Repository: vllm-project/vllm

Issue:
batching and streaming
Hi!
If i correctly understood code . Any type of entrypoints api with stream doesn't support batching . But LLMEngine has step() method for processing batch of requests and i can implement streaming with batch using step() or  there any pitfalls here? For example how handle these multiple response streams


Discussion:
I have the same question, and strongly expecting someone can help me. 
> I have the same question, and strongly expecting someone can help me.

u can use engine.step() method to synchronous executing batch of requests but probably without streaming

Resolving patch:
diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py
--- a/vllm/entrypoints/openai/serving_completion.py
+++ b/vllm/entrypoints/openai/serving_completion.py
@@ -1,6 +1,7 @@
+import asyncio
 import time
 from fastapi import Request
-from typing import AsyncGenerator, AsyncIterator
+from typing import AsyncGenerator, AsyncIterator, Callable, List, Optional
 from vllm.logger import init_logger
 from vllm.utils import random_uuid
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -18,48 +19,68 @@
 
 logger = init_logger(__name__)
 
+TypeTokenIDs = list[int]
+TypeTopLogProbs = List[Optional[dict[int, float]]]
+TypeCreateLogProbsFn = Callable[
+    [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs]
+
 
 async def completion_stream_generator(
-        request: CompletionRequest,
-        result_generator: AsyncIterator[RequestOutput],
-        echo_without_generation, create_logprobs_fn, request_id, created_time,
-        model_name) -> AsyncGenerator[str, None]:
-    previous_texts = [""] * request.n
-    previous_num_tokens = [0] * request.n
-    has_echoed = [False] * request.n
-
-    async for res in result_generator:
-        # TODO: handle client disconnect for streaming
+    request: CompletionRequest,
+    raw_request: Request,
+    on_abort,
+    result_generator: AsyncIterator[tuple[int, RequestOutput]],
+    create_logprobs_fn: TypeCreateLogProbsFn,
+    request_id: str,
+    created_time: int,
+    model_name: str,
+    num_prompts: int,
+) -> AsyncGenerator[str, None]:
+    previous_texts = [""] * request.n * num_prompts
+    previous_num_tokens = [0] * request.n * num_prompts
+    has_echoed = [False] * request.n * num_prompts
+
+    async for prompt_idx, res in result_generator:
+
+        # Abort the request if the client disconnects.
+        if await raw_request.is_disconnected():
+            await on_abort(f"{request_id}-{prompt_idx}")
+            raise StopAsyncIteration()
+
         for output in res.outputs:
-            i = output.index
-            delta_text = output.text[len(previous_texts[i]):]
-            token_ids = output.token_ids[previous_num_tokens[i]:]
-            if request.logprobs is not None:
-                top_logprobs = output.logprobs[previous_num_tokens[i]:]
-            else:
-                top_logprobs = None
-            offsets = len(previous_texts[i])
-            if request.echo and not has_echoed[i]:
-                if not echo_without_generation:
-                    delta_text = res.prompt + delta_text
-                    token_ids = res.prompt_token_ids + token_ids
-                    if top_logprobs:
-                        top_logprobs = res.prompt_logprobs + top_logprobs
-                else:  # only just return the prompt
-                    delta_text = res.prompt
-                    token_ids = res.prompt_token_ids
-                    if top_logprobs:
-                        top_logprobs = res.prompt_logprobs
+            i = output.index + prompt_idx * request.n
+            # TODO(simon): optimize the performance by avoiding full text O(n^2) sending.
+
+            if request.echo and request.max_tokens == 0:
+                # only return the prompt
+                delta_text = res.prompt
+                delta_token_ids = res.prompt_token_ids
+                top_logprobs = res.prompt_logprobs
+                has_echoed[i] = True
+            elif request.echo and request.max_tokens > 0 and not has_echoed[i]:
+                # echo the prompt and first token
+                delta_text = res.prompt + output.text
+                delta_token_ids = res.prompt_token_ids + output.token_ids
+                top_logprobs = res.prompt_logprobs + (output.logprobs or [])
                 has_echoed[i] = True
+            else:
+                # return just the delta
+                delta_text = output.text[len(previous_texts[i]):]
+                delta_token_ids = output.token_ids[previous_num_tokens[i]:]
+                top_logprobs = output.logprobs[
+                    previous_num_tokens[i]:] if output.logprobs else None
+
             if request.logprobs is not None:
+                assert top_logprobs is not None, "top_logprobs must be provided when logprobs is requested"
                 logprobs = create_logprobs_fn(
-                    token_ids=token_ids,
+                    token_ids=delta_token_ids,
                     top_logprobs=top_logprobs,
                     num_output_top_logprobs=request.logprobs,
-                    initial_text_offset=offsets,
+                    initial_text_offset=len(previous_texts[i]),
                 )
             else:
                 logprobs = None
+
             previous_texts[i] = output.text
             previous_num_tokens[i] = len(output.token_ids)
             finish_reason = output.finish_reason
@@ -77,7 +98,7 @@ async def completion_stream_generator(
                 ]).json(exclude_unset=True, ensure_ascii=False)
             yield f"data: {response_json}\n\n"
 
-            if output.finish_reason is not None:
+            if output.finish_reason is not None:  # return final usage
                 logprobs = LogProbs() if request.logprobs is not None else None
                 prompt_tokens = len(res.prompt_token_ids)
                 completion_tokens = len(output.token_ids)
@@ -129,51 +150,58 @@ def parse_prompt_format(prompt) -> tuple[bool, list]:
     return prompt_is_tokens, prompts
 
 
-def request_output_to_completion_response(final_res: RequestOutput, request,
-                                          echo_without_generation,
-                                          create_logprobs_fn, request_id,
-                                          created_time,
-                                          model_name) -> CompletionResponse:
-    assert final_res is not None
+def request_output_to_completion_response(
+    final_res_batch: list[RequestOutput],
+    request: CompletionRequest,
+    create_logprobs_fn: TypeCreateLogProbsFn,
+    request_id: str,
+    created_time: int,
+    model_name: str,
+) -> CompletionResponse:
     choices = []
-    prompt_token_ids = final_res.prompt_token_ids
-    prompt_logprobs = final_res.prompt_logprobs
-    prompt_text = final_res.prompt
-    for output in final_res.outputs:
-        if request.logprobs is not None:
-            if not echo_without_generation:
+    num_prompt_tokens = 0
+    num_generated_tokens = 0
+    for final_res in final_res_batch:
+        assert final_res is not None
+        prompt_token_ids = final_res.prompt_token_ids
+        prompt_logprobs = final_res.prompt_logprobs
+        prompt_text = final_res.prompt
+
+        for output in final_res.outputs:
+            if request.echo and request.max_tokens == 0:
+                token_ids = prompt_token_ids
+                top_logprobs = prompt_logprobs
+                output_text = prompt_text
+            elif request.echo and request.max_tokens > 0:
+                token_ids = prompt_token_ids + output.token_ids
+                top_logprobs = prompt_logprobs + output.logprobs
+                output_text = prompt_text + output.text
+            else:
                 token_ids = output.token_ids
                 top_logprobs = output.logprobs
-                if request.echo:
-                    token_ids = prompt_token_ids + token_ids
-                    top_logprobs = prompt_logprobs + top_logprobs
+                output_text = output.text
+
+            if request.logprobs is not None:
+                logprobs = create_logprobs_fn(
+                    token_ids=token_ids,
+                    top_logprobs=top_logprobs,
+                    num_output_top_logprobs=request.logprobs,
+                )
             else:
-                token_ids = prompt_token_ids
-                top_logprobs = prompt_logprobs
-            logprobs = create_logprobs_fn(
-                token_ids=token_ids,
-                top_logprobs=top_logprobs,
-                num_output_top_logprobs=request.logprobs,
+                logprobs = None
+
+            choice_data = CompletionResponseChoice(
+                index=len(choices),
+                text=output_text,
+                logprobs=logprobs,
+                finish_reason=output.finish_reason,
             )
-        else:
-            logprobs = None
-        if not echo_without_generation:
-            output_text = output.text
-            if request.echo:
-                output_text = prompt_text + output_text
-        else:
-            output_text = prompt_text
-        choice_data = CompletionResponseChoice(
-            index=output.index,
-            text=output_text,
-            logprobs=logprobs,
-            finish_reason=output.finish_reason,
-        )
-        choices.append(choice_data)
-
-    num_prompt_tokens = len(final_res.prompt_token_ids)
-    num_generated_tokens = sum(
-        len(output.token_ids) for output in final_res.outputs)
+            choices.append(choice_data)
+
+        num_prompt_tokens += len(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,
@@ -189,6 +217,36 @@ def request_output_to_completion_response(final_res: RequestOutput, request,
     )
 
 
+def merge_async_iterators(*iterators):
+    """Merge multiple asynchronous iterators into a single iterator.
+
+    This method handle the case where some iterators finish before others.
+    When it yields, it yields a tuple (i, item) where i is the index of the
+    iterator that yields the item.
+    """
+    queue = asyncio.Queue()
+
+    finished = [False] * len(iterators)
+
+    async def producer(i, iterator):
+        async for item in iterator:
+            await queue.put((i, item))
+        finished[i] = True
+
+    _tasks = [
+        asyncio.create_task(producer(i, iterator))
+        for i, iterator in enumerate(iterators)
+    ]
+
+    async def consumer():
+        while not all(finished) or not queue.empty():
+            item = await queue.get()
+            yield item
+        await asyncio.gather(*_tasks)
+
+    return consumer()
+
+
 class OpenAIServingCompletion(OpenAIServing):
 
     def __init__(self, engine: AsyncLLMEngine, served_model: str):
@@ -210,9 +268,6 @@ async def create_completion(self, request: CompletionRequest,
         if error_check_ret is not None:
             return error_check_ret
 
-        # OpenAI API supports echoing the prompt when max_tokens is 0.
-        echo_without_generation = request.echo and request.max_tokens == 0
-
         # Return error for unsupported features.
         if request.suffix is not None:
             return self.create_error_response(
@@ -226,30 +281,30 @@ async def create_completion(self, request: CompletionRequest,
         created_time = int(time.monotonic())
 
         # Schedule the request and get the result generator.
+        generators = []
         try:
             sampling_params = request.to_sampling_params()
-
             prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
 
-            if len(prompts) > 1:
-                raise ValueError(
-                    "Batching in completion API is not supported.")
-            prompt = prompts[0]
-
-            if prompt_is_tokens:
-                input_ids = self._validate_prompt_and_tokenize(
-                    request, prompt_ids=prompt)
-            else:
-                input_ids = self._validate_prompt_and_tokenize(request,
-                                                               prompt=prompt)
-
-            result_generator = self.engine.generate(None,
-                                                    sampling_params,
-                                                    request_id,
-                                                    prompt_token_ids=input_ids)
+            for i, prompt in enumerate(prompts):
+                if prompt_is_tokens:
+                    input_ids = self._validate_prompt_and_tokenize(
+                        request, prompt_ids=prompt)
+                else:
+                    input_ids = self._validate_prompt_and_tokenize(
+                        request, prompt=prompt)
+
+                generators.append(
+                    self.engine.generate(None,
+                                         sampling_params,
+                                         f"{request_id}-{i}",
+                                         prompt_token_ids=input_ids))
         except ValueError as e:
             return self.create_error_response(str(e))
 
+        result_generator: AsyncIterator[tuple[
+            int, RequestOutput]] = merge_async_iterators(*generators)
+
         # Similar to the OpenAI API, when n != best_of, we do not stream the
         # results. In addition, we do not stream the results when use beam search.
         stream = (request.stream
@@ -258,23 +313,27 @@ async def create_completion(self, request: CompletionRequest,
 
         # Streaming response
         if stream:
-            return completion_stream_generator(request, result_generator,
-                                               echo_without_generation,
+            return completion_stream_generator(request,
+                                               raw_request,
+                                               self.engine.abort,
+                                               result_generator,
                                                self._create_logprobs,
-                                               request_id, created_time,
-                                               model_name)
+                                               request_id,
+                                               created_time,
+                                               model_name,
+                                               num_prompts=len(prompts))
 
         # Non-streaming response
-        final_res: RequestOutput = None
-        async for res in result_generator:
+        final_res_batch: RequestOutput = [None] * len(prompts)
+        async for i, res in result_generator:
             if await raw_request.is_disconnected():
                 # Abort the request if the client disconnects.
-                await self.engine.abort(request_id)
+                await self.engine.abort(f"{request_id}-{i}")
                 return self.create_error_response("Client disconnected")
-            final_res = res
+            final_res_batch[i] = res
         response = request_output_to_completion_response(
-            final_res, request, echo_without_generation, self._create_logprobs,
-            request_id, created_time, model_name)
+            final_res_batch, request, self._create_logprobs, request_id,
+            created_time, model_name)
 
         # When user requests streaming but we don't stream, we still need to
         # return a streaming response with a single event.


Test patch:
diff --git a/tests/entrypoints/test_openai_server.py b/tests/entrypoints/test_openai_server.py
--- a/tests/entrypoints/test_openai_server.py
+++ b/tests/entrypoints/test_openai_server.py
@@ -1,5 +1,6 @@
-import time
+import os
 import subprocess
+import time
 
 import sys
 import pytest
@@ -17,8 +18,11 @@
 class ServerRunner:
 
     def __init__(self, args):
+        env = os.environ.copy()
+        env["PYTHONUNBUFFERED"] = "1"
         self.proc = subprocess.Popen(
             ["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
+            env=env,
             stdout=sys.stdout,
             stderr=sys.stderr,
         )
@@ -58,7 +62,8 @@ def server():
         "--dtype",
         "bfloat16",  # use half precision for speed and memory savings in CI environment
         "--max-model-len",
-        "8192"
+        "8192",
+        "--enforce-eager",
     ])
     ray.get(server_runner.ready.remote())
     yield server_runner
@@ -199,5 +204,51 @@ async def test_chat_streaming(server, client: openai.AsyncOpenAI):
     assert "".join(chunks) == output
 
 
+async def test_batch_completions(server, client: openai.AsyncOpenAI):
+    # test simple list
+    batch = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=["Hello, my name is", "Hello, my name is"],
+        max_tokens=5,
+        temperature=0.0,
+    )
+    assert len(batch.choices) == 2
+    assert batch.choices[0].text == batch.choices[1].text
+
+    # test n = 2
+    batch = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=["Hello, my name is", "Hello, my name is"],
+        n=2,
+        max_tokens=5,
+        temperature=0.0,
+        extra_body=dict(
+            # NOTE: this has to be true for n > 1 in vLLM, but not necessary for official client.
+            use_beam_search=True),
+    )
+    assert len(batch.choices) == 4
+    assert batch.choices[0].text != batch.choices[
+        1].text, "beam search should be different"
+    assert batch.choices[0].text == batch.choices[
+        2].text, "two copies of the same prompt should be the same"
+    assert batch.choices[1].text == batch.choices[
+        3].text, "two copies of the same prompt should be the same"
+
+    # test streaming
+    batch = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=["Hello, my name is", "Hello, my name is"],
+        max_tokens=5,
+        temperature=0.0,
+        stream=True,
+    )
+    texts = [""] * 2
+    async for chunk in batch:
+        assert len(chunk.choices) == 1
+        choice = chunk.choices[0]
+        texts[choice.index] += choice.text
+    assert texts[0] == texts[1]
+
+
 if __name__ == "__main__":
     pytest.main([__file__])