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- """Compare the short outputs of HF and Aphrodite when using greedy sampling.
- Run `pytest tests/basic_correctness/test_basic_correctness.py`.
- """
- import os
- import pickle
- import re
- import weakref
- from unittest.mock import patch
- import pytest
- from aphrodite import LLM
- from aphrodite.common.utils import is_hip
- from aphrodite.worker.model_runner import ModelInputForGPUWithSamplingMetadata
- from ..models.utils import check_outputs_equal
- MODELS = [
- "facebook/opt-125m",
- "meta-llama/Llama-2-7b-hf",
- ]
- def test_aphrodite_gc_ed():
- """Verify aphrodite instance is GC'ed when it is deleted"""
- llm = LLM("facebook/opt-125m")
- weak_llm = weakref.ref(llm)
- del llm
-
-
- assert weak_llm() is None
- @pytest.mark.parametrize("model", MODELS)
- @pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"])
- @pytest.mark.parametrize("dtype", ["half"])
- @pytest.mark.parametrize("max_tokens", [5])
- @pytest.mark.parametrize("enforce_eager", [False, True])
- def test_models(
- hf_runner,
- aphrodite_runner,
- example_prompts,
- model: str,
- backend: str,
- dtype: str,
- max_tokens: int,
- enforce_eager: bool,
- ) -> None:
- if backend == "FLASHINFER" and is_hip():
- pytest.skip("Flashinfer does not support ROCm/HIP.")
- os.environ["APHRODITE_ATTENTION_BACKEND"] = backend
- with hf_runner(model, dtype=dtype) as hf_model:
- hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
- with aphrodite_runner(model,
- dtype=dtype,
- enforce_eager=enforce_eager,
- gpu_memory_utilization=0.7) as aphrodite_model:
- aphrodite_outputs = aphrodite_model.generate_greedy(example_prompts,
- max_tokens)
- check_outputs_equal(
- outputs_0_lst=hf_outputs,
- outputs_1_lst=aphrodite_outputs,
- name_0="hf",
- name_1="aphrodite",
- )
- def test_model_with_failure(aphrodite_runner) -> None:
- try:
- with patch("aphrodite.modeling.models.opt.OPTForCausalLM.forward",
- side_effect=ValueError()):
- with pytest.raises(ValueError) as exc_info:
- aphrodite_runner("facebook/opt-125m",
- dtype="half",
- enforce_eager=False,
- gpu_memory_utilization=0.7)
- matches = re.search(r"input dumped to (.+).pkl",
- str(exc_info.value))
- assert matches is not None
- filename = f"{matches.group(1)}.pkl"
- with open(filename, "rb") as filep:
- inputs = pickle.load(filep)
- if any(key not in inputs for key in ("arg_1", "arg_2", "arg_3")):
- raise AssertionError("Missing keys in dumped inputs. Dumped keys: "
- f"{list(inputs.keys())}")
- assert isinstance(inputs["arg_1"],
- ModelInputForGPUWithSamplingMetadata)
- finally:
- os.remove(filename)
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