Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/tests/v1/spec_decode/test_max_len.py
+++ b/tests/v1/spec_decode/test_max_len.py
@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Test whether spec decoding handles the max model length properly."""
+
+import pytest
+
+from tests.utils import get_attn_backend_list_based_on_platform
+from vllm import LLM, SamplingParams
+from vllm.platforms import current_platform
+from vllm.sampling_params import StructuredOutputsParams
+
+_PROMPTS = [
+    "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
+    "Repeat the following sentence 10 times: Consistency is key to mastering any skill.",  # noqa: E501
+    "Who won the Turing Award in 2018, and for what contribution? Describe in detail.",  # noqa: E501
+]
+
+
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
+def test_ngram_max_len(num_speculative_tokens: int):
+    llm = LLM(
+        model="facebook/opt-125m",
+        max_model_len=100,
+        enforce_eager=True,  # For faster initialization.
+        speculative_config={
+            "method": "ngram",
+            "prompt_lookup_max": 5,
+            "prompt_lookup_min": 3,
+            "num_speculative_tokens": num_speculative_tokens,
+        },
+    )
+    sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
+    llm.generate(_PROMPTS, sampling_params)
+
+
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
+@pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
+def test_eagle_max_len(
+    monkeypatch: pytest.MonkeyPatch, num_speculative_tokens: int, attn_backend: str
+):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+
+        if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
+            pytest.skip(
+                "TRITON_ATTN does not support "
+                "multi-token eagle spec decode on current platform"
+            )
+
+        if attn_backend == "ROCM_AITER_FA" and current_platform.is_rocm():
+            m.setenv("VLLM_ROCM_USE_AITER", "1")
+
+        llm = LLM(
+            model="meta-llama/Meta-Llama-3-8B-Instruct",
+            enforce_eager=True,  # For faster initialization.
+            speculative_config={
+                "method": "eagle",
+                "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
+                "num_speculative_tokens": num_speculative_tokens,
+                "max_model_len": 80,
+            },
+            max_model_len=200,
+        )
+        sampling_params = SamplingParams(max_tokens=200, ignore_eos=True)
+        outputs = llm.generate(_PROMPTS, sampling_params)
+        for o in outputs:
+            assert o.outputs[0].finish_reason == "length", (
+                "This test is only meaningful if the output "
+                "is truncated due to max length"
+            )
+
+        sampling_params = SamplingParams(
+            max_tokens=200,
+            structured_outputs=StructuredOutputsParams(
+                regex="^" + "a b c d e " * 15 + "$"
+            ),
+        )
+        output = llm.generate(_PROMPTS, sampling_params)
+        for o in output:
+            assert o.prompt_token_ids is not None
+            assert (
+                len(o.prompt_token_ids)
+                < 80
+                < len(o.prompt_token_ids) + len(o.outputs[0].token_ids)
+                <= 200
+            ), (
+                "This test is only meaningful if the output "
+                "is longer than the eagle max length"
+            )
+            assert o.outputs[0].text == "a b c d e " * 15