[Core] Refactoring sampler and support prompt logprob for chunked pre…

…fill (vllm-project#4309)

tests/samplers/test_logprobs.py

@@ -9,26 +9,44 @@
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16, -1])
+@pytest.mark.parametrize("num_top_logprobs", [6])  # 32000 == vocab_size
 def test_get_prompt_logprobs(
     hf_runner,
     vllm_runner,
     model,
     dtype,
+    chunked_prefill_token_size: int,
+    num_top_logprobs: int,
     example_prompts,
 ):
+    max_num_seqs = 256
+    enable_chunked_prefill = False
+    max_num_batched_tokens = None
+    if chunked_prefill_token_size != -1:
+        enable_chunked_prefill = True
+        max_num_seqs = min(chunked_prefill_token_size, max_num_seqs)
+        max_num_batched_tokens = chunked_prefill_token_size
+
     max_tokens = 5
-    num_top_logprobs = 6
     hf_model = hf_runner(model, dtype=dtype)
     hf_logprobs = hf_model.generate_greedy_logprobs(
         example_prompts,
         max_tokens=max_tokens,
     )
     del hf_model
 
-    vllm_model = vllm_runner(model, dtype=dtype, max_logprobs=num_top_logprobs)
+    vllm_model = vllm_runner(
+        model,
+        dtype=dtype,
+        max_logprobs=num_top_logprobs,
+        enable_chunked_prefill=enable_chunked_prefill,
+        max_num_batched_tokens=max_num_batched_tokens,
+        max_num_seqs=max_num_seqs,
+    )
     vllm_sampling_params = SamplingParams(max_tokens=max_tokens,
                                           logprobs=num_top_logprobs,
-                                          prompt_logprobs=5,
+                                          prompt_logprobs=num_top_logprobs,
                                           temperature=0.0)
     vllm_results = vllm_model.model.generate(
         example_prompts, sampling_params=vllm_sampling_params)
@@ -52,9 +70,18 @@ def test_get_prompt_logprobs(
             "The output text from the top logprob for each token position "
             "should be the same as the output text in the result.")
 
+        # The first prompt logprob is always None
+        assert result.prompt_logprobs[0] is None
+        for prompt_logprobs in result.prompt_logprobs[1:]:
+            # If the prompt token is not included in the top X
+            # logprob, it can return 1 more data
+            assert (len(prompt_logprobs) == num_top_logprobs
+                    or len(prompt_logprobs) == num_top_logprobs + 1)
+
     # Test whether prompt logprobs are consistent with HF
     for vllm_result, hf_logprob in zip(vllm_results, hf_logprobs):
         # Check prompt logprobs
+        # The first prompt logprob is always None, so we compare it from 1:.
         vllm_prompt_logprobs = vllm_result.prompt_logprobs[1:]
         for i, vllm_prompt_logprob_dict in enumerate(vllm_prompt_logprobs):
             for token_id, logprob in vllm_prompt_logprob_dict.items():
@@ -74,6 +101,17 @@ def test_get_prompt_logprobs(
                     "The token should be decoded by the time it is returned "
                     " to the user.")
 
+    # Test if prompt logprobs are correctly set.
+    for vllm_result in vllm_results:
+        token_ids = vllm_result.prompt_token_ids
+        prompt_logprobs = vllm_result.prompt_logprobs
+
+        # The first token doesn't have logprob.
+        assert prompt_logprobs[0] is None
+
+        for token_id, logprob_dict in zip(token_ids[1:], prompt_logprobs[1:]):
+            assert token_id in logprob_dict
+
 
 def test_max_logprobs():
     runner = VllmRunner("facebook/opt-125m", max_logprobs=1)

tests/samplers/test_sampler.py

@@ -8,6 +8,7 @@
 from transformers import GenerationConfig, GenerationMixin
 
 from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_random_seed
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import Counter
@@ -54,6 +55,7 @@ def _do_sample(
     sampler: MockLogitsSampler,
     model_runner: ModelRunner,
     sampling_params: SamplingParams,
+    device: str,
 ):
     seq_group_metadata_list = []
     prompt_lens = []
@@ -68,9 +70,12 @@ def _do_sample(
             ))
         prompt_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
 
-    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
-                                                     prompt_lens,
-                                                     subquery_lens=prompt_lens)
+    sampling_metadata = SamplingMetadata.prepare(
+        seq_group_metadata_list,
+        prompt_lens,
+        subquery_lens=prompt_lens,
+        device=device,
+        pin_memory=model_runner.pin_memory)
     return sampler(logits=input_tensor, sampling_metadata=sampling_metadata)
 
 
@@ -85,7 +90,7 @@ def test_sampler_all_greedy(seed: int, device: str):
 
     sampling_params = SamplingParams(temperature=0)
     sampler_output = _do_sample(batch_size, fake_logits, sampler, model_runner,
-                                sampling_params)
+                                sampling_params, device)
     expected = torch.argmax(fake_logits, dim=-1)
     for i, sequence_output in enumerate(sampler_output):
         for nth_output in sequence_output.samples:
@@ -111,7 +116,7 @@ def test_sampler_all_random(seed: int, device: str):
         n=random.randint(1, 10),
     )
     sampler_output = _do_sample(batch_size, fake_logits, sampler, model_runner,
-                                sampling_params)
+                                sampling_params, device)
 
     for i, sequence_output in enumerate(sampler_output):
         for nth_output in sequence_output.samples:
@@ -137,7 +142,7 @@ def test_sampler_all_random_seed(seed: int, device: str):
         seed=random.randint(0, 10000),
     )
     sampler_output = _do_sample(batch_size, fake_logits, sampler, model_runner,
-                                sampling_params)
+                                sampling_params, device)
 
     for i, sequence_output in enumerate(sampler_output):
         for nth_output in sequence_output.samples:
@@ -160,10 +165,10 @@ def test_sampler_all_random_seed_deterministic(seed: int, device: str):
         seed=random.randint(0, 10000),
     )
     first_sampler_output = _do_sample(batch_size, fake_logits, sampler,
-                                      model_runner, sampling_params)
+                                      model_runner, sampling_params, device)
 
     second_sampler_output = _do_sample(batch_size, fake_logits, sampler,
-                                       model_runner, sampling_params)
+                                       model_runner, sampling_params, device)
 
     assert first_sampler_output == second_sampler_output
 
@@ -183,7 +188,8 @@ def test_sampler_all_beam(seed: int, device: str):
         best_of=2,
         use_beam_search=True,
     )
-    _do_sample(batch_size, fake_logits, sampler, model_runner, sampling_params)
+    _do_sample(batch_size, fake_logits, sampler, model_runner, sampling_params,
+               device)
     # no assertion here as I am not sure how to determine whether
     # the outputs are expected - in other words, this just tests
     # whether there are no exceptions in the sampler
@@ -443,10 +449,12 @@ def run_test_case(*,
              "batch size")
 
         _, fake_logits, sampler, model_runner = _prepare_test(batch_size)
-        sampling_metadata = model_runner._prepare_sample(
+        sampling_metadata = SamplingMetadata.prepare(
             seq_group_metadata_list,
             prompt_lens=prompt_lens if prompt_lens else None,
-            subquery_lens=prompt_lens if prompt_lens else None)
+            subquery_lens=prompt_lens if prompt_lens else None,
+            device=device,
+            pin_memory=model_runner.pin_memory)
         # the logits tensor is modified in-place by the sampler
         _ = sampler(logits=fake_logits, sampling_metadata=sampling_metadata)
 
@@ -530,8 +538,12 @@ def test_sampler_mixed(seed: int, device: str):
         prompt_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
 
     def test_sampling(model_runner: ModelRunner):
-        sampling_metadata = model_runner._prepare_sample(
-            seq_group_metadata_list, prompt_lens, subquery_lens=prompt_lens)
+        sampling_metadata = SamplingMetadata.prepare(
+            seq_group_metadata_list,
+            prompt_lens,
+            subquery_lens=prompt_lens,
+            device=device,
+            pin_memory=model_runner.pin_memory)
         sampler_output = sampler(logits=fake_logits,
                                  sampling_metadata=sampling_metadata)
 
@@ -627,9 +639,12 @@ def test_sampler_top_k_top_p(seed: int, device: str):
             ))
         prompt_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
 
-    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
-                                                     prompt_lens,
-                                                     subquery_lens=prompt_lens)
+    sampling_metadata = SamplingMetadata.prepare(
+        seq_group_metadata_list,
+        prompt_lens,
+        subquery_lens=prompt_lens,
+        device=device,
+        pin_memory=model_runner.pin_memory)
 
     sample_probs = None
 

tests/test_logits_processor.py

@@ -6,6 +6,7 @@
 import torch
 
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_random_seed
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.worker.model_runner import ModelRunner
@@ -82,9 +83,12 @@ def pick_ith(token_ids, logits):
             ))
         prompt_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
 
-    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
-                                                     prompt_lens,
-                                                     subquery_lens=prompt_lens)
+    sampling_metadata = SamplingMetadata.prepare(
+        seq_group_metadata_list,
+        prompt_lens,
+        subquery_lens=prompt_lens,
+        device=model_runner.device,
+        pin_memory=model_runner.pin_memory)
     logits_processor_output = logits_processor(
         embedding=None,
         hidden_states=input_tensor,

tests/worker/test_model_runner.py

@@ -2,6 +2,7 @@
 import torch
 
 from vllm.config import ModelConfig, SchedulerConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.worker.model_runner import ModelRunner, _get_graph_batch_size
 
@@ -97,9 +98,12 @@ def test_prepare_prompt(batch_size):
     assert len(input_positions) == sum(prompt_lens)
     torch.testing.assert_close(input_tokens, input_positions)
 
-    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
-                                                     prompt_lens,
-                                                     subquery_lens=prompt_lens)
+    sampling_metadata = SamplingMetadata.prepare(
+        seq_group_metadata_list,
+        prompt_lens,
+        subquery_lens=prompt_lens,
+        device=model_runner.device,
+        pin_memory=model_runner.pin_memory)
     assert len(input_tokens) == sum(prompt_lens)
     assert len(input_positions) == sum(prompt_lens)
     actual = sampling_metadata.selected_token_indices
@@ -195,9 +199,12 @@ def test_prepare_decode_cuda_graph(batch_size):
     for prompt_len in prompt_lens:
         expected_selected_token_indices.append(selected_token_start_idx)
         selected_token_start_idx += 1
-    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
-                                                     prompt_lens,
-                                                     subquery_lens=prompt_lens)
+    sampling_metadata = SamplingMetadata.prepare(
+        seq_group_metadata_list,
+        prompt_lens,
+        subquery_lens=prompt_lens,
+        device=model_runner.device,
+        pin_memory=model_runner.pin_memory)
     actual = sampling_metadata.selected_token_indices
     expected = torch.tensor(expected_selected_token_indices,
                             device=actual.device,

vllm/core/scheduler.py

@@ -915,6 +915,20 @@ def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
                 self.block_manager.get_common_computed_block_ids(
                     seq_group.get_seqs(status=SequenceStatus.RUNNING)))
 
+            do_sample = True
+            if seq_group.is_prefill():
+                seqs = seq_group.get_seqs()
+                # Prefill has only 1 sequence.
+                assert len(seqs) == 1
+                # In the next iteration, all prompt tokens are not computed.
+                # It means the prefill is chunked, and we don't need sampling.
+                # NOTE: We use get_len instead of get_prompt_len because when
+                # a sequence is preempted, prefill includes previous generated
+                # output tokens.
+                if (token_chunk_size + seqs[0].data.get_num_computed_tokens() <
+                        seqs[0].data.get_len()):
+                    do_sample = False
+
             # It assumes the scheduled_seq_groups is ordered by
             # prefill < decoding.
             is_prompt = seq_group.is_prefill()
@@ -924,6 +938,7 @@ def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
                 seq_data=seq_data,
                 sampling_params=seq_group.sampling_params,
                 block_tables=block_tables,
+                do_sample=do_sample,
                 token_chunk_size=token_chunk_size,
                 lora_request=seq_group.lora_request,
                 computed_block_nums=common_computed_block_nums,

vllm/engine/async_llm_engine.py

@@ -219,7 +219,7 @@ async def step_async(self) -> List[RequestOutput]:
 
         request_outputs = self._process_model_outputs(
             output, scheduler_outputs.scheduled_seq_groups,
-            scheduler_outputs.ignored_seq_groups)
+            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
 
         # Log stats.
         if self.log_stats:

vllm/engine/llm_engine.py

@@ -22,7 +22,7 @@
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (MultiModalData, SamplerOutput, Sequence,
-                           SequenceGroup, SequenceStage)
+                           SequenceGroup, SequenceGroupMetadata)
 from vllm.transformers_utils.detokenizer import Detokenizer
 from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
                                                      get_tokenizer_group)
@@ -476,9 +476,12 @@ def has_unfinished_requests(self) -> bool:
         return self.scheduler.has_unfinished_seqs()
 
     def _process_model_outputs(
-            self, output: List[SamplerOutput],
-            scheduled_seq_groups: List[SequenceGroup],
-            ignored_seq_groups: List[SequenceGroup]) -> List[RequestOutput]:
+        self,
+        output: List[SamplerOutput],
+        scheduled_seq_groups: List[SequenceGroup],
+        ignored_seq_groups: List[SequenceGroup],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> List[RequestOutput]:
         """Apply the model output to the sequences in the scheduled seq groups.
 
         Returns RequestOutputs that can be returned to the client.
@@ -492,17 +495,15 @@ def _process_model_outputs(
             sampler_outputs=output, num_seq_groups=len(scheduled_seq_groups))
 
         # Update the scheduled sequence groups with the model outputs.
-        for scheduled_seq_group, outputs in zip(scheduled_seq_groups,
-                                                output_by_sequence_group):
+        for scheduled_seq_group, outputs, seq_group_meta in zip(
+                scheduled_seq_groups, output_by_sequence_group,
+                seq_group_metadata_list):
             seq_group = scheduled_seq_group.seq_group
             seq_group.update_num_computed_tokens(
                 scheduled_seq_group.token_chunk_size)
 
-            # If all sequences in the sequence group are in DECODE, then we can
-            # process the output tokens. Otherwise, they are (chunked) prefill
-            # samples and should not be processed.
-            stages = [seq.data._stage for seq in seq_group.seqs_dict.values()]
-            if all(stage == SequenceStage.DECODE for stage in stages):
+            self.output_processor.process_prompt_logprob(seq_group, outputs)
+            if seq_group_meta.do_sample:
                 self.output_processor.process_outputs(seq_group, outputs)
 
         # Free the finished sequence groups.
@@ -585,7 +586,7 @@ def step(self) -> List[RequestOutput]:
 
         request_outputs = self._process_model_outputs(
             output, scheduler_outputs.scheduled_seq_groups,
-            scheduler_outputs.ignored_seq_groups)
+            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
 
         # Log stats.
         if self.log_stats:

vllm/engine/output_processor/interfaces.py

@@ -68,3 +68,9 @@ def process_outputs(self, sequence_group: SequenceGroup,
         scheduler.
         """
         pass
+
+    @abstractmethod
+    def process_prompt_logprob(self, seq_group: SequenceGroup,
+                               outputs: List[SequenceGroupOutput]) -> None:
+        """Update prompt logprobs received from outputs to seq_group."""
+        pass

vllm/engine/output_processor/multi_step.py

@@ -44,6 +44,15 @@ def __init__(
         self.get_tokenizer_for_seq = get_tokenizer_for_seq
         self.stop_checker = stop_checker
 
+    def process_prompt_logprob(self, seq_group: SequenceGroup,
+                               outputs: List[SequenceGroupOutput]) -> None:
+        # TODO(sang): Prompt logprob currently not implemented in multi step
+        # workers.
+        logger.warning(
+            "Prompt logprob is not supported by multi step workers. "
+            "(e.g., speculative decode uses multi step workers).")
+        pass
+
     def process_outputs(self, sequence_group: SequenceGroup,
                         outputs: List[SequenceGroupOutput]) -> None:
         """Append new tokens in the outputs to sequences in the sequence group.