[Feature][PP] Support Dynamic Chunk for Chunked Pipeline Parallelism

.github/workflows/scripts/config.yaml

@@ -166,3 +166,5 @@ e2e-multicard-4-cards:
   estimated_time: 1391
 - name: tests/e2e/multicard/4-cards/test_pipeline_parallel.py
   estimated_time: 679
+- name: tests/e2e/multicard/4-cards/test_profiling_chunk_performance.py
+  estimated_time: 1300

tests/e2e/multicard/4-cards/test_profiling_chunk_performance.py

@@ -0,0 +1,82 @@
+"""Performance guard for profiling-based dynamic chunk sizing (PP scenario).
+
+Measures Time-To-First-Token (TTFT) on 64k-token prefill requests with
+profiling_chunk_config enabled.  The test runs against
+DeepSeek-V2-Lite-Chat served with PP=2, TP=2 (4 NPU cards total).
+
+Test flow:
+  1. Create an LLM engine with profiling_chunk_config enabled.
+  2. Run NUM_WARMUP sequential requests (64k tokens, max_tokens=1) to warm
+     up both the NPU and the profiling predictor.
+  3. Run NUM_TEST sequential requests, recording TTFT for each.
+  4. Assert that the median TTFT does not exceed BASELINE_TTFT_S seconds.
+"""
+
+import os
+import statistics
+import time
+
+from tests.e2e.conftest import VllmRunner
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+os.environ["VLLM_ALLOW_LONG_MAX_MODEL_LEN"] = "1"
+os.environ["VLLM_ASCEND_ENABLE_FLASHCOMM1"] = "1"
+
+MODEL = "Qwen/Qwen3-30B-A3B"
+
+# ~64k tokens
+_WORD = "hello "
+INPUT_64K_TOKENS = _WORD * (384_000 // len(_WORD))
+
+NUM_WARMUP = 5
+NUM_TEST = 5
+
+# NOTE: Any changes to this baseline must be approved by team members.
+# Measured on Qwen3-30B-A3B, PP=2, TP=2, 64k prefill, profiling_chunk enabled.
+BASELINE_TTFT_S = 5.2
+
+
+def test_profiling_chunk_ttft_performance() -> None:
+    with VllmRunner(
+        MODEL,
+        max_model_len=70000,
+        tensor_parallel_size=2,
+        pipeline_parallel_size=2,
+        block_size=128,
+        enable_expert_parallel=True,
+        enable_prefix_caching=False,
+        gpu_memory_utilization=0.9,
+        max_num_batched_tokens=12288,
+        distributed_executor_backend="mp",
+        enforce_eager=True,
+        async_scheduling=False,
+        additional_config={"profiling_chunk_config": {"enabled":True, "smooth_factor":0.9}, "enable_cpu_binding": False},
+        hf_overrides={"rope_parameters": {"rope_type":"yarn","rope_theta":1000,"factor":5,"original_max_position_embeddings":262144}}
+    ) as vllm_model:
+        # With max_tokens=1, total latency ≈ prefill time ≈ TTFT
+        prompts = [INPUT_64K_TOKENS]
+
+        # ── Warmup ──────────────────────────────────────────────────────────
+        for _ in range(NUM_WARMUP):
+            vllm_model.generate_greedy(prompts, max_tokens=1)
+
+        # ── Measurement ─────────────────────────────────────────────────────
+        ttfts: list[float] = []
+        for _ in range(NUM_TEST):
+            start = time.perf_counter()
+            vllm_model.generate_greedy(prompts, max_tokens=1)
+            ttfts.append(time.perf_counter() - start)
+
+        median_ttft = statistics.median(ttfts)
+        ttft_str = ", ".join(f"{t:.2f}s" for t in ttfts)
+        print(
+            f"\n[profiling_chunk perf] TTFT per request: [{ttft_str}]"
+            f"\n[profiling_chunk perf] Median TTFT: {median_ttft:.2f}s  "
+            f"(baseline: {BASELINE_TTFT_S}s)"
+        )
+
+        assert median_ttft <= BASELINE_TTFT_S, (
+            f"TTFT performance regression: median TTFT {median_ttft:.2f}s "
+            f"exceeds baseline {BASELINE_TTFT_S}s. "
+            f"Individual TTFTs: [{ttft_str}]"
+        )