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61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
# Copyright (c) 2026 LightSeek Foundation
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from __future__ import annotations
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import pytest
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import torch
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from tokenspeed_kernel import hadamard_transform
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torch.manual_seed(42)
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@pytest.mark.parametrize("solution", ["triton", "fast_hadamard_transform"])
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def test_hadamard_transform(device: str, solution: str, require) -> None:
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dtype = torch.bfloat16
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require("transform", "hadamard_transform", solution, dtype, "x")
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x = torch.randn((3, 5, 128), device=device, dtype=dtype)
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scale = 128**-0.5
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out = hadamard_transform(x, scale=scale, solution=solution)
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expected = x.float().reshape(-1, x.shape[-1])
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h = 1
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while h < expected.shape[-1]:
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expected_3d = expected.reshape(
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expected.shape[0], expected.shape[1] // (2 * h), 2 * h
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)
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left = expected_3d[..., :h]
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right = expected_3d[..., h : 2 * h]
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combined = torch.cat((left + right, left - right), dim=-1)
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expected = combined.reshape_as(expected)
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h *= 2
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expected = expected.reshape_as(x) * scale
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assert out.shape == x.shape
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assert out.dtype == x.dtype
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torch.testing.assert_close(
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out.float(),
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expected.to(dtype).float(),
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atol=2.0e-2,
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rtol=2.0e-2,
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)
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