[V1] Aggregate chunked prompt logprobs in model runner (#14875)

Signed-off-by: Nick Hill <nhill@redhat.com>

vllm/v1/core/sched/scheduler.py

@@ -627,8 +627,7 @@ class Scheduler(SchedulerInterface):
 
             # Get prompt logprobs for this request.
             prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
-            # Transmit partial if chunked prefill & prompt logprobs is enabled
-            if new_token_ids or prompt_logprobs_tensors is not None:
+            if new_token_ids:
                 # Add EngineCoreOutput for this Request.
                 outputs.append(
                     EngineCoreOutput(
@@ -639,6 +638,9 @@ class Scheduler(SchedulerInterface):
                         new_prompt_logprobs_tensors=prompt_logprobs_tensors,
                         stop_reason=request.stop_reason,
                         events=request.take_events()))
+            else:
+                # Invariant: EngineCore returns no partial prefill outputs.
+                assert not prompt_logprobs_tensors
 
             self.scheduled_req_ids.remove(request.request_id)
             if not stopped:

vllm/v1/engine/logprobs.py

@@ -115,7 +115,6 @@ class LogprobsProcessor:
         num_prompt_tokens, num_logprobs = logprobs.shape
 
         # Pythonize the torch tensors.
-        # TODO(rob): experiment with doing this in EngineCore?
         prompt_token_ranks = ranks.tolist()
         prompt_logprobs = logprobs.tolist()
         token_ids = token_ids.tolist()

vllm/v1/engine/output_processor.py

@@ -105,9 +105,7 @@ class RequestState:
         finished = finish_reason is not None
         final_only = self.output_kind == RequestOutputKind.FINAL_ONLY
 
-        # In follow up, we will switch to invariant where EngineCore
-        # does not stream partial prefills.
-        if not finished and (self.is_prefilling or final_only):
+        if not finished and final_only:
             # Only the final output is required in FINAL_ONLY mode.
             return None
 
@@ -285,19 +283,7 @@ class OutputProcessor:
             finish_reason = engine_core_output.finish_reason
             stop_reason = engine_core_output.stop_reason
 
-            # TODO(andy): prompt logprobs + chunked prefill can
-            # result in engine core returning an output for a
-            # partial prefill (in order to send back partial
-            # prompt logprobs.) This breaks the invariant that
-            # process_outputs is only operating on engine core
-            # outputs associated with non-partial completions.
-            # Currently this is handled by having `is_prefilling`
-            # check for new decoded tokens, indicating that
-            # the completion is not partial.
-            #
-            # Follow up will aggregate partial prompt logprobs
-            # in the EngineCore.
-            req_state.is_prefilling = not new_token_ids
+            req_state.is_prefilling = False
 
             # 2) Detokenize the token ids into text and perform stop checks.
             stop_string = req_state.detokenizer.update(
@@ -306,8 +292,7 @@ class OutputProcessor:
                 finish_reason = FinishReason.STOP
                 stop_reason = stop_string
 
-            # 3) Compute sample and prompt logprobs for request,
-            #    if required.
+            # 3) Compute sample and prompt logprobs for request, if required.
             req_state.logprobs_processor.update_from_output(engine_core_output)
 
             # 4) Create and handle RequestOutput objects.

vllm/v1/metrics/stats.py

@@ -100,15 +100,8 @@ class IterationStats:
         num_new_generation_tokens = len(output.new_token_ids)
 
         self.num_generation_tokens += num_new_generation_tokens
-        if is_prefilling and num_new_generation_tokens > 0:
-            # TODO(andy): we used to assert that num_new_generation_tokens
-            # > 0 with an invariant that EngineCore does not stream outputs
-            # for partially completed prefills (scheduler.update_from_output
-            # makes EngineCoreOutput iff num_computed_tokens == num_tokens).
-            # When prompt logprobs are enabled, we currently stream out the
-            # partially completed prompt.
-            # This will be reverted in a follow up PR and we should re-enable
-            # this assertion / invariant.
+        if is_prefilling:
+            assert num_new_generation_tokens > 0
             self.num_prompt_tokens += prompt_len
 
             first_token_latency = self._time_since(req_stats.arrival_time)
@@ -123,16 +116,12 @@ class IterationStats:
 
         # Process the batch-level "new tokens" engine core event
         if is_prefilling:
-            # TODO: re-enable no-output-for-partial-prefills invariant as above
-            if num_new_generation_tokens > 0:
-                req_stats.first_token_ts = engine_core_timestamp
+            req_stats.first_token_ts = engine_core_timestamp
         else:
             tpot = engine_core_timestamp - req_stats.last_token_ts
             self.time_per_output_tokens_iter.append(tpot)
 
-        # TODO: re-enable no-output-for-partial-prefills invariant as above
-        if num_new_generation_tokens > 0:
-            req_stats.last_token_ts = engine_core_timestamp
+        req_stats.last_token_ts = engine_core_timestamp
 
     def update_from_events(self, req_id: str, events: list["EngineCoreEvent"],
                            is_prefilling: bool, req_stats: RequestStateStats,

vllm/v1/outputs.py

@@ -39,6 +39,25 @@ class LogprobsTensors(NamedTuple):
             self.selected_token_ranks.tolist(),
         )
 
+    @staticmethod
+    def empty_cpu(num_positions: int,
+                  num_tokens_per_position: int) -> "LogprobsTensors":
+        """Create empty LogprobsTensors on CPU."""
+
+        logprob_token_ids = torch.empty(
+            (num_positions, num_tokens_per_position),
+            dtype=torch.int32,
+            device="cpu")
+        logprobs = torch.empty_like(logprob_token_ids, dtype=torch.float32)
+        selected_token_ranks = torch.empty(num_positions,
+                                           dtype=torch.int32,
+                                           device="cpu")
+        return LogprobsTensors(
+            logprob_token_ids=logprob_token_ids,
+            logprobs=logprobs,
+            selected_token_ranks=selected_token_ranks,
+        )
+
 
 @dataclass
 class SamplerOutput:

vllm/v1/worker/gpu_input_batch.py

@@ -11,6 +11,7 @@ from vllm.lora.request import LoRARequest
 from vllm.multimodal import MultiModalKwargs
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.utils import swap_dict_values
+from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.utils import copy_slice
 from vllm.v1.worker.block_table import BlockTable
@@ -197,6 +198,9 @@ class InputBatch:
         # that are currently in the prefill phase.
         self.num_prompt_logprobs: dict[str, int] = {}
 
+        # To accumulate prompt logprobs tensor chunks across prefill steps.
+        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
+
         self.logit_bias: list[Optional[dict[int,
                                             float]]] = [None] * max_num_reqs
         self.has_allowed_token_ids: set[str] = set()
@@ -362,6 +366,7 @@ class InputBatch:
         self.generators.pop(req_index, None)
         self.num_logprobs.pop(req_id, None)
         self.num_prompt_logprobs.pop(req_id, None)
+        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
 
         # LoRA
         lora_id = self.request_lora_mapping[req_index]

vllm/v1/worker/gpu_model_runner.py

@@ -1191,6 +1191,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         if not num_prompt_logprobs_dict:
             return {}
 
+        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
         prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
 
         # Since prompt logprobs are a rare feature, prioritize simple,
@@ -1206,16 +1207,36 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
                 self.device, non_blocking=True)
 
+            # Set up target LogprobsTensors object.
+            logprobs_tensors = in_progress_dict.get(req_id)
+            if not logprobs_tensors:
+                # Create empty logprobs CPU tensors for the entire prompt.
+                # If chunked, we'll copy in slice by slice.
+                logprobs_tensors = LogprobsTensors.empty_cpu(
+                    num_prompt_tokens - 1, num_prompt_logprobs + 1)
+                in_progress_dict[req_id] = logprobs_tensors
+
             # Determine number of logits to retrieve.
-            start_tok = request.num_computed_tokens + 1
+            start_idx = request.num_computed_tokens
+            start_tok = start_idx + 1
             num_remaining_tokens = num_prompt_tokens - start_tok
-            if num_tokens < num_remaining_tokens:
+            if num_tokens <= num_remaining_tokens:
                 # This is a chunk, more tokens remain.
+                # In the == case, there are no more prompt logprobs to produce
+                # but we want to defer returning them to the next step where we
+                # have new generated tokens to return.
                 num_logits = num_tokens
             else:
                 # This is the last chunk of prompt tokens to return.
                 num_logits = num_remaining_tokens
                 completed_prefill_reqs.append(req_id)
+                prompt_logprobs_dict[req_id] = logprobs_tensors
+
+            if num_logits <= 0:
+                # This can happen for the final chunk if we prefilled exactly
+                # (num_prompt_tokens - 1) tokens for this request in the prior
+                # step. There are no more prompt logprobs to produce.
+                continue
 
             # Get the logits corresponding to this req's prompt tokens.
             # If this is a partial request (i.e. chunked prefill),
@@ -1236,19 +1257,23 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 logprobs, num_prompt_logprobs, tgt_token_ids)
 
             # Transfer GPU->CPU async.
-            prompt_logprobs_dict[req_id] = LogprobsTensors(
-                token_ids.to("cpu", non_blocking=True),
-                logprobs.to("cpu", non_blocking=True),
-                ranks.to("cpu", non_blocking=True),
-            )
+            chunk_slice = slice(start_idx, start_idx + num_logits)
+            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
+                token_ids, non_blocking=True)
+            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
+                                                         non_blocking=True)
+            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
+                ranks, non_blocking=True)
 
         # Remove requests that have completed prefill from the batch
         # num_prompt_logprobs_dict.
         for req_id in completed_prefill_reqs:
             del num_prompt_logprobs_dict[req_id]
+            del in_progress_dict[req_id]
 
         # Must synchronize the non-blocking GPU->CPU transfers.
-        torch.cuda.synchronize()
+        if prompt_logprobs_dict:
+            torch.cuda.synchronize()
 
         return prompt_logprobs_dict