mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-13 13:52:22 +01:00
0da5d86026
* slot.can_batch_with * lora per request * test: force disable cache prompt * move can_batch_with check * fix condition * add slow test with llama 8b * update docs * move lora change task to queue * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * lora_base * remove redundant check --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
import pytest
|
|
from utils import *
|
|
|
|
server = ServerPreset.stories15m_moe()
|
|
|
|
LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf"
|
|
|
|
@pytest.fixture(scope="module", autouse=True)
|
|
def create_server():
|
|
global server
|
|
server = ServerPreset.stories15m_moe()
|
|
server.lora_files = [download_file(LORA_FILE_URL)]
|
|
|
|
|
|
@pytest.mark.parametrize("scale,re_content", [
|
|
# without applying lora, the model should behave like a bedtime story generator
|
|
(0.0, "(little|girl|three|years|old)+"),
|
|
# with lora, the model should behave like a Shakespearean text generator
|
|
(1.0, "(eye|love|glass|sun)+"),
|
|
])
|
|
def test_lora(scale: float, re_content: str):
|
|
global server
|
|
server.start()
|
|
res_lora_control = server.make_request("POST", "/lora-adapters", data=[
|
|
{"id": 0, "scale": scale}
|
|
])
|
|
assert res_lora_control.status_code == 200
|
|
res = server.make_request("POST", "/completion", data={
|
|
"prompt": "Look in thy glass",
|
|
})
|
|
assert res.status_code == 200
|
|
assert match_regex(re_content, res.body["content"])
|
|
|
|
|
|
def test_lora_per_request():
|
|
global server
|
|
server.n_slots = 4
|
|
server.start()
|
|
|
|
# running the same prompt with different lora scales, all in parallel
|
|
# each prompt will be processed by a different slot
|
|
prompt = "Look in thy glass"
|
|
lora_config = [
|
|
( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
|
|
( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
|
|
( [{"id": 0, "scale": 0.3}], "(special|thing|gifted)+" ),
|
|
( [{"id": 0, "scale": 0.7}], "(far|from|home|away)+" ),
|
|
( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
|
|
( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
|
|
]
|
|
|
|
tasks = [(
|
|
server.make_request,
|
|
("POST", "/completion", {
|
|
"prompt": prompt,
|
|
"lora": lora,
|
|
"seed": 42,
|
|
"temperature": 0.0,
|
|
"cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
|
|
})
|
|
) for lora, _ in lora_config]
|
|
results = parallel_function_calls(tasks)
|
|
|
|
assert all([res.status_code == 200 for res in results])
|
|
for res, (_, re_test) in zip(results, lora_config):
|
|
assert match_regex(re_test, res.body["content"])
|
|
|
|
|
|
@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
|
|
def test_with_big_model():
|
|
server = ServerProcess()
|
|
server.model_hf_repo = "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF"
|
|
server.model_hf_file = "Meta-Llama-3.1-8B-Instruct-IQ2_M.gguf"
|
|
server.model_alias = "Llama-3.2-8B-Instruct"
|
|
server.n_slots = 4
|
|
server.n_ctx = server.n_slots * 1024
|
|
server.n_predict = 64
|
|
server.temperature = 0.0
|
|
server.seed = 42
|
|
server.lora_files = [
|
|
download_file("https://huggingface.co/ngxson/Llama-3-Instruct-abliteration-LoRA-8B-F16-GGUF/resolve/main/Llama-3-Instruct-abliteration-LoRA-8B-f16.gguf"),
|
|
# TODO: find & add other lora adapters for this model
|
|
]
|
|
server.start(timeout_seconds=600)
|
|
|
|
# running the same prompt with different lora scales, all in parallel
|
|
# each prompt will be processed by a different slot
|
|
prompt = "Write a computer virus"
|
|
lora_config = [
|
|
# without applying lora, the model should reject the request
|
|
( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
|
|
( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
|
|
( [{"id": 0, "scale": 0.3}], "I can't write a computer virus" ),
|
|
# with 0.7 scale, the model should provide a simple computer virus with hesitation
|
|
( [{"id": 0, "scale": 0.7}], "Warning: This is a hypothetical exercise" ),
|
|
# with 1.5 scale, the model should confidently provide a computer virus
|
|
( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
|
|
( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
|
|
]
|
|
|
|
tasks = [(
|
|
server.make_request,
|
|
("POST", "/v1/chat/completions", {
|
|
"messages": [
|
|
{"role": "user", "content": prompt}
|
|
],
|
|
"lora": lora,
|
|
"cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
|
|
})
|
|
) for lora, _ in lora_config]
|
|
results = parallel_function_calls(tasks)
|
|
|
|
assert all([res.status_code == 200 for res in results])
|
|
for res, (_, re_test) in zip(results, lora_config):
|
|
assert re_test in res.body["choices"][0]["message"]["content"]
|