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59 lines
1.9 KiB
59 lines
1.9 KiB
import pytest
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import torch
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from unittest.mock import Mock, patch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from swarms.models.huggingface import HuggingfaceLLM
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@pytest.fixture
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def huggingface_llm():
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# Create an instance of HuggingfaceLLM for testing.
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model_id = "gpt2-small"
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return HuggingfaceLLM(model_id=model_id)
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def test_initialization(huggingface_llm):
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# Test the initialization of the HuggingfaceLLM class.
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assert huggingface_llm.model_id == "gpt2-small"
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assert huggingface_llm.device in ["cpu", "cuda"]
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assert huggingface_llm.max_length == 20
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assert huggingface_llm.verbose == False
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assert huggingface_llm.distributed == False
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assert huggingface_llm.decoding == False
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assert huggingface_llm.model is None
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assert huggingface_llm.tokenizer is None
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def test_load_model(huggingface_llm):
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# Test loading the model.
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huggingface_llm.load_model()
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assert isinstance(huggingface_llm.model, AutoModelForCausalLM)
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assert isinstance(huggingface_llm.tokenizer, AutoTokenizer)
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def test_run(huggingface_llm):
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# Test the run method of HuggingfaceLLM.
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prompt_text = "Once upon a time"
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generated_text = huggingface_llm.run(prompt_text)
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assert isinstance(generated_text, str)
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assert len(generated_text) > 0
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def test_call_method(huggingface_llm):
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# Test the __call__ method of HuggingfaceLLM.
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prompt_text = "Once upon a time"
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generated_text = huggingface_llm(prompt_text)
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assert isinstance(generated_text, str)
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assert len(generated_text) > 0
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def test_load_model_failure():
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# Test loading model failure.
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with patch(
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"your_module.AutoModelForCausalLM.from_pretrained",
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side_effect=Exception("Model load failed"),
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):
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with pytest.raises(Exception):
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huggingface_llm = HuggingfaceLLM(model_id="gpt2-small")
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huggingface_llm.load_model()
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