[CI][DCP][Perf] reduce DCP CI execution time (#29858)

pisceskkk · web-flow · commit 46cbbca05c31 · 2025-12-04T17:28:21.000Z
Signed-off-by: QiuChunshuo &lt;qiuchunshuo@huawei.com&gt;
diff --git a/tests/distributed/test_context_parallel.py b/tests/distributed/test_context_parallel.py
@@ -16,16 +16,35 @@
 import pytest
 import torch
 
+from tests.evals.gsm8k.gsm8k_eval import evaluate_gsm8k
+from tests.utils import RemoteOpenAIServer, create_new_process_for_each_test
 from vllm.config.model import RunnerOption
 from vllm.logger import init_logger
 
 from ..models.registry import HF_EXAMPLE_MODELS
-from ..utils import compare_two_settings, create_new_process_for_each_test
 
 logger = init_logger("test_context_parallel")
 
 VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
 
+CP_TEST_MODELS = [
+    # TODO support other models
+    # [LANGUAGE GENERATION]
+    "deepseek-ai/DeepSeek-V2-Lite-Chat",
+    "Qwen/Qwen2.5-1.5B-Instruct",
+]
+
+# GSM8K eval configuration
+NUM_QUESTIONS = 256  # Fast eval for CI
+NUM_SHOTS = 5  # Few-shot examples
+# tp accuracy with 2% buffer
+MIN_ACCURACY = {
+    # .buildkite/lm-eval-harness/configs/DeepSeek-V2-Lite-Chat.yaml
+    "deepseek-ai/DeepSeek-V2-Lite-Chat": 0.64,
+    # .buildkite/lm-eval-harness/configs/Qwen2.5-1.5B-Instruct.yaml
+    "Qwen/Qwen2.5-1.5B-Instruct": 0.52,
+}
+
 
 class ParallelSetup(NamedTuple):
     tp_size: int
@@ -38,7 +57,6 @@ class ParallelSetup(NamedTuple):
 
 class CPTestOptions(NamedTuple):
     multi_node_only: bool
-    load_format: str | None = None
     attn_backend: str | None = None
 
 
@@ -54,17 +72,20 @@ def detailed(
         *,
         tp_base: int = 4,
         pp_base: int = 1,
-        dcp_base: int = 1,
+        dcp_multipliers: list[float] | None = None,
         cp_kv_cache_interleave_size: int = 1,
         multi_node_only: bool = False,
         runner: RunnerOption = "auto",
-        load_format: str | None = None,
         attn_backend: str | None = None,
     ):
         parallel_setups = []
+        if dcp_multipliers is None:
+            dcp_multipliers = [
+                0.5,
+            ]
         for eager_mode_val in [False]:
             for pp_multiplier in [1]:
-                for dcp_multiplier in [0.5, 1]:
+                for dcp_multiplier in dcp_multipliers:
                     for chunked_prefill_val in [True]:
                         parallel_setups.append(
                             ParallelSetup(
@@ -82,7 +103,6 @@ def detailed(
             runner=runner,
             test_options=CPTestOptions(
                 multi_node_only=multi_node_only,
-                load_format=load_format,
                 attn_backend=attn_backend,
             ),
         )
@@ -101,7 +121,24 @@ def iter_params(self, model_id: str):
                 )
 
 
-def _compare_cp_with_tp(
+CP_TEXT_GENERATION_MODELS = {
+    "deepseek-ai/DeepSeek-V2-Lite-Chat": [
+        CPTestSettings.detailed(
+            dcp_multipliers=[0.5, 1], cp_kv_cache_interleave_size=64
+        ),
+    ],
+    "Qwen/Qwen2.5-1.5B-Instruct": [
+        CPTestSettings.detailed(
+            cp_kv_cache_interleave_size=16, attn_backend="FLASH_ATTN"
+        ),
+        CPTestSettings.detailed(
+            cp_kv_cache_interleave_size=16, attn_backend="FLASHINFER"
+        ),
+    ],
+}
+
+
+def _test_cp_gsm8k(
     model_id: str,
     parallel_setup: ParallelSetup,
     distributed_backend: str,
@@ -121,7 +158,7 @@ def _compare_cp_with_tp(
         chunked_prefill,
     ) = parallel_setup
 
-    multi_node_only, load_format, attn_backend = test_options
+    multi_node_only, attn_backend = test_options
 
     model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
     model_info.check_transformers_version(on_fail="skip")
@@ -130,22 +167,7 @@ def _compare_cp_with_tp(
     tokenizer_mode = model_info.tokenizer_mode
     hf_overrides = model_info.hf_overrides
 
-    if load_format == "dummy":
-        # Avoid OOM
-        text_overrides = {
-            "num_hidden_layers": 4,
-            "hidden_size": 512,
-            "intermediate_size": 800,
-            "num_attention_heads": 4,
-            "num_key_value_heads": 1,
-        }
-
-        if is_multimodal:
-            hf_overrides.update({"text_config": text_overrides})
-        else:
-            hf_overrides.update(text_overrides)
-    else:
-        model_info.check_available_online(on_fail="skip")
+    model_info.check_available_online(on_fail="skip")
 
     if num_gpus_available < tp_size * pp_size:
         pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
@@ -157,90 +179,70 @@ def _compare_cp_with_tp(
     if multi_node_only and not VLLM_MULTI_NODE:
         pytest.skip("Not in multi-node setting")
 
-    common_args = [
+    server_args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
         "bfloat16",
         "--max-model-len",
-        "2048",
+        "4096",
         "--max-num-seqs",
-        "8",
+        "64",
     ]
     if chunked_prefill:
-        common_args.append("--enable-chunked-prefill")
+        server_args.append("--enable-chunked-prefill")
     if eager_mode:
-        common_args.append("--enforce-eager")
+        server_args.append("--enforce-eager")
     if runner != "auto":
-        common_args.extend(["--runner", runner])
+        server_args.extend(["--runner", runner])
     if trust_remote_code:
-        common_args.append("--trust-remote-code")
+        server_args.append("--trust-remote-code")
     if tokenizer_mode:
-        common_args.extend(["--tokenizer-mode", tokenizer_mode])
-    if load_format:
-        common_args.extend(["--load-format", load_format])
+        server_args.extend(["--tokenizer-mode", tokenizer_mode])
     if hf_overrides:
-        common_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
-
-    if not attn_backend:
-        cp_env = tp_env = {}
-    else:
-        cp_env = tp_env = {
-            "VLLM_ATTENTION_BACKEND": attn_backend,
-        }
-
-    cp_args = [
-        *common_args,
-        "--tensor-parallel-size",
-        str(tp_size),
-        "--pipeline-parallel-size",
-        str(pp_size),
-        "--decode-context-parallel-size",
-        str(dcp_size),
-        "--dcp-kv-cache-interleave-size",
-        str(cp_kv_cache_interleave_size),
-        "--distributed-executor-backend",
-        distributed_backend,
-    ]
+        server_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
+
+    server_args.extend(
+        [
+            "--tensor-parallel-size",
+            str(tp_size),
+            "--pipeline-parallel-size",
+            str(pp_size),
+            "--decode-context-parallel-size",
+            str(dcp_size),
+            "--dcp-kv-cache-interleave-size",
+            str(cp_kv_cache_interleave_size),
+            "--distributed-executor-backend",
+            distributed_backend,
+        ]
+    )
 
-    tp_args = [
-        *common_args,
-        "--tensor-parallel-size",
-        str(tp_size),
-        "--pipeline-parallel-size",
-        str(pp_size),
-        "--distributed-executor-backend",
-        distributed_backend,
-    ]
+    server_env = {}
+    if attn_backend:
+        server_env["VLLM_ATTENTION_BACKEND"] = attn_backend
 
-    compare_two_settings(
+    with RemoteOpenAIServer(
         model_id,
-        cp_args,
-        tp_args,
-        cp_env,
-        tp_env,
-        method=method,
+        server_args,
+        env_dict=server_env,
         max_wait_seconds=720,
-    )
-
-
-CP_TEXT_GENERATION_MODELS = {
-    "deepseek-ai/DeepSeek-V2-Lite-Chat": [
-        CPTestSettings.detailed(),
-        CPTestSettings.detailed(tp_base=2),
-        CPTestSettings.detailed(tp_base=2, cp_kv_cache_interleave_size=64),
-    ],
-    "bigcode/gpt_bigcode-santacoder": [
-        CPTestSettings.detailed(),
-        CPTestSettings.detailed(tp_base=2),
-    ],
-}
+    ) as remote_server:
+        host = f"http://{remote_server.host}"
+        port = remote_server.port
+
+        # Run GSM8K evaluation
+        results = evaluate_gsm8k(
+            num_questions=NUM_QUESTIONS,
+            num_shots=NUM_SHOTS,
+            host=host,
+            port=port,
+        )
 
-CP_TEST_MODELS = [
-    # TODO support other models
-    # [LANGUAGE GENERATION]
-    "deepseek-ai/DeepSeek-V2-Lite-Chat",
-    "bigcode/gpt_bigcode-santacoder",
-]
+        # Validate accuracy is reasonable
+        accuracy = results["accuracy"]
+        min_accuracy = MIN_ACCURACY[model_id]
+        assert accuracy >= min_accuracy, (
+            f"TP+DCP accuracy too low: {accuracy:.3f} < {min_accuracy:.3f}"
+        )
 
 
 @pytest.mark.parametrize(
@@ -274,12 +276,12 @@ def test_cp_generation(
     ):
         pytest.skip(reason="MLA+DCP requires compute capability of 9.0 or higher")
     if (
-        model_id == "bigcode/gpt_bigcode-santacoder"
+        model_id == "Qwen/Qwen2.5-1.5B-Instruct"
         and torch.cuda.get_device_capability() != (9, 0)
     ):
         pytest.skip(reason="GQA+DCP currently requires compute capability of 9.0")
 
-    _compare_cp_with_tp(
+    _test_cp_gsm8k(
         model_id,
         parallel_setup,
         distributed_backend,
diff --git a/tests/models/registry.py b/tests/models/registry.py
@@ -416,7 +416,11 @@ def check_available_online(
         trust_remote_code=True,
     ),
     "Qwen2ForCausalLM": _HfExamplesInfo(
-        "Qwen/Qwen2-0.5B-Instruct", extras={"2.5": "Qwen/Qwen2.5-0.5B-Instruct"}
+        "Qwen/Qwen2-0.5B-Instruct",
+        extras={
+            "2.5": "Qwen/Qwen2.5-0.5B-Instruct",
+            "2.5-1.5B": "Qwen/Qwen2.5-1.5B-Instruct",
+        },
     ),
     "Qwen2MoeForCausalLM": _HfExamplesInfo("Qwen/Qwen1.5-MoE-A2.7B-Chat"),
     "Qwen3ForCausalLM": _HfExamplesInfo("Qwen/Qwen3-8B"),
