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vllm/tests/entrypoints/openai/test_completion.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628) 
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**

commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:18:24 2025 -0500

    Add SPDX license headers to python source files
    
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
    also be easily used by tools to help manage license compliance.
    
The Linux Foundation runs license scans against the codebase to help
ensure
    we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
    
    More information can be found on the SPDX site:
    
    - https://spdx.dev/learn/handling-license-info/
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:36:32 2025 -0500

    Check for SPDX headers using pre-commit
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------

Signed-off-by: Russell Bryant <rbryant@redhat.com>
2025-02-02 11:58:18 -08:00

782 lines
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# SPDX-License-Identifier: Apache-2.0
# imports for guided decoding tests
import json
import re
import shutil
from tempfile import TemporaryDirectory
from typing import Dict, List, Optional
import jsonschema
import openai # use the official client for correctness check
import pytest
import pytest_asyncio
# downloading lora to test lora requests
from huggingface_hub import snapshot_download
from openai import BadRequestError
from transformers import AutoTokenizer
from vllm.transformers_utils.tokenizer import get_tokenizer
from ...utils import RemoteOpenAIServer
# any model with a chat template should work here
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
# technically these adapters use a different base model,
# but we're not testing generation quality here
LORA_NAME = "typeof/zephyr-7b-beta-lora"
PA_NAME = "swapnilbp/llama_tweet_ptune"
# if PA_NAME changes, PA_NUM_VIRTUAL_TOKENS might also
# need to change to match the prompt adapter
PA_NUM_VIRTUAL_TOKENS = 8
GUIDED_DECODING_BACKENDS = ["outlines", "lm-format-enforcer", "xgrammar"]
@pytest.fixture(scope="module")
def zephyr_lora_files():
return snapshot_download(repo_id=LORA_NAME)
@pytest.fixture(scope="module")
def zephyr_lora_added_tokens_files(zephyr_lora_files):
tmp_dir = TemporaryDirectory()
tmp_model_dir = f"{tmp_dir.name}/zephyr"
shutil.copytree(zephyr_lora_files, tmp_model_dir)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# Copy tokenizer to adapter and add some unique tokens
# 32000, 32001, 32002
added = tokenizer.add_tokens(["vllm1", "vllm2", "vllm3"],
special_tokens=True)
assert added == 3
tokenizer.save_pretrained(tmp_model_dir)
yield tmp_model_dir
tmp_dir.cleanup()
@pytest.fixture(scope="module")
def zephyr_pa_files():
return snapshot_download(repo_id=PA_NAME)
@pytest.fixture(scope="module")
def default_server_args(zephyr_lora_files, zephyr_lora_added_tokens_files,
zephyr_pa_files):
return [
# use half precision for speed and memory savings in CI environment
"--dtype",
"bfloat16",
"--max-model-len",
"8192",
"--max-num-seqs",
"128",
"--enforce-eager",
# lora config
"--enable-lora",
"--lora-modules",
f"zephyr-lora={zephyr_lora_files}",
f"zephyr-lora2={zephyr_lora_added_tokens_files}",
"--max-lora-rank",
"64",
"--max-cpu-loras",
"2",
# pa config
"--enable-prompt-adapter",
"--prompt-adapters",
f"zephyr-pa={zephyr_pa_files}",
f"zephyr-pa2={zephyr_pa_files}",
"--max-prompt-adapters",
"2",
"--max-prompt-adapter-token",
"128",
]
@pytest.fixture(scope="module",
params=["", "--disable-frontend-multiprocessing"])
def server(default_server_args, request):
if request.param:
default_server_args.append(request.param)
with RemoteOpenAIServer(MODEL_NAME, default_server_args) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def client(server):
async with server.get_async_client() as async_client:
yield async_client
@pytest.mark.asyncio
@pytest.mark.parametrize(
# first test base model, then test loras, then test prompt adapters
"model_name,num_virtual_tokens",
[(MODEL_NAME, 0), ("zephyr-lora", 0), ("zephyr-lora2", 0),
("zephyr-pa", PA_NUM_VIRTUAL_TOKENS),
("zephyr-pa2", PA_NUM_VIRTUAL_TOKENS)],
)
async def test_single_completion(client: openai.AsyncOpenAI, model_name: str,
num_virtual_tokens: int):
completion = await client.completions.create(model=model_name,
prompt="Hello, my name is",
max_tokens=5,
temperature=0.0)
assert completion.id is not None
assert completion.choices is not None and len(completion.choices) == 1
choice = completion.choices[0]
assert len(choice.text) >= 5
assert choice.finish_reason == "length"
assert completion.usage == openai.types.CompletionUsage(
completion_tokens=5,
prompt_tokens=6 + num_virtual_tokens,
total_tokens=11 + num_virtual_tokens)
# test using token IDs
completion = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
)
assert len(completion.choices[0].text) >= 1
assert completion.choices[0].prompt_logprobs is None
@pytest.mark.asyncio
async def test_added_lora_tokens(client: openai.AsyncOpenAI):
# test using token IDs
completion = await client.completions.create(
model="zephyr-lora2",
prompt=[0, 0, 32000, 32001, 32002],
echo=True,
max_tokens=5,
temperature=0.0,
)
# Added tokens should appear in tokenized prompt
assert completion.choices[0].text.startswith("<unk><unk>vllm1vllm2vllm3")
@pytest.mark.asyncio
async def test_added_lora_tokens_base_model(client: openai.AsyncOpenAI):
# test using token IDs
with pytest.raises(openai.BadRequestError, match="out of vocabulary"):
# Added tokens should be rejected by the base model
await client.completions.create(
model=MODEL_NAME,
prompt=[0, 0, 32000, 32001, 32002],
echo=True,
max_tokens=5,
temperature=0.0,
)
@pytest.mark.asyncio
@pytest.mark.parametrize(
# first test base model, then test loras, then test prompt adapters
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-lora2", "zephyr-pa", "zephyr-pa2"],
)
async def test_no_logprobs(client: openai.AsyncOpenAI, model_name: str):
# test using token IDs
completion = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
logprobs=None,
)
choice = completion.choices[0]
assert choice.logprobs is None
@pytest.mark.asyncio
@pytest.mark.parametrize(
# just test 1 lora and 1 pa hereafter
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_zero_logprobs(client: openai.AsyncOpenAI, model_name: str):
# test using token IDs
completion = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
logprobs=0,
)
choice = completion.choices[0]
assert choice.logprobs is not None
assert choice.logprobs.token_logprobs is not None
assert choice.logprobs.top_logprobs is not None
assert len(choice.logprobs.top_logprobs[0]) == 1
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_some_logprobs(client: openai.AsyncOpenAI, model_name: str):
# test using token IDs
completion = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
logprobs=5,
)
choice = completion.choices[0]
assert choice.logprobs is not None
assert choice.logprobs.token_logprobs is not None
assert choice.logprobs.top_logprobs is not None
assert 5 <= len(choice.logprobs.top_logprobs[0]) <= 6
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_too_many_completion_logprobs(client: openai.AsyncOpenAI,
model_name: str):
with pytest.raises(
(openai.BadRequestError, openai.APIError)): # test using token IDs
await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
# vLLM has higher default max_logprobs (20 instead of 5) to support
# both Completion API and Chat Completion API
logprobs=21,
)
...
with pytest.raises(
(openai.BadRequestError, openai.APIError)): # test using token IDs
stream = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
# vLLM has higher default max_logprobs (20 instead of 5) to support
# both Completion API and Chat Completion API
logprobs=30,
stream=True,
)
async for chunk in stream:
...
# the server should still work afterwards
completion = await client.completions.create(
model=model_name,
prompt=[0, 0, 0, 0, 0],
max_tokens=5,
temperature=0.0,
)
assert len(completion.choices[0].text) >= 0
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name, prompt_logprobs", [(MODEL_NAME, -1),
(MODEL_NAME, 0),
(MODEL_NAME, 1),
(MODEL_NAME, None)])
async def test_prompt_logprobs_completion(client: openai.AsyncOpenAI,
model_name: str,
prompt_logprobs: Optional[int]):
params: Dict = {
"prompt": ["A robot may not injure another robot", "My name is"],
"model": model_name,
}
if prompt_logprobs is not None:
params["extra_body"] = {"prompt_logprobs": prompt_logprobs}
if prompt_logprobs is not None and prompt_logprobs < 0:
with pytest.raises(BadRequestError):
await client.completions.create(**params)
else:
completion = await client.completions.create(**params)
if prompt_logprobs is not None:
assert completion.choices[0].prompt_logprobs is not None
assert len(completion.choices[0].prompt_logprobs) > 0
assert completion.choices[1].prompt_logprobs is not None
assert len(completion.choices[1].prompt_logprobs) > 0
else:
assert completion.choices[0].prompt_logprobs is None
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_completion_streaming(client: openai.AsyncOpenAI,
model_name: str):
prompt = "What is an LLM?"
single_completion = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
)
single_output = single_completion.choices[0].text
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True)
chunks: List[str] = []
finish_reason_count = 0
async for chunk in stream:
chunks.append(chunk.choices[0].text)
if chunk.choices[0].finish_reason is not None:
finish_reason_count += 1
# finish reason should only return in last block
assert finish_reason_count == 1
assert chunk.choices[0].finish_reason == "length"
assert chunk.choices[0].text
assert "".join(chunks) == single_output
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_parallel_streaming(client: openai.AsyncOpenAI, model_name: str):
"""Streaming for parallel sampling.
The tokens from multiple samples, are flattened into a single stream,
with an index to indicate which sample the token belongs to.
"""
prompt = "What is an LLM?"
n = 3
max_tokens = 5
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=max_tokens,
n=n,
stream=True)
chunks: List[List[str]] = [[] for i in range(n)]
finish_reason_count = 0
async for chunk in stream:
index = chunk.choices[0].index
text = chunk.choices[0].text
chunks[index].append(text)
if chunk.choices[0].finish_reason is not None:
finish_reason_count += 1
assert finish_reason_count == n
for chunk in chunks:
assert len(chunk) == max_tokens
print("".join(chunk))
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_completion_stream_options(client: openai.AsyncOpenAI,
model_name: str):
prompt = "What is the capital of France?"
# Test stream=True, stream_options=
# {"include_usage": False, "continuous_usage_stats": False}
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={
"include_usage": False,
"continuous_usage_stats":
False,
})
async for chunk in stream:
assert chunk.usage is None
# Test stream=True, stream_options=
# {"include_usage": False, "continuous_usage_stats": True}
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={
"include_usage": False,
"continuous_usage_stats":
True,
})
async for chunk in stream:
assert chunk.usage is None
# Test stream=True, stream_options=
# {"include_usage": True, "continuous_usage_stats": False}
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={
"include_usage": True,
"continuous_usage_stats":
False,
})
async for chunk in stream:
if chunk.choices[0].finish_reason is None:
assert chunk.usage is None
else:
assert chunk.usage is None
final_chunk = await stream.__anext__()
assert final_chunk.usage is not None
assert final_chunk.usage.prompt_tokens > 0
assert final_chunk.usage.completion_tokens > 0
assert final_chunk.usage.total_tokens == (
final_chunk.usage.prompt_tokens +
final_chunk.usage.completion_tokens)
assert final_chunk.choices == []
# Test stream=True, stream_options=
# {"include_usage": True, "continuous_usage_stats": True}
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={
"include_usage": True,
"continuous_usage_stats":
True,
})
async for chunk in stream:
assert chunk.usage is not None
assert chunk.usage.prompt_tokens > 0
assert chunk.usage.completion_tokens > 0
assert chunk.usage.total_tokens == (chunk.usage.prompt_tokens +
chunk.usage.completion_tokens)
if chunk.choices[0].finish_reason is not None:
final_chunk = await stream.__anext__()
assert final_chunk.usage is not None
assert final_chunk.usage.prompt_tokens > 0
assert final_chunk.usage.completion_tokens > 0
assert final_chunk.usage.total_tokens == (
final_chunk.usage.prompt_tokens +
final_chunk.usage.completion_tokens)
assert final_chunk.choices == []
# Test stream=False, stream_options=
# {"include_usage": None}
with pytest.raises(BadRequestError):
await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": None})
# Test stream=False, stream_options=
# {"include_usage": True}
with pytest.raises(BadRequestError):
await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": True})
# Test stream=False, stream_options=
# {"continuous_usage_stats": None}
with pytest.raises(BadRequestError):
await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"continuous_usage_stats": None})
# Test stream=False, stream_options=
# {"continuous_usage_stats": True}
with pytest.raises(BadRequestError):
await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"continuous_usage_stats": True})
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-pa"],
)
async def test_batch_completions(client: openai.AsyncOpenAI, model_name: str):
# test both text and token IDs
for prompts in (["Hello, my name is"] * 2, [[0, 0, 0, 0, 0]] * 2):
# test simple list
batch = await client.completions.create(
model=model_name,
prompt=prompts,
max_tokens=5,
temperature=0.0,
)
assert len(batch.choices) == 2
assert batch.choices[0].text == batch.choices[1].text
# test n = 2
batch = await client.completions.create(
model=model_name,
prompt=prompts,
n=2,
max_tokens=5,
temperature=0.0,
extra_body=dict(
# NOTE: this has to be true for n > 1 in vLLM, but
# not necessary for official client.
use_beam_search=True),
)
assert len(batch.choices) == 4
assert batch.choices[0].text != batch.choices[
1].text, "beam search should be different"
assert batch.choices[0].text == batch.choices[
2].text, "two copies of the same prompt should be the same"
assert batch.choices[1].text == batch.choices[
3].text, "two copies of the same prompt should be the same"
# test streaming
batch = await client.completions.create(
model=model_name,
prompt=prompts,
max_tokens=5,
temperature=0.0,
stream=True,
)
texts = [""] * 2
async for chunk in batch:
assert len(chunk.choices) == 1
choice = chunk.choices[0]
texts[choice.index] += choice.text
assert texts[0] == texts[1]
@pytest.mark.asyncio
async def test_logits_bias(client: openai.AsyncOpenAI):
prompt = "Hello, my name is"
max_tokens = 5
tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
# Test exclusive selection
token_id = 1000
completion = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=max_tokens,
temperature=0.0,
logit_bias={str(token_id): 100},
seed=42,
)
assert len(completion.choices[0].text) >= 5
response_tokens = tokenizer(completion.choices[0].text,
add_special_tokens=False)["input_ids"]
expected_tokens = tokenizer(tokenizer.decode([token_id] * 5),
add_special_tokens=False)["input_ids"]
assert all([
response == expected
for response, expected in zip(response_tokens, expected_tokens)
])
# Test ban
completion = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=max_tokens,
temperature=0.0,
)
response_tokens = tokenizer(completion.choices[0].text,
add_special_tokens=False)["input_ids"]
first_response = completion.choices[0].text
completion = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=max_tokens,
temperature=0.0,
logit_bias={str(token): -100
for token in response_tokens},
)
assert first_response != completion.choices[0].text
@pytest.mark.asyncio
async def test_allowed_token_ids(client: openai.AsyncOpenAI):
prompt = "Hello, my name is"
max_tokens = 1
tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
# Test exclusive selection
allowed_ids = [21555, 21557, 21558]
completion = await client.completions.create(
model=MODEL_NAME,
prompt=prompt,
max_tokens=max_tokens,
temperature=0.0,
seed=42,
extra_body=dict(allowed_token_ids=allowed_ids),
logprobs=1,
)
response_tokens = completion.choices[0].logprobs.tokens
assert len(response_tokens) == 1
assert tokenizer.convert_tokens_to_ids(response_tokens)[0] in allowed_ids
@pytest.mark.asyncio
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
async def test_guided_json_completion(client: openai.AsyncOpenAI,
guided_decoding_backend: str,
sample_json_schema):
completion = await client.completions.create(
model=MODEL_NAME,
prompt=f"Give an example JSON for an employee profile "
f"that fits this schema: {sample_json_schema}",
n=3,
temperature=1.0,
max_tokens=500,
extra_body=dict(guided_json=sample_json_schema,
guided_decoding_backend=guided_decoding_backend))
assert completion.id is not None
assert len(completion.choices) == 3
for i in range(3):
output_json = json.loads(completion.choices[i].text)
jsonschema.validate(instance=output_json, schema=sample_json_schema)
@pytest.mark.asyncio
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
async def test_guided_regex_completion(client: openai.AsyncOpenAI,
guided_decoding_backend: str,
sample_regex):
completion = await client.completions.create(
model=MODEL_NAME,
prompt=f"Give an example IPv4 address with this regex: {sample_regex}",
n=3,
temperature=1.0,
max_tokens=20,
extra_body=dict(guided_regex=sample_regex,
guided_decoding_backend=guided_decoding_backend))
assert completion.id is not None
assert len(completion.choices) == 3
for i in range(3):
assert re.fullmatch(sample_regex,
completion.choices[i].text) is not None
@pytest.mark.asyncio
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
async def test_guided_choice_completion(client: openai.AsyncOpenAI,
guided_decoding_backend: str,
sample_guided_choice):
completion = await client.completions.create(
model=MODEL_NAME,
prompt="The best language for type-safe systems programming is ",
n=2,
temperature=1.0,
max_tokens=10,
extra_body=dict(guided_choice=sample_guided_choice,
guided_decoding_backend=guided_decoding_backend))
assert completion.id is not None
assert len(completion.choices) == 2
for i in range(2):
assert completion.choices[i].text in sample_guided_choice
@pytest.mark.asyncio
async def test_guided_grammar(client: openai.AsyncOpenAI,
sample_sql_statements):
completion = await client.completions.create(
model=MODEL_NAME,
prompt=("Generate a sql state that select col_1 from "
"table_1 where it is equals to 1"),
temperature=1.0,
max_tokens=500,
extra_body=dict(guided_grammar=sample_sql_statements))
content = completion.choices[0].text
# 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(content)
# 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 content.strip() == ground_truth
@pytest.mark.asyncio
@pytest.mark.parametrize(
# first test base model, then test loras
"model_name",
[MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
)
@pytest.mark.parametrize("logprobs_arg", [1, 0])
async def test_echo_logprob_completion(client: openai.AsyncOpenAI,
model_name: str, logprobs_arg: int):
tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
# test using text and token IDs
for prompt in ("Hello, my name is", [0, 0, 0, 0, 0]):
completion = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
echo=True,
logprobs=logprobs_arg)
prompt_text = tokenizer.decode(prompt) if isinstance(prompt,
list) else prompt
assert re.search(r"^" + prompt_text, completion.choices[0].text)
logprobs = completion.choices[0].logprobs
assert logprobs is not None
assert len(logprobs.text_offset) > 5
assert (len(logprobs.token_logprobs) > 5
and logprobs.token_logprobs[0] is None)
assert (len(logprobs.top_logprobs) > 5
and logprobs.top_logprobs[0] is None)
for top_logprobs in logprobs.top_logprobs[1:]:
assert max(logprobs_arg,
1) <= len(top_logprobs) <= logprobs_arg + 1
assert len(logprobs.tokens) > 5
@pytest.mark.asyncio
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
async def test_guided_decoding_type_error(client: openai.AsyncOpenAI,
guided_decoding_backend: str,
sample_json_schema, sample_regex):
with pytest.raises(openai.BadRequestError):
_ = await client.completions.create(
model=MODEL_NAME,
prompt="Give an example JSON that fits this schema: 42",
extra_body=dict(guided_json=42,
guided_decoding_backend=guided_decoding_backend))
with pytest.raises(openai.BadRequestError):
_ = await client.completions.create(
model=MODEL_NAME,
prompt="Give an example string that fits this regex",
extra_body=dict(guided_regex=sample_regex,
guided_json=sample_json_schema))
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