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import unittest
import subprocess
import time
import backend_pb2
import backend_pb2_grpc
import grpc
import unittest
import subprocess
import time
import grpc
import backend_pb2_grpc
import backend_pb2
class TestBackendServicer(unittest.TestCase):
"""
TestBackendServicer is the class that tests the gRPC service.
This class contains methods to test the startup and shutdown of the gRPC service.
"""
def setUp(self):
self.service = subprocess.Popen(["python", "backend.py", "--addr", "localhost:50051"])
time.sleep(10)
def tearDown(self) -> None:
self.service.terminate()
self.service.wait()
def test_server_startup(self):
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.Health(backend_pb2.HealthMessage())
self.assertEqual(response.message, b'OK')
except Exception as err:
print(err)
self.fail("Server failed to start")
finally:
self.tearDown()
def test_load_model(self):
"""
This method tests if the model is loaded successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
print(err)
self.fail("LoadModel service failed")
finally:
self.tearDown()
def test_text(self):
"""
This method tests if the embeddings are generated successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
self.assertTrue(response.success)
req = backend_pb2.PredictOptions(Prompt="The capital of France is")
resp = stub.Predict(req)
self.assertIsNotNone(resp.message)
except Exception as err:
print(err)
self.fail("text service failed")
finally:
self.tearDown()
def test_sampling_params(self):
"""
This method tests if all sampling parameters are correctly processed
NOTE: this does NOT test for correctness, just that we received a compatible response
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
self.assertTrue(response.success)
req = backend_pb2.PredictOptions(
Prompt="The capital of France is",
TopP=0.8,
Tokens=50,
Temperature=0.7,
TopK=40,
PresencePenalty=0.1,
FrequencyPenalty=0.2,
RepetitionPenalty=1.1,
MinP=0.05,
Seed=42,
StopPrompts=["\n"],
StopTokenIds=[50256],
BadWords=["badword"],
IncludeStopStrInOutput=True,
IgnoreEOS=True,
MinTokens=5,
Logprobs=5,
PromptLogprobs=5,
SkipSpecialTokens=True,
SpacesBetweenSpecialTokens=True,
TruncatePromptTokens=10,
GuidedDecoding=True,
N=2,
)
resp = stub.Predict(req)
self.assertIsNotNone(resp.message)
self.assertIsNotNone(resp.logprobs)
except Exception as err:
print(err)
self.fail("sampling params service failed")
finally:
self.tearDown()
def test_messages_to_dicts(self):
"""
Tests _messages_to_dicts conversion of proto Messages to dicts.
"""
import sys, os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
servicer = BackendServicer()
msgs = [
backend_pb2.Message(role="user", content="hello"),
backend_pb2.Message(
role="assistant",
content="",
tool_calls='[{"id":"call_1","type":"function","function":{"name":"foo","arguments":"{}"}}]',
reasoning_content="thinking...",
),
backend_pb2.Message(role="tool", content="result", name="foo", tool_call_id="call_1"),
]
result = servicer._messages_to_dicts(msgs)
self.assertEqual(len(result), 3)
self.assertEqual(result[0], {"role": "user", "content": "hello"})
self.assertEqual(result[1]["reasoning_content"], "thinking...")
self.assertIsInstance(result[1]["tool_calls"], list)
self.assertEqual(result[1]["tool_calls"][0]["id"], "call_1")
self.assertEqual(result[2]["tool_call_id"], "call_1")
self.assertEqual(result[2]["name"], "foo")
def test_parse_options(self):
"""
Tests _parse_options correctly parses key:value strings.
"""
import sys, os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
servicer = BackendServicer()
opts = servicer._parse_options([
"tool_parser:hermes",
"reasoning_parser:deepseek_r1",
"invalid_no_colon",
"key_with_colons:a:b:c",
])
self.assertEqual(opts["tool_parser"], "hermes")
self.assertEqual(opts["reasoning_parser"], "deepseek_r1")
self.assertEqual(opts["key_with_colons"], "a:b:c")
self.assertNotIn("invalid_no_colon", opts)
def test_apply_engine_args_known_keys(self):
"""
Tests _apply_engine_args overlays user-supplied JSON onto AsyncEngineArgs.
"""
import sys, os, json as _json
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
from vllm.engine.arg_utils import AsyncEngineArgs
servicer = BackendServicer()
base = AsyncEngineArgs(model="facebook/opt-125m")
extras = _json.dumps({
"trust_remote_code": True,
"max_num_seqs": 32,
})
out = servicer._apply_engine_args(base, extras)
self.assertTrue(out.trust_remote_code)
self.assertEqual(out.max_num_seqs, 32)
# untouched fields preserved
self.assertEqual(out.model, "facebook/opt-125m")
def test_apply_engine_args_unknown_key_raises(self):
"""
Tests _apply_engine_args rejects unknown keys with a helpful suggestion.
"""
import sys, os, json as _json
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
from vllm.engine.arg_utils import AsyncEngineArgs
servicer = BackendServicer()
base = AsyncEngineArgs(model="facebook/opt-125m")
with self.assertRaises(ValueError) as ctx:
servicer._apply_engine_args(base, _json.dumps({"trustremotecode": True}))
self.assertIn("trustremotecode", str(ctx.exception))
# close-match hint for the typo
self.assertIn("trust_remote_code", str(ctx.exception))
def test_apply_engine_args_empty_passthrough(self):
"""
Tests that empty engine_args returns the base unchanged.
"""
import sys, os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
from vllm.engine.arg_utils import AsyncEngineArgs
servicer = BackendServicer()
base = AsyncEngineArgs(model="facebook/opt-125m")
self.assertIs(servicer._apply_engine_args(base, ""), base)
self.assertIs(servicer._apply_engine_args(base, None), base)
def test_tokenize_string(self):
"""
Tests the TokenizeString RPC returns valid tokens.
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
self.assertTrue(response.success)
resp = stub.TokenizeString(backend_pb2.PredictOptions(Prompt="Hello world"))
self.assertGreater(resp.length, 0)
self.assertEqual(len(resp.tokens), resp.length)
except Exception as err:
print(err)
self.fail("TokenizeString service failed")
finally:
self.tearDown()
def test_free(self):
"""
Tests the Free RPC doesn't crash.
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
self.assertTrue(response.success)
free_resp = stub.Free(backend_pb2.HealthMessage())
self.assertTrue(free_resp.success)
except Exception as err:
print(err)
self.fail("Free service failed")
finally:
self.tearDown()
def test_embedding(self):
"""
This method tests if the embeddings are generated successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="intfloat/e5-mistral-7b-instruct"))
self.assertTrue(response.success)
embedding_request = backend_pb2.PredictOptions(Embeddings="This is a test sentence.")
embedding_response = stub.Embedding(embedding_request)
self.assertIsNotNone(embedding_response.embeddings)
# assert that is a list of floats
self.assertIsInstance(embedding_response.embeddings, list)
# assert that the list is not empty
self.assertTrue(len(embedding_response.embeddings) > 0)
except Exception as err:
print(err)
self.fail("Embedding service failed")
finally:
self.tearDown()
def test_streaming_tool_parser_buffering(self):
"""
When a tool parser is active and the request carries tools, streaming
must NOT emit the model's raw tool-call markup as content, and must NOT
duplicate the buffered content. Exercises _predict(streaming=True) with a
mocked engine + tool parser (no server / GPU).
"""
import sys, os, asyncio
from types import SimpleNamespace
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backend import BackendServicer
def make_generate(chunks):
async def gen(*a, **k):
for t in chunks:
yield SimpleNamespace(
outputs=[SimpleNamespace(text=t, token_ids=[1], logprobs=None)],
prompt_token_ids=[0],
)
return lambda *a, **k: gen()
def parser_cls(called, content, calls):
class _P:
def __init__(self, tokenizer, tools=None):
pass
def extract_tool_calls(self, c, request=None):
return SimpleNamespace(tools_called=called, content=content, tool_calls=calls)
return _P
def collect(servicer, req):
async def run():
return [r async for r in servicer._predict(req, None, streaming=True)]
return asyncio.run(run())
def contents(replies):
return [cd.content for r in replies for cd in r.chat_deltas if cd.content]
tools_json = '[{"type":"function","function":{"name":"calc"}}]'
# Case 1: model emits a tool call -> no raw markup as content, tool_call present.
s = BackendServicer()
s.reasoning_parser_cls = None
s.tokenizer = None
s.llm = SimpleNamespace(generate=make_generate([
'<tool_call>\n{"name": "calc"',
'<tool_call>\n{"name": "calc", "arguments": {"x": 1}}\n</tool_call>',
]))
call = SimpleNamespace(id="call_1", function=SimpleNamespace(name="calc", arguments='{"x": 1}'))
s.tool_parser_cls = parser_cls(True, "", [call])
req = backend_pb2.PredictOptions(Prompt="x", Tools=tools_json)
replies = collect(s, req)
self.assertFalse(
any("<tool_call" in c or "<function" in c for c in contents(replies)),
"raw tool-call markup leaked as streamed content",
)
names = [tc.name for r in replies for cd in r.chat_deltas for tc in cd.tool_calls]
self.assertIn("calc", names, "structured tool_call was not emitted")
# Case 2: tools offered but model answers in plain text -> content once.
s2 = BackendServicer()
s2.reasoning_parser_cls = None
s2.tokenizer = None
s2.llm = SimpleNamespace(generate=make_generate([
"The capital ",
"The capital of France is Paris.",
]))
s2.tool_parser_cls = parser_cls(False, "", [])
req2 = backend_pb2.PredictOptions(Prompt="x", Tools=tools_json)
joined = "".join(contents(collect(s2, req2)))
self.assertEqual(
joined.count("The capital of France is Paris."), 1,
f"buffered content was duplicated: {joined!r}",
)