[Test] Only Run MLA model when user explicitly set for batch invariance (#37719)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/v1/determinism/test_batch_invariance.py

@@ -8,10 +8,10 @@ import pytest
 import torch
 from utils import (
     BACKENDS,
+    TEST_MODEL,
     _extract_step_logprobs,
     _random_prompt,
     is_device_capability_below_90,
-    resolve_model_name,
     skip_unsupported,
 )
 
@@ -57,7 +57,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     attention_config = {"backend": backend}
     # Allow overrides from environment (useful for CI tuning)
     # "facebook/opt-125m" is too small, doesn't reliably test determinism
-    model = resolve_model_name(backend)
+    model = TEST_MODEL
     num_trials = int(os.getenv("VLLM_NEEDLE_TRIALS", "5"))
     max_batch_size = int(os.getenv("VLLM_NEEDLE_BATCH_SIZE", "128"))
     min_random_prompt = int(os.getenv("VLLM_MIN_PROMPT", "1024"))
@@ -169,7 +169,6 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
 ):
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
-    model_name = resolve_model_name(backend)
     tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1"))
 
     # For batch invariance, disable custom all-reduce to ensure deterministic
@@ -186,7 +185,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
         print(f"{'=' * 80}\n")
 
     llm = LLM(
-        model=model_name,
+        model=TEST_MODEL,
         tensor_parallel_size=tp_size,
         max_num_seqs=128,
         max_model_len=8192,
@@ -395,7 +394,7 @@ def test_simple_generation(backend):
     Simple test that runs the model with a basic prompt and prints the output.
     Useful for quick smoke testing and debugging.
     """
-    model = resolve_model_name(backend)
+    model = TEST_MODEL
 
     llm = LLM(
         model=model,
@@ -458,7 +457,6 @@ def test_logprobs_without_batch_invariance_should_fail(
     monkeypatch.setattr(batch_invariant, "VLLM_BATCH_INVARIANT", False)
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
-    model_name = resolve_model_name(backend)
     tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1"))
 
     print(f"\n{'=' * 80}")
@@ -466,7 +464,7 @@ def test_logprobs_without_batch_invariance_should_fail(
     print(f"{'=' * 80}\n")
 
     llm = LLM(
-        model=model_name,
+        model=TEST_MODEL,
         tensor_parallel_size=tp_size,
         max_num_seqs=32,
         max_model_len=8192,
@@ -674,7 +672,6 @@ def test_decode_logprobs_match_prefill_logprobs(
     """
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
-    model_name = resolve_model_name(backend)
     tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1"))
 
     from vllm.model_executor.layers.batch_invariant import (
@@ -689,7 +686,7 @@ def test_decode_logprobs_match_prefill_logprobs(
         print(f"{'=' * 80}\n")
 
     llm = LLM(
-        model=model_name,
+        model=TEST_MODEL,
         tensor_parallel_size=tp_size,
         max_num_seqs=32,
         max_model_len=8192,

tests/v1/determinism/test_online_batch_invariance.py

@@ -17,7 +17,7 @@ from typing import Any
 
 import openai
 import pytest
-from utils import BACKENDS, _random_prompt, resolve_model_name, skip_unsupported
+from utils import BACKENDS, TEST_MODEL, _random_prompt, skip_unsupported
 
 from tests.utils import RemoteOpenAIServer
 
@@ -139,7 +139,6 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     backend: str,
 ) -> None:
     random.seed(int(os.getenv("VLLM_TEST_SEED", "12345")))
-    model_name = resolve_model_name(backend)
     prompts_all = [_random_prompt(10, 50) for _ in range(32)]
 
     sp_kwargs: dict[str, Any] = {
@@ -159,11 +158,11 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     if tp_size:
         server_args += ["-tp", tp_size]
 
-    with RemoteOpenAIServer(model_name, server_args) as server:
+    with RemoteOpenAIServer(TEST_MODEL, server_args) as server:
         client = server.get_client()
         _compare_bs1_vs_bsn_single_process(
             prompts=prompts_all,
             sp_kwargs=sp_kwargs,
             client=client,
-            model_name=model_name,
+            model_name=TEST_MODEL,
         )

tests/v1/determinism/utils.py

@@ -7,6 +7,10 @@ import pytest
 import torch
 
 from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
+from vllm.transformers_utils.model_arch_config_convertor import (
+    ModelArchConfigConvertorBase,
+)
 from vllm.v1.attention.backends.fa_utils import flash_attn_supports_mla
 
 skip_unsupported = pytest.mark.skipif(
@@ -16,10 +20,12 @@ skip_unsupported = pytest.mark.skipif(
     reason="Requires CUDA and >= Ampere (SM80)",
 )
 
+DEFAULT_MODEL = "Qwen/Qwen3-1.7B"
+TEST_MODEL = os.getenv("VLLM_TEST_MODEL", DEFAULT_MODEL)
+
 BACKENDS: list[str] = [
     "FLASH_ATTN",
     "TRITON_ATTN",
-    "TRITON_MLA",
 ]
 
 # FlashInfer temporarily disabled due to invariant CTA sizes.
@@ -27,19 +33,13 @@ BACKENDS: list[str] = [
 # if has_flashinfer():
 #     BACKENDS.append("FLASHINFER")
 
-if flash_attn_supports_mla():
-    BACKENDS.append("FLASH_ATTN_MLA")
-
-DEFAULT_MODEL = "Qwen/Qwen3-1.7B"
-MLA_MODEL = "deepseek-ai/DeepSeek-V2-Lite-Chat"
-
-
-def resolve_model_name(backend: str) -> str:
-    """Resolve the model name for the given backend."""
-    model = os.getenv("VLLM_TEST_MODEL", DEFAULT_MODEL)
-    if backend.endswith("MLA") and model == DEFAULT_MODEL:
-        return MLA_MODEL
-    return model
+# only run MLA backends when the requested test model is itself an MLA model.
+if os.getenv("VLLM_TEST_MODEL"):
+    config = get_config(TEST_MODEL, trust_remote_code=False)
+    if ModelArchConfigConvertorBase(config, config.get_text_config()).is_deepseek_mla():
+        BACKENDS = ["TRITON_MLA"]
+        if flash_attn_supports_mla():
+            BACKENDS.append("FLASH_ATTN_MLA")
 
 
 def _random_prompt(min_words: int = 1024, max_words: int = 1024 * 2) -> str: