import functools
import os
import signal
from collections.abc import Callable, Sequence
import torch
import torch.nn.functional as F
from typing_extensions import ParamSpec
_P = ParamSpec("_P")
def fork_new_process_for_each_test(f: Callable[_P, None]) -> Callable[_P, None]:
"""Decorator to fork a new process for each test function.
See https://github.com/vllm-project/vllm/issues/7053 for more details.
"""
@functools.wraps(f)
def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
os.setpgrp()
from _pytest.outcomes import Skipped
pid = os.fork()
print(f"Fork a new process to run a test {pid}")
if pid == 0:
try:
f(*args, **kwargs)
except Skipped as e:
print(str(e))
os._exit(0)
except Exception:
import traceback
traceback.print_exc()
os._exit(1)
else:
os._exit(0)
else:
pgid = os.getpgid(pid)
_pid, _exitcode = os.waitpid(pid, 0)
old_signal_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
os.killpg(pgid, signal.SIGTERM)
signal.signal(signal.SIGTERM, old_signal_handler)
assert _exitcode == 0, f"function {f} failed when called with args {args} and kwargs {kwargs}"
return wrapper
def matryoshka_fy(tensor: torch.Tensor, dimensions: int):
tensor = torch.tensor(tensor)
tensor = tensor[..., :dimensions]
tensor = F.normalize(tensor, p=2, dim=1)
return tensor
def check_embeddings_close(
*,
embeddings_0_lst: Sequence[list[float]],
embeddings_1_lst: Sequence[list[float]],
name_0: str,
name_1: str,
tol: float = 1e-3,
) -> None:
assert len(embeddings_0_lst) == len(embeddings_1_lst)
for prompt_idx, (embeddings_0, embeddings_1) in enumerate(zip(embeddings_0_lst, embeddings_1_lst)):
assert len(embeddings_0) == len(embeddings_1), f"Length mismatch: {len(embeddings_0)} vs. {len(embeddings_1)}"
sim = F.cosine_similarity(torch.tensor(embeddings_0), torch.tensor(embeddings_1), dim=0)
fail_msg = (
f"Test{prompt_idx}:"
f"\nCosine similarity: \t{sim:.4f}"
f"\n{name_0}:\t{embeddings_0[:16]!r}"
f"\n{name_1}:\t{embeddings_1[:16]!r}"
)
assert sim >= 1 - tol, fail_msg