# imports for guided decoding tests import json import re from typing import Dict, List, Optional import jsonschema import openai # use the official client for correctness check import pytest import pytest_asyncio import torch from openai import BadRequestError from ...utils import RemoteOpenAIServer from .test_completion import zephyr_lora_added_tokens_files # noqa: F401 from .test_completion import zephyr_lora_files # noqa: F401 # any model with a chat template should work here MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" # technically this needs Mistral-7B-v0.1 as base, but we're not testing # generation quality here LORA_NAME = "typeof/zephyr-7b-beta-lora" @pytest.fixture(scope="module") def server(zephyr_lora_files, zephyr_lora_added_tokens_files): # noqa: F811 args = [ # use half precision for speed and memory savings in CI environment "--dtype", "bfloat16", "--max-model-len", "8192", "--enforce-eager", # lora config below "--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", "--max-num-seqs", "128", ] with RemoteOpenAIServer(MODEL_NAME, 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 "model_name", [MODEL_NAME, "zephyr-lora", "zephyr-lora2"], ) async def test_no_logprobs_chat(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] chat_completion = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=5, temperature=0.0, logprobs=False) choice = chat_completion.choices[0] assert choice.logprobs is None @pytest.mark.asyncio @pytest.mark.parametrize( # just test 1 lora hereafter "model_name", [MODEL_NAME, "zephyr-lora"], ) async def test_zero_logprobs_chat(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] chat_completion = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=5, temperature=0.0, logprobs=True, top_logprobs=0) choice = chat_completion.choices[0] assert choice.logprobs is not None assert choice.logprobs.content is not None assert len(choice.logprobs.content[0].top_logprobs) == 0 @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME, "zephyr-lora"], ) async def test_some_logprobs_chat(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] chat_completion = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=5, temperature=0.0, logprobs=True, top_logprobs=5) choice = chat_completion.choices[0] assert choice.logprobs is not None assert choice.logprobs.content is not None assert len(choice.logprobs.content[0].top_logprobs) == 5 @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME, "zephyr-lora"], ) async def test_too_many_chat_logprobs(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] # Default max_logprobs is 20, so this should raise an error with pytest.raises((openai.BadRequestError, openai.APIError)): stream = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=10, logprobs=True, top_logprobs=21, stream=True) async for chunk in stream: ... with pytest.raises(openai.BadRequestError): await client.chat.completions.create(model=model_name, messages=messages, max_tokens=10, logprobs=True, top_logprobs=30, stream=False) # the server should still work afterwards chat_completion = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=10, stream=False) message = chat_completion.choices[0].message assert message.content is not None and len(message.content) >= 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_chat(client: openai.AsyncOpenAI, model_name: str, prompt_logprobs: Optional[int]): params: Dict = { "messages": [{ "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Who won the world series in 2020?" }, { "role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020." }, { "role": "user", "content": "Where was it played?" }], "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.chat.completions.create(**params) else: completion = await client.chat.completions.create(**params) if prompt_logprobs is not None: assert completion.prompt_logprobs is not None assert len(completion.prompt_logprobs) > 0 else: assert completion.prompt_logprobs is None @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME], ) async def test_more_than_one_prompt_logprobs_chat(client: openai.AsyncOpenAI, model_name: str): params: Dict = { "messages": [{ "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Who won the world series in 2020?" }, { "role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020." }, { "role": "user", "content": "Where was it played?" }], "model": model_name, "extra_body": { "prompt_logprobs": 1 } } completion_1 = await client.chat.completions.create(**params) params["extra_body"] = {"prompt_logprobs": 2} completion_2 = await client.chat.completions.create(**params) assert len(completion_1.prompt_logprobs[3]) == 1 assert len(completion_2.prompt_logprobs[3]) == 2 @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME, "zephyr-lora"], ) async def test_single_chat_session(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] # test single completion chat_completion = await client.chat.completions.create(model=model_name, messages=messages, max_tokens=10, logprobs=True, top_logprobs=5) assert chat_completion.id is not None assert len(chat_completion.choices) == 1 choice = chat_completion.choices[0] assert choice.finish_reason == "length" assert chat_completion.usage == openai.types.CompletionUsage( completion_tokens=10, prompt_tokens=37, total_tokens=47) message = choice.message assert message.content is not None and len(message.content) >= 10 assert message.role == "assistant" messages.append({"role": "assistant", "content": message.content}) # test multi-turn dialogue messages.append({"role": "user", "content": "express your result in json"}) chat_completion = await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, ) message = chat_completion.choices[0].message assert message.content is not None and len(message.content) >= 0 @pytest.mark.asyncio @pytest.mark.parametrize( # just test 1 lora hereafter "model_name", [MODEL_NAME, "zephyr-lora"], ) async def test_chat_streaming(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "what is 1+1?" }] # test single completion chat_completion = await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, ) output = chat_completion.choices[0].message.content stop_reason = chat_completion.choices[0].finish_reason # test streaming stream = await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, stream=True, ) chunks: List[str] = [] finish_reason_count = 0 async for chunk in stream: delta = chunk.choices[0].delta if delta.role: assert delta.role == "assistant" if delta.content: chunks.append(delta.content) 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 == stop_reason assert delta.content assert "".join(chunks) == output @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", ["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"], ) async def test_chat_completion_stream_options(client: openai.AsyncOpenAI, model_name: str): messages = [{ "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the capital of France?" }] # Test stream=True, stream_options={"include_usage": False} stream = await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, stream=True, stream_options={"include_usage": False}) 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.chat.completions.create(model=model_name, messages=messages, max_tokens=10, 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=False, stream_options={"include_usage": None} with pytest.raises(BadRequestError): await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, stream=False, stream_options={"include_usage": None}) # Test stream=False, stream_options={"include_usage": True} with pytest.raises(BadRequestError): await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, stream=False, stream_options={"include_usage": True}) # Test stream=True, stream_options={"include_usage": True, # "continuous_usage_stats": True} stream = await client.chat.completions.create( model=model_name, messages=messages, max_tokens=10, temperature=0.0, stream=True, stream_options={ "include_usage": True, "continuous_usage_stats": True }, ) async for chunk in stream: 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) # NOTE: Not sure why, but when I place this after `test_guided_regex_chat` # (i.e. using the same ordering as in the Completions API tests), the test # will fail on the second `guided_decoding_backend` even when I swap their order # (ref: https://github.com/vllm-project/vllm/pull/5526#issuecomment-2173772256) @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_choice_chat(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_guided_choice): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "The best language for type-safe systems programming is " }] chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=10, extra_body=dict(guided_choice=sample_guided_choice, guided_decoding_backend=guided_decoding_backend)) choice1 = chat_completion.choices[0].message.content assert choice1 in sample_guided_choice messages.append({"role": "assistant", "content": choice1}) messages.append({ "role": "user", "content": "I disagree, pick another one" }) chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=10, extra_body=dict(guided_choice=sample_guided_choice, guided_decoding_backend=guided_decoding_backend)) choice2 = chat_completion.choices[0].message.content assert choice2 in sample_guided_choice assert choice1 != choice2 @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_json_chat(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_json_schema): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": f"Give an example JSON for an employee profile that " f"fits this schema: {sample_json_schema}" }] chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, extra_body=dict(guided_json=sample_json_schema, guided_decoding_backend=guided_decoding_backend)) message = chat_completion.choices[0].message assert message.content is not None json1 = json.loads(message.content) jsonschema.validate(instance=json1, schema=sample_json_schema) messages.append({"role": "assistant", "content": message.content}) messages.append({ "role": "user", "content": "Give me another one with a different name and age" }) chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, extra_body=dict(guided_json=sample_json_schema, guided_decoding_backend=guided_decoding_backend)) message = chat_completion.choices[0].message assert message.content is not None json2 = json.loads(message.content) jsonschema.validate(instance=json2, schema=sample_json_schema) assert json1["name"] != json2["name"] assert json1["age"] != json2["age"] @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_regex_chat(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_regex): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": f"Give an example IP address with this regex: {sample_regex}" }] chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=20, extra_body=dict(guided_regex=sample_regex, guided_decoding_backend=guided_decoding_backend)) ip1 = chat_completion.choices[0].message.content assert ip1 is not None assert re.fullmatch(sample_regex, ip1) is not None messages.append({"role": "assistant", "content": ip1}) messages.append({"role": "user", "content": "Give me a different one"}) chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=20, extra_body=dict(guided_regex=sample_regex, guided_decoding_backend=guided_decoding_backend)) ip2 = chat_completion.choices[0].message.content assert ip2 is not None assert re.fullmatch(sample_regex, ip2) is not None assert ip1 != ip2 @pytest.mark.asyncio async def test_guided_decoding_type_error(client: openai.AsyncOpenAI): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "The best language for type-safe systems programming is " }] with pytest.raises(openai.BadRequestError): _ = await client.chat.completions.create(model=MODEL_NAME, messages=messages, extra_body=dict(guided_regex={ 1: "Python", 2: "C++" })) @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_choice_chat_logprobs(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_guided_choice): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": "The best language for type-safe systems programming is " }] chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=10, logprobs=True, top_logprobs=5, extra_body=dict(guided_choice=sample_guided_choice, guided_decoding_backend=guided_decoding_backend)) assert chat_completion.choices[0].logprobs is not None assert chat_completion.choices[0].logprobs.content is not None top_logprobs = chat_completion.choices[0].logprobs.content[0].top_logprobs # -9999.0 is the minimum logprob returned by OpenAI for item in top_logprobs: assert item.logprob >= -9999.0, f"Failed (top_logprobs={top_logprobs})" @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_named_tool_use(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_json_schema): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": f"Give an example JSON for an employee profile that " f"fits this schema: {sample_json_schema}" }] # non-streaming chat_completion = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, tools=[{ "type": "function", "function": { "name": "dummy_function_name", "description": "This is a dummy function", "parameters": sample_json_schema } }], tool_choice={ "type": "function", "function": { "name": "dummy_function_name" } }) message = chat_completion.choices[0].message assert len(message.content) == 0 json_string = message.tool_calls[0].function.arguments json1 = json.loads(json_string) jsonschema.validate(instance=json1, schema=sample_json_schema) messages.append({"role": "assistant", "content": json_string}) messages.append({ "role": "user", "content": "Give me another one with a different name and age" }) # streaming stream = await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, tools=[{ "type": "function", "function": { "name": "dummy_function_name", "description": "This is a dummy function", "parameters": sample_json_schema } }], tool_choice={ "type": "function", "function": { "name": "dummy_function_name" } }, stream=True) output = [] finish_reason_count = 0 async for chunk in stream: delta = chunk.choices[0].delta if delta.role: assert delta.role == "assistant" assert delta.content is None or len(delta.content) == 0 if delta.tool_calls: output.append(delta.tool_calls[0].function.arguments) 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 json2 = json.loads("".join(output)) jsonschema.validate(instance=json2, schema=sample_json_schema) assert json1["name"] != json2["name"] assert json1["age"] != json2["age"] @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines"]) async def test_required_tool_use_not_yet_supported( client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_json_schema): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": f"Give an example JSON for an employee profile that " f"fits this schema: {sample_json_schema}" }] with pytest.raises(openai.BadRequestError): await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, tools=[{ "type": "function", "function": { "name": "dummy_function_name", "description": "This is a dummy function", "parameters": sample_json_schema } }], tool_choice="required") with pytest.raises(openai.BadRequestError): await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, tools=[{ "type": "function", "function": { "name": "dummy_function_name", "description": "This is a dummy function", "parameters": sample_json_schema } }], tool_choice="auto") @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines"]) async def test_inconsistent_tool_choice_and_tools(client: openai.AsyncOpenAI, guided_decoding_backend: str, sample_json_schema): messages = [{ "role": "system", "content": "you are a helpful assistant" }, { "role": "user", "content": f"Give an example JSON for an employee profile that " f"fits this schema: {sample_json_schema}" }] with pytest.raises(openai.BadRequestError): await client.chat.completions.create(model=MODEL_NAME, messages=messages, max_tokens=1000, tool_choice={ "type": "function", "function": { "name": "dummy_function_name" } }) with pytest.raises(openai.BadRequestError): await client.chat.completions.create( model=MODEL_NAME, messages=messages, max_tokens=1000, tools=[{ "type": "function", "function": { "name": "dummy_function_name", "description": "This is a dummy function", "parameters": sample_json_schema } }], tool_choice={ "type": "function", "function": { "name": "nondefined_function_name" } }) @pytest.mark.asyncio async def test_response_format_json_object(client: openai.AsyncOpenAI): for _ in range(2): resp = await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "user", "content": ('what is 1+1? please respond with a JSON object, ' 'the format is {"result": 2}') }], response_format={"type": "json_object"}) content = resp.choices[0].message.content assert content is not None loaded = json.loads(content) assert loaded == {"result": 2}, loaded @pytest.mark.asyncio async def test_response_format_json_schema(client: openai.AsyncOpenAI): for _ in range(2): resp = await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "user", "content": ('what is 1+1? please respond with a JSON object, ' 'the format is {"result": 2}') }], response_format={ "type": "json_schema", "json_schema": { "name": "foo_test", "schema": { "type": "object", "properties": { "result": { "type": "integer" }, }, }, } }) content = resp.choices[0].message.content assert content is not None loaded = json.loads(content) assert loaded == {"result": 2}, loaded @pytest.mark.asyncio async def test_extra_fields(client: openai.AsyncOpenAI): with pytest.raises(BadRequestError) as exc_info: await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "system", "content": "You are a helpful assistant.", "extra_field": "0", }], # type: ignore temperature=0, seed=0) assert "extra_forbidden" in exc_info.value.message @pytest.mark.asyncio async def test_complex_message_content(client: openai.AsyncOpenAI): resp = await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "user", "content": [{ "type": "text", "text": "what is 1+1? please provide the result without any other text." }] }], temperature=0, seed=0) content = resp.choices[0].message.content assert content == "2" @pytest.mark.asyncio async def test_custom_role(client: openai.AsyncOpenAI): # Not sure how the model handles custom roles so we just check that # both string and complex message content are handled in the same way resp1 = await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "my-custom-role", "content": "what is 1+1?", }], # type: ignore temperature=0, seed=0) resp2 = await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "my-custom-role", "content": [{ "type": "text", "text": "what is 1+1?" }] }], # type: ignore temperature=0, seed=0) content1 = resp1.choices[0].message.content content2 = resp2.choices[0].message.content assert content1 == content2 @pytest.mark.asyncio async def test_long_seed(client: openai.AsyncOpenAI): for seed in [ torch.iinfo(torch.long).min - 1, torch.iinfo(torch.long).max + 1 ]: with pytest.raises(BadRequestError) as exc_info: await client.chat.completions.create( model=MODEL_NAME, messages=[{ "role": "system", "content": "You are a helpful assistant.", }], temperature=0, seed=seed) assert ("greater_than_equal" in exc_info.value.message or "less_than_equal" in exc_info.value.message)