Merge pull request #117 from Xaenalt/bugfix/SamplingParams

[Cherry-Pick] [Bugfix] Set SamplingParams.max_tokens for OpenAI requests if not provided by user (vllm-project#6954)

tests/entrypoints/openai/test_completion.py

@@ -537,6 +537,28 @@ async def test_logits_bias(client: openai.AsyncOpenAI):
     assert first_response != completion.choices[0].text
 
 
+@pytest.mark.asyncio
+async def test_allowed_token_ids(client: openai.AsyncOpenAI):
+    prompt = "Hello, my name is"
+    max_tokens = 1
+    tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
+
+    # Test exclusive selection
+    allowed_ids = [21555, 21557, 21558]
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=0.0,
+        seed=42,
+        extra_body=dict(allowed_token_ids=allowed_ids),
+        logprobs=1,
+    )
+    response_tokens = completion.choices[0].logprobs.tokens
+    assert len(response_tokens) == 1
+    assert tokenizer.convert_tokens_to_ids(response_tokens)[0] in allowed_ids
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])

tests/entrypoints/openai/test_serving_chat.py

@@ -1,7 +1,12 @@
 import asyncio
+from contextlib import suppress
 from dataclasses import dataclass
+from unittest.mock import MagicMock
 
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.transformers_utils.tokenizer import get_tokenizer
 
 MODEL_NAME = "openai-community/gpt2"
 CHAT_TEMPLATE = "Dummy chat template for testing {}"
@@ -42,3 +47,37 @@ async def _async_serving_chat_init():
 def test_async_serving_chat_init():
     serving_completion = asyncio.run(_async_serving_chat_init())
     assert serving_completion.chat_template == CHAT_TEMPLATE
+
+
+def test_serving_chat_should_set_correct_max_tokens():
+    mock_engine = MagicMock(spec=AsyncLLMEngine)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     MockModelConfig(),
+                                     served_model_names=[MODEL_NAME],
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     lora_modules=None,
+                                     prompt_adapters=None,
+                                     request_logger=None)
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        guided_decoding_backend="outlines",
+    )
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    # AsyncLLMEngine.generate(inputs, sampling_params, ...)
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    req.max_tokens = 10
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 10

vllm/entrypoints/openai/logits_processors.py

@@ -0,0 +1,74 @@
+from functools import lru_cache
+from typing import Dict, FrozenSet, Iterable, List, Optional, Union
+
+import torch
+from transformers import PreTrainedTokenizer
+
+from vllm.sampling_params import LogitsProcessor
+
+
+class AllowedTokenIdsLogitsProcessor:
+    """Logits processor for constraining generated tokens to a
+    specific set of token ids."""
+
+    def __init__(self, allowed_ids: Iterable[int]):
+        self.allowed_ids: Optional[List[int]] = list(allowed_ids)
+        self.mask: Optional[torch.Tensor] = None
+
+    def __call__(self, token_ids: List[int],
+                 logits: torch.Tensor) -> torch.Tensor:
+        if self.mask is None:
+            self.mask = torch.ones((logits.shape[-1], ),
+                                   dtype=torch.bool,
+                                   device=logits.device)
+            self.mask[self.allowed_ids] = False
+            self.allowed_ids = None
+        logits.masked_fill_(self.mask, float("-inf"))
+        return logits
+
+
+@lru_cache(maxsize=32)
+def _get_allowed_token_ids_logits_processor(
+    allowed_token_ids: FrozenSet[int],
+    vocab_size: int,
+) -> LogitsProcessor:
+    if not allowed_token_ids:
+        raise ValueError("Empty allowed_token_ids provided")
+    if not all(0 <= tid < vocab_size for tid in allowed_token_ids):
+        raise ValueError("allowed_token_ids contains "
+                         "out-of-vocab token id")
+    return AllowedTokenIdsLogitsProcessor(allowed_token_ids)
+
+
+def get_logits_processors(
+        logit_bias: Optional[Union[Dict[int, float], Dict[str, float]]],
+        allowed_token_ids: Optional[List[int]],
+        tokenizer: PreTrainedTokenizer) -> List[LogitsProcessor]:
+    logits_processors = []
+    if logit_bias:
+        try:
+            # Convert token_id to integer
+            # Clamp the bias between -100 and 100 per OpenAI API spec
+            clamped_logit_bias: Dict[int, float] = {
+                int(token_id): min(100.0, max(-100.0, bias))
+                for token_id, bias in logit_bias.items()
+            }
+        except ValueError as exc:
+            raise ValueError(
+                "Found token_id in logit_bias that is not "
+                "an integer or string representing an integer") from exc
+
+        def logit_bias_logits_processor(token_ids: List[int],
+                                        logits: torch.Tensor) -> torch.Tensor:
+            for token_id, bias in clamped_logit_bias.items():
+                logits[token_id] += bias
+            return logits
+
+        logits_processors.append(logit_bias_logits_processor)
+
+    if allowed_token_ids is not None:
+        logits_processors.append(
+            _get_allowed_token_ids_logits_processor(
+                frozenset(allowed_token_ids), tokenizer.vocab_size))
+
+    return logits_processors

vllm/entrypoints/openai/protocol.py

@@ -5,11 +5,13 @@
 
 import torch
 from pydantic import BaseModel, ConfigDict, Field, model_validator
+from transformers import PreTrainedTokenizer
 from typing_extensions import Annotated
 
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.entrypoints.openai.logits_processors import get_logits_processors
 from vllm.pooling_params import PoolingParams
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import LogitsProcessor, SamplingParams
 from vllm.utils import random_uuid
 
 
@@ -213,30 +215,22 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
     # doc: end-chat-completion-extra-params
 
-    def to_sampling_params(self) -> SamplingParams:
-        # We now allow logprobs being true without top_logrobs.
+    def to_sampling_params(
+            self, tokenizer: PreTrainedTokenizer,
+            guided_decode_logits_processor: Optional[LogitsProcessor],
+            default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
 
-        logits_processors = None
-        if self.logit_bias:
-            logit_bias: Dict[int, float] = {}
-            try:
-                for token_id, bias in self.logit_bias.items():
-                    # Convert token_id to integer before we add to LLMEngine
-                    # Clamp the bias between -100 and 100 per OpenAI API spec
-                    logit_bias[int(token_id)] = min(100, max(-100, bias))
-            except ValueError as exc:
-                raise ValueError(f"Found token_id `{token_id}` in logit_bias "
-                                 f"but token_id must be an integer or string "
-                                 f"representing an integer") from exc
-
-            def logit_bias_logits_processor(
-                    token_ids: List[int],
-                    logits: torch.Tensor) -> torch.Tensor:
-                for token_id, bias in logit_bias.items():
-                    logits[token_id] += bias
-                return logits
-
-            logits_processors = [logit_bias_logits_processor]
+        # We now allow logprobs being true without top_logrobs.
+        logits_processors = get_logits_processors(
+            logit_bias=self.logit_bias,
+            allowed_token_ids=None,
+            tokenizer=tokenizer,
+        )
+        if guided_decode_logits_processor:
+            logits_processors.append(guided_decode_logits_processor)
 
         return SamplingParams(
             n=self.n,
@@ -254,7 +248,7 @@ def logit_bias_logits_processor(
             logprobs=self.top_logprobs if self.logprobs else None,
             prompt_logprobs=self.top_logprobs if self.echo else None,
             ignore_eos=self.ignore_eos,
-            max_tokens=self.max_tokens,
+            max_tokens=max_tokens,
             min_tokens=self.min_tokens,
             use_beam_search=self.use_beam_search,
             early_stopping=self.early_stopping,
@@ -358,6 +352,7 @@ class CompletionRequest(OpenAIBaseModel):
     skip_special_tokens: bool = True
     spaces_between_special_tokens: bool = True
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    allowed_token_ids: Optional[List[int]] = None
     # doc: end-completion-sampling-params
 
     # doc: begin-completion-extra-params
@@ -407,30 +402,23 @@ class CompletionRequest(OpenAIBaseModel):
 
     # doc: end-completion-extra-params
 
-    def to_sampling_params(self):
+    def to_sampling_params(
+            self, tokenizer: PreTrainedTokenizer,
+            guided_decode_logits_processor: Optional[LogitsProcessor],
+            default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
         echo_without_generation = self.echo and self.max_tokens == 0
 
-        logits_processors = None
-        if self.logit_bias:
-            logit_bias: Dict[int, float] = {}
-            try:
-                for token_id, bias in self.logit_bias.items():
-                    # Convert token_id to integer
-                    # Clamp the bias between -100 and 100 per OpenAI API spec
-                    logit_bias[int(token_id)] = min(100, max(-100, bias))
-            except ValueError as exc:
-                raise ValueError(f"Found token_id `{token_id}` in logit_bias "
-                                 f"but token_id must be an integer or string "
-                                 f"representing an integer") from exc
-
-            def logit_bias_logits_processor(
-                    token_ids: List[int],
-                    logits: torch.Tensor) -> torch.Tensor:
-                for token_id, bias in logit_bias.items():
-                    logits[token_id] += bias
-                return logits
-
-            logits_processors = [logit_bias_logits_processor]
+        logits_processors = get_logits_processors(
+            logit_bias=self.logit_bias,
+            allowed_token_ids=self.allowed_token_ids,
+            tokenizer=tokenizer,
+        )
+        if guided_decode_logits_processor:
+            logits_processors.append(guided_decode_logits_processor)
 
         return SamplingParams(
             n=self.n,
@@ -447,7 +435,7 @@ def logit_bias_logits_processor(
             stop_token_ids=self.stop_token_ids,
             logprobs=self.logprobs,
             ignore_eos=self.ignore_eos,
-            max_tokens=self.max_tokens if not echo_without_generation else 1,
+            max_tokens=max_tokens if not echo_without_generation else 1,
             min_tokens=self.min_tokens,
             use_beam_search=self.use_beam_search,
             early_stopping=self.early_stopping,

vllm/entrypoints/openai/serving_chat.py

@@ -25,8 +25,6 @@
                                                     PromptAdapterPath)
 from vllm.inputs import PromptInputs
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding import (
-    get_guided_decoding_logits_processor)
 from vllm.multimodal import MultiModalDataDict
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
@@ -132,28 +130,23 @@ async def create_chat_completion(
 
         request_id = f"chat-{random_uuid()}"
         try:
-            sampling_params = request.to_sampling_params()
-            decoding_config = await self.engine.get_decoding_config()
-            guided_decoding_backend = request.guided_decoding_backend \
-                or decoding_config.guided_decoding_backend
             guided_decode_logits_processor = (
-                await
-                get_guided_decoding_logits_processor(guided_decoding_backend,
-                                                     request, tokenizer))
-            if guided_decode_logits_processor:
-                if sampling_params.logits_processors is None:
-                    sampling_params.logits_processors = []
-                sampling_params.logits_processors.append(
-                    guided_decode_logits_processor)
+                await self._guided_decode_logits_processor(request, tokenizer))
 
             prompt_inputs = self._tokenize_prompt_input(
                 request,
                 tokenizer,
                 prompt,
-                truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
                 add_special_tokens=request.add_special_tokens,
             )
 
+            sampling_params = request.to_sampling_params(
+                tokenizer,
+                guided_decode_logits_processor,
+                default_max_tokens=self.max_model_len -
+                len(prompt_inputs["prompt_token_ids"]))
+
             self._log_inputs(request_id,
                              prompt_inputs,
                              params=sampling_params,

vllm/entrypoints/openai/serving_completion.py

@@ -24,8 +24,6 @@
                                                     OpenAIServing,
                                                     PromptAdapterPath)
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding import (
-    get_guided_decoding_logits_processor)
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
@@ -93,31 +91,24 @@ async def create_completion(self, request: CompletionRequest,
 
             tokenizer = await self.engine.get_tokenizer(lora_request)
 
-            sampling_params = request.to_sampling_params()
-            decoding_config = await self.engine.get_decoding_config()
-            guided_decoding_backend = request.guided_decoding_backend \
-                or decoding_config.guided_decoding_backend
-            guided_decode_logit_processor = (
-                await
-                get_guided_decoding_logits_processor(guided_decoding_backend,
-                                                     request, tokenizer))
-            if guided_decode_logit_processor is not None:
-                if sampling_params.logits_processors is None:
-                    sampling_params.logits_processors = []
-                sampling_params.logits_processors.append(
-                    guided_decode_logit_processor)
-
+            guided_decode_logits_processor = (
+                await self._guided_decode_logits_processor(request, tokenizer))
             prompts = list(
                 self._tokenize_prompt_input_or_inputs(
                     request,
                     tokenizer,
                     request.prompt,
-                    truncate_prompt_tokens=sampling_params.
-                    truncate_prompt_tokens,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
                     add_special_tokens=request.add_special_tokens,
                 ))
 
             for i, prompt_inputs in enumerate(prompts):
+                sampling_params = request.to_sampling_params(
+                    tokenizer,
+                    guided_decode_logits_processor,
+                    default_max_tokens=self.max_model_len -
+                    len(prompt_inputs["prompt_token_ids"]))
+
                 request_id_item = f"{request_id}-{i}"
 
                 self._log_inputs(request_id_item,