test_generation_models.py

"""
Usage:

To test a specific model:
1. Add it to ALL_OTHER_MODELS
2. Run `ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerationModels.test_others`
"""

"""
Copyright 2023-2024 SGLang Team
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import dataclasses
import multiprocessing as mp
import os
import unittest
from typing import List

import torch

from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
from sglang.test.test_utils import calculate_rouge_l, is_in_ci


@dataclasses.dataclass
class ModelCase:
    model_path: str
    tp_size: int = 1
    prefill_tolerance: float = 5e-2
    decode_tolerance: float = 5e-2
    rouge_l_tolerance: float = 1
    skip_long_prompt: bool = False


# Popular models that run on the CI
CI_MODELS = [
    ModelCase("meta-llama/Llama-3.1-8B-Instruct"),
    ModelCase("google/gemma-2-2b"),
]

# All other models that do not run on the CI
ALL_OTHER_MODELS = [
    ModelCase("Qwen/Qwen2-1.5B"),
    ModelCase("Qwen/Qwen2.5-14B-Instruct"),
    ModelCase("HuggingFaceTB/SmolLM-135M-Instruct", skip_long_prompt=True),
    ModelCase("allenai/OLMo-1B-0724-hf", decode_tolerance=8e-2, skip_long_prompt=True),
    ModelCase("THUDM/glm-4-9b-chat"),
]

TORCH_DTYPES = [torch.float16]


class TestGenerationModels(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        mp.set_start_method("spawn")

    def assert_close_logits_and_output_strs(
        self,
        prompts: List[str],
        model_case: ModelCase,
        torch_dtype: torch.dtype,
    ) -> None:
        model_path = model_case.model_path
        prefill_tolerance, decode_tolerance, rouge_l_tolerance = (
            model_case.prefill_tolerance,
            model_case.decode_tolerance,
            model_case.rouge_l_tolerance,
        )
        max_new_tokens = 32

        with HFRunner(
            model_path,
            torch_dtype=torch_dtype,
            model_type="generation",
        ) as hf_runner:
            hf_outputs = hf_runner.forward(prompts, max_new_tokens=max_new_tokens)

        with SRTRunner(
            model_path,
            tp_size=model_case.tp_size,
            torch_dtype=torch_dtype,
            model_type="generation",
        ) as srt_runner:
            srt_outputs = srt_runner.forward(prompts, max_new_tokens=max_new_tokens)

        for i in range(len(prompts)):
            # Compare input logprobs
            hf_logprobs = torch.Tensor(hf_outputs.top_input_logprobs[i])
            srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
            input_len = hf_logprobs.shape[0]
            print(
                "prefill logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
            )
            if input_len <= 100:
                assert torch.all(abs(hf_logprobs - srt_logprobs) < prefill_tolerance), (
                    f"prefill logprobs are not all close with model_path={model_path} prompts={prompts} "
                    f"prefill_tolerance={prefill_tolerance}."
                    f"{hf_logprobs=}, {srt_logprobs=}"
                )

            # Compare output logprobs
            hf_logprobs = torch.Tensor(hf_outputs.top_output_logprobs[i])
            srt_logprobs = torch.Tensor(srt_outputs.top_output_logprobs[i])

            print(
                "decode logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
            )
            if input_len <= 100:
                assert torch.all(abs(hf_logprobs - srt_logprobs) < decode_tolerance), (
                    f"decode logprobs are not all close with model_path={model_path} prompts={prompts} "
                    f"decode_tolerance={decode_tolerance}."
                    f"{hf_logprobs=}, {srt_logprobs=}"
                )

        # Compare output strings
        print(f"{hf_outputs.output_strs=}")
        print(f"{srt_outputs.output_strs=}")
        rouge_l_scores = calculate_rouge_l(
            hf_outputs.output_strs, srt_outputs.output_strs
        )
        print(f"{rouge_l_scores=}")
        assert all(
            score >= rouge_l_tolerance for score in rouge_l_scores
        ), f"Not all ROUGE-L scores are greater than rouge_l_tolerance={rouge_l_tolerance}"

    def test_ci_models(self):
        for model_case in CI_MODELS:
            for torch_dtype in TORCH_DTYPES:

                # Skip long prompts for models that do not have a long context
                prompts = DEFAULT_PROMPTS
                if model_case.skip_long_prompt:
                    prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]

                # Assert the logits and output strs are close
                self.assert_close_logits_and_output_strs(
                    prompts, model_case, torch_dtype
                )

    def test_others(self):
        if is_in_ci():
            return

        for model_case in ALL_OTHER_MODELS:
            # Only run a specified model
            if (
                "ONLY_RUN" in os.environ
                and os.environ["ONLY_RUN"] != model_case.model_path
            ):
                continue

            # Skip long prompts for models that do not have a long context
            prompts = DEFAULT_PROMPTS
            if model_case.skip_long_prompt:
                prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]

            # Assert the logits and output strs are close
            self.assert_close_logits_and_output_strs(prompts, model_case, torch.float16)


if __name__ == "__main__":
    unittest.main()