from dataclasses import dataclass, field from vllm import SamplingParams from tests.e2e.conftest import VllmRunner from tests.e2e.model_utils import check_outputs_equal PROMPTS_SHORT = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] # NOTE: Randomly fill the prompt with the requested amount for # the specified capture shape to prevent accuracy issues caused by padding PROMPTS_LONG = [ ( "Solve the following math problem step by step." "The last line of your response should be of the form Answer: " "$Answer (without quotes) where $Answer is the answer to the problem.\n\n" "In triangle $ABC$, $\\sin \\angle A = \\frac{4}{5}$ and $\\angle A < 90^\\circ$. Let $D$" "be a point outside triangle $ABC$ such that $\\angle BAD = \\angle DAC$," "$\\angle BDC = 90^\\circ$. Suppose $AD = 1$ and $\\frac{BD}{CD} = \\frac{3}{2}$." "If $AB + AC$ can be expressed in the form $\\frac{a\\sqrt{b}}{c}$," "where $a, b, c$ are pairwise relatively prime integers, find $a + b + c$." ), ( "Solve the following math problem step by step." "The last line of your response should be of the form Answer: " "$Answer (without quotes) where $Answer is the answer to the problem.\n\n" "Let $ABCD$ be a unit square in the plane. Points $X$ and $Y$ are chosen" "independently and uniformly at random on the perimeter of $ABCD$." "If the expected value of the area of triangle $\\triangle AXY$" "can be expressed as $\\frac{m}{n}$, for relatively prime positive" "integers $m$ and $n$, compute $m+n$." ), ( "Solve the following math problem step by step." "The last line of your response should be of the form Answer: " "$Answer (without quotes) where $Answer is the answer to the problem.\n\n" "Let $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$" "and $x^2 + bx + c = 0$ have a common real root, and the equations $x^2 + x + a = 0$" "and $x^2 + cx + b = 0$ also have a common real root." "Compute the sum $a + b + c$." ), ] @dataclass(frozen=True) class LLMTestCase: model: str prompts: list[str] golden_answers: list[str] quantization: str | None = None sampling_params: SamplingParams = field( default_factory=lambda: SamplingParams( max_tokens=32, temperature=0.0, top_p=1.0, top_k=0, n=1, ) ) def gen_and_valid(runner_kwargs: dict, prompts: list[str], sampling_params: SamplingParams, golden_answers: list[str]): with VllmRunner(**runner_kwargs) as runner: vllm_aclgraph_outputs = runner.model.generate(prompts=prompts, sampling_params=sampling_params) outputs_gen = [] for output in vllm_aclgraph_outputs: outputs_gen.append(([output.outputs[0].index], output.outputs[0].text)) output_origin = [([0], answer) for answer in golden_answers] check_outputs_equal( outputs_0_lst=output_origin, outputs_1_lst=outputs_gen, name_0="output_origin", name_1="outputs_gen", )