# imports for guided decoding tests import json import re from typing import List import jsonschema import openai # use the official client for correctness check import pytest # using Ray for overall ease of process management, parallel requests, # and debugging. import ray import requests # downloading lora to test lora requests from huggingface_hub import snapshot_download from openai import BadRequestError from vllm.transformers_utils.tokenizer import get_tokenizer from ...utils import VLLM_PATH, RemoteOpenAIServer # 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" TEST_SCHEMA = { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" }, "skills": { "type": "array", "items": { "type": "string", "maxLength": 10 }, "minItems": 3 }, "work history": { "type": "array", "items": { "type": "object", "properties": { "company": { "type": "string" }, "duration": { "type": "string" }, "position": { "type": "string" } }, "required": ["company", "position"] } } }, "required": ["name", "age", "skills", "work history"] } TEST_REGEX = (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}" r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)") TEST_CHOICE = [ "Python", "Java", "JavaScript", "C++", "C#", "PHP", "TypeScript", "Ruby", "Swift", "Kotlin" ] @pytest.fixture(scope="module") def zephyr_lora_files(): return snapshot_download(repo_id=LORA_NAME) @pytest.fixture(scope="module") def ray_ctx(): ray.init(runtime_env={"working_dir": VLLM_PATH}) yield ray.shutdown() @pytest.fixture(scope="module") def server(zephyr_lora_files, ray_ctx): return RemoteOpenAIServer([ "--model", MODEL_NAME, # 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_files}", "--max-lora-rank", "64", "--max-cpu-loras", "2", "--max-num-seqs", "128", ]) @pytest.fixture(scope="module") def client(server): return server.get_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_single_completion(client: openai.AsyncOpenAI, model_name: str): 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, total_tokens=11) # 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) >= 5 @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(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 hereafter "model_name", [MODEL_NAME, "zephyr-lora"], ) 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"], ) 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"], ) 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", [MODEL_NAME, "zephyr-lora"], ) 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", ["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"], ) 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( # just test 1 lora hereafter "model_name", [MODEL_NAME, "zephyr-lora"], ) 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 @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_json_completion(client: openai.AsyncOpenAI, guided_decoding_backend: str): completion = await client.completions.create( model=MODEL_NAME, prompt=f"Give an example JSON for an employee profile " f"that fits this schema: {TEST_SCHEMA}", n=3, temperature=1.0, max_tokens=500, extra_body=dict(guided_json=TEST_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=TEST_SCHEMA) @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_regex_completion(client: openai.AsyncOpenAI, guided_decoding_backend: str): completion = await client.completions.create( model=MODEL_NAME, prompt=f"Give an example IPv4 address with this regex: {TEST_REGEX}", n=3, temperature=1.0, max_tokens=20, extra_body=dict(guided_regex=TEST_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(TEST_REGEX, completion.choices[i].text) is not None @pytest.mark.asyncio @pytest.mark.parametrize("guided_decoding_backend", ["outlines", "lm-format-enforcer"]) async def test_guided_choice_completion(client: openai.AsyncOpenAI, guided_decoding_backend: str): 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=TEST_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 TEST_CHOICE @pytest.mark.asyncio async def test_guided_grammar(client: openai.AsyncOpenAI): simple_sql_grammar = """ start: select_statement select_statement: "SELECT" column "from" table "where" condition column: "col_1" | "col_2" table: "table_1" | "table_2" condition: column "=" number number: "1" | "2" """ 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=simple_sql_grammar)) 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(simple_sql_grammar) 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", ["outlines", "lm-format-enforcer"]) async def test_guided_decoding_type_error(client: openai.AsyncOpenAI, guided_decoding_backend: str): 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=TEST_REGEX, guided_json=TEST_SCHEMA)) @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME], ) async def test_tokenize(client: openai.AsyncOpenAI, model_name: str): base_url = str(client.base_url)[:-3].strip("/") tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME, tokenizer_mode="fast") for add_special in [False, True]: prompt = "This is a test prompt." tokens = tokenizer.encode(prompt, add_special_tokens=add_special) response = requests.post(base_url + "/tokenize", json={ "add_special_tokens": add_special, "model": model_name, "prompt": prompt }) response.raise_for_status() assert response.json() == { "tokens": tokens, "count": len(tokens), "max_model_len": 8192 } @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME], ) async def test_detokenize(client: openai.AsyncOpenAI, model_name: str): base_url = str(client.base_url)[:-3] tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME, tokenizer_mode="fast") prompt = "This is a test prompt." tokens = tokenizer.encode(prompt, add_special_tokens=False) response = requests.post(base_url + "detokenize", json={ "model": model_name, "tokens": tokens }) response.raise_for_status() assert response.json() == {"prompt": prompt}