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Add test for loading lora from huggingface
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from typing import List | ||
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import pytest | ||
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from vllm.lora.models import LoRAModel | ||
from vllm.lora.utils import get_adapter_absolute_path | ||
from vllm.model_executor.models.llama import LlamaForCausalLM | ||
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# Provide absolute path and huggingface lora ids | ||
lora_fixture_name = ["sql_lora_files", "sql_lora_huggingface_id"] | ||
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@pytest.mark.parametrize("lora_fixture_name", lora_fixture_name) | ||
def test_load_checkpoints_from_huggingface(lora_fixture_name, request): | ||
lora_name = request.getfixturevalue(lora_fixture_name) | ||
supported_lora_modules = LlamaForCausalLM.supported_lora_modules | ||
packed_modules_mapping = LlamaForCausalLM.packed_modules_mapping | ||
embedding_modules = LlamaForCausalLM.embedding_modules | ||
embed_padding_modules = LlamaForCausalLM.embedding_padding_modules | ||
expected_lora_modules: List[str] = [] | ||
for module in supported_lora_modules: | ||
if module in packed_modules_mapping: | ||
expected_lora_modules.extend(packed_modules_mapping[module]) | ||
else: | ||
expected_lora_modules.append(module) | ||
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lora_path = get_adapter_absolute_path(lora_name) | ||
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# lora loading should work for either absolute path and hugggingface id. | ||
lora_model = LoRAModel.from_local_checkpoint( | ||
lora_path, | ||
expected_lora_modules, | ||
lora_model_id=1, | ||
device="cpu", | ||
embedding_modules=embedding_modules, | ||
embedding_padding_modules=embed_padding_modules) | ||
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# Assertions to ensure the model is loaded correctly | ||
assert lora_model is not None, "LoRAModel is not loaded correctly" |