Add support for OpenAI API parallel sampling (#640)

examples/usage/openai_parallel_sample.py

+import openai
+
+client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
+
+# Text completion
+response = client.completions.create(
+    model="default",
+    prompt="I am a robot and I want to study like humans. Now let's tell a story. Once upon a time, there was a little",
+    n=1,
+    temperature=0.8,
+    max_tokens=32,
+)
+print(response)
+
+
+# Text completion
+response = client.completions.create(
+    model="default",
+    prompt="I am a robot and I want to study like humans. Now let's tell a story. Once upon a time, there was a little",
+    n=3,
+    temperature=0.8,
+    max_tokens=32,
+)
+print(response)
+
+
+# Text completion
+response = client.completions.create(
+    model="default",
+    prompt=["The name of the famous soccer player is ", "The capital of US is"],
+    n=1,
+    temperature=0.8,
+    max_tokens=32,
+)
+print(response)
+
+
+# Text completion
+response = client.completions.create(
+    model="default",
+    prompt=["The name of the famous soccer player is ", "The capital of US is"],
+    n=3,
+    temperature=0.8,
+    max_tokens=32,
+)
+print(response)
+
+
+# Text completion
+response = client.completions.create(
+    model="default",
+    prompt=[
+        "The capital of France is",
+        "The capital of Germany is",
+        "The capital of US is",
+    ],
+    n=3,
+    temperature=0.8,
+    max_tokens=32,
+)
+print(response)
+
+# Chat completion
+response = client.chat.completions.create(
+    model="default",
+    messages=[
+        {"role": "system", "content": "You are a helpful AI assistant"},
+        {"role": "user", "content": "List 3 countries and their capitals."},
+    ],
+    temperature=0.8,
+    max_tokens=64,
+    logprobs=True,
+    n=4,
+)
+print(response)

python/sglang/srt/managers/io_struct.py

@@ -40,11 +40,13 @@ class GenerateReqInput:
             self.text is not None and self.input_ids is not None
         ):
             raise ValueError("Either text or input_ids should be provided.")
-
-        if self.text is not None:
-            is_single = isinstance(self.text, str)
+        if "n" in self.sampling_params and self.sampling_params["n"] != 1:
+            is_single = False
         else:
-            is_single = isinstance(self.input_ids[0], int)
+            if self.text is not None:
+                is_single = isinstance(self.text, str)
+            else:
+                is_single = isinstance(self.input_ids[0], int)
         self.is_single = is_single
 
         if is_single:
@@ -59,7 +61,22 @@ class GenerateReqInput:
             if self.top_logprobs_num is None:
                 self.top_logprobs_num = 0
         else:
-            num = len(self.text) if self.text is not None else len(self.input_ids)
+
+            parallel_sample_num = self.sampling_params.get("n", 1)
+
+            if parallel_sample_num != 1:
+                # parallel sampling +1 represents the original prefill stage
+                num = parallel_sample_num + 1
+                if isinstance(self.text, List):
+                    ## suppot batch operation
+                    self.batch_size = len(self.text)
+                    num = num * len(self.text)
+                else:
+                    self.batch_size = 1
+            else:
+                ## support select operation
+                num = len(self.text) if self.text is not None else len(self.input_ids)
+                self.batch_size = num
 
             if self.image_data is None:
                 self.image_data = [None] * num

python/sglang/srt/managers/tokenizer_manager.py

@@ -122,125 +122,150 @@ class TokenizerManager:
 
         obj.post_init()
         is_single = obj.is_single
-        if is_single:
-            rid = obj.rid
-
-            if obj.input_ids is None:
-                input_ids = self.tokenizer.encode(obj.text)
-            else:
-                input_ids = obj.input_ids
 
-            if len(input_ids) >= self.context_len:
-                raise ValueError(
-                    f"The input ({len(input_ids)} tokens) is longer than the "
-                    f"model's context length ({self.context_len} tokens)."
-                )
+        if is_single:
+            async for response in self._handle_single_request(obj, request):
+                yield response
+        else:
+            if obj.stream:
+                raise ValueError("Do not support stream for batch mode.")
 
-            sampling_params = SamplingParams(**obj.sampling_params)
-            if sampling_params.max_new_tokens != 0:
-                sampling_params.normalize(self.tokenizer)
-                sampling_params.verify()
+            async for response in self._handle_batch_request(obj, request):
+                yield response
 
-            if isinstance(obj.image_data, list) and len(obj.image_data) > 0:
-                pixel_values, image_hash, image_size = await self.get_pixel_values(
-                    obj.image_data[0]
-                )
-            elif isinstance(obj.image_data, str):
-                pixel_values, image_hash, image_size = await self.get_pixel_values(
-                    obj.image_data
-                )
+    async def _handle_single_request(self, obj, request, index=None, is_prefill=False):
+        if is_prefill:
+            if isinstance(obj.text, list):
+                input_text = obj.text[index]
+                rid = obj.rid[index]
             else:
-                pixel_values, image_hash, image_size = None, None, None
-            tokenized_obj = TokenizedGenerateReqInput(
-                rid=rid,
-                input_text=obj.text,
-                input_ids=input_ids,
-                pixel_values=pixel_values,
-                image_hash=image_hash,
-                image_size=image_size,
-                sampling_params=sampling_params,
-                return_logprob=obj.return_logprob,
-                logprob_start_len=obj.logprob_start_len,
-                top_logprobs_num=obj.top_logprobs_num,
-                stream=obj.stream,
+                input_text = obj.text
+                rid = obj.rid[0]
+            input_ids = self.tokenizer.encode(input_text)
+            sampling_params = SamplingParams(**obj.sampling_params[0])
+            sampling_params.max_new_tokens = 0
+            pixel_values, image_hash, image_size = await self._get_pixel_values(
+                obj.image_data[0]
+            )
+            return_logprob = obj.return_logprob[0]
+            logprob_start_len = obj.logprob_start_len[0]
+            top_logprobs_num = obj.top_logprobs_num[0]
+        else:
+            rid = obj.rid if index is None else obj.rid[index]
+            input_text = obj.text if index is None else obj.text[index]
+            input_ids = (
+                self.tokenizer.encode(input_text)
+                if obj.input_ids is None
+                else obj.input_ids
             )
-            self.send_to_router.send_pyobj(tokenized_obj)
+            if index is not None and obj.input_ids:
+                input_ids = obj.input_ids[index]
 
-            event = asyncio.Event()
-            state = ReqState([], False, event)
-            self.rid_to_state[rid] = state
+            self._validate_input_length(input_ids)
+            sampling_params = self._get_sampling_params(
+                obj.sampling_params if index is None else obj.sampling_params[index]
+            )
+            pixel_values, image_hash, image_size = await self._get_pixel_values(
+                obj.image_data if index is None else obj.image_data[index]
+            )
+            return_logprob = (
+                obj.return_logprob if index is None else obj.return_logprob[index]
+            )
+            logprob_start_len = (
+                obj.logprob_start_len if index is None else obj.logprob_start_len[index]
+            )
+            top_logprobs_num = (
+                obj.top_logprobs_num if index is None else obj.top_logprobs_num[index]
+            )
 
-            while True:
-                try:
-                    await asyncio.wait_for(event.wait(), timeout=4)
-                except asyncio.TimeoutError:
-                    if request is not None and await request.is_disconnected():
-                        self.abort_request(rid)
-                        raise ValueError(f"Abort request {rid}")
+        tokenized_obj = TokenizedGenerateReqInput(
+            rid,
+            input_text,
+            input_ids,
+            pixel_values,
+            image_hash,
+            image_size,
+            sampling_params,
+            return_logprob,
+            logprob_start_len,
+            top_logprobs_num,
+            obj.stream,
+        )
+        self.send_to_router.send_pyobj(tokenized_obj)
+
+        event = asyncio.Event()
+        state = ReqState([], False, event)
+        self.rid_to_state[rid] = state
+        if is_prefill == False:
+            async for response in self._wait_for_response(
+                event, state, obj, rid, request
+            ):
+                yield response
+        else:
+            await self._wait_for_prefill_response(event, state, obj, request, rid)
+            yield input_ids
+
+    async def _handle_batch_request(self, obj, request):
+        batch_size = obj.batch_size
+        parallel_sample_num = obj.sampling_params[0].get("n", 1)
+
+        if parallel_sample_num != 1:
+            ## send prefill requests
+            parallel_sample_num += 1
+            input_id_result = [] if obj.input_ids is None else None
+            for i in range(batch_size):
+                async for input_id in self._handle_single_request(
+                    obj, request, index=i, is_prefill=True
+                ):
+                    if input_id_result is not None:
+                        input_id_result.append(input_id)
+                    pass
+            if len(input_id_result) > 1 and input_id_result is not None:
+                obj.input_ids = input_id_result
+            elif input_id_result is not None:
+                obj.input_ids = input_id_result[0]
+        # First send out all requests
+        for i in range(batch_size):
+            for j in range(parallel_sample_num):
+                if j == 0 and parallel_sample_num != 1:
                     continue
-
-                out = self.convert_logprob_style(
-                    state.out_list[-1],
-                    obj.return_logprob,
-                    obj.top_logprobs_num,
-                    obj.return_text_in_logprobs,
+                index = i * parallel_sample_num + j
+                if parallel_sample_num != 1:
+                    # Here when using parallel sampling we shoul consider prefill stage so the index is :  j + i * (parallel_sample_num-1) + batch_size - 1
+                    index += batch_size - 1 - i
+                rid = obj.rid[index]
+                if parallel_sample_num == 1:
+                    ## select operation
+                    if obj.input_ids is None:
+                        input_text = obj.text[i]
+                        input_ids = self.tokenizer.encode(obj.text[i])
+                    else:
+                        input_text = None
+                        input_ids = obj.input_ids[i]
+                else:
+                    if batch_size == 1:
+                        input_text = obj.text
+                        input_ids = obj.input_ids
+                    else:
+                        input_text = obj.text[i]
+                        input_ids = obj.input_ids[i]
+                sampling_params = self._get_sampling_params(obj.sampling_params[index])
+                pixel_values, image_hash, image_size = await self._get_pixel_values(
+                    obj.image_data[index]
                 )
 
-                if self.server_args.log_requests and state.finished:
-                    logger.info(f"in={obj.text}, out={out}")
-
-                state.out_list = []
-                if state.finished:
-                    del self.rid_to_state[rid]
-
-                    yield out
-
-                    break
-
-                event.clear()
-
-                yield out
-        else:
-            if obj.stream:
-                raise ValueError("Do not support stream for batch mode.")
-
-            if obj.input_ids is None:
-                bs = len(obj.text)
-            else:
-                bs = len(obj.input_ids)
-
-            for i in range(bs):
-                rid = obj.rid[i]
-
-                if obj.input_ids is None:
-                    input_text = obj.text[i]
-                    input_ids = self.tokenizer.encode(obj.text[i])
-                else:
-                    input_text = None
-                    input_ids = obj.input_ids[i]
-
-                sampling_params = SamplingParams(**obj.sampling_params[i])
-                if sampling_params.max_new_tokens != 0:
-                    sampling_params.normalize(self.tokenizer)
-                    sampling_params.verify()
-                if obj.image_data[i] is None:
-                    pixel_values, image_hash, image_size = None, None, None
-                else:
-                    pixel_values, image_hash, image_size = await self.get_pixel_values(
-                        obj.image_data[i]
-                    )
                 tokenized_obj = TokenizedGenerateReqInput(
-                    rid=rid,
-                    input_text=input_text,
-                    input_ids=input_ids,
-                    pixel_values=pixel_values,
-                    image_hash=image_hash,
-                    image_size=image_size,
-                    sampling_params=sampling_params,
-                    return_logprob=obj.return_logprob[i],
-                    logprob_start_len=obj.logprob_start_len[i],
-                    top_logprobs_num=obj.top_logprobs_num[i],
-                    stream=obj.stream,
+                    rid,
+                    input_text,
+                    input_ids,
+                    pixel_values,
+                    image_hash,
+                    image_size,
+                    sampling_params,
+                    obj.return_logprob[index],
+                    obj.logprob_start_len[index],
+                    obj.top_logprobs_num[index],
+                    obj.stream,
                 )
                 self.send_to_router.send_pyobj(tokenized_obj)
 
@@ -248,9 +273,16 @@ class TokenizerManager:
                 state = ReqState([], False, event)
                 self.rid_to_state[rid] = state
 
-            output_list = []
-            for i in range(bs):
-                rid = obj.rid[i]
+        # Then wait for all responses
+        output_list = []
+        for i in range(batch_size):
+            for j in range(parallel_sample_num):
+                if j == 0 and parallel_sample_num != 1:
+                    continue
+                index = i * parallel_sample_num + j
+                if parallel_sample_num != 1:
+                    index += batch_size - 1 - i
+                rid = obj.rid[index]
                 state = self.rid_to_state[rid]
 
                 while True:
@@ -263,19 +295,86 @@ class TokenizerManager:
                                 self.abort_request(rid)
                             raise ValueError(f"Abort request {rid}")
                         continue
-
                 output_list.append(
                     self.convert_logprob_style(
                         state.out_list[-1],
-                        obj.return_logprob[i],
-                        obj.top_logprobs_num[i],
+                        obj.return_logprob[index],
+                        obj.top_logprobs_num[index],
                         obj.return_text_in_logprobs,
                     )
                 )
                 assert state.finished
                 del self.rid_to_state[rid]
 
-            yield output_list
+        yield output_list
+
+    def _validate_input_length(self, input_ids):
+        if len(input_ids) >= self.context_len:
+            raise ValueError(
+                f"The input ({len(input_ids)} tokens) is longer than the "
+                f"model's context length ({self.context_len} tokens)."
+            )
+
+    def _get_sampling_params(self, sampling_params_data, max_new_tokens=None):
+        sampling_params = SamplingParams(**sampling_params_data)
+        if max_new_tokens is not None:
+            sampling_params.max_new_tokens = max_new_tokens
+        if sampling_params.max_new_tokens != 0:
+            sampling_params.normalize(self.tokenizer)
+            sampling_params.verify()
+        return sampling_params
+
+    async def _get_pixel_values(self, image_data):
+        if isinstance(image_data, list) and len(image_data) > 0:
+            return await self.get_pixel_values(image_data[0])
+        elif isinstance(image_data, str):
+            return await self.get_pixel_values(image_data)
+        else:
+            return None, None, None
+
+    async def _wait_for_response(self, event, state, obj, rid, request):
+        while True:
+            try:
+                await asyncio.wait_for(event.wait(), timeout=4)
+            except asyncio.TimeoutError:
+                if request is not None and await request.is_disconnected():
+                    self.abort_request(rid)
+                    raise ValueError(f"Abort request {rid}")
+                continue
+
+            out = self.convert_logprob_style(
+                state.out_list[-1],
+                obj.return_logprob,
+                obj.top_logprobs_num,
+                obj.return_text_in_logprobs,
+            )
+
+            if self.server_args.log_requests and state.finished:
+                logger.info(f"in={obj.text}, out={out}")
+
+            state.out_list = []
+            if state.finished:
+                del self.rid_to_state[rid]
+                yield out
+                break
+
+            event.clear()
+            yield out
+
+    async def _wait_for_prefill_response(self, event, state, obj, request, rid):
+        while True:
+            try:
+                await asyncio.wait_for(state.event.wait(), timeout=4)
+                break
+            except asyncio.TimeoutError:
+                if request is not None and await request.is_disconnected():
+                    for rid in obj.rid:
+                        self.abort_request(rid)
+                    raise ValueError(f"Abort request {rid}")
+                continue
+
+        assert state.finished
+        del self.rid_to_state[rid]
 
     def flush_cache(self):
         req = FlushCacheReq()

python/sglang/srt/openai_api_adapter.py

@@ -95,9 +95,6 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
     request_json = await raw_request.json()
     request = CompletionRequest(**request_json)
 
-    if request.n != 1:
-        return create_error_response("n != 1 is not supported")
-
     adapted_request = GenerateReqInput(
         text=request.prompt,
         sampling_params={
@@ -108,6 +105,7 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
             "presence_penalty": request.presence_penalty,
             "frequency_penalty": request.frequency_penalty,
             "regex": request.regex,
+            "n": request.n,
         },
         return_logprob=request.logprobs is not None and request.logprobs > 0,
         top_logprobs_num=request.logprobs if request.logprobs is not None else 0,
@@ -202,46 +200,56 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
     except ValueError as e:
         return create_error_response(str(e))
 
-    ret = ret[0] if isinstance(ret, list) else ret
-    prompt_tokens = ret["meta_info"]["prompt_tokens"]
-    completion_tokens = ret["meta_info"]["completion_tokens"]
-    text = ret["text"]
-    if request.echo:
-        text = request.prompt + text
+    if not isinstance(ret, list):
+        ret = [ret]
+    choices = []
+
+    for idx, ret_item in enumerate(ret):
+        text = ret_item["text"]
 
-    if request.logprobs:
         if request.echo:
-            prefill_token_logprobs = ret["meta_info"]["prefill_token_logprobs"]
-            prefill_top_logprobs = ret["meta_info"]["prefill_top_logprobs"]
+            text = request.prompt + text
+
+        if request.logprobs:
+            if request.echo:
+                prefill_token_logprobs = ret_item["meta_info"]["prefill_token_logprobs"]
+                prefill_top_logprobs = ret_item["meta_info"]["prefill_top_logprobs"]
+            else:
+                prefill_token_logprobs = None
+                prefill_top_logprobs = None
+
+            logprobs = to_openai_style_logprobs(
+                prefill_token_logprobs=prefill_token_logprobs,
+                prefill_top_logprobs=prefill_top_logprobs,
+                decode_token_logprobs=ret_item["meta_info"]["decode_token_logprobs"],
+                decode_top_logprobs=ret_item["meta_info"]["decode_top_logprobs"],
+            )
         else:
-            prefill_token_logprobs = None
-            prefill_top_logprobs = None
-
-        logprobs = to_openai_style_logprobs(
-            prefill_token_logprobs=prefill_token_logprobs,
-            prefill_top_logprobs=prefill_top_logprobs,
-            decode_token_logprobs=ret["meta_info"]["decode_token_logprobs"],
-            decode_top_logprobs=ret["meta_info"]["decode_top_logprobs"],
+            logprobs = None
+
+        choice_data = CompletionResponseChoice(
+            index=idx,
+            text=text,
+            logprobs=logprobs,
+            finish_reason=ret_item["meta_info"]["finish_reason"],
         )
-    else:
-        logprobs = None
 
-    choice_data = CompletionResponseChoice(
-        index=0,
-        text=text,
-        logprobs=logprobs,
-        finish_reason=ret["meta_info"]["finish_reason"],
-    )
+        choices.append(choice_data)
+
     response = CompletionResponse(
-        id=ret["meta_info"]["id"],
+        id=ret[0]["meta_info"]["id"],
         model=request.model,
-        choices=[choice_data],
+        choices=choices,
         usage=UsageInfo(
-            prompt_tokens=prompt_tokens,
-            completion_tokens=completion_tokens,
-            total_tokens=prompt_tokens + completion_tokens,
+            prompt_tokens=ret[0]["meta_info"]["prompt_tokens"],
+            completion_tokens=sum(
+                item["meta_info"]["completion_tokens"] for item in ret
+            ),
+            total_tokens=ret[0]["meta_info"]["prompt_tokens"]
+            + sum(item["meta_info"]["completion_tokens"] for item in ret),
         ),
     )
+
     return response
 
 
@@ -249,9 +257,6 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
     request_json = await raw_request.json()
     request = ChatCompletionRequest(**request_json)
 
-    if request.n != 1:
-        return create_error_response("n != 1 is not supported")
-
     # Prep the data needed for the underlying GenerateReqInput:
     #  - prompt: The full prompt string.
     #  - stop: Custom stop tokens.
@@ -292,6 +297,7 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
             "presence_penalty": request.presence_penalty,
             "frequency_penalty": request.frequency_penalty,
             "regex": request.regex,
+            "n": request.n,
         },
         stream=request.stream,
     )
@@ -354,23 +360,37 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
     except ValueError as e:
         return create_error_response(str(e))
 
-    prompt_tokens = ret["meta_info"]["prompt_tokens"]
-    completion_tokens = ret["meta_info"]["completion_tokens"]
-    choice_data = ChatCompletionResponseChoice(
-        index=0,
-        message=ChatMessage(role="assistant", content=ret["text"]),
-        finish_reason=ret["meta_info"]["finish_reason"],
-    )
+    if not isinstance(ret, list):
+        ret = [ret]
+    choices = []
+    total_prompt_tokens = 0
+    total_completion_tokens = 0
+
+    for idx, ret_item in enumerate(ret):
+        prompt_tokens = ret_item["meta_info"]["prompt_tokens"]
+        completion_tokens = ret_item["meta_info"]["completion_tokens"]
+
+        choice_data = ChatCompletionResponseChoice(
+            index=idx,
+            message=ChatMessage(role="assistant", content=ret_item["text"]),
+            finish_reason=ret_item["meta_info"]["finish_reason"],
+        )
+
+        choices.append(choice_data)
+        total_prompt_tokens = prompt_tokens
+        total_completion_tokens += completion_tokens
+
     response = ChatCompletionResponse(
-        id=ret["meta_info"]["id"],
+        id=ret[0]["meta_info"]["id"],
         model=request.model,
-        choices=[choice_data],
+        choices=choices,
         usage=UsageInfo(
-            prompt_tokens=prompt_tokens,
-            completion_tokens=completion_tokens,
-            total_tokens=prompt_tokens + completion_tokens,
+            prompt_tokens=total_prompt_tokens,
+            completion_tokens=total_completion_tokens,
+            total_tokens=total_prompt_tokens + total_completion_tokens,
         ),
     )
+
     return response
 
 

python/sglang/srt/sampling_params.py

@@ -20,6 +20,7 @@ class SamplingParams:
         spaces_between_special_tokens: bool = True,
         dtype: Optional[str] = None,
         regex: Optional[str] = None,
+        n: int = 1,
     ) -> None:
         self.temperature = temperature
         self.top_p = top_p
@@ -33,6 +34,7 @@ class SamplingParams:
         self.spaces_between_special_tokens = spaces_between_special_tokens
         self.dtype = dtype
         self.regex = regex
+        self.n = n
 
         # Process some special cases
         if self.temperature < _SAMPLING_EPS: