[CI/Build] Reorganize models tests (#17459)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
323
tests/models/language/generation/test_mistral.py
Normal file
323
tests/models/language/generation/test_mistral.py
Normal file
@@ -0,0 +1,323 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""Compare the outputs of HF and vLLM for Mistral models using greedy sampling.
|
||||
|
||||
Run `pytest tests/models/test_mistral.py`.
|
||||
"""
|
||||
import copy
|
||||
import json
|
||||
|
||||
import jsonschema
|
||||
import jsonschema.exceptions
|
||||
import pytest
|
||||
|
||||
from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
|
||||
MistralToolCall, MistralToolParser)
|
||||
from vllm.sampling_params import GuidedDecodingParams, SamplingParams
|
||||
|
||||
from ...utils import check_logprobs_close
|
||||
|
||||
MODELS = [
|
||||
"mistralai/Mistral-7B-Instruct-v0.3",
|
||||
]
|
||||
|
||||
MISTRAL_FORMAT_MODELS = [
|
||||
"mistralai/Mistral-7B-Instruct-v0.3",
|
||||
# uses the v3-Tekken tokenizer
|
||||
"mistralai/Ministral-8B-Instruct-2410",
|
||||
# Mistral-Nemo is to big for CI, but passes locally
|
||||
# "mistralai/Mistral-Nemo-Instruct-2407"
|
||||
]
|
||||
|
||||
SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
|
||||
SYMBOLIC_LANG_PROMPTS = [
|
||||
"勇敢な船乗りについての詩を書く", # japanese
|
||||
"寫一首關於勇敢的水手的詩", # chinese
|
||||
"ပုံပြင်လေးပြောပြပါ်:\n", # burmese
|
||||
"Repeat the phrase 'URGENCY🌶️':\nURGENCY🌶️\nURGENCY🌶️\n", # see https://github.com/vllm-project/vllm/pull/9625
|
||||
]
|
||||
|
||||
# for function calling
|
||||
TOOLS = [{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type":
|
||||
"string",
|
||||
"description":
|
||||
"The city to find the weather for, e.g. 'San Francisco'"
|
||||
},
|
||||
"state": {
|
||||
"type":
|
||||
"string",
|
||||
"description":
|
||||
"the two-letter abbreviation for the state that the city is"
|
||||
" in, e.g. 'CA' which would mean 'California'"
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"description": "The unit to fetch the temperature in",
|
||||
"enum": ["celsius", "fahrenheit"]
|
||||
}
|
||||
},
|
||||
"required": ["city", "state", "unit"]
|
||||
}
|
||||
},
|
||||
}, {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "rewrite",
|
||||
"description": "Rewrites text",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"required": [],
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string",
|
||||
"description": "The input text to rewrite."
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}]
|
||||
MSGS = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an assistant."
|
||||
},
|
||||
{
|
||||
"role":
|
||||
"user",
|
||||
"content":
|
||||
"Could you please rewrite the below article? \n\n My English needs improvving, maybe I make errors." # noqa
|
||||
},
|
||||
{
|
||||
"role":
|
||||
"assistant",
|
||||
"content":
|
||||
"",
|
||||
"tool_calls": [{
|
||||
"id": "bbc5b7ede",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name":
|
||||
"rewrite",
|
||||
"arguments":
|
||||
'{\"text\":\"My English needs improvving, maybe I make errors.\"}' # noqa
|
||||
}
|
||||
}]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content":
|
||||
"{\"action\":\"rewrite\",\"outcome\":\"My English needs improving, maybe I make errors.\"}", # noqa
|
||||
"tool_call_id": "bbc5b7ede",
|
||||
"name": "rewrite"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "---\n\nMy English needs improving, maybe I make errors"
|
||||
},
|
||||
{
|
||||
"role":
|
||||
"user",
|
||||
"content": ("Can you tell me what the temperate"
|
||||
" will be in Dallas, in fahrenheit?")
|
||||
}
|
||||
]
|
||||
|
||||
SAMPLE_JSON_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": "number"
|
||||
},
|
||||
"position": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": ["company", "position"]
|
||||
}
|
||||
}
|
||||
},
|
||||
"required": ["name", "age", "skills", "work_history"]
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", MODELS)
|
||||
@pytest.mark.parametrize("dtype", ["bfloat16"])
|
||||
@pytest.mark.parametrize("max_tokens", [64])
|
||||
@pytest.mark.parametrize("num_logprobs", [5])
|
||||
def test_models(hf_runner, vllm_runner, example_prompts, model: str,
|
||||
dtype: str, max_tokens: int, num_logprobs: int) -> None:
|
||||
# TODO(sang): Sliding window should be tested separately.
|
||||
with hf_runner(model, dtype=dtype) as hf_model:
|
||||
hf_outputs = hf_model.generate_greedy_logprobs_limit(
|
||||
example_prompts, max_tokens, num_logprobs)
|
||||
|
||||
with vllm_runner(model, dtype=dtype,
|
||||
tokenizer_mode="mistral") as vllm_model:
|
||||
vllm_outputs = vllm_model.generate_greedy_logprobs(
|
||||
example_prompts, max_tokens, num_logprobs)
|
||||
|
||||
check_logprobs_close(
|
||||
outputs_0_lst=hf_outputs,
|
||||
outputs_1_lst=vllm_outputs,
|
||||
name_0="hf",
|
||||
name_1="vllm",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
|
||||
@pytest.mark.parametrize("dtype", ["bfloat16"])
|
||||
@pytest.mark.parametrize("max_tokens", [64])
|
||||
@pytest.mark.parametrize("num_logprobs", [5])
|
||||
def test_mistral_format(vllm_runner, example_prompts, model: str, dtype: str,
|
||||
max_tokens: int, num_logprobs: int) -> None:
|
||||
with vllm_runner(
|
||||
model,
|
||||
dtype=dtype,
|
||||
tokenizer_mode="mistral",
|
||||
load_format="mistral",
|
||||
config_format="mistral",
|
||||
) as mistral_format_model:
|
||||
mistral_format_outputs = mistral_format_model.generate_greedy_logprobs(
|
||||
example_prompts, max_tokens, num_logprobs)
|
||||
|
||||
with vllm_runner(
|
||||
model,
|
||||
dtype=dtype,
|
||||
tokenizer_mode="auto",
|
||||
load_format="safetensors",
|
||||
config_format="hf",
|
||||
) as hf_format_model:
|
||||
hf_format_outputs = hf_format_model.generate_greedy_logprobs(
|
||||
example_prompts, max_tokens, num_logprobs)
|
||||
|
||||
check_logprobs_close(
|
||||
outputs_0_lst=hf_format_outputs,
|
||||
outputs_1_lst=mistral_format_outputs,
|
||||
name_0="hf",
|
||||
name_1="mistral",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
|
||||
@pytest.mark.parametrize("dtype", ["bfloat16"])
|
||||
def test_mistral_symbolic_languages(vllm_runner, model: str,
|
||||
dtype: str) -> None:
|
||||
with vllm_runner(model,
|
||||
dtype=dtype,
|
||||
max_model_len=8192,
|
||||
tokenizer_mode="mistral",
|
||||
config_format="mistral",
|
||||
load_format="mistral") as vllm_model:
|
||||
for prompt in SYMBOLIC_LANG_PROMPTS:
|
||||
msg = {"role": "user", "content": prompt}
|
||||
outputs = vllm_model.model.chat([msg],
|
||||
sampling_params=SAMPLING_PARAMS)
|
||||
assert "<EFBFBD>" not in outputs[0].outputs[0].text.strip()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
|
||||
@pytest.mark.parametrize("dtype", ["bfloat16"])
|
||||
def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
|
||||
with vllm_runner(model,
|
||||
dtype=dtype,
|
||||
tokenizer_mode="mistral",
|
||||
config_format="mistral",
|
||||
load_format="mistral") as vllm_model:
|
||||
|
||||
msgs = copy.deepcopy(MSGS)
|
||||
outputs = vllm_model.model.chat(msgs,
|
||||
tools=TOOLS,
|
||||
sampling_params=SAMPLING_PARAMS)
|
||||
|
||||
tokenizer = vllm_model.model.get_tokenizer()
|
||||
tool_parser = MistralToolParser(tokenizer)
|
||||
|
||||
model_output = outputs[0].outputs[0].text.strip()
|
||||
assert model_output.startswith(tool_parser.bot_token), model_output
|
||||
parsed_message = tool_parser.extract_tool_calls(model_output, None)
|
||||
|
||||
assert parsed_message.tools_called
|
||||
|
||||
assert MistralToolCall.is_valid_id(parsed_message.tool_calls[0].id)
|
||||
assert parsed_message.tool_calls[
|
||||
0].function.name == "get_current_weather"
|
||||
assert parsed_message.tool_calls[
|
||||
0].function.arguments == '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}' # noqa
|
||||
assert parsed_message.content is None
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", MODELS)
|
||||
@pytest.mark.parametrize("guided_backend",
|
||||
["outlines", "lm-format-enforcer", "xgrammar"])
|
||||
def test_mistral_guided_decoding(
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
vllm_runner,
|
||||
model: str,
|
||||
guided_backend: str,
|
||||
) -> None:
|
||||
with monkeypatch.context() as m:
|
||||
# Guided JSON not supported in xgrammar + V1 yet
|
||||
m.setenv("VLLM_USE_V1", "0")
|
||||
|
||||
with vllm_runner(
|
||||
model,
|
||||
dtype='bfloat16',
|
||||
tokenizer_mode="mistral",
|
||||
guided_decoding_backend=guided_backend,
|
||||
) as vllm_model:
|
||||
guided_decoding = GuidedDecodingParams(json=SAMPLE_JSON_SCHEMA)
|
||||
params = SamplingParams(max_tokens=512,
|
||||
temperature=0.7,
|
||||
guided_decoding=guided_decoding)
|
||||
|
||||
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}"
|
||||
}]
|
||||
outputs = vllm_model.model.chat(messages, sampling_params=params)
|
||||
|
||||
generated_text = outputs[0].outputs[0].text
|
||||
json_response = json.loads(generated_text)
|
||||
assert outputs is not None
|
||||
|
||||
try:
|
||||
jsonschema.validate(instance=json_response,
|
||||
schema=SAMPLE_JSON_SCHEMA)
|
||||
except jsonschema.exceptions.ValidationError:
|
||||
pytest.fail("Generated response is not valid with JSON schema")
|
||||
Reference in New Issue
Block a user