wehub-resource-sync
491 lines
15 KiB
Python
491 lines
15 KiB
Python
# Copyright (c) 2021 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 sys
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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place, skip_check_grad_ci
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from utils import dygraph_guard
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import paddle
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from paddle import base
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from paddle.base import core
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def compiled_with_linux_and_cuda():
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return False
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# cast output to complex for numpy.linalg.eig
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def cast_to_complex(input, output):
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if input.dtype == np.float32:
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output = output.astype(np.complex64)
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elif input.dtype == np.float64:
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output = output.astype(np.complex128)
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return output
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# define eig backward function for a single square matrix
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def eig_backward(w, v, grad_w, grad_v):
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v_tran = np.transpose(v)
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v_tran = np.conjugate(v_tran)
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w_conj = np.conjugate(w)
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w_conj_l = w_conj.reshape(1, w.size)
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w_conj_r = w_conj.reshape(w.size, 1)
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w_conj_2d = w_conj_l - w_conj_r
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vhgv = np.matmul(v_tran, grad_v)
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real_vhgv = np.real(vhgv)
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diag_real = real_vhgv.diagonal()
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diag_2d = diag_real.reshape(1, w.size)
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rhs = v * diag_2d
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mid = np.matmul(v_tran, rhs)
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result = vhgv - mid
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res = np.divide(result, w_conj_2d)
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row, col = np.diag_indices_from(res)
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res[row, col] = 1.0
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tmp = np.matmul(res, v_tran)
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dx = np.linalg.solve(v_tran, tmp)
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return dx
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class TestEigOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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if not compiled_with_linux_and_cuda():
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paddle.device.set_device("cpu")
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self.op_type = "eig"
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self.python_api = paddle.linalg.eig
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self.__class__.op_type = self.op_type
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self.init_input()
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self.inputs = {'X': OpTest.np_dtype_to_base_dtype(self.x)}
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self.outputs = {'Eigenvalues': self.out[0], 'Eigenvectors': self.out[1]}
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def init_input(self):
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self.set_dtype()
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self.set_dims()
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self.x = np.random.random(self.shape).astype(self.dtype)
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self.out = np.linalg.eig(self.x)
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self.out = (
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cast_to_complex(self.x, self.out[0]),
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cast_to_complex(self.x, self.out[1]),
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)
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# for the real input, a customized checker is needed
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def checker(self, outs):
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actual_out_w = outs[0].flatten()
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expect_out_w = self.out[0].flatten()
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actual_out_v = outs[1].flatten()
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expect_out_v = self.out[1].flatten()
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length_w = len(expect_out_w)
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act_w_real = np.sort(
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np.array([np.abs(actual_out_w[i].real) for i in range(length_w)])
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)
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act_w_imag = np.sort(
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np.array([np.abs(actual_out_w[i].imag) for i in range(length_w)])
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)
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exp_w_real = np.sort(
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np.array([np.abs(expect_out_w[i].real) for i in range(length_w)])
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)
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exp_w_imag = np.sort(
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np.array([np.abs(expect_out_w[i].imag) for i in range(length_w)])
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)
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for i in range(length_w):
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np.testing.assert_allclose(
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act_w_real[i],
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exp_w_real[i],
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rtol=1e-06,
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atol=1e-05,
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err_msg='The eigenvalues real part have diff: \nExpected '
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+ str(act_w_real[i])
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+ '\n'
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+ 'But got: '
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+ str(exp_w_real[i]),
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)
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np.testing.assert_allclose(
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act_w_imag[i],
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exp_w_imag[i],
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rtol=1e-06,
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atol=1e-05,
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err_msg='The eigenvalues image part have diff: \nExpected '
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+ str(act_w_imag[i])
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+ '\n'
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+ 'But got: '
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+ str(exp_w_imag[i]),
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)
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length_v = len(expect_out_v)
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act_v_real = np.sort(
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np.array([np.abs(actual_out_v[i].real) for i in range(length_v)])
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)
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act_v_imag = np.sort(
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np.array([np.abs(actual_out_v[i].imag) for i in range(length_v)])
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)
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exp_v_real = np.sort(
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np.array([np.abs(expect_out_v[i].real) for i in range(length_v)])
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)
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exp_v_imag = np.sort(
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np.array([np.abs(expect_out_v[i].imag) for i in range(length_v)])
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)
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for i in range(length_v):
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np.testing.assert_allclose(
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act_v_real[i],
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exp_v_real[i],
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rtol=1e-06,
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atol=1e-05,
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err_msg='The eigenvectors real part have diff: \nExpected '
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+ str(act_v_real[i])
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+ '\n'
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+ 'But got: '
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+ str(exp_v_real[i]),
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)
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np.testing.assert_allclose(
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act_v_imag[i],
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exp_v_imag[i],
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rtol=1e-06,
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atol=1e-05,
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err_msg='The eigenvectors image part have diff: \nExpected '
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+ str(act_v_imag[i])
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+ '\n'
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+ 'But got: '
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+ str(exp_v_imag[i]),
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)
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def set_dtype(self):
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self.dtype = np.complex64
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def set_dims(self):
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self.shape = (10, 10)
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def init_grad(self):
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# grad_w, grad_v complex dtype
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gtype = self.dtype
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if self.dtype == np.float32:
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gtype = np.complex64
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elif self.dtype == np.float64:
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gtype = np.complex128
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self.grad_w = np.ones(self.out[0].shape, gtype)
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self.grad_v = np.ones(self.out[1].shape, gtype)
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self.grad_x = eig_backward(
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self.out[0], self.out[1], self.grad_w, self.grad_v
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)
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def test_check_output(self):
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self.check_output_with_place_customized(
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checker=self.checker,
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place=get_device_place()
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if compiled_with_linux_and_cuda()
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else core.CPUPlace(),
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check_pir=True,
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)
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@unittest.skip("skip this UT as it may cause CI to hang temporarily")
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def test_check_grad(self):
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self.init_grad()
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self.check_grad(
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['X'],
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['Eigenvalues', 'Eigenvectors'],
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_w, self.grad_v],
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check_pir=True,
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)
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class TestComplex128(TestEigOp):
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def set_dtype(self):
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self.dtype = np.complex128
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@skip_check_grad_ci(
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reason="For float dtype, numpy.linalg.eig forward outputs real or complex when input is real, therefore the grad computation may be not the same with paddle.linalg.eig"
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)
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class TestDouble(TestEigOp):
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def set_dtype(self):
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self.dtype = np.float64
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def test_check_grad(self):
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pass
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@skip_check_grad_ci(
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reason="For float dtype, numpy.linalg.eig forward outputs real or complex when input is real, therefore the grad computation may be not the same with paddle.linalg.eig"
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)
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class TestEigBatchMatrices(TestEigOp):
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def set_dtype(self):
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self.dtype = np.float64
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def set_dims(self):
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self.shape = (3, 10, 10)
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def test_check_grad(self):
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pass
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@skip_check_grad_ci(
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reason="For float dtype, numpy.linalg.eig forward outputs real or complex when input is real, therefore the grad computation may be not the same with paddle.linalg.eig"
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)
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class TestFloat(TestEigOp):
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def set_dtype(self):
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self.dtype = np.float32
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def test_check_grad(self):
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pass
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class TestEigStatic(TestEigOp):
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@unittest.skipIf(sys.platform == "darwin", reason="Skip on Mac")
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def test_check_output_with_place(self):
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paddle.enable_static()
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place = (
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get_device_place()
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if compiled_with_linux_and_cuda()
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else core.CPUPlace()
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)
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input_np = np.random.random([3, 3]).astype('complex')
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expect_val, expect_vec = np.linalg.eig(input_np)
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with base.program_guard(base.Program(), base.Program()):
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input = paddle.static.data(
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name="input", shape=[3, 3], dtype='complex'
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)
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act_val, act_vec = paddle.linalg.eig(input)
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exe = base.Executor(place)
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fetch_val, fetch_vec = exe.run(
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base.default_main_program(),
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feed={"input": input_np},
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fetch_list=[act_val, act_vec],
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)
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np.testing.assert_allclose(
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expect_val,
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fetch_val,
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rtol=1e-06,
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atol=1e-06,
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err_msg='The eigen values have diff: \nExpected '
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+ str(expect_val)
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+ '\n'
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+ 'But got: '
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+ str(fetch_val),
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)
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np.testing.assert_allclose(
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np.abs(expect_vec),
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np.abs(fetch_vec),
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rtol=1e-06,
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atol=1e-06,
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err_msg='The eigen vectors have diff: \nExpected '
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+ str(np.abs(expect_vec))
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+ '\n'
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+ 'But got: '
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+ str(np.abs(fetch_vec)),
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)
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class TestEigDyGraph(unittest.TestCase):
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def test_check_output_with_place(self):
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np.random.seed(1024)
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input_np = np.random.random([3, 3]).astype('complex')
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expect_val, expect_vec = np.linalg.eig(input_np)
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if not compiled_with_linux_and_cuda():
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paddle.set_device("cpu")
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paddle.disable_static()
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input_tensor = paddle.to_tensor(input_np)
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fetch_val, fetch_vec = paddle.linalg.eig(input_tensor)
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np.testing.assert_allclose(
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expect_val,
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fetch_val.numpy(),
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rtol=1e-06,
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atol=1e-06,
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err_msg='The eigen values have diff: \nExpected '
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+ str(expect_val)
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+ '\n'
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+ 'But got: '
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+ str(fetch_val),
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)
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np.testing.assert_allclose(
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np.abs(expect_vec),
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np.abs(fetch_vec.numpy()),
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rtol=1e-06,
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atol=1e-06,
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err_msg='The eigen vectors have diff: \nExpected '
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+ str(np.abs(expect_vec))
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+ '\n'
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+ 'But got: '
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+ str(np.abs(fetch_vec.numpy())),
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)
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def test_check_grad(self):
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test_shape = [3, 3]
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test_type = 'float64'
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if not compiled_with_linux_and_cuda():
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paddle.set_device("cpu")
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np.random.seed(1024)
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input_np = np.random.random(test_shape).astype(test_type)
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real_w, real_v = np.linalg.eig(input_np)
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grad_w = np.ones(real_w.shape, test_type)
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grad_v = np.ones(real_v.shape, test_type)
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grad_x = eig_backward(real_w, real_v, grad_w, grad_v)
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with base.dygraph.guard():
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x = paddle.to_tensor(input_np)
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x.stop_gradient = False
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w, v = paddle.linalg.eig(x)
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(w.sum() + v.sum()).backward()
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np.testing.assert_allclose(
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np.abs(x.grad.numpy()),
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np.abs(grad_x),
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rtol=1e-05,
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atol=1e-05,
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err_msg='The grad x have diff: \nExpected '
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+ str(np.abs(grad_x))
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+ '\n'
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+ 'But got: '
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+ str(np.abs(x.grad.numpy())),
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)
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def test_check_grad_none_dw_or_dv(self):
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test_shape = [3, 3]
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test_type = 'float64'
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if not compiled_with_linux_and_cuda():
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paddle.set_device("cpu")
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np.random.seed(1024)
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input_np = np.random.random(test_shape).astype(test_type)
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real_w, real_v = np.linalg.eig(input_np)
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grad_w = np.ones(real_w.shape, test_type)
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grad_v = np.ones(real_v.shape, test_type)
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grad_x = eig_backward(real_w, real_v, grad_w, grad_v)
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with base.dygraph.guard():
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x = paddle.to_tensor(input_np, stop_gradient=False)
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w, v = paddle.linalg.eig(x)
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(dw_dx,) = paddle.grad(w, x, retain_graph=True)
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(dv_dx,) = paddle.grad(v, x, retain_graph=True)
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(dwv_dx,) = paddle.grad([w, v], x)
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np.testing.assert_allclose(
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dwv_dx.numpy(),
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grad_x,
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rtol=1e-05,
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atol=1e-05,
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err_msg='The grad x have diff: \nExpected '
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+ str(np.abs(grad_x))
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+ '\n'
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+ 'But got: '
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+ str(np.abs(dwv_dx.numpy())),
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)
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np.testing.assert_allclose(
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(dw_dx + dv_dx).numpy(),
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dwv_dx.numpy(),
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rtol=1e-05,
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atol=1e-05,
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err_msg='The grad x have diff: \nExpected '
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+ str(np.abs(grad_x))
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+ '\n'
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+ 'But got: '
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+ str(np.abs((dw_dx + dv_dx).numpy())),
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)
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class TestEigWrongDimsError(unittest.TestCase):
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def test_error(self):
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if not compiled_with_linux_and_cuda():
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paddle.device.set_device("cpu")
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paddle.disable_static()
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a = np.random.random(3).astype('float32')
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x = paddle.to_tensor(a)
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self.assertRaises(ValueError, paddle.linalg.eig, x)
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class TestEigNotSquareError(unittest.TestCase):
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def test_error(self):
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if not compiled_with_linux_and_cuda():
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paddle.device.set_device("cpu")
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paddle.disable_static()
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a = np.random.random((1, 2, 3)).astype('float32')
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x = paddle.to_tensor(a)
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self.assertRaises(ValueError, paddle.linalg.eig, x)
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class TestEigUnsupportedDtypeError(unittest.TestCase):
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def test_error(self):
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if not compiled_with_linux_and_cuda():
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paddle.device.set_device("cpu")
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paddle.disable_static()
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a = (np.random.random((3, 3)) * 10).astype('int64')
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x = paddle.to_tensor(a)
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self.assertRaises(RuntimeError, paddle.linalg.eig, x)
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class TestOptionalGradInput(unittest.TestCase):
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@unittest.skip("magma is disabled by default")
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def test_eager(self):
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with dygraph_guard(), paddle.device("xpu"):
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x = paddle.randn(3, 3, requires_grad=True)
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w, v = paddle.linalg.eig(x)
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np.testing.assert_allclose(
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(x @ v).numpy(),
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(w.unsqueeze(0) * v).numpy(),
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atol=1e-5,
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rtol=1e-5,
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) # Aμ = λμ
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(dw_dx,) = paddle.grad(w, x, retain_graph=True)
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(dv_dx,) = paddle.grad(v, x, retain_graph=True)
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(dwdv_dx,) = paddle.grad([w, v], x)
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np.testing.assert_allclose(
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(dw_dx + dv_dx).numpy(),
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dwdv_dx.numpy(),
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atol=1e-5,
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rtol=1e-5,
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)
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@unittest.skip("magma is disabled by default")
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def test_dy2st(self):
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with dygraph_guard(), paddle.device("xpu"):
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x = paddle.randn(3, 3, requires_grad=True)
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def f(x):
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w, v = paddle.linalg.eig(x)
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return (
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w,
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v,
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)
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st_f = paddle.jit.to_static(f, full_graph=True, backend=None)
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w, v = st_f(x)
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np.testing.assert_allclose(
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(x @ v).numpy(),
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(w.unsqueeze(0) * v).numpy(),
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atol=1e-5,
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rtol=1e-5,
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) # Aμ = λμ
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if __name__ == "__main__":
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unittest.main()
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