api: add json_schema to OpenAI server

aphrodite/endpoints/openai/protocol.py

@@ -68,9 +68,19 @@ class UsageInfo(OpenAIBaseModel):
     completion_tokens: Optional[int] = 0
 
 
+class JsonSchemaResponseFormat(OpenAIBaseModel):
+    name: str
+    description: Optional[str] = None
+    # schema is the field in openai but that causes conflicts with pydantic so
+    # instead use json_schema with an alias
+    json_schema: Optional[Dict[str, Any]] = Field(default=None, alias='schema')
+    strict: Optional[bool] = None
+
+
 class ResponseFormat(OpenAIBaseModel):
-    # type must be "json_object" or "text"
-    type: Literal["text", "json_object"]
+    # type must be "json_schema", "json_object" or "text"
+    type: Literal["text", "json_object", "json_schema"]
+    json_schema: Optional[JsonSchemaResponseFormat] = None
 
 
 class StreamOptions(OpenAIBaseModel):

aphrodite/modeling/guided_decoding/__init__.py

@@ -6,12 +6,6 @@
     CompletionRequest)
 from aphrodite.modeling.guided_decoding.guided_fields import (
     GuidedDecodingRequest)
-from aphrodite.triton_utils import HAS_TRITON
-
-if HAS_TRITON:
-    from aphrodite.modeling.guided_decoding.outlines_decoding import (
-        get_local_outlines_guided_decoding_logits_processor,
-        get_outlines_guided_decoding_logits_processor)
 
 
 async def get_guided_decoding_logits_processor(
@@ -20,10 +14,11 @@ async def get_guided_decoding_logits_processor(
         tokenizer) -> Optional[LogitsProcessorFunc]:
     request = _adapt_request_for_tool_use(request)
     if guided_decoding_backend == 'outlines':
-        if HAS_TRITON:
-            return await get_outlines_guided_decoding_logits_processor(
-                request, tokenizer)
-        else:
+        from aphrodite.modeling.guided_decoding.outlines_decoding import (
+            get_outlines_guided_decoding_logits_processor)
+        return await get_outlines_guided_decoding_logits_processor(
+            request, tokenizer)
+    if guided_decoding_backend == 'lm-format-enforcer':
             pass
     if guided_decoding_backend == 'lm-format-enforcer':
         from aphrodite.modeling.guided_decoding.lm_format_enforcer_decoding import (  # noqa
@@ -42,6 +37,8 @@ def get_local_guided_decoding_logits_processor(
     # request = _adapt_request_for_tool_use(request)
 
     if guided_decoding_backend == 'outlines':
+        from aphrodite.modeling.guided_decoding.outlines_decoding import (
+            get_local_outlines_guided_decoding_logits_processor)
         return get_local_outlines_guided_decoding_logits_processor(
             guided_options, tokenizer)
     if guided_decoding_backend == 'lm-format-enforcer':

aphrodite/modeling/guided_decoding/lm_format_enforcer_decoding.py

@@ -16,12 +16,6 @@
 from aphrodite.modeling.guided_decoding.lm_format_enforcer_logits_processors import (  # noqa: E501
     build_aphrodite_logits_processor,
     build_aphrodite_token_enforcer_tokenizer_data)
-from aphrodite.triton_utils import HAS_TRITON
-
-if HAS_TRITON:
-    from aphrodite.modeling.guided_decoding.outlines_decoding import (
-        get_local_outlines_guided_decoding_logits_processor,
-        get_outlines_guided_decoding_logits_processor)
 
 
 async def get_lm_format_enforcer_guided_decoding_logits_processor(
@@ -47,12 +41,21 @@ async def get_lm_format_enforcer_guided_decoding_logits_processor(
         character_level_parser = RegexParser(request.guided_regex)
     elif request.guided_grammar:
         # CFG grammar not supported by LMFE, revert to outlines
+        from aphrodite.modeling.guided_decoding.outlines_decoding import (
+            get_outlines_guided_decoding_logits_processor)
         return await get_outlines_guided_decoding_logits_processor(
             request, tokenizer)
     elif (request.response_format is not None
           and request.response_format.type == "json_object"):
         character_level_parser = JsonSchemaParser(
             None)  # None means any json object
+    elif (request.response_format is not None
+          and request.response_format.type == "json_schema"
+          and request.response_format.json_schema is not None
+          and request.response_format.json_schema.json_schema is not None):
+        schema = _normalize_json_schema_object(
+            request.response_format.json_schema.json_schema)
+        character_level_parser = JsonSchemaParser(schema)
     else:
         return None
 
@@ -83,6 +86,9 @@ def get_local_lm_format_enforcer_guided_decoding_logits_processor(
     elif guided_options.guided_regex:
         character_level_parser = RegexParser(guided_options.guided_regex)
     elif guided_options.guided_grammar:
+        from aphrodite.modeling.guided_decoding.outlines_decoding import (
+            get_local_outlines_guided_decoding_logits_processor)
+
         # CFG grammar not supported by LMFE, revert to outlines
         return get_local_outlines_guided_decoding_logits_processor(
             guided_options, tokenizer)

aphrodite/modeling/guided_decoding/outlines_decoding.py

@@ -126,6 +126,13 @@ def _get_guide_and_mode(
           and request.response_format is not None
           and request.response_format.type == "json_object"):
         return JSON_GRAMMAR, GuidedDecodingMode.GRAMMAR
+    elif (not isinstance(request, GuidedDecodingRequest)
+          and request.response_format is not None
+          and request.response_format.type == "json_schema"
+          and request.response_format.json_schema is not None
+          and request.response_format.json_schema.json_schema is not None):
+        json = json_dumps(request.response_format.json_schema.json_schema)
+        return json, GuidedDecodingMode.JSON
     else:
         return None, None
 

tests/endpoints/llm/test_lazy_outlines.py

@@ -0,0 +1,43 @@
+import sys
+
+from aphrodite import LLM, SamplingParams
+
+
+def test_lazy_outlines(sample_regex):
+    """If users don't use guided decoding, outlines should not be imported.
+    """
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+    llm = LLM(model="facebook/opt-125m",
+              enforce_eager=True,
+              gpu_memory_utilization=0.3)
+    outputs = llm.generate(prompts, sampling_params)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    # make sure outlines is not imported
+    assert 'outlines' not in sys.modules
+    llm = LLM(model="facebook/opt-125m",
+              enforce_eager=True,
+              guided_decoding_backend="lm-format-enforcer",
+              gpu_memory_utilization=0.3)
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+    outputs = llm.generate(
+        prompts=[
+            f"Give an example IPv4 address with this regex: {sample_regex}"
+        ] * 2,
+        sampling_params=sampling_params,
+        use_tqdm=True,
+        guided_options_request=dict(guided_regex=sample_regex))
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    # make sure outlines is not imported
+    assert 'outlines' not in sys.modules

tests/endpoints/openai/test_chat.py

@@ -17,7 +17,7 @@
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
 # technically this needs Mistral-7B-v0.1 as base, but we're not testing
 # generation quality here
-LORA_NAME = "typeof/zephyr-7b-beta-lora"
+LORA_NAME = "alpindale/zephyr-7b-beta-lora"
 
 
 @pytest.fixture(scope="module")
@@ -755,22 +755,6 @@ async def test_response_format_json_object(client: openai.AsyncOpenAI):
         assert loaded == {"result": 2}, loaded
 
 
-@pytest.mark.asyncio
-async def test_extra_fields(client: openai.AsyncOpenAI):
-    with pytest.raises(BadRequestError) as exc_info:
-        await client.chat.completions.create(
-            model=MODEL_NAME,
-            messages=[{
-                "role": "system",
-                "content": "You are a helpful assistant.",
-                "extra_field": "0",
-            }],  # type: ignore
-            temperature=0,
-            seed=0)
-
-    assert "extra_forbidden" in exc_info.value.message
-
-
 @pytest.mark.asyncio
 async def test_complex_message_content(client: openai.AsyncOpenAI):
     resp = await client.chat.completions.create(
@@ -840,3 +824,34 @@ async def test_long_seed(client: openai.AsyncOpenAI):
 
         assert ("greater_than_equal" in exc_info.value.message
                 or "less_than_equal" in exc_info.value.message)
+
+
+@pytest.mark.asyncio
+async def test_response_format_json_schema(client: openai.AsyncOpenAI):
+    for _ in range(2):
+        resp = await client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{
+                "role":
+                "user",
+                "content": ('what is 1+1? please respond with a JSON object, '
+                            'the format is {"result": 2}')
+            }],
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "foo_test",
+                    "schema": {
+                        "type": "object",
+                        "properties": {
+                            "result": {
+                                "type": "integer"
+                            },
+                        },
+                    },
+                }
+            })
+        content = resp.choices[0].message.content
+        assert content is not None
+        loaded = json.loads(content)
+        assert loaded == {"result": 2}, loaded