wehub-resource-sync
280 lines
9.4 KiB
Python
280 lines
9.4 KiB
Python
# 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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reduce_api_list = [
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paddle.sum,
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paddle.mean,
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paddle.nansum,
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paddle.nanmean,
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paddle.median,
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paddle.nanmedian,
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paddle.min,
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paddle.max,
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paddle.amin,
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paddle.amax,
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paddle.prod,
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paddle.logsumexp,
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paddle.all,
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paddle.any,
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paddle.count_nonzero,
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]
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# Use to test zero-dim of reduce API
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class TestReduceAPI(unittest.TestCase):
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def assertShapeEqual(self, out, target_tuple):
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if not paddle.framework.in_pir_mode():
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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_dygraph_reduce(self):
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paddle.disable_static()
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for api in reduce_api_list:
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# 1) x is 0D
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, []).astype('bool')
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else:
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x = paddle.rand([])
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x.stop_gradient = False
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out = api(x, axis=None)
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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 api not in [paddle.count_nonzero]:
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np.testing.assert_allclose(out.numpy(), x.numpy())
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if api not in [paddle.median, paddle.nanmedian]:
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out_empty_list = api(x, axis=[])
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self.assertEqual(out_empty_list, out)
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self.assertEqual(out_empty_list.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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np.testing.assert_allclose(x.grad.numpy(), np.array(1.0))
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np.testing.assert_allclose(out.grad.numpy(), np.array(1.0))
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out1 = api(x, axis=0)
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self.assertEqual(out1.shape, [])
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self.assertEqual(out1, out)
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out1.backward()
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out2 = api(x, axis=-1)
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self.assertEqual(out2.shape, [])
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self.assertEqual(out2, out)
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out2.backward()
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if x.grad is not None:
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self.assertEqual(x.grad.shape, [])
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np.testing.assert_allclose(x.grad.numpy(), np.array(3.0))
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# 2) x is 1D, axis=0, reduce to 0D
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, [5]).astype('bool')
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else:
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x = paddle.rand([5])
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x.stop_gradient = False
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out = api(x, axis=0)
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out.retain_grads()
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out.backward()
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self.assertEqual(out.shape, [])
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if x.grad is not None:
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self.assertEqual(out.grad.shape, [])
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self.assertEqual(x.grad.shape, [5])
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# 3) x is ND, reduce to 0D
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, [3, 5]).astype('bool')
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else:
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x = paddle.rand([3, 5])
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x.stop_gradient = False
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out = api(x, axis=None)
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out.retain_grads()
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out.backward()
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self.assertEqual(out.shape, [])
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if x.grad is not None:
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self.assertEqual(out.grad.shape, [])
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self.assertEqual(x.grad.shape, [3, 5])
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# 4) x is ND, reduce to 0D, keepdim=True
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, [3, 5]).astype('bool')
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else:
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x = paddle.rand([3, 5])
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x.stop_gradient = False
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out = api(x, keepdim=True)
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out.retain_grads()
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out.backward()
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self.assertEqual(out.shape, [1, 1])
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if x.grad is not None:
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self.assertEqual(out.grad.shape, [1, 1])
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self.assertEqual(x.grad.shape, [3, 5])
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paddle.enable_static()
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def test_static_reduce_x_0D(self):
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paddle.enable_static()
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for api in reduce_api_list:
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main_prog = paddle.static.Program()
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exe = paddle.static.Executor()
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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
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, []).astype('bool')
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else:
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x = paddle.rand([])
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x.stop_gradient = False
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out = api(x, axis=None)
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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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if api not in [paddle.median, paddle.nanmedian]:
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out_empty_list = api(x, axis=[])
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self.assertShapeEqual(out_empty_list, [])
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out1 = api(x, axis=0)
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self.assertShapeEqual(out1, [])
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out2 = api(x, axis=-1)
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self.assertShapeEqual(out2, [])
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fetch_list = [x, out]
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fetch_list.extend(
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[
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_grad
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for _param, _grad in grad_list
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if isinstance(
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_grad,
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(paddle.pir.Value, paddle.base.framework.Variable),
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)
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]
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)
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res = exe.run(main_prog, fetch_list=fetch_list)
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for res_data in res:
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self.assertEqual(res_data.shape, ())
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if api not in [paddle.count_nonzero]:
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np.testing.assert_allclose(res[0], res[1])
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if len(res) > 3:
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np.testing.assert_allclose(res[-2], np.array(1.0))
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np.testing.assert_allclose(res[-1], np.array(1.0))
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if len(res) > 2:
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np.testing.assert_allclose(res[-1], np.array(1.0))
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def test_static_reduce_ND_0D(self):
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paddle.enable_static()
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for api in reduce_api_list:
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main_prog = paddle.static.Program()
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exe = paddle.static.Executor()
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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 ND, reduce to 0D
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, [3, 5]).astype('bool')
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else:
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x = paddle.rand([3, 5])
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x.stop_gradient = False
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out = api(x, axis=None)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[out, x]
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)
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fetch_list = [out]
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fetch_list.extend(
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[
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_grad
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for _param, _grad in grad_list
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if isinstance(
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_grad,
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(paddle.pir.Value, paddle.base.framework.Variable),
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)
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]
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)
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res = exe.run(main_prog, fetch_list=fetch_list)
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self.assertEqual(res[0].shape, ())
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if len(res) > 1:
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self.assertEqual(res[1].shape, ())
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if len(res) > 2:
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self.assertEqual(res[2].shape, (3, 5))
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def test_static_reduce_x_1D(self):
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paddle.enable_static()
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for api in reduce_api_list:
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main_prog = paddle.static.Program()
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exe = paddle.static.Executor()
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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 1D, axis=0, reduce to 0D
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if api in [paddle.all, paddle.any]:
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x = paddle.randint(0, 2, [5]).astype('bool')
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else:
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x = paddle.rand([5])
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x.stop_gradient = False
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out = api(x, axis=0)
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grad_list = paddle.static.append_backward(
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out, parameter_list=[out, x]
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)
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fetch_list = [out]
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fetch_list.extend(
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[
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_grad
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for _param, _grad in grad_list
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if isinstance(
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_grad,
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(paddle.pir.Value, paddle.base.framework.Variable),
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)
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]
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)
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res = exe.run(main_prog, fetch_list=fetch_list)
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self.assertEqual(res[0].shape, ())
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if len(res) > 1:
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self.assertEqual(res[1].shape, ())
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if len(res) > 2:
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self.assertEqual(res[2].shape, (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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