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import unittest
from unittest.mock import patch
from training_coach.parser_llama_cpp import (
DEFAULT_LLAMA_CPP_MAX_TOKENS,
DEFAULT_LLAMA_CPP_MODEL_FILE,
DEFAULT_LLAMA_CPP_MODEL_REPO,
DEFAULT_LLAMA_CPP_N_CTX,
MINIFIED_JSON_GBNF,
LlamaCppRuntimeUnavailableError,
_load_llama_cpp,
build_completion_prompt,
generate_parser_response_llama_cpp,
llama_cpp_json_grammar,
load_llama_cpp_model,
parse_check_in_with_llama_cpp,
warm_up_llama_cpp_parser,
)
class FakeLlama:
from_pretrained_calls = []
completion_calls = []
@classmethod
def from_pretrained(cls, **kwargs):
cls.from_pretrained_calls.append(kwargs)
return cls()
def create_completion(self, **kwargs):
self.completion_calls.append(kwargs)
return {
"choices": [
{
"text": json.dumps(
{
"check_in": {
"raw_text": "60 min",
"time_available_minutes": 60,
}
}
),
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 100, "completion_tokens": 20},
}
class FakeGrammar:
grammars = []
@classmethod
def from_string(cls, grammar, verbose=True):
cls.grammars.append((grammar, verbose))
return {"grammar": grammar, "verbose": verbose}
class LlamaCppParserTest(unittest.TestCase):
def setUp(self):
load_llama_cpp_model.cache_clear()
llama_cpp_json_grammar.cache_clear()
FakeLlama.from_pretrained_calls = []
FakeLlama.completion_calls = []
FakeGrammar.grammars = []
def tearDown(self):
load_llama_cpp_model.cache_clear()
llama_cpp_json_grammar.cache_clear()
def test_llama_cpp_defaults_are_locked(self):
self.assertEqual(DEFAULT_LLAMA_CPP_MODEL_REPO, "unsloth/Qwen3-1.7B-GGUF")
self.assertEqual(DEFAULT_LLAMA_CPP_MODEL_FILE, "Qwen3-1.7B-Q4_K_M.gguf")
self.assertEqual(DEFAULT_LLAMA_CPP_MAX_TOKENS, 512)
self.assertEqual(DEFAULT_LLAMA_CPP_N_CTX, 2048)
def test_runtime_can_import_llama_cpp_or_reports_clear_error(self):
try:
loaded = _load_llama_cpp()
except LlamaCppRuntimeUnavailableError as error:
self.assertIn("Install llama-cpp-python", str(error))
else:
self.assertEqual(len(loaded), 2)
def test_load_model_uses_repo_file_and_runtime_settings(self):
with patch(
"training_coach.parser_llama_cpp._load_llama_cpp",
return_value=(FakeLlama, FakeGrammar),
):
load_llama_cpp_model(
repo_id="repo/model",
filename="model.gguf",
n_ctx=4096,
n_threads=2,
n_threads_batch=4,
)
self.assertEqual(
FakeLlama.from_pretrained_calls,
[
{
"repo_id": "repo/model",
"filename": "model.gguf",
"n_ctx": 4096,
"verbose": False,
"n_threads": 2,
"n_threads_batch": 4,
}
],
)
def test_build_completion_prompt_prefills_empty_think_block(self):
prompt = build_completion_prompt(
[
{"role": "system", "content": "system text"},
{"role": "user", "content": "user text"},
]
)
self.assertIn("<|im_start|>system\nsystem text<|im_end|>\n", prompt)
self.assertIn("<|im_start|>user\nuser text<|im_end|>\n", prompt)
self.assertTrue(
prompt.endswith("<|im_start|>assistant\n<think>\n\n</think>\n\n")
)
def test_generate_parser_response_uses_generic_json_grammar(self):
with patch(
"training_coach.parser_llama_cpp._load_llama_cpp",
return_value=(FakeLlama, FakeGrammar),
):
response = generate_parser_response_llama_cpp(
"60 min",
repo_id="repo/model",
filename="model.gguf",
max_tokens=128,
n_ctx=2048,
n_threads=None,
)
completion_call = FakeLlama.completion_calls[0]
self.assertEqual(completion_call["max_tokens"], 128)
self.assertEqual(completion_call["temperature"], 0)
self.assertEqual(completion_call["stop"], ["<|im_end|>"])
self.assertEqual(completion_call["grammar"]["grammar"], MINIFIED_JSON_GBNF)
self.assertEqual(FakeGrammar.grammars, [(MINIFIED_JSON_GBNF, False)])
self.assertIn("60 min", completion_call["prompt"])
self.assertIn("</think>", completion_call["prompt"])
self.assertIn("check_in", response)
def test_parse_check_in_with_llama_cpp_reads_env_and_validates_response(self):
with patch.dict(
"os.environ",
{
"LLAMA_CPP_MODEL_REPO": "repo/model",
"LLAMA_CPP_MODEL_FILE": "model.gguf",
"LLAMA_CPP_MAX_TOKENS": "128",
"LLAMA_CPP_N_CTX": "4096",
"LLAMA_CPP_N_THREADS": "2",
},
clear=True,
), patch(
"training_coach.parser_llama_cpp._load_llama_cpp",
return_value=(FakeLlama, FakeGrammar),
):
parsed = parse_check_in_with_llama_cpp("60 min")
self.assertEqual(parsed.check_in.time_available_minutes, 60)
self.assertEqual(FakeLlama.from_pretrained_calls[0]["repo_id"], "repo/model")
self.assertEqual(FakeLlama.from_pretrained_calls[0]["filename"], "model.gguf")
self.assertEqual(FakeLlama.from_pretrained_calls[0]["n_ctx"], 4096)
self.assertEqual(FakeLlama.from_pretrained_calls[0]["n_threads"], 2)
self.assertEqual(FakeLlama.from_pretrained_calls[0]["n_threads_batch"], 2)
def test_threads_batch_env_overrides_decode_thread_default(self):
with patch.dict(
"os.environ",
{
"LLAMA_CPP_N_THREADS": "2",
"LLAMA_CPP_N_THREADS_BATCH": "6",
},
clear=True,
), patch(
"training_coach.parser_llama_cpp._load_llama_cpp",
return_value=(FakeLlama, FakeGrammar),
):
parse_check_in_with_llama_cpp("60 min")
self.assertEqual(FakeLlama.from_pretrained_calls[0]["n_threads"], 2)
self.assertEqual(FakeLlama.from_pretrained_calls[0]["n_threads_batch"], 6)
def test_warm_up_runs_single_token_generation(self):
with patch.dict("os.environ", {}, clear=True), patch(
"training_coach.parser_llama_cpp._load_llama_cpp",
return_value=(FakeLlama, FakeGrammar),
):
warm_up_llama_cpp_parser()
self.assertEqual(len(FakeLlama.completion_calls), 1)
completion_call = FakeLlama.completion_calls[0]
self.assertEqual(completion_call["max_tokens"], 1)
self.assertIn("warmup", completion_call["prompt"])
if __name__ == "__main__":
unittest.main()
|