[Cherry-Pick] add mm token usage (#4648)

* [Feature] add mm token usage (#4570)

* add mm token usage

* fix unit test

* fix unit test

* fix unit test

* fix model path

* fix unit test

* fix unit test

* fix unit test

* remove uncomment

* change var name

* fix code style

* fix code style

* fix code style

* fix code style

* fix unit test

* update doc

* update doc

---------

Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>

docs/offline_inference.md

@@ -198,6 +198,8 @@ For ``LLM`` configuration, refer to [Parameter Documentation](parameters.md).
 * finished(bool): Completion status
 * metrics(fastdeploy.engine.request.RequestMetrics): Performance metrics
 * num_cached_tokens(int): Cached token count (only valid when enable_prefix_caching``` is enabled)
+* num_input_image_tokens(int): Number of input image tokens.
+* num_input_video_tokens(int): Number of input video tokens.
 * error_code(int): Error code
 * error_msg(str): Error message
 

docs/online_serving/README.md

@@ -238,6 +238,19 @@ ChatMessage:
     completion_token_ids: Optional[List[int]] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
+UsageInfo:
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
+    completion_tokens_details: Optional[CompletionTokenUsageInfo] = None
+PromptTokenUsageInfo:
+    cached_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
+    video_tokens: Optional[int] = None
+CompletionTokenUsageInfo:
+    reasoning_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
 ToolCall:
     id: str = None
     type: Literal["function"] = "function"
@@ -414,6 +427,19 @@ CompletionResponseChoice:
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
     tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+UsageInfo:
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
+    completion_tokens_details: Optional[CompletionTokenUsageInfo] = None
+PromptTokenUsageInfo:
+    cached_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
+    video_tokens: Optional[int] = None
+CompletionTokenUsageInfo:
+    reasoning_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
 ToolCall:
     id: str = None
     type: Literal["function"] = "function"

docs/zh/offline_inference.md

@@ -198,6 +198,8 @@ for output in outputs:
 * finished(bool)：标识当前query 是否推理结束
 * metrics(fastdeploy.engine.request.RequestMetrics)：记录推理耗时指标
 * num_cached_tokens(int): 缓存的token数量, 仅在开启 ``enable_prefix_caching``时有效
+* num_input_image_tokens(int): 输入图片token的数量
+* num_input_video_tokens(int): 输入视频token的数量
 * error_code(int): 错误码
 * error_msg(str): 错误信息
 

docs/zh/online_serving/README.md

@@ -237,6 +237,19 @@ ChatMessage:
     completion_token_ids: Optional[List[int]] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
+UsageInfo:
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
+    completion_tokens_details: Optional[CompletionTokenUsageInfo] = None
+PromptTokenUsageInfo:
+    cached_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
+    video_tokens: Optional[int] = None
+CompletionTokenUsageInfo:
+    reasoning_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
 ToolCall:
     id: str = None
     type: Literal["function"] = "function"
@@ -410,6 +423,19 @@ CompletionResponseChoice:
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
     tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+UsageInfo:
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
+    completion_tokens_details: Optional[CompletionTokenUsageInfo] = None
+PromptTokenUsageInfo:
+    cached_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
+    video_tokens: Optional[int] = None
+CompletionTokenUsageInfo:
+    reasoning_tokens: Optional[int] = None
+    image_tokens: Optional[int] = None
 ToolCall:
     id: str = None
     type: Literal["function"] = "function"

fastdeploy/engine/request.py

@@ -447,6 +447,8 @@ class RequestOutput:
         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.
+        num_input_image_tokens: The number of input image tokens.
+        num_input_video_tokens: The number of input video tokens.
     """
 
     def __init__(
@@ -459,6 +461,8 @@ class RequestOutput:
         finished: bool = False,
         metrics: Optional[RequestMetrics] = None,
         num_cached_tokens: Optional[int] = 0,
+        num_input_image_tokens: Optional[int] = 0,
+        num_input_video_tokens: Optional[int] = 0,
         error_code: Optional[int] = 200,
         error_msg: Optional[str] = None,
     ) -> None:
@@ -470,6 +474,8 @@ class RequestOutput:
         self.finished = finished
         self.metrics = metrics
         self.num_cached_tokens = num_cached_tokens
+        self.num_input_image_tokens = num_input_image_tokens
+        self.num_input_video_tokens = num_input_video_tokens
         self.error_code = error_code
         self.error_msg = error_msg
 
@@ -512,6 +518,8 @@ class RequestOutput:
             f"outputs={self.outputs}, "
             f"finished={self.finished}, "
             f"num_cached_tokens={self.num_cached_tokens}, "
+            f"num_input_image_tokens={self.num_input_image_tokens}, "
+            f"num_input_video_tokens={self.num_input_video_tokens}, "
             f"metrics={self.metrics}, "
         )
 
@@ -534,6 +542,8 @@ class RequestOutput:
             "metrics": None if self.metrics is None else self.metrics.to_dict(),
             "finished": self.finished,
             "num_cached_tokens": self.num_cached_tokens,
+            "num_input_image_tokens": self.num_input_image_tokens,
+            "num_input_video_tokens": self.num_input_video_tokens,
             "error_code": self.error_code,
             "error_msg": self.error_msg,
         }

fastdeploy/entrypoints/openai/serving_chat.py

@@ -276,6 +276,8 @@ class OpenAIServingChat:
                     if first_iteration:
                         num_prompt_tokens = len(prompt_token_ids)
                         num_cached_tokens = res.get("num_cached_tokens", 0)
+                        num_input_image_tokens = res.get("num_input_image_tokens", 0)
+                        num_input_video_tokens = res.get("num_input_video_tokens", 0)
                         for i in range(num_choices):
                             choice = ChatCompletionResponseStreamChoice(
                                 index=i,
@@ -312,7 +314,11 @@ class OpenAIServingChat:
                                     prompt_tokens=num_prompt_tokens,
                                     completion_tokens=0,
                                     total_tokens=num_prompt_tokens,
-                                    prompt_tokens_details=PromptTokenUsageInfo(cached_tokens=num_cached_tokens),
+                                    prompt_tokens_details=PromptTokenUsageInfo(
+                                        cached_tokens=num_cached_tokens,
+                                        image_tokens=num_input_image_tokens,
+                                        video_tokens=num_input_video_tokens,
+                                    ),
                                     completion_tokens_details=CompletionTokenUsageInfo(reasoning_tokens=0),
                                 )
                             yield f"data: {chunk.model_dump_json(exclude_unset=True)} \n\n"
@@ -476,6 +482,8 @@ class OpenAIServingChat:
             draft_logprob_contents = [[] for _ in range(num_choices)]
             completion_token_ids = [[] for _ in range(num_choices)]
             num_cached_tokens = [0] * num_choices
+            num_input_image_tokens = [0] * num_choices
+            num_input_video_tokens = [0] * num_choices
             num_image_tokens = [0] * num_choices
             response_processor = ChatResponseProcessor(
                 data_processor=self.engine_client.data_processor,
@@ -546,14 +554,15 @@ class OpenAIServingChat:
                             previous_num_tokens[idx] += data["outputs"].get("image_token_num")
                             num_image_tokens[idx] = data["outputs"].get("image_token_num")
                         choice = await self._create_chat_completion_choice(
-                            output=output,
-                            index=idx,
+                            data=data,
                             request=request,
-                            previous_num_tokens=previous_num_tokens[idx],
                             prompt_token_ids=prompt_token_ids,
                             prompt_tokens=prompt_tokens,
                             completion_token_ids=completion_token_ids[idx],
+                            previous_num_tokens=previous_num_tokens[idx],
                             num_cached_tokens=num_cached_tokens,
+                            num_input_image_tokens=num_input_image_tokens,
+                            num_input_video_tokens=num_input_video_tokens,
                             num_image_tokens=num_image_tokens,
                             logprob_contents=logprob_contents,
                             response_processor=response_processor,
@@ -571,11 +580,16 @@ class OpenAIServingChat:
             prompt_tokens=num_prompt_tokens,
             completion_tokens=num_generated_tokens,
             total_tokens=num_prompt_tokens + num_generated_tokens,
-            prompt_tokens_details=PromptTokenUsageInfo(cached_tokens=sum(num_cached_tokens)),
+            prompt_tokens_details=PromptTokenUsageInfo(
+                cached_tokens=sum(num_cached_tokens),
+                image_tokens=sum(num_input_image_tokens),
+                video_tokens=sum(num_input_video_tokens),
+            ),
             completion_tokens_details=CompletionTokenUsageInfo(
                 reasoning_tokens=num_reasoning_tokens, image_tokens=sum(num_image_tokens)
             ),
         )
+
         choices = sorted(choices, key=lambda x: x.index)
         res = ChatCompletionResponse(
             id=request_id,
@@ -589,18 +603,21 @@ class OpenAIServingChat:
 
     async def _create_chat_completion_choice(
         self,
-        output: dict,
-        index: int,
+        data: dict,
         request: ChatCompletionRequest,
-        previous_num_tokens: int,
         prompt_token_ids: list,
         prompt_tokens: str,
         completion_token_ids: list,
+        previous_num_tokens: int,
         num_cached_tokens: list,
+        num_input_image_tokens: list,
+        num_input_video_tokens: list,
         num_image_tokens: list,
         logprob_contents: list,
         response_processor: ChatResponseProcessor,
     ) -> ChatCompletionResponseChoice:
+        idx = int(data["request_id"].split("_")[-1])
+        output = data["outputs"]
 
         if output is not None and output.get("metrics") and output["metrics"].get("request_start_time"):
             work_process_metrics.e2e_request_latency.observe(
@@ -621,13 +638,15 @@ class OpenAIServingChat:
             message.content = output["text"]
 
         logprobs_full_res = None
-        if logprob_contents[index]:
-            logprobs_full_res = LogProbs(content=logprob_contents[index])
+        if logprob_contents[idx]:
+            logprobs_full_res = LogProbs(content=logprob_contents[idx])
 
         has_no_token_limit = request.max_tokens is None and request.max_completion_tokens is None
         max_tokens = request.max_completion_tokens or request.max_tokens
-        num_cached_tokens[index] = output.get("num_cached_tokens", 0)
-        num_image_tokens[index] = output.get("num_image_tokens", 0)
+        num_cached_tokens[idx] = data.get("num_cached_tokens", 0)
+        num_input_image_tokens[idx] = data.get("num_input_image_tokens", 0)
+        num_input_video_tokens[idx] = data.get("num_input_video_tokens", 0)
+        num_image_tokens[idx] = output.get("num_image_tokens", 0)
 
         finish_reason = "stop"
         if has_no_token_limit or previous_num_tokens != max_tokens:
@@ -640,7 +659,7 @@ class OpenAIServingChat:
             finish_reason = "recover_stop"
 
         return ChatCompletionResponseChoice(
-            index=index,
+            index=idx,
             message=message,
             logprobs=logprobs_full_res,
             finish_reason=finish_reason,

fastdeploy/input/ernie4_5_vl_processor/process.py

@@ -193,6 +193,8 @@ class DataProcessor:
             "labels": [],
             "cur_position": 0,
             "video_cnt": 0,
+            "num_input_image_tokens": 0,
+            "num_input_video_tokens": 0,
             "mm_positions": [],
             "mm_hashes": [],
         }
@@ -357,6 +359,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
+        outputs["num_input_image_tokens"] += num_tokens
 
         pos_ids = self._compute_3d_positions(1, patches_h, patches_w, outputs["cur_position"])
         outputs["position_ids"].extend(pos_ids)
@@ -428,6 +431,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
+        outputs["num_input_video_tokens"] += num_tokens
 
         pos_ids = self._compute_3d_positions(num_frames, patches_h, patches_w, outputs["cur_position"])
         outputs["position_ids"].extend(pos_ids)

fastdeploy/input/paddleocr_vl_processor/process.py

@@ -143,9 +143,10 @@ class DataProcessor:
             "labels": [],
             "cur_position": 0,
             "video_cnt": 0,
+            "num_input_image_tokens": 0,
+            "num_input_video_tokens": 0,
             "fps": [],
             "mm_positions": [],
-            "mm_hashes": [],
             "vit_seqlen": [],
             "vit_position_ids": [],
         }
@@ -354,6 +355,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.image_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
+        outputs["num_input_image_tokens"] += int(num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
         if not uuid:
@@ -414,6 +416,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.video_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
+        outputs["num_input_video_tokens"] += int(num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
         if not uuid:

fastdeploy/input/qwen_vl_processor/process.py

@@ -142,6 +142,8 @@ class DataProcessor:
             "labels": [],
             "cur_position": 0,
             "video_cnt": 0,
+            "num_input_image_tokens": 0,
+            "num_input_video_tokens": 0,
             "fps": [],
             "mm_positions": [],
             "mm_hashes": [],
@@ -351,6 +353,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.image_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
+        outputs["num_input_image_tokens"] += int(num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
         if not uuid:
@@ -409,6 +412,7 @@ class DataProcessor:
         outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.video_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
+        outputs["num_input_video_tokens"] += int(num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
         if not uuid:

fastdeploy/output/token_processor.py

@@ -289,6 +289,9 @@ class TokenProcessor:
                     if task.messages is not None:
                         result.prompt = task.messages
                     result.num_cached_tokens = task.num_cached_tokens
+                    if task.get("multimodal_inputs", None):
+                        result.num_input_image_tokens = task.multimodal_inputs.get("num_input_image_tokens", 0)
+                        result.num_input_video_tokens = task.multimodal_inputs.get("num_input_video_tokens", 0)
 
                 is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
                 result = self._process_per_token(task, i, token_ids, result, is_prefill)
@@ -655,6 +658,9 @@ class TokenProcessor:
                 if task.messages is not None:
                     result.prompt = task.messages
             result.num_cached_tokens = task.num_cached_tokens
+            if task.get("multimodal_inputs", None):
+                result.num_input_image_tokens = task.multimodal_inputs.get("num_input_image_tokens", 0)
+                result.num_input_video_tokens = task.multimodal_inputs.get("num_input_video_tokens", 0)
 
             is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
 

tests/ce/server/test_logprobs.py

@@ -32,6 +32,7 @@ def test_unstream_with_logprobs():
         "bytes": [231, 137, 155, 233, 161, 191],
         "top_logprobs": None,
     }
+
     assert resp_json["usage"]["prompt_tokens"] == 22
     assert resp_json["usage"]["completion_tokens"] == 3
     assert resp_json["usage"]["total_tokens"] == 25

tests/entrypoints/openai/test_max_streaming_tokens.py

@@ -387,10 +387,10 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
                         "text": "Normal AI response",
                         "reasoning_content": "Normal reasoning",
                         "tool_call": None,
-                        "num_cached_tokens": 3,
                         "num_image_tokens": 2,
                         "raw_prediction": "raw_answer_0",
                     },
+                    "num_cached_tokens": 3,
                     "finished": True,
                     "previous_num_tokens": 2,
                 },
@@ -416,10 +416,10 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
                         "text": "Edge case response",
                         "reasoning_content": None,
                         "tool_call": None,
-                        "num_cached_tokens": 0,
                         "num_image_tokens": 0,
                         "raw_prediction": None,
                     },
+                    "num_cached_tokens": 0,
                     "finished": True,
                     "previous_num_tokens": 1,
                 },
@@ -446,18 +446,21 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
         mock_response_processor.enable_multimodal_content.return_value = False
         completion_token_ids = [[], []]
         num_cached_tokens = [0, 0]
+        num_input_image_tokens = [0, 0]
+        num_input_video_tokens = [0, 0]
         num_image_tokens = [0, 0]
 
         for idx, case in enumerate(test_cases):
             actual_choice = await self.chat_serving._create_chat_completion_choice(
-                output=case["test_data"]["outputs"],
-                index=idx,
+                data=case["test_data"],
                 request=case["mock_request"],
-                previous_num_tokens=case["test_data"]["previous_num_tokens"],
                 prompt_token_ids=prompt_token_ids,
                 prompt_tokens=prompt_tokens,
                 completion_token_ids=completion_token_ids[idx],
+                previous_num_tokens=case["test_data"]["previous_num_tokens"],
                 num_cached_tokens=num_cached_tokens,
+                num_input_image_tokens=num_input_image_tokens,
+                num_input_video_tokens=num_input_video_tokens,
                 num_image_tokens=num_image_tokens,
                 logprob_contents=logprob_contents,
                 response_processor=mock_response_processor,

tests/output/test_process_batch_output.py

@@ -50,7 +50,7 @@ class MockTask:
     def get(self, key: str, default_value=None):
         if hasattr(self, key):
             return getattr(self, key)
-        elif hasattr(self.sampling_params, key):
+        elif hasattr(self, "sampling_params") and hasattr(self.sampling_params, key):
             return getattr(self.sampling_params, key)
         else:
             return default_value