[Bug fix][Core] assert num_new_tokens == 1 fails when SamplingParams.n is not...

[Bug fix][Core] assert num_new_tokens == 1 fails when SamplingParams.n is not 1 and max_tokens is large & Add tests for preemption (#4451)

.buildkite/test-pipeline.yaml

@@ -17,6 +17,7 @@ steps:
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_basic_correctness.py
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
+  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py
 
 - label: Core Test
   command: pytest -v -s core

tests/basic_correctness/test_chunked_prefill.py

@@ -55,7 +55,6 @@ def test_models(
     )
     vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
     del vllm_model
-    print(vllm_outputs[0])
 
     for i in range(len(example_prompts)):
         hf_output_ids, hf_output_str = hf_outputs[i]

tests/basic_correctness/test_preemption.py

+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 has to be set before running this test.
+
+Run `VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1
+pytest tests/basic_correctness/test_preemption.py`.
+"""
+import pytest
+
+from vllm.core.scheduler import (ARTIFICIAL_PREEMPTION_MAX_CNT,
+                                 ENABLE_ARTIFICIAL_PREEMPT)
+
+MODELS = [
+    "facebook/opt-125m",
+]
+
+assert ENABLE_ARTIFICIAL_PREEMPT is True, (
+    "Use an env var VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1. "
+    "`VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest "
+    "tests/basic_correctness/test_preemption.py`")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [96])
+@pytest.mark.parametrize("chunked_prefill_token_size", [16])
+def test_chunked_prefill_recompute(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    chunked_prefill_token_size: int,
+) -> None:
+    """Ensure that chunked prefill works with preemption."""
+    max_num_seqs = min(chunked_prefill_token_size, 256)
+    enable_chunked_prefill = False
+    max_num_batched_tokens = None
+    if chunked_prefill_token_size != -1:
+        enable_chunked_prefill = True
+        max_num_batched_tokens = chunked_prefill_token_size
+
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(
+        model,
+        dtype=dtype,
+        max_num_batched_tokens=max_num_batched_tokens,
+        enable_chunked_prefill=enable_chunked_prefill,
+        max_num_seqs=max_num_seqs,
+    )
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    assert (vllm_model.model.llm_engine.scheduler.artificial_preempt_cnt <
+            ARTIFICIAL_PREEMPTION_MAX_CNT)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [96])
+def test_preemption(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    """By default, recompute preemption is enabled"""
+
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(
+        model,
+        dtype=dtype,
+    )
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    assert (vllm_model.model.llm_engine.scheduler.artificial_preempt_cnt <
+            ARTIFICIAL_PREEMPTION_MAX_CNT)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [96])
+@pytest.mark.parametrize("beam_width", [4])
+def test_swap(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    beam_width: int,
+) -> None:
+    """Use beam search enables swapping."""
+    example_prompts = example_prompts[:1]
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_beam_search(example_prompts, beam_width,
+                                               max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(model, dtype=dtype, swap_space=10)
+    vllm_outputs = vllm_model.generate_beam_search(example_prompts, beam_width,
+                                                   max_tokens)
+    assert (vllm_model.model.llm_engine.scheduler.artificial_preempt_cnt <
+            ARTIFICIAL_PREEMPTION_MAX_CNT)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, _ = hf_outputs[i]
+        vllm_output_ids, _ = vllm_outputs[i]
+        assert len(hf_output_ids) == len(vllm_output_ids)
+        for j in range(len(hf_output_ids)):
+            assert hf_output_ids[j] == vllm_output_ids[j], (
+                f"Test{i} output{j}:\nHF: {hf_output_ids}\n"
+                f"vLLM: {vllm_output_ids}")

tests/conftest.py

@@ -296,6 +296,7 @@ class VllmRunner:
         tensor_parallel_size: int = 1,
         block_size: int = 16,
         enable_chunked_prefill: bool = False,
+        swap_space=4,
         **kwargs,
     ) -> None:
         self.model = LLM(
@@ -303,7 +304,7 @@ class VllmRunner:
             tokenizer=tokenizer_name,
             trust_remote_code=True,
             dtype=dtype,
-            swap_space=0,
+            swap_space=swap_space,
             disable_log_stats=disable_log_stats,
             tensor_parallel_size=tensor_parallel_size,
             max_model_len=max_model_len,

tests/spec_decode/test_spec_decode_worker.py

@@ -33,7 +33,7 @@ def test_correctly_calls_draft_model(k: int, batch_size: int):
     worker = SpecDecodeWorker(draft_worker, target_worker, rejection_sampler,
                               metrics_collector)
 
-    exception_secret = 'artifical stop'
+    exception_secret = 'artificial stop'
     draft_worker.get_spec_proposals.side_effect = ValueError(exception_secret)
 
     execute_model_data, _, _ = create_batch(batch_size, k)
@@ -101,7 +101,7 @@ def test_correctly_calls_target_model(k: int, batch_size: int):
         proposal_probs=proposal_probs,
         proposal_lens=proposal_lens)
 
-    exception_secret = 'artifical stop'
+    exception_secret = 'artificial stop'
     target_worker.execute_model.side_effect = ValueError(exception_secret)
 
     with pytest.raises(ValueError, match=exception_secret):
@@ -197,7 +197,7 @@ def test_correctly_calls_rejection_sampler(k: int, batch_size: int):
 
     target_worker.execute_model.return_value = [target_output[0]]
 
-    exception_secret = 'artifical stop'
+    exception_secret = 'artificial stop'
     rejection_sampler.side_effect = ValueError(exception_secret)
 
     with pytest.raises(ValueError, match=exception_secret):

vllm/core/scheduler.py

 import enum
+import os
+import random
 import time
 from collections import deque
 from dataclasses import dataclass, field
@@ -15,6 +17,13 @@ from vllm.utils import merge_dicts
 
 logger = init_logger(__name__)
 
+# Test-only. If configured, decode is preempted with
+# ARTIFICIAL_PREEMPTION_PROB% probability.
+ENABLE_ARTIFICIAL_PREEMPT = bool(
+    os.getenv("VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT", False))  # noqa
+ARTIFICIAL_PREEMPTION_PROB = 0.5
+ARTIFICIAL_PREEMPTION_MAX_CNT = 500
+
 
 class PreemptionMode(enum.Enum):
     """Preemption modes.
@@ -286,6 +295,13 @@ class Scheduler:
         # Latency of the last prompt step
         self.last_prompt_latency = 0.0
 
+        # The following field is test-only. It is used to inject artificial
+        # preemption.
+        self.enable_artificial_preemption = ENABLE_ARTIFICIAL_PREEMPT
+        self.artificial_preempt_cnt = (ARTIFICIAL_PREEMPTION_MAX_CNT
+                                       if self.enable_artificial_preemption
+                                       else 0)
+
     @property
     def lora_enabled(self) -> bool:
         return bool(self.lora_config)
@@ -386,15 +402,13 @@ class Scheduler:
         # groups to preempt.
         now = time.time()
         running_queue = policy.sort_by_priority(now, running_queue)
-
         while running_queue:
             seq_group = running_queue[0]
             num_running_tokens = self._get_num_new_tokens(
                 seq_group, SequenceStatus.RUNNING, enable_chunking, budget)
 
-            # We can have up to 1 running prefill at any given time in running
-            # queue, which means we can guarantee chunk size is at least 1.
-            assert num_running_tokens != 0
+            if num_running_tokens == 0:
+                break
 
             running_queue.popleft()
             while not self._can_append_slots(seq_group):
@@ -449,9 +463,6 @@ class Scheduler:
                 if curr_loras is not None and seq_group.lora_int_id > 0:
                     curr_loras.add(seq_group.lora_int_id)
 
-        # Make sure all queues are updated.
-        assert len(running_queue) == 0
-
         return running_queue, SchedulerRunningOutputs(
             decode_seq_groups=decode_seq_groups,
             prefill_seq_groups=prefill_seq_groups,
@@ -545,7 +556,6 @@ class Scheduler:
                     ScheduledSequenceGroup(seq_group,
                                            token_chunk_size=num_new_tokens))
             else:
-                assert num_new_tokens == 1
                 decode_seq_groups.append(
                     ScheduledSequenceGroup(seq_group, token_chunk_size=1))
             budget.add_num_batched_tokens(seq_group.request_id, num_new_tokens)
@@ -868,6 +878,13 @@ class Scheduler:
         """Determine whether or not we have enough space in the KV cache to
         continue generation of the sequence group.
         """
+        # It is True only for testing case to trigger artificial preemption.
+        if (self.enable_artificial_preemption
+                and random.uniform(0, 1) < ARTIFICIAL_PREEMPTION_PROB
+                and self.artificial_preempt_cnt > 0):
+            self.artificial_preempt_cnt -= 1
+            return False
+
         # Appending slots only occurs in decoding.
         is_prefill = False
 
@@ -1116,11 +1133,14 @@ class Scheduler:
         if `enable_chunking` is True. If a sequence group has multiple
         sequences (e.g., running beam search), it means it is in decoding
         phase, so chunking doesn't happen.
+
+        Returns 0 if the new token cannot be computed due to token budget.
         """
         num_new_tokens = 0
         seqs = seq_group.get_seqs(status=status)
         for seq in seqs:
             num_new_tokens += seq.get_num_new_tokens()
+        assert num_new_tokens > 0
         # Chunk if a running request cannot fit in.
         # If number of seq > 1, it means it is doing beam search in a
         # decode phase. Do not chunk in that case.