[Doc] Basic guide for writing unit tests for new models (vllm-project#11951)

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

docs/source/contributing/model/basic.md

@@ -1,6 +1,6 @@
 (new-model-basic)=
 
-# Basic Implementation
+# Implementing a Basic Model
 
 This guide walks you through the steps to implement a basic vLLM model.
 

docs/source/contributing/model/index.md

@@ -10,6 +10,7 @@ This section provides more information on how to integrate a [PyTorch](https://p
 
 basic
 registration
+tests
 multimodal
 ```
 

docs/source/contributing/model/registration.md

@@ -1,6 +1,6 @@
 (new-model-registration)=
 
-# Model Registration
+# Registering a Model to vLLM
 
 vLLM relies on a model registry to determine how to run each model.
 A list of pre-registered architectures can be found [here](#supported-models).
@@ -15,7 +15,6 @@ This gives you the ability to modify the codebase and test your model.
 
 After you have implemented your model (see [tutorial](#new-model-basic)), put it into the <gh-dir:vllm/model_executor/models> directory.
 Then, add your model class to `_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py> so that it is automatically registered upon importing vLLM.
-You should also include an example HuggingFace repository for this model in <gh-file:tests/models/registry.py> to run the unit tests.
 Finally, update our [list of supported models](#supported-models) to promote your model!
 
 ```{important}

docs/source/contributing/model/tests.md

@@ -0,0 +1,63 @@
+(new-model-tests)=
+
+# Writing Unit Tests
+
+This page explains how to write unit tests to verify the implementation of your model.
+
+## Required Tests
+
+These tests are necessary to get your PR merged into vLLM library.
+Without them, the CI for your PR will fail.
+
+### Model loading
+
+Include an example HuggingFace repository for your model in <gh-file:tests/models/registry.py>.
+This enables a unit test that loads dummy weights to ensure that the model can be initialized in vLLM.
+
+```{important}
+The list of models in each section should be maintained in alphabetical order.
+```
+
+```{tip}
+If your model requires a development version of HF Transformers, you can set
+`min_transformers_version` to skip the test in CI until the model is released.
+```
+
+## Optional Tests
+
+These tests are optional to get your PR merged into vLLM library.
+Passing these tests provides more confidence that your implementation is correct, and helps avoid future regressions.
+
+### Model correctness
+
+These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of <gh-dir:tests/models>.
+
+#### Generative models
+
+For [generative models](#generative-models), there are two levels of correctness tests, as defined in <gh-file:tests/models/utils.py>:
+
+- Exact correctness (`check_outputs_equal`): The text outputted by vLLM should exactly match the text outputted by HF.
+- Logprobs similarity (`check_logprobs_close`): The logprobs outputted by vLLM should be in the top-k logprobs outputted by HF, and vice versa.
+
+#### Pooling models
+
+For [pooling models](#pooling-models), we simply check the cosine similarity, as defined in <gh-file:tests/models/embedding/utils.py>.
+
+(mm-processing-tests)=
+
+### Multi-modal processing
+
+#### Common tests
+
+Adding your model to <gh-file:tests/models/multimodal/processing/test_common.py> verifies that the following input combinations result in the same outputs:
+
+- Text + multi-modal data
+- Tokens + multi-modal data
+- Text + cached multi-modal data
+- Tokens + cached multi-modal data
+
+#### Model-specific tests
+
+You can add a new file under <gh-dir:tests/models/multimodal/processing> to run tests that only apply to your model.
+
+For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in <gh-file:tests/models/multimodal/processing/test_phi3v.py>.

tests/models/registry.py

@@ -22,6 +22,11 @@ class _HfExamplesInfo:
     for speculative decoding.
     """
 
+    min_transformers_version: Optional[str] = None
+    """
+    The minimum version of HF Transformers that is required to run this model.
+    """
+
     is_available_online: bool = True
     """
     Set this to ``False`` if the name of this architecture no longer exists on

tests/models/test_initialization.py

@@ -1,7 +1,9 @@
 from unittest.mock import patch
 
 import pytest
+from packaging.version import Version
 from transformers import PretrainedConfig
+from transformers import __version__ as TRANSFORMERS_VERSION
 
 from vllm import LLM
 
@@ -13,6 +15,14 @@ def test_can_initialize(model_arch):
     model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
     if not model_info.is_available_online:
         pytest.skip("Model is not available online")
+    if model_info.min_transformers_version is not None:
+        current_version = TRANSFORMERS_VERSION
+        required_version = model_info.min_transformers_version
+        if Version(current_version) < Version(required_version):
+            pytest.skip(
+                f"You have `transformers=={current_version}` installed, but "
+                f"`transformers>={required_version}` is required to run this "
+                "model")
 
     # Avoid OOM
     def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig: