102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pytest
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from vllm import LLM, SamplingParams
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from vllm.assets.image import ImageAsset
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from ..utils import create_new_process_for_each_test
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@create_new_process_for_each_test()
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def test_plugin(
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monkeypatch: pytest.MonkeyPatch,
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dummy_opt_path: str,
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):
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# V1 shuts down rather than raising an error here.
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_V1", "0")
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m.setenv("VLLM_PLUGINS", "")
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with pytest.raises(Exception) as excinfo:
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LLM(model=dummy_opt_path, load_format="dummy")
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error_msg = "has no vLLM implementation and the Transformers implementation is not compatible with vLLM" # noqa: E501
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assert (error_msg in str(excinfo.value))
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@create_new_process_for_each_test()
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def test_oot_registration_text_generation(
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monkeypatch: pytest.MonkeyPatch,
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dummy_opt_path: str,
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):
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with monkeypatch.context() as m:
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m.setenv("VLLM_PLUGINS", "register_dummy_model")
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prompts = ["Hello, my name is", "The text does not matter"]
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sampling_params = SamplingParams(temperature=0)
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llm = LLM(model=dummy_opt_path, load_format="dummy")
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first_token = llm.get_tokenizer().decode(0)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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generated_text = output.outputs[0].text
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# make sure only the first token is generated
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rest = generated_text.replace(first_token, "")
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assert rest == ""
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@create_new_process_for_each_test()
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def test_oot_registration_embedding(
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monkeypatch: pytest.MonkeyPatch,
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dummy_gemma2_embedding_path: str,
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):
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with monkeypatch.context() as m:
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m.setenv("VLLM_PLUGINS", "register_dummy_model")
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prompts = ["Hello, my name is", "The text does not matter"]
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llm = LLM(model=dummy_gemma2_embedding_path, load_format="dummy")
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outputs = llm.embed(prompts)
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for output in outputs:
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assert all(v == 0 for v in output.outputs.embedding)
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image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
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@create_new_process_for_each_test()
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def test_oot_registration_multimodal(
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monkeypatch: pytest.MonkeyPatch,
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dummy_llava_path: str,
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):
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with monkeypatch.context() as m:
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m.setenv("VLLM_PLUGINS", "register_dummy_model")
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prompts = [{
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"prompt": "What's in the image?<image>",
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"multi_modal_data": {
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"image": image
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},
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}, {
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"prompt": "Describe the image<image>",
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"multi_modal_data": {
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"image": image
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},
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}]
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sampling_params = SamplingParams(temperature=0)
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llm = LLM(model=dummy_llava_path,
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load_format="dummy",
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max_num_seqs=1,
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trust_remote_code=True,
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gpu_memory_utilization=0.98,
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max_model_len=4096,
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enforce_eager=True,
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limit_mm_per_prompt={"image": 1})
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first_token = llm.get_tokenizer().decode(0)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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generated_text = output.outputs[0].text
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# make sure only the first token is generated
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rest = generated_text.replace(first_token, "")
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assert rest == ""
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