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wehub-resource-sync e25996e7db chore: import upstream snapshot with attribution
2026-07-13 12:40:42 +08:00

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Note:
# 0D Tensor indicates that the tensor's dimension is 0
# 0D Tensor's shape is always [], numel is 1
# which can be created by paddle.rand([])
import os
import unittest
import numpy as np
import paddle
import paddle.nn.functional as F
paddle.set_device('xpu')
paddle.disable_static()
unary_api_list = [
paddle.nn.functional.elu,
paddle.nn.functional.gelu,
paddle.nn.functional.hardsigmoid,
paddle.nn.functional.hardswish,
paddle.nn.functional.hardshrink,
paddle.nn.functional.hardtanh,
paddle.nn.functional.leaky_relu,
paddle.nn.functional.log_sigmoid,
paddle.nn.functional.relu,
paddle.nn.functional.relu6,
paddle.nn.functional.sigmoid,
paddle.nn.functional.softplus,
paddle.nn.functional.softshrink,
paddle.nn.functional.softsign,
paddle.nn.functional.swish,
paddle.nn.functional.tanhshrink,
paddle.nn.functional.thresholded_relu,
paddle.stanh,
paddle.nn.functional.celu,
paddle.nn.functional.selu,
paddle.nn.functional.mish,
paddle.nn.functional.silu,
paddle.nn.functional.tanh,
paddle.nn.functional.dropout,
paddle.cosh,
paddle.sinh,
paddle.abs,
paddle.acos,
paddle.asin,
paddle.atan,
paddle.ceil,
paddle.cos,
paddle.exp,
paddle.floor,
paddle.log,
paddle.log1p,
paddle.reciprocal,
paddle.round,
paddle.sin,
paddle.sqrt,
paddle.square,
paddle.tanh,
paddle.acosh,
paddle.asinh,
paddle.atanh,
paddle.expm1,
paddle.log10,
paddle.log2,
paddle.tan,
paddle.erf,
paddle.erfinv,
paddle.rsqrt,
paddle.sign,
paddle.deg2rad,
paddle.rad2deg,
paddle.neg,
paddle.logit,
paddle.trunc,
paddle.digamma,
paddle.lgamma,
paddle.poisson,
paddle.bernoulli,
paddle.nn.functional.softmax,
paddle.nn.functional.log_softmax,
paddle.nn.functional.gumbel_softmax,
paddle.nn.functional.alpha_dropout,
]
inplace_api_list = [
paddle.nn.functional.relu_,
paddle.nn.functional.tanh_,
]
# Use to test zero-dim in unary API.
class TestUnaryAPI(unittest.TestCase):
def test_dygraph_unary(self):
for api in unary_api_list:
x = paddle.rand([])
x.stop_gradient = False
out = api(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
for api in inplace_api_list:
x = paddle.rand([])
out = api(x)
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
reduce_api_list = [
paddle.sum,
paddle.mean,
paddle.nansum,
paddle.nanmean,
paddle.min,
paddle.max,
paddle.amin,
paddle.amax,
paddle.prod,
paddle.logsumexp,
paddle.all,
paddle.any,
]
# Use to test zero-dim of reduce API
class TestReduceAPI(unittest.TestCase):
def test_dygraph_reduce(self):
for api in reduce_api_list:
# 1) x is 0D
if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, []).astype('bool')
else:
x = paddle.rand([])
x.stop_gradient = False
out = api(x, None)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
np.testing.assert_allclose(out.numpy(), x.numpy())
out_empty_list = api(x, [])
self.assertEqual(out_empty_list, out)
self.assertEqual(out_empty_list.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
np.testing.assert_allclose(x.grad.numpy(), np.array(1.0))
np.testing.assert_allclose(out.grad.numpy(), np.array(1.0))
out1 = api(x, 0)
self.assertEqual(out1.shape, [])
self.assertEqual(out1, out)
out1.backward()
out2 = api(x, -1)
self.assertEqual(out2.shape, [])
self.assertEqual(out2, out)
out2.backward()
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
np.testing.assert_allclose(x.grad.numpy(), np.array(3.0))
# 2) x is ND, reduce to 0D
if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, [3, 5]).astype('bool')
else:
x = paddle.rand([3, 5])
x.stop_gradient = False
out = api(x, None)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [3, 5])
# 3) x is 1D, axis=0, reduce to 0D
if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, [5]).astype('bool')
else:
x = paddle.rand([5])
x.stop_gradient = False
out = api(x, 0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [5])
binary_api_list = [
{'func': paddle.add, 'cls_method': '__add__'},
{'func': paddle.subtract, 'cls_method': '__sub__'},
{'func': paddle.multiply, 'cls_method': '__mul__'},
{'func': paddle.divide, 'cls_method': '__div__'},
{'func': paddle.pow, 'cls_method': '__pow__'},
{'func': paddle.equal, 'cls_method': '__eq__'},
{'func': paddle.not_equal, 'cls_method': '__ne__'},
{'func': paddle.greater_equal, 'cls_method': '__ge__'},
{'func': paddle.greater_than, 'cls_method': '__gt__'},
{'func': paddle.less_equal, 'cls_method': '__le__'},
{'func': paddle.less_than, 'cls_method': '__lt__'},
{'func': paddle.remainder, 'cls_method': '__mod__'},
paddle.mod,
paddle.floor_mod,
paddle.logical_and,
paddle.logical_or,
paddle.logical_xor,
paddle.maximum,
paddle.minimum,
paddle.fmax,
paddle.fmin,
paddle.complex,
paddle.kron,
]
binary_int_api_list = [
paddle.bitwise_and,
paddle.bitwise_or,
paddle.bitwise_xor,
paddle.gcd,
paddle.lcm,
]
# Use to test zero-dim of binary API
class TestBinaryAPI(unittest.TestCase):
def test_dygraph_binary(self):
for api in binary_api_list:
# 1) x is 0D, y is 0D
x = paddle.rand([])
y = paddle.rand([])
x.stop_gradient = False
y.stop_gradient = False
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [])
# 2) x is ND, y is 0D
x = paddle.rand([2, 3, 4])
y = paddle.rand([])
x.stop_gradient = False
y.stop_gradient = False
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [2, 3, 4])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [2, 3, 4])
if x.grad is not None:
self.assertEqual(x.grad.shape, [2, 3, 4])
self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [2, 3, 4])
# 3) x is 0D , y is ND
x = paddle.rand([])
y = paddle.rand([2, 3, 4])
x.stop_gradient = False
y.stop_gradient = False
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [2, 3, 4])
self.assertEqual(out.shape, [2, 3, 4])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [2, 3, 4])
self.assertEqual(out.grad.shape, [2, 3, 4])
# 4) x is 0D , y is scalar
x = paddle.rand([])
x.stop_gradient = False
y = 0.5
if isinstance(api, dict):
out = getattr(paddle.Tensor, api['cls_method'])(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
for api in binary_int_api_list:
# 1) x is 0D, y is 0D
x_np = np.random.randint(-10, 10, [])
y_np = np.random.randint(-10, 10, [])
out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
out = api(x, y)
self.assertEqual(out.shape, [])
np.testing.assert_array_equal(out.numpy(), out_np)
# 2) x is ND, y is 0D
x_np = np.random.randint(-10, 10, [3, 5])
y_np = np.random.randint(-10, 10, [])
out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
out = api(x, y)
self.assertEqual(out.shape, [3, 5])
np.testing.assert_array_equal(out.numpy(), out_np)
# 3) x is 0D , y is ND
x_np = np.random.randint(-10, 10, [])
y_np = np.random.randint(-10, 10, [3, 5])
out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
out = api(x, y)
self.assertEqual(out.shape, [3, 5])
np.testing.assert_array_equal(out.numpy(), out_np)
# Use to test zero-dim of Sundry API, which is unique and can not be classified
# with others. It can be implemented here flexibly.
class TestSundryAPI(unittest.TestCase):
def setUp(self):
self.x = paddle.rand([])
def test_getitem(self):
# case1: When all axis have a scalar indice, output should be a 0-d Tensor;
x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
x.stop_gradient = False
out = x[1, 2, 3, 4]
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
np.testing.assert_allclose(out, np.array(119))
self.assertEqual(out.grad.shape, [])
np.testing.assert_allclose(out.grad, 1.0)
self.assertEqual(x.grad.shape, [2, 3, 4, 5])
x_grad_expected = np.zeros((2, 3, 4, 5))
x_grad_expected[1, 2, 3, 4] = 1.0
np.testing.assert_allclose(x.grad, x_grad_expected)
# case2: When one axis has a 0-d Tensor indice, the output should be same as int indice.
x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
out1 = x[1, 2]
out2 = x[
paddle.full([], 1, dtype='int32'), paddle.full([], 2, dtype='int32')
]
np.testing.assert_allclose(out1, out2)
# case3: When all axis have a scalar indice (i.e. case1) and has None indice,
# ndim of output should be same with numbers of None.
x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
out1 = x[1, 2, None, 3, 4]
self.assertEqual(out1.shape, [1])
np.testing.assert_allclose(out1, np.array([119]))
out2 = x[1, None, 2, None, 3, 4]
self.assertEqual(out2.shape, [1, 1])
np.testing.assert_allclose(out2, np.array([[119]]))
# case4: 1-D Tensor will be treated as vector, no axis decrease will happen.
x = paddle.ones((2, 3, 4))
indice = paddle.ones([1], dtype='int32')
out1 = x[indice]
self.assertEqual(out1.shape, [1, 3, 4])
np.testing.assert_allclose(out1, np.ones((1, 3, 4)))
out2 = x[indice, indice]
self.assertEqual(out2.shape, [1, 4])
np.testing.assert_allclose(out2, np.ones((1, 4)))
def test_setitem(self):
# case1: all axis have a scalar indice
x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
x.stop_gradient = False
out = x * 2
out[1, 2, 3, 4] = 10
out.backward()
self.assertEqual(out.shape, x.shape)
np.testing.assert_allclose(out[1, 2, 3, 4], np.array(10))
self.assertEqual(x.grad.shape, [2, 3, 4, 5])
x_grad_expected = np.ones((2, 3, 4, 5)) * 2
x_grad_expected[1, 2, 3, 4] = 0
np.testing.assert_allclose(x.grad, x_grad_expected)
# case2: 0-D Tensor indice in some axis
# NOTE(zoooo0820): Now, int/slice with 0-D Tensor will still be
# treated as combined indexing, which is not support backward.
# There should have more test cases such as out[1, indice, :] = 0.5 when this
# problem is fixed.
x = paddle.randn((2, 3, 4, 5))
x.stop_gradient = False
indice = paddle.full([], 1, dtype='int32')
out = x * 1
out[indice, indice] = 0.5
out.backward()
self.assertEqual(out.shape, x.shape)
np.testing.assert_allclose(out[1, 1], np.ones((4, 5)) * 0.5)
x_grad_expected = np.ones((2, 3, 4, 5))
x_grad_expected[1, 1] = 0
np.testing.assert_allclose(x.grad, x_grad_expected)
# case3：0-D Tensor indice in some axis, value is a Tensor
# and there is broadcast
x = paddle.randn((2, 3, 4, 5))
x.stop_gradient = False
v = paddle.ones((4, 5), dtype='float32') * 5
v.stop_gradient = False
indice = paddle.full([], 1, dtype='int32')
out = x * 1
out[indice] = v
out.backward()
self.assertEqual(out.shape, x.shape)
np.testing.assert_allclose(out[1], np.ones((3, 4, 5)) * 5)
x_grad_expected = np.ones((2, 3, 4, 5))
x_grad_expected[1] = 0
np.testing.assert_allclose(x.grad, x_grad_expected)
value_grad_expected = np.ones((4, 5)) * 3
np.testing.assert_allclose(v.grad, value_grad_expected)
# case4: value is a 0-D tensor and there is broadcast
x = paddle.randn((2, 3, 4, 5))
x.stop_gradient = False
v = paddle.ones([], dtype='float32') * 5
v.stop_gradient = False
out = x * 1
indice = paddle.full([], 0, dtype='int32')
out[indice] = v
out.backward()
self.assertEqual(out.shape, x.shape)
self.assertEqual(v.grad.shape, [])
np.testing.assert_allclose(out[0], np.ones((3, 4, 5)) * 5)
x_grad_expected = np.ones((2, 3, 4, 5))
x_grad_expected[0] = 0
np.testing.assert_allclose(x.grad, x_grad_expected)
value_grad_expected = np.ones(()) * 3 * 4 * 5
np.testing.assert_allclose(v.grad, value_grad_expected)
# case5: indice / value is 0-D Tensor, and there is no broadcast
x = paddle.randn((2, 3, 4, 5))
x.stop_gradient = False
v = paddle.ones([], dtype='float32') * 2
v.stop_gradient = False
out = x * 1
indice = paddle.full([], 0, dtype='int32')
out[indice, indice, indice, indice] = v
out.backward()
self.assertEqual(out.shape, x.shape)
self.assertEqual(v.grad.shape, [])
np.testing.assert_allclose(out[0, 0, 0, 0], np.ones(()) * 2)
x_grad_expected = np.ones((2, 3, 4, 5))
x_grad_expected[0, 0, 0, 0] = 0
np.testing.assert_allclose(x.grad, x_grad_expected)
value_grad_expected = np.ones(())
np.testing.assert_allclose(v.grad, value_grad_expected)
def test_expand(self):
# case1
x = paddle.full([], 1, 'float32')
x.stop_gradient = False
out = paddle.expand(x, shape=[1])
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [1])
np.testing.assert_allclose(out, 1.0)
self.assertEqual(x.grad.shape, [])
np.testing.assert_allclose(x.grad, 1.0)
self.assertEqual(out.grad.shape, [1])
np.testing.assert_allclose(out.grad, 1.0)
# case2
x1 = paddle.full([], 1, 'float32')
x1.stop_gradient = False
out1 = paddle.expand(x1, shape=[])
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
np.testing.assert_allclose(out1, 1.0)
self.assertEqual(x1.grad.shape, [])
np.testing.assert_allclose(x1.grad, 1.0)
self.assertEqual(out1.grad.shape, [])
np.testing.assert_allclose(out1.grad, 1.0)
# case3
x2 = paddle.full([], 1, 'float32')
x2.stop_gradient = False
out2 = paddle.expand(x2, shape=[1, 1])
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [1, 1])
np.testing.assert_allclose(out2, 1.0)
self.assertEqual(x2.grad.shape, [])
np.testing.assert_allclose(x2.grad, 1.0)
self.assertEqual(out2.grad.shape, [1, 1])
np.testing.assert_allclose(out2.grad, 1.0)
# case4
x3 = paddle.full([], 1, 'float32')
x3.stop_gradient = False
out3 = paddle.expand(x3, shape=[3, 3])
out3.retain_grads()
out3.backward()
self.assertEqual(out3.shape, [3, 3])
np.testing.assert_allclose(out3, 1.0)
self.assertEqual(x3.grad.shape, [])
np.testing.assert_allclose(x3.grad, 9.0)
self.assertEqual(out3.grad.shape, [3, 3])
np.testing.assert_allclose(out3.grad, 1.0)
def test_expand_as(self):
x = paddle.full([], 1, 'float32')
x.stop_gradient = False
y = paddle.full([], 1, 'float32')
y.stop_gradient = False
out = paddle.expand_as(x, y)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(x.item(), 1.0)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad.item(), 1.0)
self.assertEqual(out.shape, [])
self.assertEqual(out.item(), 1.0)
self.assertEqual(out.grad, None)
x1 = paddle.full([], 1, 'float32')
x1.stop_gradient = False
y1 = paddle.full([1], 1, 'float32')
out1 = paddle.expand_as(x1, y1)
out1.backward()
self.assertEqual(x1.shape, [])
self.assertEqual(x1.item(), 1.0)
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x1.grad.item(0), 1.0)
self.assertEqual(out1.shape, [1])
self.assertEqual(out1.item(0), 1.0)
self.assertEqual(out1.grad, None)
x2 = paddle.full([], 1, 'float32')
x2.stop_gradient = False
y2 = paddle.full([3, 3], 1, 'float32')
out2 = paddle.expand_as(x2, y2)
out2.backward()
self.assertEqual(x2.shape, [])
self.assertEqual(x2.item(), 1.0)
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x2.grad.item(0), 9.0)
self.assertEqual(out2.shape, [3, 3])
self.assertEqual(out2.item(0), 1.0)
self.assertEqual(out2.grad, None)
def test_top_k(self):
x = paddle.full([], 1, 'float32')
x.stop_gradient = False
out, indices = paddle.topk(x, k=1, axis=0)
out.retain_grads()
out.backward()
self.assertEqual(indices.shape, [])
self.assertEqual(indices.item(), 0)
self.assertEqual(x.shape, [])
self.assertEqual(x.item(), 1.0)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad.item(0), 1.0)
self.assertEqual(out.shape, [])
self.assertEqual(out.item(), 1.0)
self.assertEqual(out.grad, 1.0)
x1 = paddle.full([], 1, 'float32')
x1.stop_gradient = False
out1, indices1 = paddle.topk(x1, k=1, axis=-1)
out1.retain_grads()
out1.backward()
self.assertEqual(indices1.shape, [])
self.assertEqual(indices1.item(), 0)
self.assertEqual(x1.shape, [])
self.assertEqual(x1.item(), 1.0)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad.item(0), 1.0)
self.assertEqual(out1.shape, [])
self.assertEqual(out1.item(), 1.0)
self.assertEqual(out1.grad, 1.0)
with self.assertRaises(ValueError):
tmp = paddle.topk(x1, k=1, axis=2)
def test_broadcast_to(self):
x = paddle.full([], 1, 'float32')
x.stop_gradient = False
out = paddle.broadcast_to(x, shape=[1])
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [1])
np.testing.assert_allclose(out, 1.0)
self.assertEqual(x.grad.shape, [])
np.testing.assert_allclose(x.grad, 1.0)
self.assertEqual(out.grad.shape, [1])
np.testing.assert_allclose(out.grad, 1.0)
# case2
x1 = paddle.full([], 1, 'float32')
x1.stop_gradient = False
out1 = paddle.broadcast_to(x1, shape=[])
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
np.testing.assert_allclose(out1, 1.0)
self.assertEqual(x1.grad.shape, [])
np.testing.assert_allclose(x1.grad, 1.0)
self.assertEqual(out1.grad.shape, [])
np.testing.assert_allclose(out1.grad, 1.0)
# case3
x2 = paddle.full([], 1, 'float32')
x2.stop_gradient = False
out2 = paddle.broadcast_to(x2, shape=[1, 1])
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [1, 1])
np.testing.assert_allclose(out2, 1.0)
self.assertEqual(x2.grad.shape, [])
np.testing.assert_allclose(x2.grad, 1.0)
self.assertEqual(out2.grad.shape, [1, 1])
np.testing.assert_allclose(out2.grad, 1.0)
# case4
x3 = paddle.full([], 1, 'float32')
x3.stop_gradient = False
out3 = paddle.broadcast_to(x3, shape=[3, 3])
out3.retain_grads()
out3.backward()
self.assertEqual(out3.shape, [3, 3])
np.testing.assert_allclose(out3, 1.0)
self.assertEqual(x3.grad.shape, [])
np.testing.assert_allclose(x3.grad, 9.0)
self.assertEqual(out3.grad.shape, [3, 3])
np.testing.assert_allclose(out3.grad, 1.0)
def test_broadcast_tensors(self):
# 1) x is 0D, y is 0D
x1 = paddle.full([], 2.0)
x1.stop_gradient = False
x2 = paddle.full([], 2.0)
x2.stop_gradient = False
out1, out2 = paddle.broadcast_tensors([x1, x2])
# backward has bug now
# out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
# self.assertEqual(x1.grad.shape, [])
# 2) x is ND , y is 0D
x1 = paddle.full([2, 3], 2.0)
x1.stop_gradient = False
x2 = paddle.full([], 2.0)
x2.stop_gradient = False
out1, out2 = paddle.broadcast_tensors([x1, x2])
# out1.backward()
self.assertEqual(out1.shape, [2, 3])
self.assertEqual(out2.shape, [2, 3])
# self.assertEqual(x1.grad.shape, [2, 3])
# 3) x is 0D , y is ND
x1 = paddle.full([], 2.0)
x1.stop_gradient = False
x2 = paddle.full([2, 3], 2.0)
x2.stop_gradient = False
out1, out2 = paddle.broadcast_tensors([x1, x2])
# out1.backward()
self.assertEqual(out1.shape, [2, 3])
self.assertEqual(out2.shape, [2, 3])
# self.assertEqual(x1.grad.shape, [2, 3])
def test_argmin(self):
# 1) x is 0D
x = paddle.rand([])
out1 = paddle.argmin(x, 0)
out2 = paddle.argmin(x, -1)
out3 = paddle.argmin(x, None)
self.assertEqual(out1.shape, [])
np.testing.assert_allclose(out1, 0)
self.assertEqual(out2.shape, [])
np.testing.assert_allclose(out2, 0)
self.assertEqual(out3.shape, [])
np.testing.assert_allclose(out3, 0)
# 2) x is 1D
x = paddle.rand([5])
x.stop_gradient = False
out = paddle.argmin(x, 0)
out.backward()
self.assertEqual(out.shape, [])
# 3) x is ND
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.argmin(x)
out.backward()
self.assertEqual(out.shape, [])
# 4) x is ND, keepdim=True
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.argmin(x, keepdim=True)
out.backward()
self.assertEqual(out.shape, [1, 1])
def test_argmax(self):
# 1) x is 0D
x = paddle.rand([])
out1 = paddle.argmax(x, 0)
out2 = paddle.argmax(x, -1)
out3 = paddle.argmax(x, None)
self.assertEqual(out1.shape, [])
np.testing.assert_allclose(out1, 0)
self.assertEqual(out2.shape, [])
np.testing.assert_allclose(out2, 0)
self.assertEqual(out3.shape, [])
np.testing.assert_allclose(out3, 0)
# 2) x is 1D
x = paddle.rand([5])
out = paddle.argmax(x, 0)
self.assertEqual(out.shape, [])
# 3) x is ND
x = paddle.rand([3, 5])
out = paddle.argmax(x)
self.assertEqual(out.shape, [])
# 4) x is ND, keepdim=True
x = paddle.rand([3, 5])
out = paddle.argmax(x, keepdim=True)
self.assertEqual(out.shape, [1, 1])
def test_median(self):
# 1) x is 0D
x = paddle.rand([])
x.stop_gradient = False
out1 = paddle.median(x, 0)
out2 = paddle.median(x, -1)
out3 = paddle.median(x, None)
out1.backward()
out2.backward()
out3.backward()
self.assertEqual(out1.shape, [])
np.testing.assert_allclose(out1, x)
self.assertEqual(out2.shape, [])
np.testing.assert_allclose(out2, x)
self.assertEqual(out3.shape, [])
np.testing.assert_allclose(out3, x)
self.assertEqual(x.grad.shape, [])
np.testing.assert_allclose(x.grad, 3.0)
# 2) x is 1D
x = paddle.rand([5])
x.stop_gradient = False
out = paddle.median(x, 0)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [5])
# 3) x is ND
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.median(x, None)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [3, 5])
# 4) x is ND, keepdim=True
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.median(x, keepdim=True)
out.backward()
self.assertEqual(out.shape, [1, 1])
self.assertEqual(x.grad.shape, [3, 5])
def test_kthvalue(self):
# 1) x is 0D
x = paddle.randn([])
x.stop_gradient = False
out, index = paddle.kthvalue(x, 1)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out, x)
self.assertEqual(index.shape, [])
self.assertEqual(index, 0)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad, 1.0)
# 2) x is 1D
x1 = paddle.randn([5])
x1.stop_gradient = False
out1, index1 = paddle.kthvalue(x1, 1)
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(index1.shape, [])
self.assertEqual(x1.grad.shape, [5])
def test_mode(self):
# 1) x is 0D
x = paddle.randn([])
x.stop_gradient = False
out, index = paddle.mode(x)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out, x)
self.assertEqual(index.shape, [])
self.assertEqual(index, 0)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad, 1.0)
# 2) x is 1D
x1 = paddle.randn([5])
x1.stop_gradient = False
out1, index1 = paddle.mode(x1)
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(index1.shape, [])
self.assertEqual(x1.grad.shape, [5])
def test_is_empty(self):
# 1) x is 0D
x = paddle.rand([])
out = paddle.is_empty(x)
self.assertFalse(out)
self.assertEqual(out.shape, [])
# 2) x is 1D
x = paddle.rand([5])
out = paddle.is_empty(x)
self.assertFalse(out)
self.assertEqual(out.shape, [])
# 3) x is ND
x = paddle.rand([3, 5])
out = paddle.is_empty(x)
self.assertFalse(out)
self.assertEqual(out.shape, [])
x = paddle.rand([3, 0, 5])
out = paddle.is_empty(x)
self.assertTrue(out)
self.assertEqual(out.shape, [])
def test_squeeze_(self):
# 1) x is 0D
x = paddle.rand([])
x.squeeze_(0)
self.assertEqual(x.shape, [])
# 2) x is 1D
x = paddle.rand([1])
x.squeeze_(0)
self.assertEqual(x.shape, [])
# 3）x is ND
x = paddle.rand([2, 1])
x.squeeze_(1)
self.assertEqual(x.shape, [2])
def test_as_complex(self):
x = paddle.rand([2])
x.stop_gradient = False
out = paddle.as_complex(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [2])
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [2])
self.assertEqual(out.grad.shape, [])
def test_dot(self):
# 1) x is 1D
x = paddle.rand([2])
x.stop_gradient = False
y = paddle.rand([2])
y.stop_gradient = False
out = paddle.dot(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [2])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
# 2) x is 2D
x1 = paddle.rand([2, 2])
x1.stop_gradient = False
y1 = paddle.rand([2, 2])
y1.stop_gradient = False
out1 = paddle.dot(x1, y1)
out1.retain_grads()
out1.backward()
self.assertEqual(x1.grad.shape, [2, 2])
self.assertEqual(out1.shape, [2])
self.assertEqual(out1.grad.shape, [2])
def test_inner(self):
# 0) input is 0D
x = paddle.rand([])
x.stop_gradient = False
y = paddle.rand([])
y.stop_gradient = False
out = paddle.inner(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
# 1) input is 1D
x = paddle.rand([2])
x.stop_gradient = False
y = paddle.rand([2])
y.stop_gradient = False
out = paddle.inner(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [2])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
# 2) input is 2D
x = paddle.rand([2, 3])
x.stop_gradient = False
y = paddle.rand([3, 3])
y.stop_gradient = False
out = paddle.inner(x, y)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.shape, [2, 3])
self.assertEqual(out.grad.shape, [2, 3])
def test_tensordot(self):
# 1) input is 1D
x = paddle.arange(10, dtype='float64')
x.stop_gradient = False
y = paddle.arange(10, dtype='float64')
y.stop_gradient = False
out = paddle.tensordot(x, y, axes=1)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [10])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
# 2) input is 2D
x = paddle.arange(6, dtype='float64').reshape([2, 3])
y = paddle.arange(6, dtype='float64').reshape([2, 3])
x.stop_gradient = False
out = paddle.tensordot(x, y, axes=2)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
def test_metric_accuracy(self):
x = paddle.full(shape=[2, 4], fill_value=0.25)
y = paddle.full(shape=[2, 1], fill_value=1, dtype="int64")
out = paddle.metric.accuracy(input=x, label=y, k=1)
self.assertEqual(out.shape, [])
def test_std(self):
# 1) x is 0D
x = paddle.rand([])
x.stop_gradient = False
out1 = paddle.std(x)
out2 = paddle.std(x, [])
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertTrue(np.isnan(out1.numpy()))
self.assertTrue(np.isnan(out2.numpy()))
self.assertEqual(x.grad.shape, [])
self.assertTrue(np.isnan(x.grad.numpy()))
# 2) x is ND
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.std(x)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [3, 5])
def test_var(self):
# 1) x is 0D
x = paddle.rand([])
x.stop_gradient = False
out1 = paddle.var(x)
out2 = paddle.var(x, [])
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertTrue(np.isnan(out1.numpy()))
self.assertTrue(np.isnan(out2.numpy()))
self.assertEqual(x.grad.shape, [])
self.assertTrue(np.isnan(x.grad.numpy()))
# 2) x is ND
x = paddle.rand([3, 5])
x.stop_gradient = False
out = paddle.std(x)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [3, 5])
def test_quantile(self):
# 1) x is 0D
x = paddle.rand([])
x.stop_gradient = False
out = paddle.quantile(x, 0.5, axis=None)
out.retain_grads()
out.backward()
out_empty_list = paddle.quantile(x, 0.5, axis=[])
self.assertEqual(out_empty_list, out)
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out, x)
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad, 1.0)
self.assertEqual(out.grad.shape, [])
self.assertEqual(out.grad, 1.0)
# 2) x is ND
x = paddle.rand([2, 3])
x.stop_gradient = False
out = paddle.quantile(x, 0.5, axis=None)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(out.grad, 1.0)
self.assertEqual(x.grad.shape, [2, 3])
def test_flip(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.flip(x, axis=[])
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
def test_linear(self):
x = paddle.randn([3, 2])
w = paddle.full(shape=[2, 4], fill_value=0.5)
b = paddle.zeros([])
np.testing.assert_array_equal(
F.linear(x, w, b).numpy(), F.linear(x, w).numpy()
)
def test_is_floating_point(self):
self.assertTrue(paddle.is_floating_point(self.x))
def test_is_integer(self):
x = paddle.randint(0, 10, [])
self.assertTrue(paddle.is_integer(x))
def test_is_tensor(self):
self.assertTrue(paddle.is_tensor(self.x))
def test_isfinite(self):
out = paddle.isfinite(self.x)
np.testing.assert_array_equal(out.numpy(), np.array(True))
def test_isinf(self):
x = paddle.to_tensor(np.array(float('-inf')))
out = paddle.isinf(x)
np.testing.assert_array_equal(out.numpy(), np.array(True))
def test_isnan(self):
x = paddle.to_tensor(np.array(float('nan')))
out = paddle.isnan(x)
np.testing.assert_array_equal(out.numpy(), np.array(True))
def test_isclose(self):
out = paddle.isclose(self.x, self.x)
np.testing.assert_array_equal(out.numpy(), np.array(True))
def test_clone(self):
out = paddle.clone(self.x)
np.testing.assert_array_equal(out.numpy(), self.x.numpy())
def test_assign(self):
out = paddle.assign(self.x)
np.testing.assert_array_equal(out.numpy(), self.x.numpy())
def test_item(self):
x = paddle.full([], 0.5)
self.assertEqual(x.item(), 0.5)
def test_tolist(self):
x = paddle.full([], 0.5)
self.assertEqual(x.tolist(), 0.5)
def test_numpy(self):
x = paddle.full([], 0.5)
x_np = x.numpy()
np.testing.assert_array_equal(x_np.shape, ())
np.testing.assert_array_equal(x_np, np.array(0.5))
x_np = x.numpy(False)
np.testing.assert_array_equal(x_np.shape, ())
np.testing.assert_array_equal(x_np, np.array(0.5))
def test_numel(self):
# 1) x is 0D
out = paddle.numel(self.x)
self.assertEqual(out.shape, [])
np.testing.assert_array_equal(out.numpy(), np.array(1))
# 2) x is ND
x = paddle.full([3, 5], 0.5)
out = paddle.numel(x)
self.assertEqual(out.shape, [])
np.testing.assert_array_equal(out.numpy(), np.array(15))
def test_rank(self):
# 1) x is 0D
x = paddle.rand([])
out = paddle.rank(x)
self.assertEqual(out.shape, [])
np.testing.assert_array_equal(out.numpy(), np.array(0))
# 1) x is ND
x = paddle.full([3, 5], 0.5)
out = paddle.rank(x)
self.assertEqual(out.shape, [])
np.testing.assert_array_equal(out.numpy(), np.array(2))
def test_shape(self):
out = paddle.shape(self.x)
np.testing.assert_array_equal(out.numpy(), np.array([]))
self.assertEqual(out.shape, [0])
def test_equal_scalar(self):
x = paddle.rand([])
out = paddle.equal(x, 2.0)
self.assertEqual(out.shape, [])
self.assertEqual(out, False)
x1 = paddle.full([], 2.0)
out1 = paddle.equal(x1, 2.0)
self.assertEqual(out1.shape, [])
self.assertEqual(out1, True)
def test_pow_scalar(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.pow(x, 2.0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
def test_cast(self):
x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False
out = paddle.cast(x, 'int32')
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
def test_cumprod(self):
x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False
out = paddle.cumprod(x, 0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
with self.assertRaises(ValueError):
tmp = paddle.cumprod(x, 2)
def test_clip(self):
x = paddle.uniform([], None, -10, 10)
x.stop_gradient = False
out = paddle.clip(x, -5, 5)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
x1 = paddle.uniform([], None, -10, 10)
x1.stop_gradient = False
out1 = paddle.clip(x1, paddle.full([], 5.0), paddle.full([], 5.0))
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out1.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
def test_increment(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.increment(x, 1.0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
def test_bitwise_not(self):
x = paddle.randint(-1, 1, [])
out1 = ~x
out2 = paddle.bitwise_not(x)
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
def test_logical_not(self):
x = paddle.randint(0, 1, [])
out = paddle.logical_not(x)
self.assertEqual(out.shape, [])
def test_searchsorted(self):
# have no backward
x = paddle.to_tensor([1, 3, 5, 7, 9])
y = paddle.rand([])
out = paddle.searchsorted(x, y)
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 0)
def test_transpose(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.transpose(x, [])
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out, x)
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad, 1.0)
with self.assertRaises(ValueError):
x = paddle.transpose(x, [0])
def test_moveaxis(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.moveaxis(x, [], [])
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out, x)
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad, 1.0)
with self.assertRaises(AssertionError):
x = paddle.moveaxis(x, [1], [0])
def test_gather_1D(self):
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
index = paddle.full([], 2, 'int64')
out = paddle.gather(x, index)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 5)
self.assertEqual(x.grad.shape, [5])
self.assertEqual(out.grad.shape, [])
def test_gather_xD_axis_0(self):
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [3])
np.testing.assert_array_equal(out.numpy(), x.numpy()[1, :])
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [3])
def test_gather_xD_axis_1(self):
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index, axis=1)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [2])
np.testing.assert_array_equal(out.numpy(), [2.0, 5.0])
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [2])
def test_gather_nd(self):
x1 = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
x2 = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index1 = paddle.full([1], 1, 'int64')
index2 = paddle.full([2], 1, 'int64')
out1 = paddle.gather_nd(x1, index1)
out2 = paddle.gather_nd(x2, index2)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
np.testing.assert_array_equal(out1, np.array(3.0))
np.testing.assert_array_equal(out2, np.array(5.0))
self.assertEqual(x1.grad.shape, [5])
self.assertEqual(x2.grad.shape, [2, 3])
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
def test_einsum(self):
os.environ['FLAGS_new_einsum'] = "0"
x = paddle.rand([5])
# sum
out1 = paddle.einsum('i->', x)
expect1 = np.einsum('i->', x)
# dot
out2 = paddle.einsum('i,i->', x, x)
expect2 = np.einsum('i,i->', x, x)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
np.testing.assert_allclose(out1, expect1, rtol=1e-03)
np.testing.assert_allclose(out2, expect2, rtol=1e-03)
def test_einsum_V2(self):
os.environ['FLAGS_new_einsum'] = "1"
x = paddle.rand([5])
# sum
out1 = paddle.einsum('i->', x)
expect1 = np.einsum('i->', x)
# dot
out2 = paddle.einsum('i,i->', x, x)
expect2 = np.einsum('i,i->', x, x)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
np.testing.assert_allclose(out1, expect1, rtol=1e-03)
np.testing.assert_allclose(out2, expect2, rtol=1e-03)
def test_scatter_1D(self):
# have no backward now
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4.0)
out = paddle.scatter(x, index, updates)
self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[2], 4)
def test_scatter_XD(self):
# have no backward now
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter(x, index, updates)
self.assertEqual(out.shape, [2, 3])
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
def test_scatter_grad_xpu_index_empty(self):
x = paddle.randn([100, 1], dtype='float32')
x.stop_gradient = False
index = paddle.to_tensor([], dtype='int64')
updates = paddle.randn([0, 1], dtype='float32')
updates.stop_gradient = False
out = paddle.scatter(x, index, updates)
out.backward()
self.assertEqual(x.shape, [100, 1])
self.assertEqual(index.shape, [0])
self.assertEqual(updates.shape, [0, 1])
self.assertEqual(out.shape, [100, 1])
self.assertEqual(x.grad.shape, [100, 1])
self.assertEqual(updates.grad.shape, [0, 1])
def test_diagflat(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x3 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x3.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
x3.retain_grads()
out1 = paddle.diagflat(x1, 1)
out2 = paddle.diagflat(x2, -1)
out3 = paddle.diagflat(x3, 0)
out1.retain_grads()
out2.retain_grads()
out3.retain_grads()
out1.backward()
out2.backward()
out3.backward()
self.assertEqual(out1.shape, [2, 2])
self.assertEqual(out2.shape, [2, 2])
self.assertEqual(out3.shape, [1, 1])
self.assertEqual(out1.grad.shape, [2, 2])
self.assertEqual(out2.grad.shape, [2, 2])
self.assertEqual(out3.grad.shape, [1, 1])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x3.grad.shape, [])
def test_scatter__1D(self):
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0])
index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4.0)
out = paddle.scatter_(x, index, updates)
self.assertEqual(out.numpy()[2], 4)
def test_scatter__XD(self):
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter_(x, index, updates)
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
def test_scatter_nd(self):
index = paddle.to_tensor([3], dtype="int64")
updates = paddle.full([], 2, dtype='float32')
out = paddle.scatter_nd(index, updates, [5])
self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[3], 2)
def test_flatten(self):
x = paddle.rand([])
x.stop_gradient = False
start_axis = 0
stop_axis = -1
out = paddle.flatten(x, start_axis=start_axis, stop_axis=stop_axis)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
self.assertEqual(x.grad.shape, [])
def test_histogram(self):
x = paddle.rand([])
out = paddle.histogram(x, bins=5, min=1, max=5)
self.assertEqual(out.shape, [5])
def test_scale(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.scale(x, scale=2.0, bias=1.0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
def test_scale_(self):
x = paddle.rand([])
out = x.scale_(scale=2.0, bias=1.0)
self.assertEqual(out.shape, [])
def test_floor_divide(self):
# 1-d // 0-d
x = paddle.to_tensor([1, -2, 3], dtype="int64")
y = paddle.full([], 2, dtype='int64')
out1_1 = paddle.floor_divide(x, y)
out1_2 = paddle.Tensor.__floordiv__(x, y)
np.testing.assert_array_equal(out1_1.numpy(), out1_2.numpy())
np.testing.assert_array_equal(out1_1.numpy(), np.asarray([0, -1, 1]))
# 0-d // 1-d
out2_1 = paddle.floor_divide(y, x)
out2_2 = paddle.Tensor.__floordiv__(y, x)
np.testing.assert_array_equal(out2_1.numpy(), out2_2.numpy())
np.testing.assert_array_equal(out2_2.numpy(), np.asarray([2, -1, 0]))
# 0-d // 0-d
x = paddle.full([], 3, dtype='int64')
out3_1 = paddle.floor_divide(x, y)
out3_2 = paddle.Tensor.__floordiv__(x, y)
np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy())
np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1))
def test_cumsum(self):
x1 = paddle.rand([])
x1.stop_gradient = False
out1 = paddle.cumsum(x1)
out2 = paddle.cumsum(x1, axis=0)
out3 = paddle.cumsum(x1, axis=-1)
out1.retain_grads()
out2.retain_grads()
out3.retain_grads()
out1.backward()
out2.backward()
out3.backward()
self.assertEqual(x1.grad.shape, [])
self.assertTrue(x1.grad.numpy() == 3)
self.assertEqual(out1.shape, [1])
self.assertEqual(out1.grad.shape, [1])
self.assertTrue(out1.grad.numpy() == 1)
self.assertEqual(out2.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertTrue(out2.grad.numpy() == 1)
self.assertEqual(out3.shape, [])
self.assertEqual(out3.grad.shape, [])
self.assertTrue(out3.grad.numpy() == 1)
def test_add_n(self):
x1 = paddle.rand([])
x1.stop_gradient = False
x2 = paddle.rand([])
x2.stop_gradient = False
x3 = paddle.rand([])
x3.stop_gradient = False
out1 = paddle.add_n(x1)
out2 = paddle.add_n([x2, x3])
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(x1.grad.shape, [])
self.assertTrue(x1.grad.numpy() == 1)
self.assertEqual(x2.grad.shape, [])
self.assertTrue(x2.grad.numpy() == 1)
self.assertEqual(x3.grad.shape, [])
self.assertTrue(x3.grad.numpy() == 1)
self.assertEqual(out1.shape, [])
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out2.grad.shape, [])
def test_reshape_list(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.reshape(x, [])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
out = paddle.reshape(x, [1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1, 1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1, 1])
self.assertEqual(out.grad.shape, [1, 1])
def test_reshape_tensor(self):
x = paddle.rand([1, 1])
x.stop_gradient = False
out = paddle.reshape(x, [])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
new_shape = paddle.to_tensor([1, 1, 1], "int32")
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1, 1, 1])
self.assertEqual(out.grad.shape, [1, 1, 1])
new_shape = paddle.to_tensor([-1], "int32")
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1, 1])
self.assertEqual(out.grad.shape, [1, 1])
def test_reshape__list(self):
x = paddle.rand([])
out = paddle.reshape_(x, [])
self.assertEqual(out.shape, [])
out = paddle.reshape_(x, [1])
self.assertEqual(out.shape, [1])
out = paddle.reshape_(x, [-1])
self.assertEqual(out.shape, [1])
out = paddle.reshape_(x, [-1, 1])
self.assertEqual(out.shape, [1, 1])
def test_reshape__tensor(self):
x = paddle.rand([1, 1])
out = paddle.reshape_(x, [])
self.assertEqual(out.shape, [])
new_shape = paddle.full([1], 1, "int32")
out = paddle.reshape_(x, new_shape)
self.assertEqual(out.shape, [1])
new_shape = paddle.full([1], -1, "int32")
out = paddle.reshape_(x, new_shape)
self.assertEqual(out.shape, [1])
new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
out = paddle.reshape_(x, new_shape)
self.assertEqual(out.shape, [1, 1])
def test_reverse(self):
x = paddle.rand([])
x.stop_gradient = False
out = paddle.reverse(x, axis=[])
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
def test_sort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.sort(x1, axis=-1)
out2 = paddle.sort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), x1.numpy())
self.assertEqual(out2.numpy(), x2.numpy())
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 1)
self.assertEqual(x2.grad.numpy(), 1)
def test_argsort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.argsort(x1, axis=-1)
out2 = paddle.argsort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), 0)
self.assertEqual(out2.numpy(), 0)
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 0)
self.assertEqual(x2.grad.numpy(), 0)
def test_lerp(self):
# 0D + 0D, weight is float scalar
x = paddle.rand([])
y = paddle.rand([])
x.stop_gradient = False
y.stop_gradient = False
out = paddle.lerp(x, y, 0.5)
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [])
# 0D + 0D, weigh is 0D
x0 = paddle.rand([])
y0 = paddle.rand([])
w0 = paddle.rand([])
x0.stop_gradient = False
y0.stop_gradient = False
y0.retain_grads()
out0 = paddle.lerp(x0, y0, w0)
out0.backward()
self.assertEqual(out0.shape, [])
self.assertEqual(x0.grad.shape, [])
self.assertEqual(y0.grad.shape, [])
# 0D + ND
x1 = paddle.rand([])
y1 = paddle.rand([64, 64])
w1 = paddle.rand([])
x1.stop_gradient = False
y1.stop_gradient = False
x1.retain_grads()
y1.retain_grads()
out1 = paddle.lerp(x1, y1, w1)
out1.backward()
self.assertEqual(out1.shape, [64, 64])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(y1.grad.shape, [64, 64])
# ND + 0D
x2 = paddle.rand([64, 64])
y2 = paddle.rand([])
w2 = paddle.rand([])
x2.stop_gradient = False
y2.stop_gradient = False
x2.retain_grads()
y2.retain_grads()
out2 = paddle.lerp(x2, y2, w2)
out2.backward()
self.assertEqual(out2.shape, [64, 64])
self.assertEqual(x2.grad.shape, [64, 64])
self.assertEqual(y2.grad.shape, [])
def test_repeat_interleave(self):
x = paddle.randn(())
x.stop_gradient = False
out = paddle.repeat_interleave(x, 2, None)
out.backward()
# check shape of output
self.assertEqual(out.shape, [2])
# check grad shape
self.assertEqual(x.grad.shape, [])
repeats = paddle.to_tensor([3], dtype='int32')
out = paddle.repeat_interleave(x, repeats, None)
# check shape of output with 1D repeats
self.assertEqual(out.shape, [3])
# check grad shape with 1D repeats
self.assertEqual(x.grad.shape, [])
def test_allclose(self):
# 1) x is 0D
x = paddle.full([], 0.5)
y = paddle.full([], 0.6)
out = paddle.allclose(x, y)
self.assertEqual(out.shape, [])
self.assertFalse(out)
# 2) x is ND
x = paddle.full([2, 3], 0.5)
y = paddle.full([2, 3], 0.6)
out = paddle.allclose(x, y)
self.assertEqual(out.shape, [])
self.assertFalse(out)
def test_equal_all(self):
# 1) x is 0D
x = paddle.full([], 0.5)
y = paddle.full([], 0.6)
out = paddle.equal_all(x, y)
self.assertEqual(out.shape, [])
self.assertFalse(out)
# 2) x is ND
x = paddle.full([2, 3], 0.5)
y = paddle.full([2, 3], 0.6)
out = paddle.equal_all(x, y)
self.assertEqual(out.shape, [])
self.assertFalse(out)
def test_where(self):
x1 = paddle.full([], 1)
x2 = paddle.full([], 2)
x1.stop_gradient = False
x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out = paddle.where(x1 > x2, x1, x2)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 2)
self.assertEqual(out.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 0)
self.assertEqual(x2.grad.numpy(), 1)
def test_atan2(self):
x1 = paddle.full([], 0)
x2 = paddle.full([], 2)
x1.retain_grads()
x2.retain_grads()
x1.stop_gradient = False
x2.stop_gradient = False
out = paddle.atan2(x1, x2)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 0)
self.assertEqual(out.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 0.5)
self.assertEqual(x2.grad.numpy(), 0)
def test_interpolate(self):
from paddle.nn.functional import interpolate
input_x = paddle.rand([2, 3, 6, 6])
input_x.stop_gradient = False
origin_result = interpolate(
x=input_x, size=[12, 12], mode="bilinear", align_corners=False
)
output_size = [
paddle.full([], 12, dtype="int32"),
paddle.full([], 12, dtype="int32"),
]
out1 = interpolate(
x=input_x, size=output_size, mode="bilinear", align_corners=False
)
out1.backward()
self.assertEqual(out1.shape, [2, 3, 12, 12])
self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])
scale_1 = [paddle.full([], 2), paddle.full([], 2)]
out2 = interpolate(
x=input_x,
scale_factor=scale_1,
mode="bilinear",
align_corners=False,
)
out2.backward()
self.assertEqual(out2.shape, [2, 3, 12, 12])
self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])
scale_2 = paddle.full([], 2)
out3 = interpolate(
x=input_x,
scale_factor=scale_2,
mode="bilinear",
align_corners=False,
)
out3.backward()
self.assertEqual(out3.shape, [2, 3, 12, 12])
self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])
np.testing.assert_allclose(
origin_result.numpy(), out1.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
origin_result.numpy(), out2.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
origin_result.numpy(), out3.numpy(), rtol=1e-05
)
def test_upsample(self):
from paddle.nn.functional import upsample
input_x = paddle.rand([2, 3, 6, 6])
input_x.stop_gradient = False
output_size = [
paddle.full([], 12, dtype="int32"),
paddle.full([], 12, dtype="int32"),
]
out1 = upsample(
x=input_x, size=output_size, mode="bilinear", align_corners=False
)
out1.backward()
self.assertEqual(out1.shape, [2, 3, 12, 12])
self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])
def test_unstack(self):
x1 = paddle.full([1], 0)
x2 = paddle.full([2], 2)
x1.retain_grads()
x2.retain_grads()
x1.stop_gradient = False
x2.stop_gradient = False
[out1] = paddle.unstack(x1, 0)
out1.retain_grads()
out1.backward()
[out2_1, out2_2] = paddle.unstack(x2, 0)
out2 = paddle.add_n([out2_1, out2_2])
out2.retain_grads()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out1.numpy(), 0)
self.assertEqual(out2_1.shape, [])
self.assertEqual(out2_1.numpy(), 2)
self.assertEqual(out2_2.shape, [])
self.assertEqual(out2_2.numpy(), 2)
self.assertEqual(x2.grad.shape, [2])
def test_unbind(self):
x1 = paddle.full([1], 0)
x2 = paddle.full([2], 2)
x1.retain_grads()
x2.retain_grads()
x1.stop_gradient = False
x2.stop_gradient = False
[out1] = paddle.unbind(x1, 0)
out1.retain_grads()
out1.backward()
[out2_1, out2_2] = paddle.unbind(x2, 0)
out2 = paddle.add_n([out2_1, out2_2])
out2.retain_grads()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out1.numpy(), 0)
self.assertEqual(out2_1.shape, [])
self.assertEqual(out2_1.numpy(), 2)
self.assertEqual(out2_2.shape, [])
self.assertEqual(out2_2.numpy(), 2)
self.assertEqual(x2.grad.shape, [2])
def test_masked_select(self):
x = paddle.rand([])
x.stop_gradient = False
mask = paddle.full([], True, dtype='bool')
y = paddle.masked_select(x, mask)
y.retain_grads()
y.backward()
self.assertEqual(y.shape, [1])
self.assertEqual(y.numpy(), x.numpy())
self.assertEqual(y.grad.shape, [1])
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad.numpy(), 1)
def test_squeeze(self):
x1 = paddle.full([], 2)
x1.stop_gradient = False
x1.retain_grads()
out1 = paddle.squeeze(x1, axis=0)
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(x1.grad.shape, [])
x2 = paddle.full([], 3)
x3 = paddle.full([1], 0, dtype='int32')
x2.stop_gradient = False
x2.retain_grads()
out2 = paddle.squeeze(x2, axis=x3)
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [])
self.assertEqual(x2.grad.shape, [])
def test_unsqueeze(self):
x1 = paddle.full([], 2)
x1.stop_gradient = False
x1.retain_grads()
out1 = paddle.unsqueeze(x1, axis=0)
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [1])
self.assertEqual(x1.grad.shape, [])
x2 = paddle.full([], 0, dtype='int32')
out2 = paddle.unsqueeze(x1, axis=x2)
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [1])
self.assertEqual(x1.grad.shape, [])
def test_t(self):
x = paddle.full([], 2.0)
x.stop_gradient = False
x.retain_grads()
out = paddle.t(x)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, [])
def test_prelu(self):
x1 = paddle.full([], 1.0, 'float32')
x1.stop_gradient = False
w1 = paddle.full([], 0.25, dtype='float32')
w1.stop_gradient = False
out1 = paddle.nn.functional.prelu(x1, w1)
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out1.numpy(), 1.0)
self.assertEqual(out1.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 1.0)
x2 = paddle.full([], -1.0, 'float32')
x2.stop_gradient = False
w2 = paddle.full([], 0.25, dtype='float32')
w2.stop_gradient = False
out2 = paddle.nn.functional.prelu(x2, w2)
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [])
self.assertEqual(out2.numpy(), -0.25)
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x2.grad.numpy(), 0.25)
def test_prelu_grad_xpu_zero_batch(self):
x = paddle.full([0, 128, 28, 28], 1.0, 'float32')
x.stop_gradient = False
w = paddle.full([128], 0.25, dtype='float32')
w.stop_gradient = False
out = paddle.nn.functional.prelu(x, w, data_format="NCHW")
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [0, 128, 28, 28])
self.assertEqual(x.grad.shape, [0, 128, 28, 28])
self.assertEqual(out.grad.shape, [0, 128, 28, 28])
self.assertEqual(w.grad.shape, [128])
self.assertEqual(w.grad.numpy().sum(), 0.0)
def test_while_loop(self):
def cond(i, x):
return paddle.less_than(i, eleven)
def body(i, x):
x = x + i
i = i + 1
return [i, x]
i = paddle.full([], 1.0, dtype='float32')
i.stop_gradient = False
eleven = paddle.full([], 11, dtype='float32')
x = paddle.full([], 0.0, dtype='float32')
x.stop_gradient = False
out_i, out_x = paddle.static.nn.while_loop(cond, body, [i, x])
out_x.backward()
self.assertEqual(out_i.shape, [])
np.testing.assert_allclose(out_i, np.array(11))
self.assertEqual(out_x.shape, [])
np.testing.assert_allclose(out_x, np.array(55))
self.assertEqual(i.grad.shape, [])
np.testing.assert_allclose(i.grad, np.array(10))
self.assertEqual(x.grad.shape, [])
np.testing.assert_allclose(x.grad, np.array(1.0))
def test_to_tensor(self):
out1 = paddle.to_tensor(1)
out2 = paddle.to_tensor(2.5)
out1.retain_grads()
out1.backward()
out2.retain_grads()
out2.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(out1, 1)
self.assertEqual(out2.shape, [])
self.assertEqual(out2, 2.5)
def test_matmul(self):
# 1) no transpose
x = paddle.randn([10])
x.stop_gradient = False
y = paddle.randn([10])
y.stop_gradient = False
out1 = paddle.matmul(x, y)
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(x.grad.shape, [10])
self.assertEqual(y.grad.shape, [10])
# 2) transpose x and y
x = paddle.randn([10])
x.stop_gradient = False
y = paddle.randn([10])
y.stop_gradient = False
out2 = paddle.matmul(x, y, True, True)
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [])
self.assertEqual(x.grad.shape, [10])
self.assertEqual(y.grad.shape, [10])
def test_linalg_slogdet(self):
# 2-D input
x = paddle.randn([3, 3])
x.stop_gradient = False
out = paddle.linalg.slogdet(x)
out.retain_grads()
out.backward()
self.assertTrue(out.shape, [2])
self.assertTrue(x.grad.shape, [3, 3])
# 3-D input
x1 = paddle.randn([3, 3, 3])
x1.stop_gradient = False
out1 = paddle.linalg.slogdet(x1)
out1.retain_grads()
out1.backward()
self.assertTrue(out1.shape, [2, 3])
self.assertTrue(x1.grad.shape, [3, 3, 3])
def test_compat_slogdet(self):
# 2-D input
x = paddle.randn([3, 3])
x.stop_gradient = False
sign, logabsdet = paddle.linalg.slogdet(x)
loss = logabsdet.sum()
loss.backward()
self.assertEqual(sign.shape, [])
self.assertEqual(logabsdet.shape, [])
self.assertTrue(x.grad.shape, [3, 3])
# 3-D input
x1 = paddle.randn([3, 3, 3])
x1.stop_gradient = False
sign1, logabsdet1 = paddle.linalg.slogdet(x1)
loss1 = logabsdet1.sum()
loss1.backward()
self.assertTrue(sign1.shape, [3])
self.assertTrue(logabsdet1.shape, [3])
self.assertTrue(x1.grad.shape, [3, 3, 3])
def test_multi_dot(self):
a = paddle.randn([4])
a.stop_gradient = False
b = paddle.randn([4, 5])
b.stop_gradient = False
c = paddle.randn([5])
c.stop_gradient = False
out = paddle.linalg.multi_dot([a, b, c])
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(a.grad.shape, [4])
self.assertEqual(b.grad.shape, [4, 5])
self.assertEqual(c.grad.shape, [5])
def test_cov(self):
xt = paddle.randn((3, 4))
xt.stop_gradient = False
xt_1 = paddle.randn((12,))
xt_1.stop_gradient = False
xt_out = paddle.linalg.cov(xt)
xt_out.retain_grads()
xt_out.backward()
self.assertEqual(xt_out.shape, [3, 3])
self.assertEqual(xt.grad.shape, [3, 4])
xt_1_out = paddle.linalg.cov(xt_1)
xt_1.retain_grads()
xt_1_out.backward()
self.assertEqual(xt_1_out.shape, [])
self.assertEqual(xt_1.grad.shape, [12])
def test_det(self):
xt = paddle.randn([3, 3, 3])
xt.stop_gradient = False
xt_1 = paddle.randn([3, 3])
xt_1.stop_gradient = False
xt_out = paddle.linalg.det(xt)
xt.retain_grads()
xt_out.backward()
self.assertEqual(xt_out.shape, [3])
self.assertEqual(xt.grad.shape, [3, 3, 3])
xt_1_out = paddle.linalg.det(xt_1)
xt_1.retain_grads()
xt_1_out.backward()
self.assertEqual(xt_1_out.shape, [])
self.assertEqual(xt_1.grad.shape, [3, 3])
def test_dist(self):
x = paddle.to_tensor([[3, 3], [3, 3]], dtype="float32")
y = paddle.to_tensor([[3, 3], [3, 1]], dtype="float32")
x.stop_gradient = False
y.stop_gradient = False
out = paddle.dist(x, y, 0)
out.backward()
self.assertEqual(out.shape, [])
np.testing.assert_allclose(out, np.array(1))
self.assertEqual(x.grad.shape, [2, 2])
self.assertEqual(y.grad.shape, [2, 2])
def test_linalg_norm(self):
# 1D input, p = fro ,axis = None, using reduceInferMeta
x_1 = paddle.arange(24, dtype="float32") - 12
x_1.stop_gradient = False
out_1 = paddle.linalg.norm(x_1)
out_1.retain_grads()
out_1.backward()
self.assertEqual(out_1.shape, [])
self.assertTrue(x_1.grad.shape, [24])
# 1D input, p = 1 ,axis = None,
# using p_norm, as_vector = True
x_2 = paddle.arange(24, dtype="float32") - 12
x_2.stop_gradient = False
out_2 = paddle.linalg.norm(x_2, p=1)
out_2.retain_grads()
out_2.backward()
self.assertEqual(out_2.shape, [])
self.assertEqual(x_2.grad.shape, [24])
# 1D input, p = 1 ,axis = 0,
# using p_norm, as_vector = False
x_2_p = paddle.arange(24, dtype="float32") - 12
x_2_p.stop_gradient = False
out_2_p = paddle.linalg.norm(x_2_p, p=1, axis=0)
out_2_p.retain_grads()
out_2_p.backward()
self.assertEqual(out_2_p.shape, [])
self.assertEqual(x_2_p.grad.shape, [24])
# 1D input, p = fro ,axis = 0,
# using p_norm, as_vector = False
x_2_fro = paddle.arange(24, dtype="float32") - 12
x_2_fro.stop_gradient = False
out_2_fro = paddle.linalg.norm(x_2_fro, p="fro", axis=0)
out_2_fro.retain_grads()
out_2_fro.backward()
self.assertEqual(out_2_fro.shape, [])
self.assertEqual(x_2_fro.grad.shape, [24])
# 2D input, p = 1, axis = [0, 1]
# using p_matrix_norm ,depends on paddle.sum
x_3 = paddle.arange(24, dtype="float32").reshape([4, 6])
x_3.stop_gradient = False
out_3 = paddle.linalg.norm(x_3, p=1, axis=[0, 1])
out_3.retain_grads()
out_3.backward()
self.assertEqual(out_3.shape, [])
self.assertEqual(x_3.grad.shape, [4, 6])
# 2D input, p = 1, axis = None
# using p_matrix_norm, depends on paddle.sum
x_4 = paddle.arange(24, dtype="float32").reshape([4, 6])
x_4.stop_gradient = False
out_4 = paddle.linalg.norm(x_4)
out_4.retain_grads()
out_4.backward()
self.assertEqual(out_4.shape, [])
self.assertEqual(x_4.grad.shape, [4, 6])
# 2D input, p = inf, axis = [0, 1]
# using p_matrix_norm, depends on paddle.sum
x_5 = paddle.arange(24, dtype="float32").reshape([4, 6])
x_5.stop_gradient = False
out_5 = paddle.linalg.norm(x_5, p=2, axis=[0, 1])
out_5.retain_grads()
out_5.backward()
self.assertEqual(out_5.shape, [])
self.assertEqual(x_5.grad.shape, [4, 6])
# 2D input, p = -inf, axis = [0, 1]
x_6 = paddle.arange(24, dtype="float32").reshape([4, 6])
x_6.stop_gradient = False
out_6 = paddle.linalg.norm(x_6, p=-float("inf"), axis=[0, 1])
out_6.retain_grads()
out_6.backward()
self.assertEqual(out_6.shape, [])
self.assertEqual(x_6.grad.shape, [4, 6])
def test_linalg_cond(self):
def assert_shape(out):
self.assertEqual(out.shape, [])
x1 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x1.stop_gradient = False
# p = 2 : use paddle.sum
out = paddle.linalg.cond(x1)
out.backward()
assert_shape(out)
self.assertEqual(x1.grad.shape, [3, 3])
# p = fro : use paddle.sum
x2 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x2.stop_gradient = False
out_fro = paddle.linalg.cond(x2, p='fro')
out_fro.backward()
assert_shape(out_fro)
self.assertEqual(x2.grad.shape, [3, 3])
# p = nuc : use paddle.sum
x3 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x3.stop_gradient = False
out_nuc = paddle.linalg.cond(x3, p='nuc')
out_nuc.backward()
assert_shape(out_nuc)
self.assertEqual(x3.grad.shape, [3, 3])
# p in (-1, 1) : use paddle.sum
x4 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x4.stop_gradient = False
out_1 = paddle.linalg.cond(x4, p=1)
out_1.backward()
assert_shape(out_1)
self.assertEqual(x4.grad.shape, [3, 3])
x5 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x5.stop_gradient = False
out_minus_1 = paddle.linalg.cond(x5, p=-1)
out_minus_1.backward()
assert_shape(out_minus_1)
self.assertEqual(x5.grad.shape, [3, 3])
# p in (-2, 2) depends on paddle.sum
x6 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x6.stop_gradient = False
out_2 = paddle.linalg.cond(x6, p=2)
out_2.backward()
assert_shape(out_2)
self.assertEqual(x6.grad.shape, [3, 3])
# p in (-inf, inf):use paddle.sum
x8 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
x8.stop_gradient = False
out_inf = paddle.linalg.cond(x8, p=float("inf"))
out_inf.backward()
assert_shape(out_inf)
self.assertEqual(x8.grad.shape, [3, 3])
a = paddle.randn([2, 4, 4])
a.stop_gradient = False
a_cond_fro = paddle.linalg.cond(a, p='fro')
a_cond_fro.backward()
self.assertEqual(len(a_cond_fro.shape), 1)
self.assertEqual(a.grad.shape, [2, 4, 4])
def test_trace(self):
x = paddle.to_tensor([[3, 2], [1, 9]], dtype="float32")
x.stop_gradient = False
out = paddle.trace(x)
out.backward()
self.assertEqual(out.shape, [])
np.testing.assert_allclose(out, np.array(12))
self.assertEqual(x.grad.shape, [2, 2])
# Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
class TestNoBackwardAPI(unittest.TestCase):
def setUp(self):
self.shape = [
paddle.full([], 2, 'int32'),
paddle.full([], 3, 'int32'),
paddle.full([], 4, 'int32'),
]
def test_slice(self):
starts = [paddle.full([], 1, 'int32'), paddle.full([], 1, 'int32')]
ends = [paddle.full([], 3, 'int32'), paddle.full([], 3, 'int32')]
x = paddle.rand([5, 3, 3])
out = paddle.slice(x, [1, 2], starts, ends)
self.assertEqual(out.shape, [5, 2, 2])
def test_strided_slice(self):
starts = [paddle.full([], 0, 'int32'), paddle.full([], 0, 'int32')]
ends = [paddle.full([], 4, 'int32'), paddle.full([], 4, 'int32')]
strides = [paddle.full([], 2, 'int32'), paddle.full([], 2, 'int32')]
x = paddle.rand([5, 5, 5])
out = paddle.strided_slice(x, [1, 2], starts, ends, strides)
self.assertEqual(out.shape, [5, 2, 2])
def test_linspace(self):
start = paddle.full([], 1.0)
stop = paddle.full([], 5.0)
num = paddle.full([], 5, 'int32')
out = paddle.linspace(start, stop, num)
np.testing.assert_array_equal(out.numpy(), [1.0, 2.0, 3.0, 4.0, 5.0])
def test_arange(self):
start = paddle.full([], 1.0)
stop = paddle.full([], 6.0)
step = paddle.full([], 1.0)
out = paddle.arange(start, stop, step)
np.testing.assert_array_equal(out.numpy(), [1.0, 2.0, 3.0, 4.0, 5.0])
def test_normal(self):
mean = paddle.full([], 0.0)
std = paddle.full([], 0.0)
out = paddle.normal(mean, std)
self.assertEqual(out.shape, [])
out = paddle.normal(0.0, 1.0, [])
self.assertEqual(out.shape, [])
out = paddle.normal(0.0, 1.0, self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_rand(self):
out = paddle.rand([])
self.assertEqual(out.shape, [])
out = paddle.rand(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_randn(self):
out = paddle.randn([])
self.assertEqual(out.shape, [])
out = paddle.randn(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_randint_and_randint_like(self):
out = paddle.randint(-10, 10, [])
self.assertEqual(out.shape, [])
out = paddle.randint_like(out, -10, 10)
self.assertEqual(out.shape, [])
out = paddle.randint(-10, 10, self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_standard_normal(self):
out = paddle.standard_normal([])
self.assertEqual(out.shape, [])
out = paddle.standard_normal(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_uniform(self):
out = paddle.uniform([])
self.assertEqual(out.shape, [])
out = paddle.uniform(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_empty_and_empty_like(self):
out = paddle.empty([])
self.assertEqual(out.shape, [])
out = paddle.empty_like(out)
self.assertEqual(out.shape, [])
out = paddle.empty(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_full_and_full_like(self):
out = paddle.full([], 0.5)
self.assertEqual(out.shape, [])
out = paddle.full_like(out, 0.5)
self.assertEqual(out.shape, [])
out = paddle.full(self.shape, 0.5)
self.assertEqual(out.shape, [2, 3, 4])
def test_ones_and_ones_like(self):
out = paddle.ones([])
self.assertEqual(out.shape, [])
out = paddle.ones_like(out)
self.assertEqual(out.shape, [])
out = paddle.ones(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_zeros_and_zeros_like(self):
out = paddle.zeros([])
self.assertEqual(out.shape, [])
out = paddle.zeros_like(out)
self.assertEqual(out.shape, [])
out = paddle.zeros(self.shape)
self.assertEqual(out.shape, [2, 3, 4])
def test_embedding(self):
ids = paddle.full(shape=[], fill_value=1, dtype='int64')
w0 = paddle.arange(3, 9).reshape((3, 2)).astype(paddle.float32)
w = paddle.to_tensor(w0, stop_gradient=False)
emb = paddle.nn.functional.embedding(
x=ids, weight=w, sparse=True, name="embedding"
)
self.assertEqual(emb.shape, [2])
res = [5.0, 6.0]
for i in range(len(res)):
self.assertEqual(emb.numpy()[i], res[i])
def test_embedding_alias(self):
ids = paddle.full(shape=[], fill_value=1, dtype='int64')
w0 = paddle.arange(3, 9).reshape((3, 2)).astype(paddle.float32)
w = paddle.to_tensor(w0, stop_gradient=False)
emb = paddle.nn.functional.embedding(
input=ids, weight=w, sparse=True, name="embedding"
)
self.assertEqual(emb.shape, [2])
res = [5.0, 6.0]
for i in range(len(res)):
self.assertEqual(emb.numpy()[i], res[i])
def test_embedding_grad_ids_3_weight_20_32(self):
ids = paddle.to_tensor([], dtype='int64').reshape([0, 1, 1])
w = paddle.randn([20, 32], dtype='float32')
ids.stop_gradient = False
w.stop_gradient = False
out = paddle.nn.functional.embedding(
input=ids,
weight=w,
padding_idx=None,
max_norm=None,
norm_type=2.0,
sparse=False,
scale_grad_by_freq=False,
name=None,
)
loss = out.sum()
loss.backward()
self.assertEqual(out.shape, [0, 1, 1, 32])
def test_one_hot_label(self):
label = paddle.full(shape=[], fill_value=2, dtype='int64')
one_hot_label = paddle.nn.functional.one_hot(label, num_classes=4)
self.assertEqual(one_hot_label.shape, [4])
self.assertEqual(one_hot_label.numpy()[2], 1)
def test_unique_consecutive(self):
x = paddle.rand([])
y, inverse, counts = paddle.unique_consecutive(
x,
return_inverse=True,
return_counts=True,
)
self.assertEqual(y, x)
self.assertEqual(inverse, 0)
self.assertEqual(counts, 1)
self.assertEqual(y.shape, [1])
self.assertEqual(inverse.shape, [1])
self.assertEqual(counts.shape, [1])
def test_unique(self):
x = paddle.rand([])
y, index, inverse, counts = paddle.unique(
x,
return_index=True,
return_inverse=True,
return_counts=True,
)
self.assertEqual(y, x)
self.assertEqual(index, 0)
self.assertEqual(inverse, 0)
self.assertEqual(counts, 1)
self.assertEqual(y.shape, [1])
self.assertEqual(index.shape, [1])
self.assertEqual(inverse.shape, [1])
self.assertEqual(counts.shape, [1])
def test_matrix_rank(self):
x = paddle.eye(10)
x.stop_gradient = False
out = paddle.linalg.matrix_rank(x)
self.assertEqual(out.shape, [])
np.testing.assert_equal(out, np.array(10))
c = paddle.ones(shape=[3, 4, 5])
c.stop_gradient = False
out_c = paddle.linalg.matrix_rank(c)
self.assertEqual(out_c.shape, [3])
np.testing.assert_equal(out_c, np.array([1, 1, 1]))
# 2D, tol->float : OUTPUT 0D
x_tol = paddle.eye(10)
x_tol.stop_gradient = False
out_tol = paddle.linalg.matrix_rank(x_tol, tol=0.1)
self.assertEqual(out_tol.shape, [])
# 3D, tol->float : OUTPUT 1D
c_tol = paddle.ones(shape=[3, 4, 5])
c_tol.stop_gradient = False
out_c_tol = paddle.linalg.matrix_rank(c_tol, tol=0.1)
self.assertEqual(out_c_tol.shape, [3])
tol_2 = paddle.randn([2])
# 2D, tol->Tensor[1,2] : OUTPUT 1D
d = paddle.eye(10)
out_d = paddle.linalg.matrix_rank(d, tol=tol_2)
self.assertEqual(out_d.shape, [2])
class TestSwishZeroSizeXPU(unittest.TestCase):
def test_swish_zero_size(self):
x = paddle.randn([0, 10, 1, 1], dtype='float16')
out = F.swish(x)
self.assertEqual(out.shape, [0, 10, 1, 1])
if __name__ == "__main__":
unittest.main()
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