[Test] Add basic correctness test (#2908)

.buildkite/test-pipeline.yaml

@@ -11,8 +11,16 @@ steps:
 - label: AsyncEngine Test
   command: pytest -v -s async_engine
 
-- label: Distributed Test
-  command: pytest -v -s test_comm_ops.py
+- label: Basic Correctness Test
+  command: pytest -v -s --forked basic_correctness
+
+- label: Distributed Comm Ops Test
+  command: pytest -v -s --forked test_comm_ops.py
+  working_dir: "/vllm-workspace/tests/distributed"
+  num_gpus: 2 # only support 1 or 2 for now.
+
+- label: Distributed Correctness Test
+  command: pytest -v -s --forked test_basic_distributed_correctness.py
   working_dir: "/vllm-workspace/tests/distributed"
   num_gpus: 2 # only support 1 or 2 for now.
 

tests/basic_correctness/test_basic_correctness.py

@@ -0,0 +1,38 @@
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/basic_correctness/test_basic_correctness.py --forked`.
+"""
+import pytest
+
+MODELS = [
+    "facebook/opt-125m",
+    "meta-llama/Llama-2-7b-hf",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [5])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(model, dtype=dtype)
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")

tests/conftest.py

@@ -165,6 +165,7 @@ class VllmRunner:
         model_name: str,
         tokenizer_name: Optional[str] = None,
         dtype: str = "half",
+        tensor_parallel_size: int = 1,
     ) -> None:
         self.model = LLM(
             model=model_name,
@@ -172,6 +173,7 @@ class VllmRunner:
             trust_remote_code=True,
             dtype=dtype,
             swap_space=0,
+            tensor_parallel_size=tensor_parallel_size,
         )
 
     def generate(

tests/distributed/test_basic_distributed_correctness.py

@@ -0,0 +1,41 @@
+"""Compare the outputs of HF and distributed vLLM when using greedy sampling.
+
+Run `pytest tests/distributed/test_basic_distributed_correctness.py --forked`.
+"""
+import pytest
+import torch
+
+MODELS = [
+    "facebook/opt-125m",
+    "meta-llama/Llama-2-7b-hf",
+]
+
+
+@pytest.mark.skipif(torch.cuda.device_count() < 2,
+                    reason="Need at least 2 GPUs to run the test.")
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [5])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(model, dtype=dtype, tensor_parallel_size=2)
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")