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67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import weakref
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import pytest
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import torch
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from vllm import LLM, PoolingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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from ...models.utils import softmax
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MODEL_NAME = "internlm/internlm2-1_8b-reward"
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prompts = ["The chef prepared a delicious meal."]
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@pytest.fixture(autouse=True)
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def v1(run_with_both_engines):
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# Simple autouse wrapper to run both engines for each test
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# This can be promoted up to conftest.py to run for every
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# test in a package
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pass
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@pytest.fixture(scope="module")
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def llm():
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# pytest caches the fixture so we use weakref.proxy to
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# enable garbage collection
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llm = LLM(model=MODEL_NAME,
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max_num_batched_tokens=32768,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.75,
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enforce_eager=True,
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trust_remote_code=True,
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seed=0)
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with llm.deprecate_legacy_api():
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yield weakref.proxy(llm)
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del llm
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cleanup_dist_env_and_memory()
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@pytest.mark.skip_global_cleanup
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def test_pooling_params(llm: LLM):
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def get_outputs(softmax):
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outputs = llm.reward(prompts,
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pooling_params=PoolingParams(softmax=softmax),
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use_tqdm=False)
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return torch.cat([x.outputs.data for x in outputs])
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default = get_outputs(softmax=None)
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w_softmax = get_outputs(softmax=True)
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wo_softmax = get_outputs(softmax=False)
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assert torch.allclose(default, w_softmax,
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atol=1e-2), "Default should use softmax."
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assert not torch.allclose(w_softmax, wo_softmax,
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atol=1e-2), "wo_softmax should not use softmax."
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assert torch.allclose(
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softmax(wo_softmax), w_softmax,
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atol=1e-2), "w_softmax should be close to softmax(wo_softmax)."
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