wehub-resource-sync
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# Copyright (c) 2023 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 import _pir_ops, nn
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from paddle.autograd.ir_backward import grad
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from paddle.decomposition import decomp
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from paddle.framework import core
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paddle.enable_static()
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class SimpNet(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x, linear1_weight, linear2_weight):
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x2 = _pir_ops.matmul(x, linear1_weight, False, False)
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x3 = _pir_ops.gelu(x2, False)
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res = _pir_ops.matmul(x3, linear2_weight, False, False)
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return res
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class TestPrimMode(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [2, 1024, 1024]
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self.shape_y = [2, 1024, 1024]
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self.shape_l1_w = [2, 1024, 4096]
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self.shape_l2_w = [2, 4096, 1024]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.y = np.random.random(self.shape_y).astype("float32")
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self.l1_w = np.random.random(self.shape_l1_w).astype("float32")
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self.l2_w = np.random.random(self.shape_l2_w).astype("float32")
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def base_net(self, flag=None):
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if flag == "all":
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core._set_prim_all_enabled(True)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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net = SimpNet()
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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y = paddle.static.data('y', self.shape_y, dtype='float32')
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x.stop_gradient = False
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y.stop_gradient = False
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l1_w = paddle.static.data('l1_w', self.shape_l1_w, dtype='float32')
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l2_w = paddle.static.data('l2_w', self.shape_l2_w, dtype='float32')
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divide_out = paddle.divide(x, y)
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res = net(divide_out, l1_w, l2_w)
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[res2] = decomp.decompose(main_program, [res])
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gradients = grad(res2, (x, y))
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exe = paddle.static.Executor()
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outs = exe.run(
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feed={
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'x': self.x,
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'y': self.y,
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'l1_w': self.l1_w,
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'l2_w': self.l2_w,
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},
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fetch_list=[res2, gradients[0], gradients[1]],
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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if flag == "all":
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core._set_prim_all_enabled(False)
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assert (
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'pd_op.gelu' not in whole_ops
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and 'pd_op.divide_grad' not in whole_ops
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)
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return outs
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def test_prim_all(self):
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res_ref = self.base_net()
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res = self.base_net("all")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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if __name__ == "__main__":
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unittest.main()
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