[Bug] Fix batch invariant test issue, bs=1 with `max_seq_num = 1` (#39320)

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

tests/v1/determinism/test_batch_invariance.py

@@ -36,10 +36,10 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     using the high-level v1 LLM() API only (no manual batching).
 
     Strategy:
-    - Create two LLM engines with identical config except max_num_seqs: 1 vs N.
-    - Compute a baseline output for the needle prompt with the bs=1 engine.
-    - For many trials, generate a batch (size N) where the needle appears at a
-      random position among random filler prompts using the bs=N engine.
+    - Create a single LLM engine configured for the larger batch limit (N).
+    - Compute a baseline output for the needle prompt when it is run alone.
+    - For many trials, generate a mixed batch (size N) where the needle appears
+      at a random position among random filler prompts using the same engine.
     - Track how many trials match vs mismatch, and report totals at the end.
       The test fails if any mismatches occur, but we still dump pass/fail
       counts.
@@ -83,11 +83,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     needle_prompt = "There once was a "
 
-    llm_bs1 = None
-    llm_bsN = None
+    llm = None
     try:
-        # Engine with bs=1 behavior
-        llm_bs1 = LLM_with_max_seqs(
+        llm = LLM_with_max_seqs(
             model=model,
             max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
@@ -96,20 +94,11 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
         )
 
         # Baseline generation for the needle prompt alone.
-        baseline_out = llm_bs1.generate([needle_prompt], sampling)
+        baseline_out = llm.generate([needle_prompt], sampling)
         assert len(baseline_out) == 1
         assert len(baseline_out[0].outputs) >= 1
         baseline_text = baseline_out[0].outputs[0].text
 
-        # Engine with larger batch limit (e.g., 64)
-        llm_bsN = LLM_with_max_seqs(
-            model=model,
-            max_num_seqs=max_batch_size,
-            gpu_memory_utilization=gpu_mem_util,
-            max_model_len=max_model_len,
-            attention_config=attention_config,
-        )
-
         mismatches = 0
 
         for trial in range(num_trials):
@@ -124,8 +113,8 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
                 else:
                     prompts.append(_random_prompt(min_random_prompt, max_random_prompt))
 
-            # Generate with the larger-batch engine
-            outputs = llm_bsN.generate(prompts, sampling)
+            # Generate with the same engine but in a larger batch.
+            outputs = llm.generate(prompts, sampling)
             # Find the needle output by position
             needle_output = outputs[needle_pos]
             assert needle_output.prompt == needle_prompt
@@ -151,12 +140,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     finally:
         # Ensure engines are shutdown to free GPU/VRAM across test sessions
-        if llm_bs1 is not None:
+        if llm is not None:
             with contextlib.suppress(Exception):
-                llm_bs1.shutdown()
-        if llm_bsN is not None:
-            with contextlib.suppress(Exception):
-                llm_bsN.shutdown()
+                llm.shutdown()
 
 
 @skip_unsupported