157 lines
5.0 KiB
Python
157 lines
5.0 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Usage:
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To test a specific model:
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1. Add it to ALL_OTHER_MODELS
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2. Run `ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerationModels.test_others`
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"""
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import dataclasses
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import multiprocessing as mp
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import os
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import unittest
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from typing import List
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import torch
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from sglang.test.runners import (
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DEFAULT_PROMPTS,
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HFRunner,
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SRTRunner,
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check_close_model_outputs,
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)
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from sglang.test.test_utils import CustomTestCase, is_in_ci
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@dataclasses.dataclass
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class ModelCase:
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model_path: str
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tp_size: int = 1
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prefill_tolerance: float = 5e-2
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decode_tolerance: float = 5e-2
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rouge_l_tolerance: float = 1
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skip_long_prompt: bool = False
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trust_remote_code: bool = False
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# Popular models that run on the CI
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CI_MODELS = [
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ModelCase("meta-llama/Llama-3.1-8B-Instruct"),
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ModelCase("google/gemma-2-2b"),
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]
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# All other models that do not run on the CI
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ALL_OTHER_MODELS = [
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ModelCase("Qwen/Qwen2-1.5B"),
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ModelCase("Qwen/Qwen2.5-14B-Instruct"),
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ModelCase("HuggingFaceTB/SmolLM-135M-Instruct", skip_long_prompt=True),
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ModelCase("allenai/OLMo-1B-0724-hf", decode_tolerance=8e-2, skip_long_prompt=True),
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ModelCase(
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"THUDM/glm-4-9b-chat", tp_size=2, trust_remote_code=True, skip_long_prompt=True
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),
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ModelCase("openai-community/gpt2"),
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ModelCase("microsoft/Phi-3-small-8k-instruct"),
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ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
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ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),
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]
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TORCH_DTYPES = [torch.float16]
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class TestGenerationModels(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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mp.set_start_method("spawn", force=True)
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def assert_close_logits_and_output_strs(
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self,
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prompts: List[str],
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model_case: ModelCase,
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torch_dtype: torch.dtype,
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) -> None:
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model_path = model_case.model_path
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prefill_tolerance, decode_tolerance, rouge_l_tolerance = (
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model_case.prefill_tolerance,
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model_case.decode_tolerance,
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model_case.rouge_l_tolerance,
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)
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max_new_tokens = 32
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with HFRunner(
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model_path,
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torch_dtype=torch_dtype,
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model_type="generation",
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trust_remote_code=model_case.trust_remote_code,
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) as hf_runner:
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hf_outputs = hf_runner.forward(prompts, max_new_tokens=max_new_tokens)
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with SRTRunner(
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model_path,
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tp_size=model_case.tp_size,
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torch_dtype=torch_dtype,
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model_type="generation",
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trust_remote_code=model_case.trust_remote_code,
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) as srt_runner:
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srt_outputs = srt_runner.forward(prompts, max_new_tokens=max_new_tokens)
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check_close_model_outputs(
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hf_outputs=hf_outputs,
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srt_outputs=srt_outputs,
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prefill_tolerance=model_case.prefill_tolerance,
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decode_tolerance=model_case.decode_tolerance,
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rouge_l_tolerance=model_case.rouge_l_tolerance,
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debug_text=f"model_path={model_path} prompts={prompts}",
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)
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def test_ci_models(self):
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for model_case in CI_MODELS:
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for torch_dtype in TORCH_DTYPES:
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# Skip long prompts for models that do not have a long context
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prompts = DEFAULT_PROMPTS
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if model_case.skip_long_prompt:
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prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
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# Assert the logits and output strs are close
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self.assert_close_logits_and_output_strs(
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prompts, model_case, torch_dtype
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)
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def test_others(self):
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if is_in_ci():
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return
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for model_case in ALL_OTHER_MODELS:
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# Only run a specified model
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if (
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"ONLY_RUN" in os.environ
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and os.environ["ONLY_RUN"] != model_case.model_path
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):
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continue
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# Skip long prompts for models that do not have a long context
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prompts = DEFAULT_PROMPTS
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if model_case.skip_long_prompt:
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prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
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# Assert the logits and output strs are close
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self.assert_close_logits_and_output_strs(prompts, model_case, torch.float16)
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if __name__ == "__main__":
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unittest.main()
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