[Frontend] Add per-request number of cached token stats (#10174)

tests/prefix_caching/test_prefix_caching.py

@@ -27,6 +27,7 @@ UNSTABLE_PROMPT_SEQUENCE = [
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [5])
 @pytest.mark.parametrize("cached_position", [0, 1])
+@pytest.mark.parametrize("block_size", [16])
 def test_mixed_requests(
     hf_runner,
     vllm_runner,
@@ -36,11 +37,12 @@ def test_mixed_requests(
     dtype: str,
     max_tokens: int,
     cached_position: int,
+    block_size: int,
     monkeypatch,
 ) -> None:
     """
     Test the case when some sequences have the prefix cache hit
-    and the others don't. The cached position determines where 
+    and the others don't. The cached position determines where
     the sequence is at among the batch of prefills.
     """
     override_backend_env_variable(monkeypatch, backend)
@@ -53,12 +55,30 @@ def test_mixed_requests(
             model,
             dtype=dtype,
             enable_prefix_caching=True,
+            block_size=block_size,
     ) as vllm_model:
         # Run the first prompt so the cache is populated
         vllm_outputs = vllm_model.generate_greedy([cached_prompt], max_tokens)
 
         # Run all the promopts
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
+        req_outputs = vllm_model.model.generate(example_prompts, greedy_params)
+
+        # Verify number of cached tokens
+        for i in range(len(req_outputs)):
+            if i == cached_position:
+                expected_num_cached_tokens = (
+                    len(req_outputs[i].prompt_token_ids) //
+                    block_size) * block_size
+            else:
+                expected_num_cached_tokens = 0
+            assert req_outputs[
+                i].num_cached_tokens == expected_num_cached_tokens
+
+        vllm_outputs = [
+            (output.prompt_token_ids + list(output.outputs[0].token_ids),
+             output.prompt + output.outputs[0].text) for output in req_outputs
+        ]
 
     check_outputs_equal(
         outputs_0_lst=hf_outputs,

vllm/entrypoints/openai/api_server.py

@@ -540,6 +540,7 @@ def init_app_state(
         return_tokens_as_token_ids=args.return_tokens_as_token_ids,
         enable_auto_tools=args.enable_auto_tool_choice,
         tool_parser=args.tool_call_parser,
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
     ) if model_config.task == "generate" else None
     state.openai_serving_completion = OpenAIServingCompletion(
         engine_client,

vllm/entrypoints/openai/cli_args.py

@@ -228,6 +228,11 @@ def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         default=False,
         help="Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint"
     )
+    parser.add_argument(
+        "--enable-prompt-tokens-details",
+        action='store_true',
+        default=False,
+        help="If set to True, enable prompt_tokens_details in usage.")
 
     return parser
 

vllm/entrypoints/openai/protocol.py

@@ -99,10 +99,15 @@ class ModelList(OpenAIBaseModel):
     data: List[ModelCard] = Field(default_factory=list)
 
 
+class PromptTokenUsageInfo(OpenAIBaseModel):
+    cached_tokens: Optional[int] = None
+
+
 class UsageInfo(OpenAIBaseModel):
     prompt_tokens: int = 0
     total_tokens: int = 0
     completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
 
 
 class RequestResponseMetadata(BaseModel):

vllm/entrypoints/openai/run_batch.py

@@ -78,6 +78,11 @@ def parse_args():
         help="Port number for the Prometheus metrics server "
         "(only needed if enable-metrics is set).",
     )
+    parser.add_argument(
+        "--enable-prompt-tokens-details",
+        action='store_true',
+        default=False,
+        help="If set to True, enable prompt_tokens_details in usage.")
 
     return parser.parse_args()
 
@@ -217,6 +222,7 @@ async def main(args):
         prompt_adapters=None,
         request_logger=request_logger,
         chat_template=None,
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
     ) if model_config.task == "generate" else None
     openai_serving_embedding = OpenAIServingEmbedding(
         engine,

vllm/entrypoints/openai/serving_chat.py

@@ -18,8 +18,8 @@ from vllm.entrypoints.openai.protocol import (
     ChatCompletionRequest, ChatCompletionResponse,
     ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
     ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
-    DeltaToolCall, ErrorResponse, FunctionCall, RequestResponseMetadata,
-    ToolCall, UsageInfo)
+    DeltaToolCall, ErrorResponse, FunctionCall, PromptTokenUsageInfo,
+    RequestResponseMetadata, ToolCall, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
                                                     LoRAModulePath,
                                                     OpenAIServing,
@@ -49,7 +49,8 @@ class OpenAIServingChat(OpenAIServing):
                  chat_template: Optional[str],
                  return_tokens_as_token_ids: bool = False,
                  enable_auto_tools: bool = False,
-                 tool_parser: Optional[str] = None):
+                 tool_parser: Optional[str] = None,
+                 enable_prompt_tokens_details: bool = False):
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
                          base_model_paths=base_model_paths,
@@ -80,6 +81,8 @@ class OpenAIServingChat(OpenAIServing):
                                 f"tool_parser:'{tool_parser}' which has not "
                                 "been registered") from e
 
+        self.enable_prompt_tokens_details = enable_prompt_tokens_details
+
     async def create_chat_completion(
         self,
         request: ChatCompletionRequest,
@@ -252,6 +255,7 @@ class OpenAIServingChat(OpenAIServing):
         previous_num_tokens = [0] * num_choices
         finish_reason_sent = [False] * num_choices
         num_prompt_tokens = 0
+        num_cached_tokens = None
 
         if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
             tool_choice_function_name = request.tool_choice.function.name
@@ -305,6 +309,7 @@ class OpenAIServingChat(OpenAIServing):
                 # the result_generator, it needs to be sent as the FIRST
                 # response (by the try...catch).
                 if first_iteration:
+                    num_cached_tokens = res.num_cached_tokens
                     # Send first response for each request.n (index) with
                     # the role
                     role = self.get_chat_request_role(request)
@@ -530,11 +535,13 @@ class OpenAIServingChat(OpenAIServing):
             # is sent, send the usage
             if include_usage:
                 completion_tokens = sum(previous_num_tokens)
-                final_usage = UsageInfo(
-                    prompt_tokens=num_prompt_tokens,
-                    completion_tokens=completion_tokens,
-                    total_tokens=num_prompt_tokens + completion_tokens,
-                )
+                final_usage = UsageInfo(prompt_tokens=num_prompt_tokens,
+                                        completion_tokens=completion_tokens,
+                                        total_tokens=num_prompt_tokens +
+                                        completion_tokens)
+                if self.enable_prompt_tokens_details and num_cached_tokens:
+                    final_usage.prompt_tokens_details = PromptTokenUsageInfo(
+                        cached_tokens=num_cached_tokens)
 
                 final_usage_chunk = ChatCompletionStreamResponse(
                     id=request_id,
@@ -702,11 +709,13 @@ class OpenAIServingChat(OpenAIServing):
             num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
         num_generated_tokens = sum(
             len(output.token_ids) for output in final_res.outputs)
-        usage = UsageInfo(
-            prompt_tokens=num_prompt_tokens,
-            completion_tokens=num_generated_tokens,
-            total_tokens=num_prompt_tokens + num_generated_tokens,
-        )
+        usage = UsageInfo(prompt_tokens=num_prompt_tokens,
+                          completion_tokens=num_generated_tokens,
+                          total_tokens=num_prompt_tokens +
+                          num_generated_tokens)
+        if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
+            usage.prompt_tokens_details = PromptTokenUsageInfo(
+                cached_tokens=final_res.num_cached_tokens)
 
         request_metadata.final_usage_info = usage
 

vllm/outputs.py

@@ -83,10 +83,11 @@ class RequestOutput:
         finished: Whether the whole request is finished.
         metrics: Metrics associated with the request.
         lora_request: The LoRA request that was used to generate the output.
-        encoder_prompt: The encoder prompt string of the request; 
-                        None if decoder-only
-        encoder_prompt_token_ids: The token IDs of the encoder prompt;
-                                  None if decoder-only
+        encoder_prompt: The encoder prompt string of the request.
+                        None if decoder-only.
+        encoder_prompt_token_ids: The token IDs of the encoder prompt.
+                                  None if decoder-only.
+        num_cached_tokens: The number of tokens with prefix cache hit.
     """
 
     def __init__(
@@ -101,6 +102,7 @@ class RequestOutput:
         lora_request: Optional[LoRARequest] = None,
         encoder_prompt: Optional[str] = None,
         encoder_prompt_token_ids: Optional[List[int]] = None,
+        num_cached_tokens: Optional[int] = None,
     ) -> None:
         self.request_id = request_id
         self.prompt = prompt
@@ -112,6 +114,7 @@ class RequestOutput:
         self.lora_request = lora_request
         self.encoder_prompt = encoder_prompt
         self.encoder_prompt_token_ids = encoder_prompt_token_ids
+        self.num_cached_tokens = num_cached_tokens
 
     @classmethod
     def new(
@@ -192,6 +195,8 @@ class RequestOutput:
 
         outputs = []
         include_prompt = True
+        # num_cached_tokens should be the same for all the sequences
+        num_cached_tokens = None
         for i, seq in enumerate(top_n_seqs):
             output_text = seq.get_output_text_to_return(
                 text_buffer_length, delta)
@@ -199,6 +204,7 @@ class RequestOutput:
             output_token_ids = seq.get_output_token_ids_to_return(delta)
             num_output_tokens = 1 if isinstance(output_token_ids,
                                                 int) else len(output_token_ids)
+            num_cached_tokens = seq.data.get_num_cached_tokens()
 
             output_logprobs = seq.output_logprobs if include_logprobs else None
 
@@ -272,7 +278,7 @@ class RequestOutput:
         init_args = (seq_group.request_id, prompt, prompt_token_ids,
                      prompt_logprobs, outputs, finished, seq_group.metrics,
                      seq_group.lora_request, encoder_prompt,
-                     encoder_prompt_token_ids)
+                     encoder_prompt_token_ids, num_cached_tokens)
 
         if use_cache:
             request_output = seq_group.cached_request_output
@@ -293,7 +299,8 @@ class RequestOutput:
                 f"outputs={self.outputs}, "
                 f"finished={self.finished}, "
                 f"metrics={self.metrics}, "
-                f"lora_request={self.lora_request})")
+                f"lora_request={self.lora_request}, "
+                f"num_cached_tokens={self.num_cached_tokens})")
 
 
 class EmbeddingRequestOutput:

vllm/sequence.py

@@ -167,6 +167,8 @@ class SequenceData(msgspec.Struct,
                                    ...] = msgspec.field(default_factory=tuple)
     # The number of tokens that are computed (that run against the model).
     _num_computed_tokens: int = 0
+    # The number of tokens with prefix cache hit.
+    _num_cached_tokens: int = 0
     _stage: SequenceStage = SequenceStage.PREFILL
     _cached_all_token_ids: List[int] = msgspec.field(default_factory=list)
 
@@ -323,6 +325,14 @@ class SequenceData(msgspec.Struct,
         if self.get_num_uncomputed_tokens() == 0:
             self._stage = SequenceStage.DECODE
 
+    def get_num_cached_tokens(self) -> int:
+        """Return the number of tokens with prefix cache hit."""
+        return self._num_cached_tokens
+
+    def update_num_cached_tokens(self, num_cached_tokens: int):
+        """Update the number of tokens with prefix cache hit."""
+        self._num_cached_tokens = num_cached_tokens
+
     def reset_state_for_recompute(self) -> None:
         """Reset the number of computed tokens from this sequence. It is
         supposed to be called when a sequence needs to be started from
@@ -379,7 +389,7 @@ class SequenceData(msgspec.Struct,
 
 class Sequence:
     """Stores the data, status, and block information of a sequence.
-    
+
     The sequence is constructed from the :data:`DecoderOnlyInputs`
     (for decoder-only) or :data:`EncoderDecoderInputs` (for encoder-decoder)
     instance passed in through the :code:`inputs` constructor argument.
@@ -906,7 +916,7 @@ class SequenceGroupMetadata(
         multi_modal_data: Multi modal data.
         mm_processor_kwargs: Multimodal input processor / mapper overrides.
         encoder_seq_data: Optional sequence data for encoder prompt
-                          (SequenceGroup.encoder_seq). Should be None 
+                          (SequenceGroup.encoder_seq). Should be None
                           unless you are working with an encoder/decoder
                           model.
         cross_block_table: Optional cross-attention block table associated

vllm/worker/model_runner.py

@@ -542,6 +542,9 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         # this may be larger than the sequence length if chunked
         # prefill is enabled.
         prefix_cache_len = len(computed_block_nums) * self.block_size
+        seq_group_metadata.seq_data[inter_data.seq_ids[
+            seq_idx]].update_num_cached_tokens(prefix_cache_len)
+
         # The number of so far computed prompt tokens in this sequence.
         context_len = inter_data.context_lens[seq_idx]
         # The total number of prompt tokens in this sequence.