[CI/Build] Reorganize models tests (#7820)

tests/basic_correctness/test_chunked_prefill.py

@@ -6,11 +6,13 @@ prefill requests are chunked.
 
 Run `pytest tests/models/test_chunked_prefill.py`.
 """
+import os
 from contextlib import nullcontext
 
 import pytest
 
 from ..models.utils import check_logprobs_close, check_outputs_equal
+from ..utils import multi_gpu_test
 
 MODELS = [
     "facebook/opt-125m",
@@ -66,6 +68,59 @@ def test_models(
     )
 
 
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
+@pytest.mark.parametrize("model", MODELS)
+def test_models_distributed(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    distributed_executor_backend: str,
+) -> None:
+    if (model == "meta-llama/Llama-2-7b-hf"
+            and distributed_executor_backend == "ray"):
+        # test ray adag
+        os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
+        os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
+
+    dtype = "half"
+    max_tokens = 5
+    chunked_prefill_token_size = 16
+
+    # Add a chunked prefill config.
+    max_num_seqs = min(chunked_prefill_token_size, 256)
+    assert chunked_prefill_token_size != -1
+    enable_chunked_prefill = True
+    max_num_batched_tokens = chunked_prefill_token_size
+
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+
+    with vllm_runner(
+            model,
+            dtype=dtype,
+            tensor_parallel_size=2,
+            max_num_seqs=max_num_seqs,
+            enable_chunked_prefill=enable_chunked_prefill,
+            max_num_batched_tokens=max_num_batched_tokens,
+            distributed_executor_backend=distributed_executor_backend,
+    ) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+
+    with hf_runner(model, dtype=dtype) as hf_model:
+        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
 @pytest.mark.parametrize(
     "kv_cache_dtype,model",
     [("fp8_e4m3",