init v0.11.0rc0

tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

@@ -1,14 +1,10 @@
 from __future__ import annotations
 
-import os
-
 import pytest
 from vllm import SamplingParams
 
 from tests.e2e.conftest import VllmRunner
 
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
 
 @pytest.fixture
 def sampling_config():
@@ -20,9 +16,10 @@ def model_name():
     return "wemaster/deepseek_mtp_main_random_bf16"
 
 
-def test_mtp_correctness(
+def mtp_correctness(
     sampling_config: SamplingParams,
     model_name: str,
+    num_speculative_tokens: int,
 ):
     example_prompts = [
         "Hello, my name is",
@@ -38,7 +35,7 @@ def test_mtp_correctness(
                     tensor_parallel_size=1,
                     gpu_memory_utilization=0.7,
                     max_model_len=256,
-                    enforce_eager=True) as ref_llm:
+                    enforce_eager=False) as ref_llm:
         ref_outputs = ref_llm.generate(example_prompts, sampling_config)
 
     with VllmRunner(
@@ -50,9 +47,9 @@ def test_mtp_correctness(
             enable_expert_parallel=True,
             speculative_config={
                 "method": "deepseek_mtp",
-                "num_speculative_tokens": 1,
+                "num_speculative_tokens": num_speculative_tokens,
             },
-            enforce_eager=True,
+            enforce_eager=False,
             max_model_len=2000,
             additional_config={"ascend_scheduler_config": {
                 "enabled": False
@@ -74,3 +71,18 @@ def test_mtp_correctness(
     # Heuristic: expect at least 66% of the prompts to match exactly
     # Upon failure, inspect the outputs to check for inaccuracy.
     assert matches > int(0.66 * len(ref_outputs))
+    del spec_llm
+
+
+def test_mtp1_correctness(
+    sampling_config: SamplingParams,
+    model_name: str,
+):
+    mtp_correctness(sampling_config, model_name, 1)
+
+
+def test_mtp2_correctness(
+    sampling_config: SamplingParams,
+    model_name: str,
+):
+    mtp_correctness(sampling_config, model_name, 2)

tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_torchair_correctness.py

@@ -1,14 +1,10 @@
 from __future__ import annotations
 
-import os
-
 import pytest
 from vllm import SamplingParams
 
 from tests.e2e.conftest import VllmRunner
 
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
 
 @pytest.fixture
 def sampling_config():

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -99,7 +99,6 @@ def test_ngram_correctness(
     assert matches > int(0.7 * len(ref_outputs))
 
 
-@pytest.mark.skipif(True, reason="oom in CI, fix me")
 @pytest.mark.parametrize("use_eagle3", [False, True], ids=["eagle", "eagle3"])
 def test_eagle_correctness(
     test_prompts: list[list[dict[str, Any]]],
@@ -111,8 +110,6 @@ def test_eagle_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using eagle speculative decoding.
     '''
-    if not use_eagle3:
-        pytest.skip("Not current support for the test.")
 
     ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
@@ -121,7 +118,6 @@ def test_eagle_correctness(
     spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
     with VllmRunner(
             model_name,
-            trust_remote_code=True,
             enable_chunked_prefill=True,
             max_num_seqs=1,
             max_num_batched_tokens=2048,