[V1] Add VLLM_ALLOW_INSECURE_SERIALIZATION env var (vllm-project#17490)

Signed-off-by: Russell Bryant <[email protected]>
Signed-off-by: Nick Hill <[email protected]>
Co-authored-by: Nick Hill <[email protected]>

Author: 2 people

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -105,8 +105,9 @@ def test_structured_output(
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(json=sample_json_schema))
     outputs = llm.generate(prompts=[
-        f"Give an example JSON for an employee profile "
-        f"that fits this schema: {sample_json_schema}"
+        (f"Give an example JSON for an employee profile that fits this "
+         f"schema. Make the response as short as possible. Schema: "
+         f"{sample_json_schema}")
     ] * 2,
                            sampling_params=sampling_params,
                            use_tqdm=True)
@@ -136,7 +137,8 @@ def test_structured_output(
 
     outputs = llm.generate(
         prompts=("Generate a JSON object with curly braces for a person with "
-                 "name and age fields for John Smith who is 31 years old."),
+                 "name and age fields for John Smith who is 31 years old. "
+                 "Make the response as short as possible."),
         sampling_params=sampling_params,
         use_tqdm=True)
 
@@ -165,19 +167,20 @@ def test_structured_output(
         with pytest.raises(ValueError,
                            match="The provided JSON schema contains features "
                            "not supported by xgrammar."):
-            llm.generate(prompts=[
-                f"Give an example JSON for an employee profile "
-                f"that fits this schema: {unsupported_json_schema}"
-            ] * 2,
-                         sampling_params=sampling_params,
-                         use_tqdm=True)
+            llm.generate(
+                prompts=[(f"Give an example JSON for an employee profile that "
+                          f"fits this schema: {unsupported_json_schema}. "
+                          f"Make the response as short as possible.")] * 2,
+                sampling_params=sampling_params,
+                use_tqdm=True)
     else:
-        outputs = llm.generate(
-            prompts=("Give an example JSON object for a grade "
-                     "that fits this schema: "
-                     f"{unsupported_json_schema}"),
-            sampling_params=sampling_params,
-            use_tqdm=True)
+        outputs = llm.generate(prompts=(
+            "Give an example JSON object for a grade "
+            "that fits this schema: "
+            f"{unsupported_json_schema}. Make the response as short as "
+            "possible."),
+                               sampling_params=sampling_params,
+                               use_tqdm=True)
         assert outputs is not None
         for output in outputs:
             assert output is not None
@@ -199,8 +202,10 @@ def test_structured_output(
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(grammar=sample_sql_ebnf))
     outputs = llm.generate(
-        prompts=("Generate a sql statement that selects col_1 from "
-                 "table_1 where it is equal to 1"),
+        prompts=(
+            "Generate a sql statement that selects col_1 from "
+            "table_1 where it is equal to 1. Make the response as short as "
+            "possible."),
         sampling_params=sampling_params,
         use_tqdm=True,
     )
@@ -231,8 +236,10 @@ def test_structured_output(
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(grammar=sample_sql_lark))
     outputs = llm.generate(
-        prompts=("Generate a sql statement that selects col_1 from "
-                 "table_1 where it is equal to 1"),
+        prompts=(
+            "Generate a sql statement that selects col_1 from "
+            "table_1 where it is equal to 1. Make the response as short as "
+            "possible."),
         sampling_params=sampling_params,
         use_tqdm=True,
     )
@@ -269,8 +276,10 @@ def test_structured_output(
         guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
     with pytest.raises(ValueError, match="Failed to convert the grammar "):
         llm.generate(
-            prompts=("Generate a sql statement that selects col_1 from "
-                     "table_1 where it is equal to 1"),
+            prompts=(
+                "Generate a sql statement that selects col_1 from "
+                "table_1 where it is equal to 1. Make the response as short "
+                "as possible."),
             sampling_params=sampling_params,
             use_tqdm=True,
         )
@@ -284,7 +293,8 @@ def test_structured_output(
         guided_decoding=GuidedDecodingParams(regex=sample_regex))
     outputs = llm.generate(
         prompts=[
-            f"Give an example IPv4 address with this regex: {sample_regex}"
+            (f"Give an example IPv4 address with this regex: {sample_regex}. "
+             f"Make the response as short as possible.")
         ] * 2,
         sampling_params=sampling_params,
         use_tqdm=True,
@@ -309,7 +319,8 @@ def test_structured_output(
         top_p=0.95,
         guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
     outputs = llm.generate(
-        prompts="The best language for type-safe systems programming is ",
+        prompts=("The best language for type-safe systems programming is "
+                 "(Make the response as short as possible.) "),
         sampling_params=sampling_params,
         use_tqdm=True)
     assert outputs is not None
@@ -331,11 +342,12 @@ def test_structured_output(
         temperature=1.0,
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(json=json_schema))
-    outputs = llm.generate(
-        prompts="Generate a JSON with the brand, model and car_type of"
-        "the most iconic car from the 90's",
-        sampling_params=sampling_params,
-        use_tqdm=True)
+    outputs = llm.generate(prompts=(
+        "Generate a JSON with the brand, model and car_type of the most "
+        "iconic car from the 90's. Make the response as short as "
+        "possible."),
+                           sampling_params=sampling_params,
+                           use_tqdm=True)
 
     assert outputs is not None
 
@@ -373,7 +385,8 @@ def test_structured_output(
         guided_decoding=GuidedDecodingParams(json=json_schema))
 
     outputs = llm.generate(
-        prompts="Generate a description of a frog using 50 characters.",
+        prompts=("Generate a description of a frog using 50 characters. "
+                 "Make the response as short as possible."),
         sampling_params=sampling_params,
         use_tqdm=True)
 
@@ -452,7 +465,8 @@ def test_structured_output(
 
 You are a helpful assistant.
          
-Given the previous instructions, what is the weather in New York City?
+Given the previous instructions, what is the weather in New York City? \
+Make the response as short as possible.
 """
 
     # Change this once other backends support structural_tag
@@ -509,9 +523,10 @@ def test_structured_output_auto_mode(
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(json=unsupported_json_schema))
 
-    prompts = ("Give an example JSON object for a grade "
-               "that fits this schema: "
-               f"{unsupported_json_schema}")
+    prompts = (
+        "Give an example JSON object for a grade "
+        "that fits this schema: "
+        f"{unsupported_json_schema}. Make the response as short as possible.")
     # This would fail with the default of "xgrammar", but in "auto"
     # we will handle fallback automatically.
     outputs = llm.generate(prompts=prompts,
@@ -566,7 +581,8 @@ def test_guidance_no_additional_properties(monkeypatch: pytest.MonkeyPatch):
     prompt = (
         "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a "
         "helpful assistant.<|im_end|>\n<|im_start|>user\nPlease generate a "
-        "large JSON object with key-value pairs a1=b1, a2=b2, ..., a20=b20"
+        "large JSON object with key-value pairs a1=b1, a2=b2, ..., a20=b20. "
+        "Make the response as short as possible."
         "<|im_end|>\n<|im_start|>assistant\n")
 
     def generate_with_backend(backend):