feat: add json_object support in response_format

lmdeploy/pytorch/engine/guided_process.py

@@ -26,7 +26,8 @@ def get_processors(self, session_ctx: List[Dict[str, Any]],
         processors = {}
         for i, _format in enumerate(response_formats):
             if isinstance(_format, Dict) and _format.get('type', 'text') != 'text':
-                if _format['type'] == 'json_schema':
+                schema_type = _format['type']
+                if schema_type == 'json_schema':
                     schema = _format['json_schema']
                     if isinstance(schema, Dict):
                         for key in ['json_schema', 'schema']:
@@ -37,15 +38,17 @@ def get_processors(self, session_ctx: List[Dict[str, Any]],
                         raise ValueError(f'Cannot parse schema {schema}. The schema must be '
                                          'either a dictionary or a string that contains the'
                                          ' JSON Schema specification')
-                elif _format['type'] == 'regex_schema':
+                elif schema_type == 'regex_schema':
                     schema = _format.get('regex_schema', '')
+                elif schema_type == 'json_object':
+                    schema = '{"type" : "object", "additionalProperties": true}'
                 else:
-                    raise ValueError(f"unsupported format type: {_format['type']}")
+                    raise ValueError(f'unsupported format type: {schema_type}')
 
                 session_id = session_ctx[i]['session_id']
                 seq_id = session_ctx[i]['seq_id']
 
-                processors[i] = self.get_processor(session_id, seq_id, schema, _format['type'])
+                processors[i] = self.get_processor(session_id, seq_id, schema, schema_type)
 
         return processors
 
@@ -63,7 +66,9 @@ def get_processor(self, session_id: int, seq_id: int, schema: str, type: str) ->
             assert isinstance(schema, dict)
             compiled = self.compiler.compile_json_schema(schema)
         elif type == 'regex_schema':
-            compiled = self.compiler.compile_regex_grammar(schema)
+            compiled = self.compiler.compile_regex(schema)
+        elif type == 'json_object':
+            compiled = self.compiler.compile_json_schema(schema)
         else:
             assert False, f'Do not support schema type {type}'
 

lmdeploy/serve/openai/api_server.py

@@ -590,7 +590,7 @@ async def completion_stream_generator() -> AsyncGenerator[str, None]:
         tool_calls = None
         reasoning_content = None
         if request.tool_choice != 'none' and VariableInterface.tool_parser is not None:
-            try:  # TODO add json_schema guidance to turbomind
+            try:
                 tool_call_info = VariableInterface.tool_parser.extract_tool_calls(text, request=request)
                 text, tool_calls = tool_call_info.content, tool_call_info.tool_calls
                 if isinstance(tool_calls, List) and len(tool_calls):

lmdeploy/turbomind/turbomind.py

@@ -720,7 +720,12 @@ async def async_stream_infer(self,
             try:
                 tokenizer_info = TokenizerInfo.from_huggingface(tokenizer.model.model, vocab_size=vocab_size)
                 decode_grammar_type = gen_config.response_format['type']
-                decode_grammar = gen_config.response_format[decode_grammar_type]['schema']
+                if decode_grammar_type == 'json_schema':
+                    decode_grammar = gen_config.response_format[decode_grammar_type]['schema']
+                elif decode_grammar_type == 'regex_schema':
+                    decode_grammar = gen_config.response_format[decode_grammar_type]
+                elif decode_grammar_type == 'json_object':
+                    decode_grammar = '{"type" : "object", "additionalProperties": true}'
 
                 compiler = _xgr.GrammarCompiler(tokenizer_info)
 
@@ -730,9 +735,12 @@ async def async_stream_infer(self,
                 elif decode_grammar_type == 'regex_schema':
                     decode_grammar = str(decode_grammar)
                     grammar = compiler.compile_regex(decode_grammar)
+                elif decode_grammar_type == 'json_object':
+                    decode_grammar = str(decode_grammar)
+                    grammar = compiler.compile_json_schema(decode_grammar)
                 else:
                     assert False, f'Decode grammar type {decode_grammar_type} should be in ' \
-                                   '["json_schema", "regex_schema"]'
+                                   '["json_schema", "regex_schema", "json_object"]'
 
                 self.model_inst.set_grammar(grammar)
             except ValueError as e:

tests/test_lmdeploy/test_grammar.py

@@ -1,4 +1,5 @@
 import json
+import re
 
 import pytest
 from jsonschema import validate
@@ -16,64 +17,81 @@
     ('pt', lambda: PytorchEngineConfig(max_batch_size=1, session_len=1024)),
 ]
 
-GUIDE_SCHEMA = {
-    'type': 'object',
-    'properties': {
-        'name': {
-            'type': 'string'
-        },
-        'skills': {
-            'type': 'array',
-            'items': {
-                'type': 'string',
-                'maxLength': 10
+SCHEMA_MAP = {
+    'json_schema': {
+        'type': 'object',
+        'properties': {
+            'name': {
+                'type': 'string'
             },
-            'minItems': 3,
-            'maxItems': 10,
-        },
-        'work history': {
-            'type': 'array',
-            'items': {
-                'type': 'object',
-                'properties': {
-                    'company': {
-                        'type': 'string'
-                    },
-                    'duration': {
-                        'type': 'string'
+            'skills': {
+                'type': 'array',
+                'items': {
+                    'type': 'string',
+                    'maxLength': 10
+                },
+                'minItems': 3,
+                'maxItems': 10,
+            },
+            'work history': {
+                'type': 'array',
+                'items': {
+                    'type': 'object',
+                    'properties': {
+                        'company': {
+                            'type': 'string'
+                        },
+                        'duration': {
+                            'type': 'string'
+                        },
                     },
+                    'required': ['company'],
                 },
-                'required': ['company'],
             },
         },
+        'required': ['name', 'skills', 'work history'],
     },
-    'required': ['name', 'skills', 'work history'],
+    'regex_schema': 'call me [A-Za-z]{1,10}',
+    'json_object': None,
 }
 
 
 @pytest.mark.parametrize('model_id', MODEL_IDS)
 @pytest.mark.parametrize('backend_name,backend_factory', BACKEND_FACTORIES)
-@pytest.mark.parametrize('enable_guide', [True, False])
-def test_guided_matrix(model_id, backend_name, backend_factory, enable_guide):
+@pytest.mark.parametrize('schema_type', list(SCHEMA_MAP.keys()) + [None])
+def test_guided_matrix(model_id, backend_name, backend_factory, schema_type):
     pipe = pipeline(
         model_id,
         backend_config=backend_factory(),
         log_level='INFO',
     )
 
+    if schema_type is None:
+        enable_guide = False
+    else:
+        enable_guide = True
+        response_format = {'type': schema_type}
+        schema = SCHEMA_MAP[schema_type]
+        if schema_type == 'json_schema':
+            response_format[schema_type] = dict(name='test', schema=schema)
+        elif schema_type == 'regex_schema':
+            response_format[schema_type] = schema
+
     try:
         if enable_guide:
-            gen_config = GenerationConfig(response_format=dict(
-                type='json_schema',
-                json_schema=dict(name='test', schema=GUIDE_SCHEMA),
-            ), )
+            gen_config = GenerationConfig(response_format=response_format)
         else:
             gen_config = GenerationConfig()
 
         response = pipe(['Make a self introduction please.'] * 3, gen_config=gen_config)
         assert response and response[0].text
 
         if enable_guide:
-            validate(instance=json.loads(response[0].text), schema=GUIDE_SCHEMA)
+            if schema_type == 'json_schema':
+                validate(instance=json.loads(response[0].text), schema=schema)
+            elif schema_type == 'json_object':
+                validate(instance=json.loads(response[0].text), schema={'type': 'object', 'additionalProperties': True})
+            elif schema_type == 'regex_schema':
+                assert re.fullmatch(schema, response[0].text)
     finally:
         pipe.close()