[SpecDecode] Add spec decode support

.github/workflows/vllm_ascend_test.yaml

@@ -122,10 +122,10 @@ jobs:
           VLLM_USE_V1: 0
         run: |
           if [[ "${{ matrix.os }}" == "linux-arm64-npu-1" ]]; then
-            pytest -sv tests/singlecard
+            pytest -sv tests/singlecard/test_offline_inference.py
             pytest -sv tests/ops
           else
-            pytest -sv tests/multicard
+            pytest -sv tests/multicard/test_offline_inference_distributed.py
             pytest -sv tests/ops
           fi
 
@@ -135,13 +135,35 @@ jobs:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
         run: |
           if [[ "${{ matrix.os }}" == "linux-arm64-npu-1" ]]; then
-            pytest -sv tests/singlecard
+            pytest -sv tests/singlecard/test_offline_inference.py
             pytest -sv tests/ops
           else
-            pytest -sv tests/multicard
+            pytest -sv tests/multicard/test_offline_inference_distributed.py
             pytest -sv tests/ops
           fi
 
+      - name: Check for changes in Speculative Decode
+        id: filter_spec_decode
+        uses: dorny/paths-filter@v2
+        with:
+          filters: |
+            speculative_tests_changed:
+              - "tests/singlecard/spec_decode/**"
+              - "tests/multicard/spec_decode_e2e/**"
+              - "vllm_ascend/worker/multi_step_runner.py"
+              - "vllm_ascend/worker/multi_step_worker.py"
+              - "vllm_ascend/patch/patch_rejection_sampler.py"
+              - "vllm_ascend/patch/patch_spec_decode_worker.py"
+              - "vllm_ascend/patch/patch_multi_step_worker.py"
+      - name: Run vllm-project/vllm-ascend Speculative Decode test
+        env:
+          HF_ENDPOINT: https://hf-mirror.com
+        if: steps.filter_spec_decode.outputs.speculative_tests_changed
+        run: |
+          if [[ "${{ matrix.os }}" == "linux-arm64-npu-1" ]]; then
+            pytest -sv tests/singlecard/spec_decode
+          fi
+
       - name: Run vllm-project/vllm test for V0 Engine
         env:
           VLLM_USE_V1: 0

requirements-dev.txt

@@ -3,4 +3,4 @@ modelscope
 pytest >= 6.0
 pytest-asyncio
 lm-eval
-ray
+ray

tests/multicard/test_offline_inference_distributed.py

@@ -24,7 +24,8 @@
 
 import pytest
 import vllm  # noqa: F401
-from conftest import VllmRunner
+
+from tests.conftest import VllmRunner
 
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 

tests/singlecard/spec_decode/conftest.py

@@ -0,0 +1,28 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/tests/spec_decode/conftest.py
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import pytest
+
+
+@pytest.fixture(scope="function", autouse=True)
+def use_v0_only(monkeypatch):
+    """
+    Since this module is V0 only, set VLLM_USE_V1=0 for
+    all tests in the module.
+    """
+    monkeypatch.setenv('VLLM_USE_V1', '0')

tests/singlecard/spec_decode/e2e/conftest.py

@@ -0,0 +1,256 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/tests/spec_decode/e2e/conftest.py
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from itertools import cycle
+from typing import List, Optional, Sequence, Tuple, Union
+
+import pytest
+import torch
+from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.model_executor.utils import set_random_seed
+from vllm.sequence import PromptLogprobs, SampleLogprobs
+
+from ....model_utils import (TokensTextLogprobs,
+                             TokensTextLogprobsPromptLogprobs,
+                             check_logprobs_close, check_outputs_equal)
+
+PROMPTS = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+    "San Francisco is know for its",
+    "Facebook was created in 2004 by",
+    "Curious George is a",
+    "Python 3.11 brings improvements to its",
+]
+
+
+@pytest.fixture
+def test_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                       test_llm_kwargs, seed):
+
+    def generate():
+        kwargs = {
+            **common_llm_kwargs,
+            **per_test_common_llm_kwargs,
+            **test_llm_kwargs,
+        }
+
+        llm = LLM(**kwargs)
+
+        if seed is not None:
+            set_random_seed(seed)
+
+        yield llm
+
+        del llm
+        cleanup_dist_env_and_memory()
+
+    return generate
+
+
+def maybe_assert_ngram_worker(llm):
+    # Verify the proposer worker is ngram if ngram is specified.
+    if (llm.llm_engine.speculative_config is not None
+            and llm.llm_engine.speculative_config.method == "ngram"):
+        from vllm.spec_decode.ngram_worker import NGramWorker
+        assert isinstance(
+            llm.llm_engine.model_executor.driver_worker.proposer_worker,
+            NGramWorker)
+
+
+def get_output_from_llm_generator(
+        llm_generator, prompts,
+        sampling_params) -> Tuple[List[str], List[List[int]], float]:
+    tokens: List[str] = []
+    token_ids: List[List[int]] = []
+    acceptance_rate: float = -1.0
+    for llm in llm_generator():
+        maybe_assert_ngram_worker(llm)
+
+        outputs = llm.generate(prompts, sampling_params, use_tqdm=True)
+
+        token_ids = [output.outputs[0].token_ids for output in outputs]
+        tokens = [output.outputs[0].text for output in outputs]
+
+        # Fetch acceptance rate if logging is enabled.
+        if stat_loggers := getattr(llm.llm_engine, "stat_loggers", None):
+            stat_logger = stat_loggers["prometheus"]
+            acceptance_rate = (stat_logger.metrics.
+                               gauge_spec_decode_draft_acceptance_rate.labels(
+                                   **stat_logger.labels)._value.get())
+        del llm
+
+    return tokens, token_ids, acceptance_rate
+
+
+def check_logprobs_correctness(
+    spec_outputs: Sequence[Union[TokensTextLogprobs,
+                                 TokensTextLogprobsPromptLogprobs]],
+    baseline_outputs: Sequence[Union[TokensTextLogprobs,
+                                     TokensTextLogprobsPromptLogprobs]],
+    disable_logprobs: bool = False,
+):
+    """Compare sampled and prompt logprobs between baseline and spec decoding
+    """
+    if not disable_logprobs:
+        return check_logprobs_close(
+            outputs_0_lst=baseline_outputs,
+            outputs_1_lst=spec_outputs,
+            name_0="org",
+            name_1="sd",
+        )
+
+    # Check correctness when disable_logprobs == True
+    for spec_output, baseline_output in zip(spec_outputs, baseline_outputs):
+        # Check generated token logprobs.
+        spec_logprobs = spec_output[2]
+        baseline_logprobs = baseline_output[2]
+        _check_logprobs_when_output_disabled(spec_logprobs,
+                                             baseline_logprobs,
+                                             is_prompt_logprobs=False)
+
+        # Check prompt logprobs too, if they exist
+        if len(baseline_output) == 4:
+            assert len(spec_output) == 4
+            spec_prompt_logprobs = spec_output[3]
+            baseline_prompt_logprobs = baseline_output[3]
+            _check_logprobs_when_output_disabled(spec_prompt_logprobs,
+                                                 baseline_prompt_logprobs,
+                                                 is_prompt_logprobs=True)
+
+
+def _check_logprobs_when_output_disabled(
+    spec_logprobs: Union[Optional[PromptLogprobs], SampleLogprobs],
+    baseline_logprobs: Union[Optional[PromptLogprobs], SampleLogprobs],
+    is_prompt_logprobs: bool = False,
+):
+    # Prompt logprobs are optional
+    if is_prompt_logprobs and baseline_logprobs is None:
+        assert spec_logprobs is None
+        return
+
+    assert spec_logprobs is not None
+    assert baseline_logprobs is not None
+    assert len(spec_logprobs) == len(baseline_logprobs)
+
+    # For each generated position of the sequence.
+    for pos, (spec_pos_logprobs, baseline_pos_logprobs) in enumerate(
+            zip(spec_logprobs, baseline_logprobs)):
+
+        # First prompt logprob is expected to be None
+        if is_prompt_logprobs and baseline_pos_logprobs is None:
+            assert spec_pos_logprobs is None
+            assert pos == 0
+            continue
+
+        assert spec_pos_logprobs is not None
+        assert baseline_pos_logprobs is not None
+
+        # When disabled, the 1 logprob is returned with dummy values for the
+        # score and rank, but the token id should match the baseline model
+        assert len(spec_pos_logprobs) == 1
+        (spec_pos_logprob_token_id,
+         spec_pos_logprob) = next(iter(spec_pos_logprobs.items()))
+        assert spec_pos_logprob.rank == -1
+        assert spec_pos_logprob.logprob == 0.0
+        if isinstance(spec_pos_logprob_token_id, torch.Tensor):
+            spec_pos_logprob_token_id = spec_pos_logprob_token_id.item()
+        assert spec_pos_logprob_token_id in baseline_pos_logprobs
+
+
+def run_equality_correctness_test(
+        vllm_runner,
+        common_llm_kwargs,
+        per_test_common_llm_kwargs,
+        baseline_llm_kwargs,
+        test_llm_kwargs,
+        batch_size: int,
+        max_output_len: int,
+        seed: Optional[int] = 0,
+        temperature: float = 0.0,
+        disable_seed: bool = False,
+        ignore_eos: bool = True,
+        ensure_all_accepted: bool = False,
+        expected_acceptance_rate: Optional[float] = None,
+        logprobs: Optional[int] = None,
+        prompt_logprobs: Optional[int] = None,
+        disable_logprobs: bool = False):
+
+    org_args = {
+        **common_llm_kwargs,
+        **per_test_common_llm_kwargs,
+        **baseline_llm_kwargs,
+    }
+
+    sd_args = {
+        **common_llm_kwargs,
+        **per_test_common_llm_kwargs,
+        **test_llm_kwargs,
+    }
+
+    prompts = [prompt for prompt, _ in zip(cycle(PROMPTS), range(batch_size))]
+
+    if disable_seed:
+        seed = None
+
+    sampling_params = SamplingParams(temperature=temperature,
+                                     max_tokens=max_output_len,
+                                     seed=seed,
+                                     ignore_eos=ignore_eos,
+                                     logprobs=logprobs,
+                                     prompt_logprobs=prompt_logprobs)
+
+    with vllm_runner(**org_args) as vllm_model:
+        org_outputs = vllm_model.generate_w_logprobs(prompts, sampling_params)
+
+    with vllm_runner(**sd_args) as vllm_model:
+        if ensure_all_accepted or expected_acceptance_rate is not None:
+            # Force log interval to be 0 to catch all metrics.
+            stat_logger = vllm_model.model.llm_engine.stat_loggers[
+                'prometheus']
+            stat_logger.local_interval = -100
+
+        sd_outputs = vllm_model.generate_w_logprobs(prompts, sampling_params)
+
+        if ensure_all_accepted or expected_acceptance_rate is not None:
+            acceptance_rate = (stat_logger.metrics.
+                               gauge_spec_decode_draft_acceptance_rate.labels(
+                                   **stat_logger.labels)._value.get())
+
+            if ensure_all_accepted:
+                assert True
+                # FIXME: ci fails to log acceptance rate.
+                # It works locally.
+                # assert acceptance_rate == 1.0
+
+            if expected_acceptance_rate is not None:
+                assert acceptance_rate >= expected_acceptance_rate - 1e-2
+
+    # Only pass token entries, not the logprobs
+    check_outputs_equal(outputs_0_lst=[out[0:2] for out in org_outputs],
+                        outputs_1_lst=[out[0:2] for out in sd_outputs],
+                        name_0="org",
+                        name_1="sd")
+
+    # Check logprobs if requested
+    if logprobs is not None or prompt_logprobs is not None:
+        check_logprobs_correctness(sd_outputs, org_outputs, disable_logprobs)