Support unifying axis ordering in dump comparator (#19456)
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import sys
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from typing import Optional
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import pytest
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import torch
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from sglang.srt.debug_utils.comparator.aligner.axis_swapper import (
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AxisSwapperPlan,
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compute_axis_swapper_plan,
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execute_axis_swapper_plan,
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)
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from sglang.srt.debug_utils.comparator.utils import Pair
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from sglang.srt.debug_utils.comparator.warning_sink import warning_sink
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from sglang.test.ci.ci_register import register_cpu_ci
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register_cpu_ci(est_time=15, suite="default", nightly=True)
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class TestComputeAxisSwapperPlan:
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def test_no_dims_returns_none(self) -> None:
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assert compute_axis_swapper_plan(Pair(x=None, y=None)) is None
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assert compute_axis_swapper_plan(Pair(x="t h d", y=None)) is None
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assert compute_axis_swapper_plan(Pair(x=None, y="t h d")) is None
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def test_same_order_returns_none(self) -> None:
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result: Optional[AxisSwapperPlan] = compute_axis_swapper_plan(
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Pair(x="t h d", y="t h d")
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)
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assert result is None
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def test_different_order(self) -> None:
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result: Optional[AxisSwapperPlan] = compute_axis_swapper_plan(
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Pair(x="t h d", y="t d h")
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)
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assert result is not None
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assert result.pattern == "t h d -> t d h"
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def test_name_mismatch_returns_none_with_warning(self) -> None:
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with warning_sink.context() as warnings:
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result: Optional[AxisSwapperPlan] = compute_axis_swapper_plan(
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Pair(x="t h d", y="t h e")
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)
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assert result is None
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assert len(warnings) == 1
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assert warnings[0].category == "axis_swapper_dim_mismatch"
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assert "dim name sets differ" in warnings[0].message
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def test_modifiers_ignored_for_name_extraction(self) -> None:
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result: Optional[AxisSwapperPlan] = compute_axis_swapper_plan(
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Pair(x="t h(tp) d", y="t d h(tp)")
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)
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assert result is not None
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assert result.pattern == "t h d -> t d h"
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class TestExecuteAxisSwapperPlan:
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def test_rearrange(self) -> None:
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torch.manual_seed(42)
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tensor: torch.Tensor = torch.randn(4, 8, 16)
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plan = AxisSwapperPlan(pattern="t h d -> t d h")
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result: torch.Tensor = execute_axis_swapper_plan(tensor=tensor, plan=plan)
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assert result.shape == (4, 16, 8)
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for i in range(4):
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assert torch.equal(
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result[i],
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tensor[i].T,
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)
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if __name__ == "__main__":
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sys.exit(pytest.main([__file__]))
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