Files
FastDeploy/tests/deterministic/test_determinism_offline_single_gpu.py
T
gongweibao 30f9f33f34 [Feature][BugFix][OP] Enhance Deterministic Inference Mode with Kernel-level Fixes and Batch-invariant BMM (#6610)
* add fa deter

* add ut

* add long sentence

* fix basic

* fix bugs

* fix adn

* fix first

* fix single

* fix single

* fix single test

* refine

* add more test

* refine comments

* add comments of bmm

* fix ci

* remove probe

* add

* remove not need

* refine tests

* fix comments and refine code

* refine code

* refine test

* refine test

* mv 4cards tests

* fix tests

* add

* fix comments

* fix cover

* fix cover

---------

Co-authored-by: gongweibao <gognweibao@baidu.com>
2026-03-09 10:27:53 +08:00

130 lines
3.9 KiB
Python

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Single-GPU determinism offline inference tests for coverage.
Simplified from tests/e2e/4cards_cases/test_determinism_offline.py
for single-GPU coverage testing.
Usage:
CUDA_VISIBLE_DEVICES=0 pytest tests/deterministic/test_determinism_offline_single_gpu.py -v
"""
import os
from contextlib import contextmanager
import pytest
pytestmark = pytest.mark.gpu
DEFAULT_MODEL_DIR = "./models"
MODEL_NAME = "Qwen2-7B-Instruct"
@contextmanager
def env_override(mapping):
"""Temporarily set env vars, restoring original values on exit."""
old = {k: os.environ.get(k) for k in mapping}
os.environ.update(mapping)
try:
yield
finally:
for k, v in old.items():
if v is None:
os.environ.pop(k, None)
else:
os.environ[k] = v
@pytest.fixture(scope="module")
def model_path():
model_dir = os.getenv("MODEL_PATH", DEFAULT_MODEL_DIR)
return os.path.join(model_dir, MODEL_NAME)
@pytest.fixture(autouse=True)
def _reset_deterministic_mode():
"""Ensure every test starts with deterministic mode ON."""
os.environ["FD_DETERMINISTIC_MODE"] = "1"
yield
os.environ["FD_DETERMINISTIC_MODE"] = "1"
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
@pytest.fixture(scope="module", autouse=True)
def _module_env():
"""Set env vars before importing fastdeploy (must happen first)."""
with env_override(
{
"CUDA_VISIBLE_DEVICES": os.environ.get("CUDA_VISIBLE_DEVICES", "0"),
"FD_DETERMINISTIC_MODE": "1",
"FD_CUSTOM_AR_MAX_SIZE_MB": "64",
}
):
# Lazy import: env vars must be set before importing fastdeploy
global LLM, SamplingParams # noqa: PLW0603
from fastdeploy import LLM, SamplingParams
yield
@pytest.fixture(scope="module")
def llm(model_path, _module_env):
return LLM(
model=model_path,
tensor_parallel_size=1, # Single GPU
max_model_len=4096,
enable_prefix_caching=False,
graph_optimization_config={"use_cudagraph": False},
)
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _generate_text(llm, prompt, sp):
"""Generate once, return (text, token_ids)."""
out = llm.generate([prompt], sp)[0]
return out.outputs.text, list(out.outputs.token_ids)
def _assert_deterministic(llm, prompt, sp, runs=2):
"""Run *runs* times and assert all outputs are identical."""
results = [_generate_text(llm, prompt, sp) for _ in range(runs)]
texts = [r[0] for r in results]
token_ids = [r[1] for r in results]
assert all(t == texts[0] for t in texts), "Text outputs differ across runs"
assert all(t == token_ids[0] for t in token_ids), "Token IDs differ across runs"
return texts[0], token_ids[0]
# ===================== Core determinism tests =====================
def test_deterministic_same_prompt(llm):
"""Same prompt + same seed produces identical output across 3 runs."""
sp = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=30, seed=123)
_assert_deterministic(llm, "What is AI?", sp, runs=3)
if __name__ == "__main__":
pytest.main(["-sv", __file__])