chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,167 @@
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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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import unittest
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import numpy as np
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import paddle
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from paddle.nn.quant import weight_quantize
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paddle.seed(3)
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np.random.seed(3)
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# fmt: off
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# 预先计算得到的权重矩阵，作为ref用于测试
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ref_out = [[-103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103],
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[-86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86],
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[-69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69],
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[-52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52],
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[-35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35],
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[-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],
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[34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34],
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[51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51],
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[68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68],
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[85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85],
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[102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102],
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[119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119],
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[-103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103],
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[-86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86],
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[-69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69],
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[-52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52],
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[-35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35],
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[-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],
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[34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34],
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[51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51],
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[68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68],
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[85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85],
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[102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102],
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[119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119],
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[-103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103],
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[-86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86],
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[-69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69],
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[-52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52],
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[-35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35],
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[-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],
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[34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34],
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[51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51],
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[68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68],
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[85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85],
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[102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102],
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[119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119],
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[-103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103],
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[-86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86],
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[-69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69],
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[-52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52],
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[-35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35, -35],
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[-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],
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[34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34],
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[51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51],
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[68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68],
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[85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85],
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[102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102],
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[119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119],
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[-103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103, -103],
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[-86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86, -86],
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[-69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69, -69],
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[-52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52, -52]]
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# fmt: off
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np.set_printoptions(threshold=100000000)
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paddle.set_printoptions(threshold=100000000)
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def arrange_cols(rows, cols):
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weight = []
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for i in range(rows):
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for j in range(cols):
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weight.append((j % 15) - 7)
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# pack them
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res = np.array(weight).reshape([rows, cols])
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res0 = res[0::2,:] & 0x0F
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res1 = (res[1::2,:] & 0x0F) << 4
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res = res0 | res1
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return res
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class WeightQuantizeW4a8TestCase(unittest.TestCase):
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def setUp(self):
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self.rows = 64
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self.cols = 64
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self.weight = arrange_cols(self.rows, self.cols)
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self.weight = paddle.to_tensor(self.weight).astype('int8')
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def run_test(self):
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out = weight_quantize(self.weight, algo="w4a8")[0]
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out = out.reshape([-1])
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baseline = np.array(ref_out).reshape([-1])
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np.allclose(baseline, out.astype("int32").numpy(), atol=1e-2)
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class WeightQuantizeW4a8ZeroSizeTensorTestCase(unittest.TestCase):
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def setUp(self):
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self.x_shape = [1, 0, 10]
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self.dtype = "float32"
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def _test_dygraph(self):
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paddle.disable_static()
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x = paddle.rand(shape= self.x_shape, dtype=paddle.float16)
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x.stop_gradient=False
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out, scale = weight_quantize(x, algo='weight_only_int8')
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loss = out.sum()
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loss.backward()
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np.assert_allclose(x.grad.shape,x.shape)
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def run_test(self):
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self.setUp()
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self._test_dygraph()
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class WeightQuantizeW4afp8TestCase(unittest.TestCase):
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def setUp(self):
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self.rows = 128
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self.cols = 128
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weight = np.random.randint(-7, 7, size=[self.rows, self.cols], dtype='int8') # shape: [K, N]
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self.weight_trans = weight.transpose() + 7
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weight1 = weight[0::2, :] & 0x0F
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weight2 = (weight[1::2, :] & 0x0F) << 4
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weight_packed = weight1 | weight2
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self.weight_packed = paddle.to_tensor(weight_packed)
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def test(self):
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out = weight_quantize(self.weight_packed, algo="w4afp8")[0] # shape: [N, K/2]
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out_np = np.array(out.reshape([-1, 32]))
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out_np_1 = (out_np >> 4) & 0x0F
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out_np_2 = out_np & 0x0F
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result = np.zeros((out_np_1.shape[0], out_np_1.shape[1]*2), dtype=out_np.dtype)
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result[:, 1::2] = out_np_1
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result[:, 0::2] = out_np_2
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# ref out
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tmp = self.weight_trans.reshape([-1, 64])
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tmp1 = tmp[:, 0:32] & 0x0F
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tmp2 = (tmp[:, 32:64] & 0x0F) << 4
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ref_out = tmp1 | tmp2
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ref_out = ref_out.reshape([-1, self.rows])
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np.allclose(ref_out.astype("int32"), out.astype("int32").numpy(), atol=1e-2)
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if __name__ == '__main__':
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unittest.main()
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