[Speculative decoding] Add periodic log with time spent in proposal/scoring/verification (#6963)

tests/spec_decode/test_spec_decode_worker.py

@@ -34,8 +34,11 @@ def test_correctly_calls_draft_model(k: int, batch_size: int,
     target_worker = mock_worker()
     metrics_collector = MagicMock(spec=AsyncMetricsCollector)
     worker = SpecDecodeWorker(
-        draft_worker, target_worker,
-        mock_spec_decode_sampler(acceptance_sampler_method), metrics_collector)
+        draft_worker,
+        target_worker,
+        mock_spec_decode_sampler(acceptance_sampler_method),
+        disable_logprobs=False,
+        metrics_collector=metrics_collector)
     exception_secret = 'artificial stop'
     draft_worker.get_spec_proposals.side_effect = ValueError(exception_secret)
 
@@ -74,8 +77,11 @@ def test_correctly_calls_target_model(k: int, batch_size: int,
     set_random_seed(1)
 
     worker = SpecDecodeWorker(
-        draft_worker, target_worker,
-        mock_spec_decode_sampler(acceptance_sampler_method), metrics_collector)
+        draft_worker,
+        target_worker,
+        mock_spec_decode_sampler(acceptance_sampler_method),
+        disable_logprobs=False,
+        metrics_collector=metrics_collector)
     worker.init_device()
 
     vocab_size = 32_000
@@ -159,8 +165,11 @@ def test_correctly_calls_spec_decode_sampler(k: int, batch_size: int,
 
     set_random_seed(1)
 
-    worker = SpecDecodeWorker(draft_worker, target_worker, spec_decode_sampler,
-                              metrics_collector)
+    worker = SpecDecodeWorker(draft_worker,
+                              target_worker,
+                              spec_decode_sampler,
+                              disable_logprobs=False,
+                              metrics_collector=metrics_collector)
     worker.init_device()
 
     proposal_token_ids = torch.randint(low=0,
@@ -249,8 +258,11 @@ def test_correctly_formats_output(k: int, batch_size: int,
 
     set_random_seed(1)
     spec_decode_sampler = mock_spec_decode_sampler(acceptance_sampler_method)
-    worker = SpecDecodeWorker(draft_worker, target_worker, spec_decode_sampler,
-                              metrics_collector)
+    worker = SpecDecodeWorker(draft_worker,
+                              target_worker,
+                              spec_decode_sampler,
+                              disable_logprobs=False,
+                              metrics_collector=metrics_collector)
     worker.init_device()
 
     proposal_token_ids = torch.randint(low=0,
@@ -479,9 +491,13 @@ def test_k_equals_zero(k: int, batch_size: int,
     set_random_seed(1)
 
     worker = SpecDecodeWorker(
-        draft_worker, target_worker,
-        mock_spec_decode_sampler(acceptance_sampler_method), False,
-        metrics_collector)
+        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,
+    )
 
     seq_group_metadata_list, _, _ = create_batch(batch_size,
                                                  k,
@@ -526,9 +542,13 @@ def test_empty_input_batch(k: int, batch_size: int,
     set_random_seed(1)
 
     worker = SpecDecodeWorker(
-        draft_worker, target_worker,
-        mock_spec_decode_sampler(acceptance_sampler_method), False,
-        metrics_collector)
+        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,
+    )
 
     seq_group_metadata_list, _, _ = create_batch(batch_size,
                                                  k,
@@ -560,8 +580,13 @@ def test_init_device(acceptance_sampler_method: str):
     spec_decode_sampler = mock_spec_decode_sampler(acceptance_sampler_method)
     metrics_collector = MagicMock(spec=AsyncMetricsCollector)
 
-    worker = SpecDecodeWorker(draft_worker, target_worker, spec_decode_sampler,
-                              False, metrics_collector)
+    worker = SpecDecodeWorker(
+        proposer_worker=draft_worker,
+        scorer_worker=target_worker,
+        spec_decode_sampler=spec_decode_sampler,
+        disable_logprobs=False,
+        metrics_collector=metrics_collector,
+    )
     worker.init_device()
 
     draft_worker.init_device.assert_called_once()
@@ -583,9 +608,11 @@ def test_initialize_cache(acceptance_sampler_method):
     target_worker = mock_worker()
     metrics_collector = MagicMock(spec=AsyncMetricsCollector)
 
-    worker = SpecDecodeWorker(
-        draft_worker, target_worker,
-        mock_spec_decode_sampler(acceptance_sampler_method), metrics_collector)
+    worker = SpecDecodeWorker(proposer_worker=draft_worker,
+                              scorer_worker=target_worker,
+                              spec_decode_sampler=mock_spec_decode_sampler(
+                                  acceptance_sampler_method),
+                              metrics_collector=metrics_collector)
 
     kwargs = {"num_gpu_blocks": 1024, "num_cpu_blocks": 1023}
     worker.initialize_cache(**kwargs)
@@ -725,7 +752,8 @@ def test_populate_seq_ids_with_bonus_tokens():
         seq_group_metadata_list=seq_group_metadata_list,
         accepted_token_ids=accepted_token_ids,
         target_logprobs=target_token_logprobs,
-        k=k)
+        k=k,
+        stage_times=(0, 0, 0))
     # Verify that _seq_with_bonus_token_in_last_step contains the following:
     # 1. Sequence IDs that were already present in
     #    _seq_with_bonus_token_in_last_step but were not part of the current

vllm/config.py

@@ -907,6 +907,7 @@ class SpeculativeConfig:
         speculative_max_model_len: Optional[int],
         enable_chunked_prefill: bool,
         use_v2_block_manager: bool,
+        disable_log_stats: bool,
         speculative_disable_by_batch_size: Optional[int],
         ngram_prompt_lookup_max: Optional[int],
         ngram_prompt_lookup_min: Optional[int],
@@ -1095,7 +1096,8 @@ class SpeculativeConfig:
                 typical_acceptance_sampler_posterior_threshold,
             typical_acceptance_sampler_posterior_alpha=\
                 typical_acceptance_sampler_posterior_alpha,
-            disable_logprobs=disable_logprobs
+            disable_logprobs=disable_logprobs,
+            disable_log_stats=disable_log_stats,
         )
 
     @staticmethod
@@ -1189,6 +1191,7 @@ class SpeculativeConfig:
         typical_acceptance_sampler_posterior_threshold: float,
         typical_acceptance_sampler_posterior_alpha: float,
         disable_logprobs: bool,
+        disable_log_stats: bool,
     ):
         """Create a SpeculativeConfig object.
 
@@ -1221,6 +1224,8 @@ class SpeculativeConfig:
                 sampling, target sampling, and after accepted tokens are
                 determined. If set to False, log probabilities will be
                 returned.
+            disable_log_stats: Whether to disable periodic printing of stage
+                times in speculative decoding.
         """
         self.draft_model_config = draft_model_config
         self.draft_parallel_config = draft_parallel_config
@@ -1235,6 +1240,7 @@ class SpeculativeConfig:
         self.typical_acceptance_sampler_posterior_alpha = \
             typical_acceptance_sampler_posterior_alpha
         self.disable_logprobs = disable_logprobs
+        self.disable_log_stats = disable_log_stats
 
         self._verify_args()
 

vllm/engine/arg_utils.py

@@ -792,6 +792,7 @@ class EngineArgs:
             speculative_max_model_len=self.speculative_max_model_len,
             enable_chunked_prefill=self.enable_chunked_prefill,
             use_v2_block_manager=self.use_v2_block_manager,
+            disable_log_stats=self.disable_log_stats,
             ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
             ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
             draft_token_acceptance_method=\

vllm/spec_decode/spec_decode_worker.py

@@ -27,7 +27,7 @@ from vllm.spec_decode.ngram_worker import NGramWorker
 from vllm.spec_decode.proposer_worker_base import ProposerWorkerBase
 from vllm.spec_decode.smaller_tp_proposer_worker import SmallerTpProposerWorker
 from vllm.spec_decode.target_model_runner import TargetModelRunner
-from vllm.spec_decode.util import (create_sequence_group_output,
+from vllm.spec_decode.util import (Timer, create_sequence_group_output,
                                    get_all_num_logprobs,
                                    get_sampled_token_logprobs, nvtx_range,
                                    split_batch_by_proposal_len)
@@ -75,7 +75,9 @@ def create_spec_worker(*args, **kwargs) -> "SpecDecodeWorker":
         typical_acceptance_sampler_posterior_threshold,
         typical_acceptance_sampler_posterior_alpha=speculative_config.
         typical_acceptance_sampler_posterior_alpha,
-        disable_logprobs=speculative_config.disable_logprobs)
+        disable_logprobs=speculative_config.disable_logprobs,
+        disable_log_stats=speculative_config.disable_log_stats,
+    )
 
     return spec_decode_worker
 
@@ -116,6 +118,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         typical_acceptance_sampler_posterior_threshold: float,
         typical_acceptance_sampler_posterior_alpha: float,
         disable_logprobs: bool,
+        disable_log_stats: bool,
     ) -> "SpecDecodeWorker":
 
         allow_zero_draft_token_step = True
@@ -171,6 +174,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             proposer_worker,
             scorer_worker,
             disable_logprobs=disable_logprobs,
+            disable_log_stats=disable_log_stats,
             disable_by_batch_size=disable_by_batch_size,
             spec_decode_sampler=spec_decode_sampler,
             allow_zero_draft_token_step=allow_zero_draft_token_step)
@@ -180,7 +184,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         proposer_worker: ProposerWorkerBase,
         scorer_worker: WorkerBase,
         spec_decode_sampler: SpecDecodeBaseSampler,
-        disable_logprobs: bool,
+        disable_logprobs: bool = False,
+        disable_log_stats: bool = False,
         metrics_collector: Optional[AsyncMetricsCollector] = None,
         disable_by_batch_size: Optional[int] = None,
         allow_zero_draft_token_step: Optional[bool] = True,
@@ -203,6 +208,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             disable_logprobs: If set to True, token log probabilities will
                 not be output in both the draft worker and the target worker.
                 If set to False, log probabilities will be output by both.
+            disable_log_stats: If set to True, disable periodic printing of
+                speculative stage times.
             disable_by_batch_size: If the batch size is larger than this,
                 disable speculative decoding for new incoming requests.
             metrics_collector: Helper class for collecting metrics; can be set
@@ -240,6 +247,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         # in the subsequent step.
         self.previous_hidden_states: Optional[HiddenStates] = None
         self._disable_logprobs = disable_logprobs
+        self._disable_log_stats = disable_log_stats
 
     def init_device(self) -> None:
         """Initialize both scorer and proposer models.
@@ -525,28 +533,37 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         execute_model_req.previous_hidden_states = self.previous_hidden_states
         self.previous_hidden_states = None
 
-        # Generate proposals using draft worker.
-        proposals = self.proposer_worker.get_spec_proposals(
-            execute_model_req, self._seq_with_bonus_token_in_last_step)
+        with Timer() as proposal_timer:
+            # Generate proposals using draft worker.
+            proposals = self.proposer_worker.get_spec_proposals(
+                execute_model_req, self._seq_with_bonus_token_in_last_step)
 
         if not self._allow_zero_draft_token_step and proposals.no_proposals:
             #TODO: Fix it #5814
             raise RuntimeError("Cannot handle cases where distributed draft "
                                "workers generate no tokens")
 
-        proposal_scores = self.scorer.score_proposals(
-            execute_model_req,
-            proposals,
-        )
-        accepted_token_ids, target_logprobs = self._verify_tokens(
-            execute_model_req.seq_group_metadata_list, proposal_scores,
-            proposals, execute_model_req.num_lookahead_slots)
+        with Timer() as scoring_timer:
+            proposal_scores = self.scorer.score_proposals(
+                execute_model_req,
+                proposals,
+            )
+
+        with Timer() as verification_timer:
+            accepted_token_ids, target_logprobs = self._verify_tokens(
+                execute_model_req.seq_group_metadata_list, proposal_scores,
+                proposals, execute_model_req.num_lookahead_slots)
+
+        stage_times = (proposal_timer.elapsed_time_ms / num_lookahead_slots,
+                       scoring_timer.elapsed_time_ms,
+                       verification_timer.elapsed_time_ms)
 
         return self._create_output_sampler_list(
             execute_model_req.seq_group_metadata_list,
             accepted_token_ids,
             target_logprobs=target_logprobs,
-            k=execute_model_req.num_lookahead_slots)
+            k=execute_model_req.num_lookahead_slots,
+            stage_times=stage_times)
 
     @nvtx_range("spec_decode_worker._verify_tokens")
     def _verify_tokens(
@@ -645,6 +662,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         accepted_token_ids: torch.Tensor,  # shape: [batch_size, k+1]
         target_logprobs: torch.Tensor,  # shape: [batch_size, k+1, vocab_size]
         k: int,
+        stage_times: Tuple[float, float, float],
     ) -> List[SamplerOutput]:
         """Given the accepted token ids, create a list of SamplerOutput.
 
@@ -722,8 +740,30 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         if maybe_rejsample_metrics is not None:
             sampler_output_list[
                 0].spec_decode_worker_metrics = maybe_rejsample_metrics
+
+            # Log time spent in each stage periodically.
+            # This is periodic because the rejection sampler emits metrics
+            # periodically.
+            self._maybe_log_stage_times(*stage_times)
+
         return sampler_output_list
 
+    def _maybe_log_stage_times(self, average_time_per_proposal_tok_ms: float,
+                               scoring_time_ms: float,
+                               verification_time_ms: float) -> None:
+        """Log the speculative stage times. If stat logging is disabled, do
+        nothing.
+        """
+        if self._disable_log_stats:
+            return
+
+        logger.info(
+            "SpecDecodeWorker stage times: "
+            "average_time_per_proposal_tok_ms=%.02f "
+            "scoring_time_ms=%.02f verification_time_ms=%.02f",
+            average_time_per_proposal_tok_ms, scoring_time_ms,
+            verification_time_ms)
+
     def _create_dummy_logprob_lists(
         self,
         batch_size: int,

vllm/spec_decode/util.py

+import time
 from contextlib import contextmanager
 from typing import Dict, List, Optional, Tuple
 
@@ -214,3 +215,17 @@ def nvtx_range(msg, *args, **kwargs):
         yield
     finally:
         torch.cuda.nvtx.range_pop()
+
+
+class Timer:
+    """Basic timer context manager for measuring CPU time.
+    """
+
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time_s = self.end_time - self.start_time
+        self.elapsed_time_ms = self.elapsed_time_s * 1000