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320 行
10 KiB
Python
320 行
10 KiB
Python
import unittest
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import torch
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from sglang.srt.speculative.eagle_utils import (
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build_tree_kernel_efficient,
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organize_draft_results,
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)
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from sglang.srt.utils import get_device
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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register_cuda_ci(est_time=6, stage="base-b", runner_config="1-gpu-small")
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register_amd_ci(est_time=3, suite="stage-b-test-1-gpu-small-amd")
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class TestBuildEagleTree(unittest.TestCase):
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"""Unit tests for build_eagle_tree functionality."""
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def test_build_tree_kernel_efficient(self):
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"""Test the build_tree_kernel_efficient function with known inputs and expected outputs."""
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bonus_tokens = torch.tensor([29974, 13], device=get_device(), dtype=torch.int32)
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score_list = [
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torch.tensor(
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[
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[[7.1127e-01, 2.8292e-01, 2.2995e-03, 1.7357e-03]],
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[[9.7476e-01, 2.2219e-02, 6.5031e-04, 1.3212e-04]],
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],
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dtype=torch.float32,
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device=get_device(),
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),
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torch.tensor(
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[
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[
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[6.9142e-01, 1.2863e-02, 1.6873e-03, 1.1871e-03],
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[2.4787e-01, 1.8818e-02, 1.4204e-02, 9.2235e-04],
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[2.2971e-03, 1.6700e-06, 1.8737e-07, 8.3146e-08],
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[1.2771e-03, 2.4374e-04, 1.7832e-04, 1.1947e-05],
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],
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[
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[8.4832e-02, 6.6068e-02, 5.8304e-02, 5.7851e-02],
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[2.3616e-03, 1.1243e-03, 5.4368e-04, 2.7768e-04],
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[2.5286e-04, 1.5578e-04, 2.8817e-05, 1.2888e-05],
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[1.2834e-04, 2.5417e-06, 1.1279e-06, 1.6088e-08],
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],
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],
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dtype=torch.float32,
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device=get_device(),
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),
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torch.tensor(
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[
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[
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[6.6438e-01, 2.6997e-02, 2.4236e-05, 4.0821e-06],
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[2.4402e-01, 2.8409e-03, 5.0935e-04, 2.9022e-04],
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[1.6178e-02, 2.0567e-03, 4.5892e-04, 3.0034e-05],
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[1.3023e-02, 5.0497e-04, 3.6371e-04, 8.7750e-05],
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],
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[
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[2.3263e-02, 2.0054e-02, 9.3990e-03, 2.7783e-03],
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[6.4156e-02, 5.5506e-04, 1.0429e-04, 9.7211e-05],
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[4.9950e-02, 5.0630e-03, 9.0068e-04, 3.3656e-04],
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[7.5817e-03, 8.5731e-04, 6.9972e-04, 6.0793e-04],
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],
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],
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dtype=torch.float32,
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device=get_device(),
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),
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torch.tensor(
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[
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[
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[6.6420e-01, 1.0525e-04, 6.5864e-05, 1.2253e-06],
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[1.3019e-01, 1.0461e-01, 5.2083e-03, 1.6777e-03],
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[2.0103e-02, 6.7335e-03, 1.2625e-04, 1.0364e-05],
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[1.5142e-02, 7.0819e-04, 9.6595e-05, 8.7951e-05],
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],
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[
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[5.8608e-02, 1.8840e-03, 7.8535e-04, 4.4400e-04],
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[1.2185e-02, 2.0684e-03, 1.7418e-03, 1.4327e-03],
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[6.2455e-03, 6.1487e-03, 2.6862e-03, 1.8034e-03],
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[1.8590e-03, 1.6151e-03, 1.2481e-03, 3.6038e-04],
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],
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],
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dtype=torch.float32,
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device=get_device(),
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),
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]
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token_list = [
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torch.tensor(
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[[29896, 29906, 29900, 29945], [13, 2, 29871, 28956]],
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dtype=torch.int64,
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device=get_device(),
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),
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torch.tensor(
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[
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[
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29889,
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29974,
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29945,
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29900,
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29974,
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29922,
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29930,
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29958,
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29889,
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29974,
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29930,
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29945,
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29974,
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29922,
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29930,
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29958,
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],
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[
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22550,
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4136,
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16492,
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8439,
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29871,
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2,
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3001,
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13,
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2,
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13,
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29906,
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29946,
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2,
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13,
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29871,
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259,
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],
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],
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device=get_device(),
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),
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torch.tensor(
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[
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[
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29946,
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29945,
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29953,
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29906,
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29896,
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29945,
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29900,
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29906,
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29896,
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29945,
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29906,
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29953,
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29896,
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29945,
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29906,
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29946,
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],
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[
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29871,
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2,
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29901,
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29889,
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29871,
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2,
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395,
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259,
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29901,
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29871,
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2,
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29889,
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3001,
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1234,
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7146,
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2186,
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],
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],
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device=get_device(),
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),
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torch.tensor(
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[
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[
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29946,
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29974,
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29945,
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29930,
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29889,
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29922,
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29974,
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29930,
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29974,
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29946,
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29930,
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29922,
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29889,
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29974,
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29945,
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29922,
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],
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[
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29941,
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29906,
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2,
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29946,
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29871,
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450,
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319,
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14990,
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29946,
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29941,
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2,
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29906,
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29871,
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2,
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3001,
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13,
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],
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],
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device=get_device(),
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),
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]
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parents_list = [
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torch.tensor(
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[[-1, 0, 1, 2, 3], [-1, 0, 1, 2, 3]],
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dtype=torch.int64,
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device=get_device(),
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),
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torch.tensor(
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[[4, 8, 9, 10], [4, 5, 6, 7]], dtype=torch.int64, device=get_device()
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),
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torch.tensor(
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[[20, 24, 21, 28], [24, 28, 20, 21]],
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dtype=torch.int64,
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device=get_device(),
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),
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torch.tensor(
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[[36, 40, 41, 44], [36, 40, 44, 45]],
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dtype=torch.int64,
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device=get_device(),
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),
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]
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seq_lens = torch.tensor([5, 10], dtype=torch.int64, device=get_device())
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topk = 4
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depth = 4
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num_draft_token = 8
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parent_list, top_scores_index, draft_tokens = organize_draft_results(
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score_list, token_list, parents_list, num_draft_token
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)
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(
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tree_mask,
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position,
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retrieve_index,
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retrieve_next_token,
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retrieve_next_sibling,
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draft_tokens,
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) = build_tree_kernel_efficient(
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bonus_tokens=bonus_tokens,
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parent_list=parent_list,
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top_scores_index=top_scores_index,
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draft_tokens=draft_tokens,
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seq_lens=seq_lens,
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seq_lens_sum=torch.sum(seq_lens).item(),
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topk=topk,
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spec_steps=depth,
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num_verify_tokens=num_draft_token,
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)
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# Verify expected outputs
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self.assertEqual(
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position.tolist(),
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[5, 6, 6, 7, 7, 8, 8, 9, 10, 11, 12, 12, 12, 12, 13, 14],
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"Position tensor does not match expected values",
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)
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self.assertEqual(
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retrieve_index.tolist(),
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[
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[0, 1, 2, 3, 4, 5, 6, 7],
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[8, 9, 10, 11, 12, 13, 14, 15],
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],
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"Retrieve index tensor does not match expected values",
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)
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self.assertEqual(
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retrieve_next_token.tolist(),
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[
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[1, 3, 4, 5, 6, 7, -1, -1],
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[1, 2, -1, 6, -1, -1, 7, -1],
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],
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"Retrieve next token tensor does not match expected values",
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)
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self.assertEqual(
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retrieve_next_sibling.tolist(),
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[
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[-1, 2, -1, -1, -1, -1, -1, -1],
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[-1, -1, 3, 4, 5, -1, -1, -1],
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],
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"Retrieve next sibling tensor does not match expected values",
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)
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self.assertEqual(
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draft_tokens.tolist(),
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[
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29974,
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29896,
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29906,
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29889,
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29974,
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29946,
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29896,
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29946,
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13,
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13,
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22550,
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4136,
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16492,
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8439,
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29871,
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29941,
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],
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"Draft tokens tensor does not match expected values",
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)
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if __name__ == "__main__":
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unittest.main()
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