Support logit bias for OpenAI API (#3027)

tests/entrypoints/test_openai_server.py

@@ -9,6 +9,8 @@ import ray  # using Ray for overall ease of process management, parallel request
 import openai  # use the official client for correctness check
 from huggingface_hub import snapshot_download  # downloading lora to test lora requests
 
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
 MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"  # any model with a chat template should work here
 LORA_NAME = "typeof/zephyr-7b-beta-lora"  # technically this needs Mistral-7B-v0.1 as base, but we're not testing generation quality here
@@ -310,5 +312,51 @@ async def test_batch_completions(server, client: openai.AsyncOpenAI,
     assert texts[0] == texts[1]
 
 
+async def test_logits_bias(server, client: openai.AsyncOpenAI):
+    prompt = "Hello, my name is"
+    max_tokens = 5
+    tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
+
+    # Test exclusive selection
+    token_id = 1000
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=0.0,
+        logit_bias={str(token_id): 100},
+    )
+    assert completion.choices[0].text is not None and len(
+        completion.choices[0].text) >= 5
+    response_tokens = tokenizer(completion.choices[0].text,
+                                add_special_tokens=False)["input_ids"]
+    expected_tokens = tokenizer(tokenizer.decode([token_id] * 5),
+                                add_special_tokens=False)["input_ids"]
+    assert all([
+        response == expected
+        for response, expected in zip(response_tokens, expected_tokens)
+    ])
+
+    # Test ban
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=0.0,
+    )
+    response_tokens = tokenizer(completion.choices[0].text,
+                                add_special_tokens=False)["input_ids"]
+    first_response = completion.choices[0].text
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=0.0,
+        logit_bias={str(token): -100
+                    for token in response_tokens},
+    )
+    assert first_response != completion.choices[0].text
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

vllm/entrypoints/openai/protocol.py

@@ -8,6 +8,8 @@ from pydantic import BaseModel, Field
 from vllm.utils import random_uuid
 from vllm.sampling_params import SamplingParams
 
+import torch
+
 
 class ErrorResponse(BaseModel):
     object: str = "error"
@@ -88,6 +90,21 @@ class ChatCompletionRequest(BaseModel):
     def to_sampling_params(self) -> SamplingParams:
         if self.logprobs and not self.top_logprobs:
             raise ValueError("Top logprobs must be set when logprobs is.")
+
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                    token_ids: List[int],
+                    logits: torch.Tensor) -> torch.Tensor:
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
         return SamplingParams(
             n=self.n,
             presence_penalty=self.presence_penalty,
@@ -111,6 +128,7 @@ class ChatCompletionRequest(BaseModel):
             spaces_between_special_tokens=self.spaces_between_special_tokens,
             include_stop_str_in_output=self.include_stop_str_in_output,
             length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
         )
 
 
@@ -149,6 +167,20 @@ class CompletionRequest(BaseModel):
     def to_sampling_params(self):
         echo_without_generation = self.echo and self.max_tokens == 0
 
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                    token_ids: List[int],
+                    logits: torch.Tensor) -> torch.Tensor:
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
         return SamplingParams(
             n=self.n,
             best_of=self.best_of,
@@ -172,6 +204,7 @@ class CompletionRequest(BaseModel):
             spaces_between_special_tokens=(self.spaces_between_special_tokens),
             include_stop_str_in_output=self.include_stop_str_in_output,
             length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
         )
 
 

vllm/entrypoints/openai/serving_chat.py

@@ -39,19 +39,13 @@ class OpenAIServingChat(OpenAIServing):
         See  https://platform.openai.com/docs/api-reference/chat/create
         for the API specification. This API mimics the OpenAI ChatCompletion API.
 
-        NOTE: Currently we do not support the following features:
+        NOTE: Currently we do not support the following feature:
             - function_call (Users should implement this by themselves)
-            - logit_bias (to be supported by vLLM engine)
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             return error_check_ret
 
-        if request.logit_bias is not None and len(request.logit_bias) > 0:
-            # TODO: support logit_bias in vLLM engine.
-            return self.create_error_response(
-                "logit_bias is not currently supported")
-
         try:
             prompt = self.tokenizer.apply_chat_template(
                 conversation=request.messages,

vllm/entrypoints/openai/serving_completion.py

@@ -264,10 +264,9 @@ class OpenAIServingCompletion(OpenAIServing):
         See https://platform.openai.com/docs/api-reference/completions/create
         for the API specification. This API mimics the OpenAI Completion API.
 
-        NOTE: Currently we do not support the following features:
+        NOTE: Currently we do not support the following feature:
             - suffix (the language models we currently support do not support
             suffix)
-            - logit_bias (to be supported by vLLM engine)
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
@@ -277,9 +276,6 @@ class OpenAIServingCompletion(OpenAIServing):
         if request.suffix is not None:
             return self.create_error_response(
                 "suffix is not currently supported")
-        if request.logit_bias is not None and len(request.logit_bias) > 0:
-            return self.create_error_response(
-                "logit_bias is not currently supported")
 
         model_name = request.model
         request_id = f"cmpl-{random_uuid()}"