Add integration test for JSON schema

outlines/generate/api.py

@@ -248,5 +248,11 @@ def json(
         regex_str = build_regex_from_object(schema)
         generator = regex(model, regex_str, max_tokens, sampler)
         generator.format_sequence = lambda x: pyjson.loads(x)
+    else:
+        raise ValueError(
+            f"Cannot parse schema {schema_object}. The schema must be either "
+            + "a Pydantic object, a function or a string that contains the JSON "
+            + "Schema specification"
+        )
 
     return generator

tests/generate/test_generator.py

@@ -21,10 +21,10 @@
 
 def test_sequence_generator_class():
     class MockFSM:
-        def next_state(self, state, next_token_ids):
+        def next_state(self, state, next_token_ids, _):
             return 4
 
-        def allowed_token_ids(self, _):
+        def allowed_token_ids(self, *_):
             return [4]
 
         def is_final_state(self, _, idx=0):

tests/generate/test_integration_transfomers.py

@@ -276,6 +276,30 @@ class Spam(BaseModel):
     assert len(result.spam) <= 10
 
 
+def test_transformers_json_schema():
+    model_name = "hf-internal-testing/tiny-random-GPTJForCausalLM"
+    model = models.transformers(model_name, device="cpu")
+    prompt = "Output some JSON "
+
+    schema = """{
+      "title": "spam",
+      "type": "object",
+      "properties": {
+           "foo" : {"type": "integer"},
+           "bar": {"type": "string", "maxLength": 4}
+        }
+      }
+    """
+
+    rng = torch.Generator()
+    rng.manual_seed(0)  # make sure that `bar` is not an int
+
+    result = generate.json(model, schema, max_tokens=500)(prompt, rng=rng)
+    assert isinstance(result, dict)
+    assert isinstance(result["foo"], int)
+    assert isinstance(result["bar"], str)
+
+
 def test_transformers_json_batch():
     model_name = "hf-internal-testing/tiny-random-GPTJForCausalLM"
     model = models.transformers(model_name, device="cpu")