add qwen3

vllm-v0.6.2/tests/spec_decode/test_dynamic_spec_decode.py

@@ -0,0 +1,91 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+
+from vllm.sequence import ExecuteModelRequest
+from vllm.spec_decode.mlu_metrics import MLUAsyncMetricsCollector
+from vllm.spec_decode.mlu_multi_step_worker import MLUMultiStepWorker
+from vllm.spec_decode.mlu_spec_decode_worker import MLUSpecDecodeWorker
+from vllm.spec_decode.top1_proposer import Top1Proposer
+
+from .test_utils import mock_spec_decode_sampler
+from .utils import create_batch, mock_worker
+
+MultiStepWorker = MLUMultiStepWorker
+AsyncMetricsCollector = MLUAsyncMetricsCollector
+SpecDecodeWorker = MLUSpecDecodeWorker
+
+
+@pytest.mark.parametrize('queue_size', [4])
+@pytest.mark.parametrize('batch_size', [1])
+@pytest.mark.parametrize('k', [1])
+@pytest.mark.parametrize("acceptance_sampler_method",
+                         ["rejection_sampler", "typical_acceptance_sampler"])
+@torch.inference_mode()
+def test_disable_spec_tokens(queue_size: int, batch_size: int, k: int,
+                             acceptance_sampler_method: str):
+    """Verify that speculative tokens are disabled when the batch size
+    exceeds the threshold.
+    """
+    disable_by_batch_size = 3
+    draft_worker = mock_worker(cls=MultiStepWorker)
+    target_worker = mock_worker()
+    metrics_collector = MagicMock(spec=AsyncMetricsCollector)
+    worker = SpecDecodeWorker(proposer_worker=draft_worker,
+                              scorer_worker=target_worker,
+                              spec_decode_sampler=mock_spec_decode_sampler(
+                                  acceptance_sampler_method),
+                              disable_logprobs=False,
+                              metrics_collector=metrics_collector,
+                              disable_by_batch_size=disable_by_batch_size)
+
+    exception_secret = 'artificial stop'
+    draft_worker.get_spec_proposals.side_effect = ValueError(exception_secret)
+
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
+    execute_model_req = ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k,
+        running_queue_size=queue_size)
+
+    if queue_size > disable_by_batch_size:
+        with patch.object(worker,
+                          '_run_no_spec',
+                          side_effect=ValueError(exception_secret)), \
+            pytest.raises(ValueError, match=exception_secret):
+            worker.execute_model(execute_model_req=execute_model_req)
+
+    # When the batch size is larger than the threshold,
+    # we expect no speculative tokens (0).
+    expected_num_spec_tokens = None if queue_size < disable_by_batch_size else 0
+    assert seq_group_metadata_list[
+        0].num_speculative_tokens == expected_num_spec_tokens
+
+    draft_worker.sampler_output.side_effect = ValueError(exception_secret)
+
+    proposer = Top1Proposer(
+        worker=draft_worker,
+        device='cpu',  # not used
+        vocab_size=100,  # not used
+        # Must be long enough to avoid being skipped due to length.
+        max_proposal_len=1024,
+    )
+
+    if queue_size < disable_by_batch_size:
+        # Should raise exception when executing the mocked draft model.
+        with pytest.raises(ValueError, match=exception_secret):
+            proposer.get_spec_proposals(
+                execute_model_req=ExecuteModelRequest(
+                    seq_group_metadata_list=seq_group_metadata_list,
+                    num_lookahead_slots=k),
+                seq_ids_with_bonus_token_in_last_step=set())
+    else:
+        # Should not execute the draft model because spec decode is disabled
+        # for all requests. Accordingly, the proposal length should be 0.
+        proposals = proposer.get_spec_proposals(
+            execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list,
+                num_lookahead_slots=k),
+            seq_ids_with_bonus_token_in_last_step=set())
+        assert proposals.proposal_lens.tolist() == [0] * batch_size