n_reinit} lora_B matrices with tiny gaussian.")
print(
f"[make_lora_adapter] Reinitialized {n_reinit} lora_B matrices with tiny gaussian."
)
peft_model.save_pretrained(str(out_dir))
tok.save_pretrained(str(out_dir))
# Sanity: verify safetensors file present and non-trivial.
from safetensors import safe_open
st_path = out_dir / "adapter_model.safetensors"
n_zero_tensors = 0
n_tensors = 0
with safe_open(str(st_path), framework="pt") as f:
with safe_open(str(st_path), framework = "pt") as f:
for key in f.keys():
t = f.get_tensor(key)
n_tensors += 1
if (t == 0).all().item():
n_zero_tensors += 1
print(f"[make_lora_adapter] Wrote {n_tensors} tensors to {st_path} "
f"({n_zero_tensors} all-zero).")
print(
f"[make_lora_adapter] Wrote {n_tensors} tensors to {st_path} "
f"({n_zero_tensors} all-zero)."
)
print(f"[make_lora_adapter] Adapter saved to {out_dir}")