wehub-resource-sync
114 lines
3.1 KiB
Python
114 lines
3.1 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 os
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import sys
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import unittest
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from os.path import dirname
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import numpy as np
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os.environ['FLAGS_prim_all'] = 'true'
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os.environ['FLAGS_prim_enable_dynamic'] = 'true'
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os.environ['FLAGS_use_cinn'] = '1'
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os.environ['FLAGS_deny_cinn_ops'] = 'slice;'
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import paddle
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from paddle import nn
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sys.path.append(dirname(dirname(__file__)))
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import utils
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class BroadcastSubgraph(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(
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self, x, in_1, in_2, in_3, in_5
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): # [512, 512] [512, 1], [512, 512], [512, 1] , [512, 512]
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t0 = paddle.transpose(x, [1, 0])
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t2 = in_1 * in_1
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t3 = paddle.expand(t2, [512, 512])
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t4 = in_2 / t3
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t5 = t4 * -1
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t6 = t5 * t0
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t7 = t6.sum([1], keepdim=False)
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t8 = t7.reshape([512, 1])
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t10 = 1 / in_1
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t12 = t10 * t0
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t14 = t12 * in_2
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t16 = 1 / in_3
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t17 = t16 * t8
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t18 = t17 * in_5
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t19 = t17 * in_5
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t21 = t14 + t18
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t22 = t21 + t19
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return t22
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class TestIfSubgraph(unittest.TestCase):
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def setUp(self):
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paddle.seed(2024)
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self.prepare_data()
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def prepare_data(self):
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# self.shape = [22, 64, 56]
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self.x = paddle.randn([512, 512], dtype="float32")
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self.x.stop_gradient = False
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self.in_1 = paddle.randn([512, 1], dtype="float32")
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self.in_1.stop_gradient = False
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self.in_2 = paddle.randn([512, 512], dtype="float32")
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self.in_2.stop_gradient = False
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self.in_3 = paddle.randn([512, 1], dtype="float32")
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self.in_3.stop_gradient = False
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self.in_5 = paddle.randn([512, 512], dtype="float32")
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self.in_5.stop_gradient = False
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def check_jit_kernel_info(self, static_fn):
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utils.check_jit_kernel_number(static_fn, 2)
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utils.check_jit_kernel_structure(
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static_fn,
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{
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'if_0': {utils.JIT_KERNEL_NAME: 1},
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'else_0': {},
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utils.JIT_KERNEL_NAME: 1,
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},
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)
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def eval(self, use_cinn):
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net = BroadcastSubgraph()
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net = utils.apply_to_static(net, use_cinn)
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net.eval()
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out = net(self.x, self.in_1, self.in_2, self.in_3, self.in_5)
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return out
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def test_eval(self):
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dy_out = self.eval(use_cinn=False)
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cinn_out = self.eval(use_cinn=True)
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np.testing.assert_allclose(
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cinn_out.numpy(), dy_out.numpy(), atol=1e-2, rtol=1e-2
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)
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if __name__ == '__main__':
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unittest.main()
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