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import pdb
from pricer.ci import Pricer
from unittest.mock import patch, MagicMock
import torch
import pytest
from transformers import BitsAndBytesConfig
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
PROJECT_NAME = "pricer"
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
RUN_NAME = "2024-09-13_13.04.39"
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
MODEL_DIR = "hf-cache/"
BASE_DIR = MODEL_DIR + BASE_MODEL
FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL
@pytest.fixture
def pricer():
return Pricer()
def test_wake_up():
pricer = Pricer()
assert pricer.wake_up() == "ok"
@patch('transformers.AutoTokenizer')
@patch('peft.PeftModel')
@patch('transformers.AutoModelForCausalLM')
def test_setup(MockAutoModel, MockPeftModel, MockAutoTokenizer, pricer):
# Setup mocks
mock_tokenizer = MockAutoTokenizer.from_pretrained.return_value
mock_model = MockAutoModel.from_pretrained.return_value
mock_peft_model = MockPeftModel.from_pretrained.return_value
# Call the setup method
pricer.setup()
# Assertions to ensure the setup method works correctly
MockAutoTokenizer.from_pretrained.assert_called_once_with(BASE_DIR)
assert pricer.tokenizer == mock_tokenizer
assert pricer.tokenizer.pad_token == pricer.tokenizer.eos_token
assert pricer.tokenizer.padding_side == "right"
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
MockAutoModel.from_pretrained.assert_called_once_with(
BASE_DIR,
quantization_config=quant_config,
device_map="auto"
)
assert pricer.base_model == mock_model
MockPeftModel.from_pretrained.assert_called_once_with(mock_model, FINETUNED_DIR, revision=REVISION)
assert pricer.fine_tuned_model == mock_peft_model
@patch('transformers.AutoTokenizer')
@patch('peft.PeftModel')
def test_price(MockPeftModel, MockAutoTokenizer, pricer):
# Setup mocks
mock_tokenizer = MockAutoTokenizer.return_value
mock_tokenizer.encode.return_value = torch.tensor([[1, 2, 3]])
mock_tokenizer.decode.return_value = "Price is $123.45"
mock_model = MockPeftModel.return_value
mock_model.generate.return_value = torch.tensor([[1, 2, 3, 4, 5]])
# Assign mocks to the pricer instance
pricer.tokenizer = mock_tokenizer
pricer.fine_tuned_model = mock_model
# Call the method
description = "Test description"
result = pricer.price(description)
# Assert the result
assert result == 123.45
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