chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,415 @@
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# Copyright (c) 2018 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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# Note:
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# 0D Tensor indicates that the tensor's dimension is 0
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# 0D Tensor's shape is always [], numel is 1
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# which can be created by paddle.rand([])
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import unittest
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import numpy as np
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import paddle
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from paddle.framework import use_pir_api
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binary_api_list = [
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{'func': paddle.add, 'cls_method': '__add__'},
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{'func': paddle.subtract, 'cls_method': '__sub__'},
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{'func': paddle.multiply, 'cls_method': '__mul__'},
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{'func': paddle.divide, 'cls_method': '__div__'},
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{'func': paddle.pow, 'cls_method': '__pow__'},
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{'func': paddle.equal, 'cls_method': '__eq__'},
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{'func': paddle.not_equal, 'cls_method': '__ne__'},
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{'func': paddle.greater_equal, 'cls_method': '__ge__'},
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{'func': paddle.greater_than, 'cls_method': '__gt__'},
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{'func': paddle.less_equal, 'cls_method': '__le__'},
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{'func': paddle.less_than, 'cls_method': '__lt__'},
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{'func': paddle.remainder, 'cls_method': '__mod__'},
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paddle.mod,
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paddle.floor_mod,
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paddle.logical_and,
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paddle.logical_or,
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paddle.logical_xor,
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paddle.maximum,
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paddle.minimum,
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paddle.fmax,
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paddle.fmin,
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paddle.complex,
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paddle.kron,
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paddle.logaddexp,
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paddle.nextafter,
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paddle.ldexp,
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paddle.polar,
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paddle.heaviside,
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]
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binary_int_api_list = [
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paddle.bitwise_and,
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paddle.bitwise_or,
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paddle.bitwise_xor,
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paddle.gcd,
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paddle.lcm,
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]
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inplace_binary_api_list = [
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paddle.tensor.add_,
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paddle.tensor.subtract_,
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paddle.tensor.multiply_,
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paddle.tensor.remainder_,
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paddle.tensor.remainder_,
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]
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# Use to test zero-dim of binary API
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class TestBinaryAPI(unittest.TestCase):
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def test_dygraph_binary(self):
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paddle.disable_static()
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for api in binary_api_list:
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# 1) x is 0D, y is 0D
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x = paddle.rand([])
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y = paddle.rand([])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
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np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
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else:
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out = api(x, y)
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out.retain_grads()
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out.backward()
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self.assertEqual(x.shape, [])
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self.assertEqual(y.shape, [])
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self.assertEqual(out.shape, [])
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if x.grad is not None:
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self.assertEqual(x.grad.shape, [])
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self.assertEqual(y.grad.shape, [])
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self.assertEqual(out.grad.shape, [])
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# 2) x is ND, y is 0D
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x = paddle.rand([2, 3, 4])
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y = paddle.rand([])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
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np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
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else:
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out = api(x, y)
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out.retain_grads()
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out.backward()
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self.assertEqual(x.shape, [2, 3, 4])
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self.assertEqual(y.shape, [])
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self.assertEqual(out.shape, [2, 3, 4])
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if x.grad is not None:
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self.assertEqual(x.grad.shape, [2, 3, 4])
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self.assertEqual(y.grad.shape, [])
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self.assertEqual(out.grad.shape, [2, 3, 4])
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# 3) x is 0D , y is ND
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x = paddle.rand([])
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y = paddle.rand([2, 3, 4])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
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np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
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else:
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out = api(x, y)
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out.retain_grads()
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out.backward()
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self.assertEqual(x.shape, [])
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self.assertEqual(y.shape, [2, 3, 4])
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self.assertEqual(out.shape, [2, 3, 4])
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if x.grad is not None:
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self.assertEqual(x.grad.shape, [])
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self.assertEqual(y.grad.shape, [2, 3, 4])
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self.assertEqual(out.grad.shape, [2, 3, 4])
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# 4) x is 0D , y is scalar
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x = paddle.rand([])
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x.stop_gradient = False
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y = 0.5
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if isinstance(api, dict):
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out = getattr(paddle.Tensor, api['cls_method'])(x, y)
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out.retain_grads()
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out.backward()
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self.assertEqual(x.shape, [])
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self.assertEqual(out.shape, [])
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if x.grad is not None:
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self.assertEqual(x.grad.shape, [])
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self.assertEqual(out.grad.shape, [])
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for api in binary_int_api_list:
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# 1) x is 0D, y is 0D
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x_np = np.random.randint(-10, 10, [])
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y_np = np.random.randint(-10, 10, [])
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out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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out = api(x, y)
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self.assertEqual(out.shape, [])
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np.testing.assert_array_equal(out.numpy(), out_np)
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# 2) x is ND, y is 0D
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x_np = np.random.randint(-10, 10, [3, 5])
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y_np = np.random.randint(-10, 10, [])
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out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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out = api(x, y)
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self.assertEqual(out.shape, [3, 5])
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np.testing.assert_array_equal(out.numpy(), out_np)
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# 3) x is 0D , y is ND
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x_np = np.random.randint(-10, 10, [])
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y_np = np.random.randint(-10, 10, [3, 5])
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out_np = eval(f"np.{api.__name__.lstrip('_')}(x_np, y_np)")
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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out = api(x, y)
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self.assertEqual(out.shape, [3, 5])
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np.testing.assert_array_equal(out.numpy(), out_np)
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for api in inplace_binary_api_list:
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with paddle.no_grad():
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x = paddle.rand([])
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y = paddle.rand([])
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out = api(x, y)
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self.assertEqual(x.shape, [])
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self.assertEqual(out.shape, [])
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x = paddle.rand([3, 5])
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y = paddle.rand([])
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out = api(x, y)
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self.assertEqual(x.shape, [3, 5])
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self.assertEqual(out.shape, [3, 5])
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paddle.enable_static()
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def assertShapeEqual(self, out, target_tuple):
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if not use_pir_api():
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out_shape = list(out.shape)
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else:
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out_shape = out.shape
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self.assertEqual(out_shape, target_tuple)
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def test_static_binary_0D_0D(self):
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paddle.enable_static()
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for api in binary_api_list:
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(
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main_prog, paddle.static.Program()
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):
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# 1) x is 0D, y is 0D
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x = paddle.rand([])
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y = paddle.rand([])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(
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(
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paddle.pir.Value
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if use_pir_api()
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else paddle.static.Variable
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),
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api['cls_method'],
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)(x, y)
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self.assertEqual(out.shape, out_cls.shape)
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else:
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out = api(x, y)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[x, y, out]
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)
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self.assertShapeEqual(x, [])
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self.assertShapeEqual(y, [])
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self.assertShapeEqual(out, [])
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if len(grad_list) != 0 and grad_list[0][1] is not None:
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# x_grad
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self.assertShapeEqual(grad_list[0][1], [])
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# y_grad
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self.assertShapeEqual(grad_list[1][1], [])
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# out_grad
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self.assertShapeEqual(grad_list[2][1], [])
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paddle.disable_static()
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def test_static_binary_0D_ND(self):
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paddle.enable_static()
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for api in binary_api_list:
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(
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main_prog, paddle.static.Program()
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):
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# 2) x is 0D, y is ND
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x = paddle.rand([])
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y = paddle.rand([2, 3, 4])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(
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(
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paddle.pir.Value
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if use_pir_api()
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else paddle.static.Variable
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),
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api['cls_method'],
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)(x, y)
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self.assertEqual(out.shape, out_cls.shape)
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else:
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out = api(x, y)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[x, y, out]
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)
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self.assertShapeEqual(x, [])
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self.assertShapeEqual(y, [2, 3, 4])
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self.assertShapeEqual(out, [2, 3, 4])
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if len(grad_list) != 0 and grad_list[0][1] is not None:
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# x_grad
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self.assertShapeEqual(grad_list[0][1], [])
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# y_grad
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self.assertShapeEqual(grad_list[1][1], [2, 3, 4])
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# out_grad
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self.assertShapeEqual(grad_list[2][1], [2, 3, 4])
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paddle.disable_static()
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def test_static_binary_ND_0D(self):
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paddle.enable_static()
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for api in binary_api_list:
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(
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main_prog, paddle.static.Program()
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):
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# 3) x is ND, y is 0d
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x = paddle.rand([2, 3, 4])
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y = paddle.rand([])
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x.stop_gradient = False
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y.stop_gradient = False
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if isinstance(api, dict):
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out = api['func'](x, y)
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out_cls = getattr(
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(
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paddle.pir.Value
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if use_pir_api()
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else paddle.static.Variable
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),
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api['cls_method'],
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)(x, y)
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self.assertEqual(out.shape, out_cls.shape)
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else:
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out = api(x, y)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[x, y, out]
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)
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self.assertShapeEqual(x, [2, 3, 4])
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self.assertShapeEqual(y, [])
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self.assertShapeEqual(out, [2, 3, 4])
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if len(grad_list) != 0 and grad_list[0][1] is not None:
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# x_grad
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self.assertShapeEqual(grad_list[0][1], [2, 3, 4])
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# y_grad
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self.assertShapeEqual(grad_list[1][1], [])
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# out_grad
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self.assertShapeEqual(grad_list[2][1], [2, 3, 4])
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paddle.disable_static()
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def test_static_binary_0D_scalar(self):
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paddle.enable_static()
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for api in binary_api_list:
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(
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main_prog, paddle.static.Program()
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):
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# 4) x is 0D , y is scalar
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x = paddle.rand([])
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x.stop_gradient = False
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y = 0.5
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if isinstance(api, dict):
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out = getattr(
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(
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paddle.pir.Value
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if use_pir_api()
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else paddle.static.Variable
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),
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api['cls_method'],
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)(x, y)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[x, out]
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)
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self.assertShapeEqual(x, [])
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self.assertShapeEqual(out, [])
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if len(grad_list) != 0 and grad_list[0][1] is not None:
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# x_grad
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self.assertShapeEqual(grad_list[0][1], [])
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# out_grad
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self.assertShapeEqual(grad_list[1][1], [])
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paddle.disable_static()
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def test_static_binary_int_api(self):
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paddle.enable_static()
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for api in binary_int_api_list:
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(
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main_prog, paddle.static.Program()
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):
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# 1) x is 0D, y is 0D
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x = paddle.randint(-10, 10, [])
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y = paddle.randint(-10, 10, [])
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out = api(x, y)
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self.assertShapeEqual(out, [])
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# 2) x is ND , y is 0D
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x = paddle.randint(-10, 10, [3, 5])
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y = paddle.randint(-10, 10, [])
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out = api(x, y)
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self.assertShapeEqual(out, [3, 5])
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# 3) x is 0D , y is ND
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x = paddle.randint(-10, 10, [])
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y = paddle.randint(-10, 10, [3, 5])
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out = api(x, y)
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self.assertShapeEqual(out, [3, 5])
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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