Unit tests for pricer-module
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@@ -1,18 +1,5 @@
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# Run Continuous Integration (CI) Tests on Modal
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Note!
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The HF secret in Modal is named "huggingface-secret". Pls rename if your secret has another name.
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## Test modal deployment
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You can test pricer.ci in Modal:
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(`modal deploy -m pricer.ci`)
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In python CLI:
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(`import modal`)
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(`Pricer = modal.Cls.lookup("pricer-ci-testing", "Pricer")`)
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(`pricer = Pricer()`)
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(`reply = pricer.price.remote("Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio")`)
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(`print(reply)`)
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## Unit testing
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Unit test strategy created like in
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[This example repo](https://github.com/modal-labs/ci-on-modal)
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@@ -20,11 +7,12 @@ Unit test strategy created like in
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## Usage
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All commands below are run from the root of the repository (this directory).
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_Note_: I removed modal-decorators from pricer.ci-module to be able to run unit tests.
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### Run tests remotely on Modal
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```bash
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modal run pricer.ci
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modal run pricer.ci::pytest
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```
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On the first execution, the [container image](https://modal.com/docs/guide/custom-container)
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@@ -39,9 +27,15 @@ To debug the tests, you can open a shell
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in the exact same environment that the tests are run in:
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```bash
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modal shell pricer.ci
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modal shell pricer.ci::pytest
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```
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_Note_: On the Modal worker, the `pytest` command is run from the home directory, `/root`,
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which contains the `tests` folder, but the `modal shell` command will
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drop you at the top of the filesystem, `/`.
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To run test:
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```bash
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cd root
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pytest
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```
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@@ -39,17 +39,16 @@ FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL
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QUESTION = "How much does this cost to the nearest dollar?"
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PREFIX = "Price is $"
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@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800)
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class Pricer:
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@modal.build()
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def download_model_to_folder(self):
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from huggingface_hub import snapshot_download
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import os
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os.makedirs(MODEL_DIR, exist_ok=True)
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snapshot_download(BASE_MODEL, local_dir=BASE_DIR)
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snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR)
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print(f"Using this HF Token: {hf_token}")
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snapshot_download(BASE_MODEL, local_dir=BASE_DIR, use_auth_token=hf_token)
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snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR, use_auth_token=hf_token)
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@modal.enter()
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def setup(self):
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import os
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import torch
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@@ -78,7 +77,6 @@ class Pricer:
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self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION)
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@modal.method()
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def price(self, description: str) -> float:
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import os
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import re
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@@ -98,6 +96,5 @@ class Pricer:
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match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
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return float(match.group()) if match else 0
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@modal.method()
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def wake_up(self) -> str:
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return "ok"
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@@ -4,3 +4,4 @@ transformers
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bitsandbytes
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accelerate
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peft
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dotenv
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@@ -1,5 +0,0 @@
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from my_pkg.lib import has_gpu
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def test_torch_cuda():
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assert has_gpu()
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@@ -0,0 +1,84 @@
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import pdb
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from pricer.ci import Pricer
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from unittest.mock import patch, MagicMock
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import torch
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import pytest
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from transformers import BitsAndBytesConfig
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
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PROJECT_NAME = "pricer"
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HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
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RUN_NAME = "2024-09-13_13.04.39"
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PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
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REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
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FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
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MODEL_DIR = "hf-cache/"
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BASE_DIR = MODEL_DIR + BASE_MODEL
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FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL
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@pytest.fixture
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def pricer():
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return Pricer()
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def test_wake_up():
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pricer = Pricer()
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assert pricer.wake_up() == "ok"
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@patch('transformers.AutoTokenizer')
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@patch('peft.PeftModel')
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@patch('transformers.AutoModelForCausalLM')
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def test_setup(MockAutoModel, MockPeftModel, MockAutoTokenizer, pricer):
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# Setup mocks
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mock_tokenizer = MockAutoTokenizer.from_pretrained.return_value
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mock_model = MockAutoModel.from_pretrained.return_value
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mock_peft_model = MockPeftModel.from_pretrained.return_value
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# Call the setup method
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pricer.setup()
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# Assertions to ensure the setup method works correctly
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MockAutoTokenizer.from_pretrained.assert_called_once_with(BASE_DIR)
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assert pricer.tokenizer == mock_tokenizer
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assert pricer.tokenizer.pad_token == pricer.tokenizer.eos_token
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assert pricer.tokenizer.padding_side == "right"
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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MockAutoModel.from_pretrained.assert_called_once_with(
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BASE_DIR,
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quantization_config=quant_config,
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device_map="auto"
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)
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assert pricer.base_model == mock_model
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MockPeftModel.from_pretrained.assert_called_once_with(mock_model, FINETUNED_DIR, revision=REVISION)
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assert pricer.fine_tuned_model == mock_peft_model
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@patch('transformers.AutoTokenizer')
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@patch('peft.PeftModel')
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def test_price(MockPeftModel, MockAutoTokenizer, pricer):
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# Setup mocks
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mock_tokenizer = MockAutoTokenizer.return_value
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mock_tokenizer.encode.return_value = torch.tensor([[1, 2, 3]])
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mock_tokenizer.decode.return_value = "Price is $123.45"
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mock_model = MockPeftModel.return_value
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mock_model.generate.return_value = torch.tensor([[1, 2, 3, 4, 5]])
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# Assign mocks to the pricer instance
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pricer.tokenizer = mock_tokenizer
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pricer.fine_tuned_model = mock_model
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# Call the method
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description = "Test description"
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result = pricer.price(description)
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# Assert the result
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assert result == 123.45
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