Support for guided decoding for offline LLM (#6878)
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
This commit is contained in:
142
tests/entrypoints/llm/test_guided_generate.py
Normal file
142
tests/entrypoints/llm/test_guided_generate.py
Normal file
@@ -0,0 +1,142 @@
|
||||
import json
|
||||
import re
|
||||
import weakref
|
||||
|
||||
import jsonschema
|
||||
import pytest
|
||||
|
||||
from vllm.entrypoints.llm import LLM
|
||||
from vllm.outputs import RequestOutput
|
||||
from vllm.sampling_params import SamplingParams
|
||||
|
||||
from ...conftest import cleanup
|
||||
|
||||
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def llm():
|
||||
# pytest caches the fixture so we use weakref.proxy to
|
||||
# enable garbage collection
|
||||
llm = LLM(model=MODEL_NAME, max_model_len=1024)
|
||||
|
||||
with llm.deprecate_legacy_api():
|
||||
yield weakref.proxy(llm)
|
||||
del llm
|
||||
cleanup()
|
||||
|
||||
|
||||
@pytest.mark.skip_global_cleanup
|
||||
def test_guided_regex(sample_regex, llm):
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.8,
|
||||
top_p=0.95,
|
||||
)
|
||||
outputs = llm.generate(
|
||||
prompts=[
|
||||
f"Give an example IPv4 address with this regex: {sample_regex}"
|
||||
] * 2,
|
||||
sampling_params=sampling_params,
|
||||
use_tqdm=True,
|
||||
guided_options_request=dict(guided_regex=sample_regex))
|
||||
|
||||
assert outputs is not None
|
||||
for output in outputs:
|
||||
assert output is not None
|
||||
assert isinstance(output, RequestOutput)
|
||||
prompt = output.prompt
|
||||
generated_text = output.outputs[0].text
|
||||
print(generated_text)
|
||||
assert generated_text is not None
|
||||
assert re.fullmatch(sample_regex, generated_text) is not None
|
||||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||||
|
||||
|
||||
@pytest.mark.skip_global_cleanup
|
||||
def test_guided_json_completion(sample_json_schema, llm):
|
||||
sampling_params = SamplingParams(
|
||||
temperature=1.0,
|
||||
max_tokens=1000,
|
||||
)
|
||||
outputs = llm.generate(
|
||||
prompts=[
|
||||
f"Give an example JSON for an employee profile "
|
||||
f"that fits this schema: {sample_json_schema}"
|
||||
] * 2,
|
||||
sampling_params=sampling_params,
|
||||
use_tqdm=True,
|
||||
guided_options_request=dict(guided_json=sample_json_schema))
|
||||
|
||||
assert outputs is not None
|
||||
|
||||
for output in outputs:
|
||||
assert output is not None
|
||||
assert isinstance(output, RequestOutput)
|
||||
prompt = output.prompt
|
||||
|
||||
generated_text = output.outputs[0].text
|
||||
assert generated_text is not None
|
||||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||||
output_json = json.loads(generated_text)
|
||||
jsonschema.validate(instance=output_json, schema=sample_json_schema)
|
||||
|
||||
|
||||
@pytest.mark.skip_global_cleanup
|
||||
def test_guided_choice_completion(sample_guided_choice, llm):
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.8,
|
||||
top_p=0.95,
|
||||
)
|
||||
outputs = llm.generate(
|
||||
prompts="The best language for type-safe systems programming is ",
|
||||
sampling_params=sampling_params,
|
||||
use_tqdm=True,
|
||||
guided_options_request=dict(guided_choice=sample_guided_choice))
|
||||
|
||||
assert outputs is not None
|
||||
for output in outputs:
|
||||
assert output is not None
|
||||
assert isinstance(output, RequestOutput)
|
||||
prompt = output.prompt
|
||||
generated_text = output.outputs[0].text
|
||||
print(generated_text)
|
||||
assert generated_text is not None
|
||||
assert generated_text in sample_guided_choice
|
||||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||||
|
||||
|
||||
@pytest.mark.skip_global_cleanup
|
||||
def test_guided_grammar(sample_sql_statements, llm):
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.8,
|
||||
top_p=0.95,
|
||||
max_tokens=1000,
|
||||
)
|
||||
outputs = llm.generate(
|
||||
prompts=("Generate a sql state that select col_1 from "
|
||||
"table_1 where it is equals to 1"),
|
||||
sampling_params=sampling_params,
|
||||
use_tqdm=True,
|
||||
guided_options_request=dict(guided_grammar=sample_sql_statements))
|
||||
|
||||
assert outputs is not None
|
||||
for output in outputs:
|
||||
assert output is not None
|
||||
assert isinstance(output, RequestOutput)
|
||||
prompt = output.prompt
|
||||
|
||||
generated_text = output.outputs[0].text
|
||||
assert generated_text is not None
|
||||
# use Lark to parse the output, and make sure it's a valid parse tree
|
||||
from lark import Lark
|
||||
parser = Lark(sample_sql_statements)
|
||||
parser.parse(generated_text)
|
||||
|
||||
# remove spaces for comparison b/c we removed them in the grammar
|
||||
ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
|
||||
" ", "")
|
||||
|
||||
assert generated_text.strip() == ground_truth
|
||||
|
||||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||||
Reference in New Issue
Block a user