wehub-resource-sync
128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
# Copyright (c) 2024 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 struct
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import unittest
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import numpy as np
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import paddle
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import paddle.nn.quant as Q
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from paddle.base import core
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def convert_uint16_to_float(in_list):
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in_list = np.asarray(in_list)
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out = np.vectorize(
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lambda x: struct.unpack(
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'<f', struct.pack('<I', np.uint32(x) << np.uint32(16))
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)[0],
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otypes=[np.float32],
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)(in_list.flat)
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return np.reshape(out, in_list.shape)
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class ApplyPerChannelScaleTest(unittest.TestCase):
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def config(self):
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self.rows = 32
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self.cols = 128
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self.rtol = 1e-5
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self.atol = 1e-8
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self.dtype = 'float16'
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self.static = False
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def setUp(self):
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self.config()
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paddle.set_default_dtype(self.dtype)
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self.x = paddle.to_tensor(
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np.random.random(size=(self.rows, self.cols)), self.dtype
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)
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self.scales = paddle.to_tensor(
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np.random.uniform(0, 1, size=(self.cols)), self.dtype
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)
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self.out_expected = paddle.multiply(self.x, self.scales)
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def get_out_static(self):
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paddle.enable_static()
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main = paddle.static.Program()
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start = paddle.static.Program()
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with paddle.static.program_guard(main, start):
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x = paddle.static.data("x", self.x.shape, dtype=self.dtype)
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scales = paddle.static.data(
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"scales", self.scales.shape, dtype=self.dtype
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)
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x.stop_gradient = True
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scales.stop_gradient = True
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out = Q.apply_per_channel_scale(x, scales)
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feed_dict = {
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'x': self.x.numpy(),
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'scales': self.scales.numpy(),
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}
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exe = paddle.static.Executor(paddle.CUDAPlace(0))
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exe.run(start)
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(out,) = exe.run(main, feed=feed_dict, fetch_list=[out])
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paddle.disable_static()
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return out
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def test_apply_per_channel_scale(self):
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if self.static:
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self.out_real = self.get_out_static()
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else:
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paddle.disable_static()
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self.out_real = Q.apply_per_channel_scale(
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x=self.x,
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scales=self.scales,
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)
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out_expected = self.out_expected
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if self.dtype == 'bfloat16' and isinstance(
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self.out_real, paddle.Tensor
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):
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self.out_real = convert_uint16_to_float(self.out_real)
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out_expected = convert_uint16_to_float(self.out_expected)
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np.testing.assert_allclose(
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out_expected, self.out_real, rtol=self.rtol, atol=self.atol
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)
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@unittest.skipIf(
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not core.is_compiled_with_cuda()
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or paddle.device.cuda.get_device_capability()[0] < 8
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or not core.is_bfloat16_supported(core.CUDAPlace(0)),
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"quantized_matmul requires CUDA >= 11.2 and CUDA_ARCH >= 8 or core is not support bfloat16",
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)
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class ApplyPerChannelScaleTestCase1(ApplyPerChannelScaleTest):
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def config(self):
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super().config()
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self.dtype = 'bfloat16'
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class ApplyPerChannelScaleTestCase2(ApplyPerChannelScaleTest):
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def config(self):
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super().config()
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self.rows = 1024
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self.cols = 128
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class ApplyPerChannelScaleStaticTest(ApplyPerChannelScaleTest):
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def config(self):
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super().config()
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self.static = True
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if __name__ == '__main__':
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unittest.main()
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