wehub-resource-sync
868 lines
28 KiB
Python
868 lines
28 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle.base import core
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RTOL = 1e-5
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ATOL = 1e-8
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DTYPE_ALL_CPU = {
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'float64',
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'float16',
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'float32',
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'bool',
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'uint8',
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'int32',
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'int64',
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}
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# add `bfloat16` if core is compiled with CUDA and support the bfloat16
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DTYPE_ALL_GPU = DTYPE_ALL_CPU | (
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{'bfloat16'}
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if (core.is_compiled_with_cuda() or is_custom_device())
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and core.is_bfloat16_supported(get_device_place())
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else set()
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)
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PLACES = [paddle.CPUPlace()] + (
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[get_device_place()]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else []
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)
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def generate_data(shape, dtype='int64'):
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"""generate test data
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Args:
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shape(list of int): shape of inputs
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dtype(str): dtype
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Returns:
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x, dtype, shape, name
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"""
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return {
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# bfloat16 convert to uint16 for numpy
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'x': np.random.randint(0, 255, size=shape).astype(
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dtype if dtype != 'bfloat16' else 'uint16'
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),
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'dtype': dtype,
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'shape': shape,
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'name': f'{shape}_{dtype}',
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}
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class BaseTest(unittest.TestCase):
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"""Test in each `PLACES` and in `static/dygraph`"""
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def _test_static_api(
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self,
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func_paddle,
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func_numpy,
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x,
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dtype,
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shape,
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name,
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split_paddle,
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split_numpy,
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places=None,
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):
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"""Test `static`
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Args:
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func_paddle: `hsplit`, `vsplit`, `dsplit`, `tensor_split`
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func_numpy: `hsplit`, `vsplit`, `dsplit`, `array_split`
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x: input tensor
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dtype: input tensor's dtype
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shape: input tensor's shape
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name: input tensor's name
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split_paddle: num_or_sections or indices_or_sections in paddle
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split_numpy: `hsplit`, `vsplit`, `dsplit` should convert num_or_sections in paddle to indices_or_sections in numpy. For test error, `split_numpy` is None and skip compare result, ensure the error only raised from paddle.
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places: exec place, default to PLACES
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"""
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paddle.enable_static()
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places = PLACES if places is None else places
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for place in places:
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program = paddle.static.Program()
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exe = paddle.static.Executor(place)
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with paddle.static.program_guard(program):
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input = paddle.static.data(name, shape, dtype)
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input.stop_gradient = False
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feed = {name: x}
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out = func_paddle(input, split_paddle)
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if paddle.framework.in_pir_mode():
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fetch_list = [out]
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grads = paddle.autograd.ir_backward.grad(out, [input])
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out_grad = grads[0]
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fetch_list.append(out_grad)
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*res, res_grad = exe.run(feed=feed, fetch_list=fetch_list)
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self.assertEqual(list(res_grad.shape), list(input.shape))
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else:
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res = exe.run(feed=feed, fetch_list=[out])
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if split_numpy is not None:
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out_ref = func_numpy(x, split_numpy)
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for n, p in zip(out_ref, res):
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np.testing.assert_allclose(n, p, rtol=RTOL, atol=ATOL)
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def _test_dygraph_api(
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self,
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func_paddle,
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func_numpy,
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x,
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dtype,
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shape,
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name,
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split_paddle,
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split_numpy,
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places=None,
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):
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"""Test `dygraph`, and check grads"""
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paddle.disable_static()
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places = PLACES if places is None else places
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for place in places:
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out = func_paddle(paddle.to_tensor(x).astype(dtype), split_paddle)
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if split_numpy is not None:
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out_ref = func_numpy(x, split_numpy)
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for n, p in zip(out_ref, out):
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np.testing.assert_allclose(
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n, p.numpy(), rtol=RTOL, atol=ATOL
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)
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# check grads for the first tensor
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out = out[0]
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for y in out:
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y.stop_gradient = False
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z = y * 123
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grads = paddle.grad(z, y)
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self.assertTrue(len(grads), 1)
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self.assertEqual(grads[0].dtype, y.dtype)
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self.assertEqual(grads[0].shape, y.shape)
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def _test_all(
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self,
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kwargs,
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):
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self._test_dygraph_api(self.func_paddle, self.func_numpy, **kwargs)
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self._test_static_api(self.func_paddle, self.func_numpy, **kwargs)
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class TestHSplit(BaseTest):
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def setUp(self):
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self.func_paddle = paddle.hsplit
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self.func_numpy = np.hsplit
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def test_split_dim(self):
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x = generate_data([6])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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x = generate_data([4, 6])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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x = generate_data([4, 6, 3])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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def test_dtype(self):
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for dtype in DTYPE_ALL_CPU:
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self._test_all(
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{
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**generate_data([6], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [paddle.CPUPlace()],
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},
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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for dtype in DTYPE_ALL_GPU:
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self._test_all(
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{
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**generate_data([6], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [get_device_place()],
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},
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)
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def test_error_dim(self):
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# test 0-d
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x = generate_data([])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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def test_error_split(self):
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x = generate_data([5])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 0, 'split_numpy': None})
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class TestVSplit(BaseTest):
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def setUp(self):
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self.func_paddle = paddle.vsplit
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self.func_numpy = np.vsplit
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def test_split_dim(self):
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x = generate_data([6, 4])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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x = generate_data([6, 4, 3])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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def test_dtype(self):
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for dtype in DTYPE_ALL_CPU:
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self._test_all(
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{
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**generate_data([6, 4], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [paddle.CPUPlace()],
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},
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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for dtype in DTYPE_ALL_GPU:
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self._test_all(
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{
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**generate_data([6, 4], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [get_device_place()],
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},
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)
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def test_error_dim(self):
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# test 0-d
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x = generate_data([])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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# test 1-d
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x = generate_data([6])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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def test_error_split(self):
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x = generate_data([5, 4])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 0, 'split_numpy': None})
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class TestDSplit(BaseTest):
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def setUp(self):
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self.func_paddle = paddle.dsplit
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self.func_numpy = np.dsplit
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def test_split_dim(self):
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x = generate_data([4, 3, 6])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all(
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{
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**x,
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'split_paddle': [2, 4],
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'split_numpy': [2, 4],
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}
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)
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self._test_all(
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{
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**x,
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'split_paddle': (2, 1, 3),
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'split_numpy': (2, 1, 3),
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}
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)
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self._test_all(
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{**x, 'split_paddle': [-1, 1, 3], 'split_numpy': [-1, 1, 3]}
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)
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self._test_all({**x, 'split_paddle': [-1], 'split_numpy': [-1]})
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def test_dtype(self):
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for dtype in DTYPE_ALL_CPU:
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self._test_all(
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{
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**generate_data([4, 2, 6], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [paddle.CPUPlace()],
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},
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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for dtype in DTYPE_ALL_GPU:
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self._test_all(
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{
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**generate_data([4, 2, 6], dtype=dtype),
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'split_paddle': 3,
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'split_numpy': 3,
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'places': [get_device_place()],
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},
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)
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def test_error_dim(self):
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# test 0-d
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x = generate_data([])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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# test 1-d
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x = generate_data([6])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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# test 2-d
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x = generate_data([4, 6])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
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def test_error_split(self):
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x = generate_data([3, 6, 5])
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with self.assertRaises(ValueError):
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self._test_all({**x, 'split_paddle': 0, 'split_numpy': None})
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class TestTensorSplit(BaseTest):
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def setUp(self):
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self.func_paddle = paddle.tensor_split
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self.func_numpy = np.array_split
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def test_split_dim(self):
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x = generate_data([6])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all({**x, 'split_paddle': [2, 4], 'split_numpy': [2, 4]})
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self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
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self._test_all({**x, 'split_paddle': (2, 5), 'split_numpy': (2, 5)})
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self._test_all(
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{**x, 'split_paddle': [2, 4, 5], 'split_numpy': [2, 4, 5]}
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)
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# not evenly split
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x = generate_data([7])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all({**x, 'split_paddle': [2, 4], 'split_numpy': [2, 4]})
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self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
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self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
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self._test_all(
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{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
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)
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x = generate_data([7, 4])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all({**x, 'split_paddle': [2, 4], 'split_numpy': [2, 4]})
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self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
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self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
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self._test_all(
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{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
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)
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x = generate_data([7, 4, 3])
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self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
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self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
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self._test_all({**x, 'split_paddle': [2, 4], 'split_numpy': [2, 4]})
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self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
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self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
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self._test_all(
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{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
def test_split_axis(self):
|
|
# 1-d
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=0)
|
|
self.func_numpy = functools.partial(np.array_split, axis=0)
|
|
|
|
x = generate_data([7])
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
|
|
self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
|
|
self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
|
|
self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
# 2-d
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=1)
|
|
self.func_numpy = functools.partial(np.array_split, axis=1)
|
|
|
|
x = generate_data([4, 7])
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
|
|
self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
|
|
self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
|
|
self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
# 3-d
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=2)
|
|
self.func_numpy = functools.partial(np.array_split, axis=2)
|
|
|
|
x = generate_data([4, 4, 7])
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
|
|
self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
|
|
self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
|
|
self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
# n-d
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=3)
|
|
self.func_numpy = functools.partial(np.array_split, axis=3)
|
|
|
|
x = generate_data([4, 4, 4, 7])
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
|
|
self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
|
|
self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
|
|
self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
# axis -2
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=-2)
|
|
self.func_numpy = functools.partial(np.array_split, axis=-2)
|
|
|
|
x = generate_data([4, 4, 7, 4])
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': 3})
|
|
self._test_all({**x, 'split_paddle': 2, 'split_numpy': 2})
|
|
self._test_all({**x, 'split_paddle': [2, 3], 'split_numpy': [2, 3]})
|
|
self._test_all({**x, 'split_paddle': (2, 6), 'split_numpy': (2, 6)})
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 4, 6], 'split_numpy': [2, 4, 6]}
|
|
)
|
|
|
|
def test_special_indices(self):
|
|
"""indices in a mess, negative index, index out of range"""
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=0)
|
|
self.func_numpy = functools.partial(np.array_split, axis=0)
|
|
|
|
x = generate_data([7])
|
|
# indices' order in a mess
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 1, 3], 'split_numpy': [2, 1, 3]}
|
|
)
|
|
|
|
# index out of range
|
|
self._test_all(
|
|
{**x, 'split_paddle': [2, 3, 16], 'split_numpy': [2, 3, 16]}
|
|
)
|
|
|
|
# index with -1
|
|
self._test_all(
|
|
{**x, 'split_paddle': [3, -1, 16], 'split_numpy': [3, -1, 16]}
|
|
)
|
|
|
|
# mix index
|
|
self._test_all(
|
|
{
|
|
**x,
|
|
'split_paddle': [3, -1, 5, 2, 16],
|
|
'split_numpy': [3, -1, 5, 2, 16],
|
|
}
|
|
)
|
|
|
|
def test_dtype(self):
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=0)
|
|
self.func_numpy = functools.partial(np.array_split, axis=0)
|
|
|
|
for dtype in DTYPE_ALL_CPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [paddle.CPUPlace()],
|
|
},
|
|
)
|
|
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
for dtype in DTYPE_ALL_GPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [get_device_place()],
|
|
},
|
|
)
|
|
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=1)
|
|
self.func_numpy = functools.partial(np.array_split, axis=1)
|
|
|
|
for dtype in DTYPE_ALL_CPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([4, 6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [paddle.CPUPlace()],
|
|
},
|
|
)
|
|
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
for dtype in DTYPE_ALL_GPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([4, 6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [get_device_place()],
|
|
},
|
|
)
|
|
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=2)
|
|
self.func_numpy = functools.partial(np.array_split, axis=2)
|
|
|
|
for dtype in DTYPE_ALL_CPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([4, 4, 6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [paddle.CPUPlace()],
|
|
},
|
|
)
|
|
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
for dtype in DTYPE_ALL_GPU:
|
|
self._test_all(
|
|
{
|
|
**generate_data([4, 4, 6], dtype=dtype),
|
|
'split_paddle': 3,
|
|
'split_numpy': 3,
|
|
'places': [get_device_place()],
|
|
},
|
|
)
|
|
|
|
def test_error_dim(self):
|
|
# axis 0
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=0)
|
|
self.func_numpy = functools.partial(np.array_split, axis=0)
|
|
|
|
# test 0-d
|
|
x = generate_data([])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
# axis 1
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=1)
|
|
self.func_numpy = functools.partial(np.array_split, axis=1)
|
|
|
|
# test 0-d
|
|
x = generate_data([])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
# test 1-d
|
|
x = generate_data([6])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
# axis 2
|
|
self.func_paddle = functools.partial(paddle.tensor_split, axis=2)
|
|
self.func_numpy = functools.partial(np.array_split, axis=2)
|
|
|
|
# test 0-d
|
|
x = generate_data([])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
# test 1-d
|
|
x = generate_data([6])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
# test 2-d
|
|
x = generate_data([4, 6])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 3, 'split_numpy': None})
|
|
|
|
def test_error_split(self):
|
|
x = generate_data([6])
|
|
with self.assertRaises(ValueError):
|
|
self._test_all({**x, 'split_paddle': 0, 'split_numpy': None})
|
|
|
|
|
|
class SplitCompatibilityTest(unittest.TestCase):
|
|
def test_a(
|
|
self,
|
|
):
|
|
"""Test `dygraph`, and check grads"""
|
|
paddle.disable_static()
|
|
x = generate_data([4, 6, 3])["x"]
|
|
places = PLACES
|
|
for place in places:
|
|
out = paddle.tensor_split(
|
|
input=paddle.to_tensor(x).astype("float32"),
|
|
dim=1,
|
|
indices_or_sections=[2, 4],
|
|
)
|
|
out_ref = np.array_split(x, [2, 4], 1)
|
|
|
|
for n, p in zip(out_ref, out):
|
|
np.testing.assert_allclose(n, p.numpy(), rtol=RTOL, atol=ATOL)
|
|
|
|
# check grads for the first tensor
|
|
out = out[0]
|
|
|
|
for y in out:
|
|
y.stop_gradient = False
|
|
z = y * 123
|
|
grads = paddle.grad(z, y)
|
|
self.assertTrue(len(grads), 1)
|
|
self.assertEqual(grads[0].dtype, y.dtype)
|
|
self.assertEqual(grads[0].shape, y.shape)
|
|
|
|
def test_b(
|
|
self,
|
|
):
|
|
"""Test `dygraph`, and check grads"""
|
|
paddle.disable_static()
|
|
x = generate_data([4, 6, 3])["x"]
|
|
places = PLACES
|
|
for place in places:
|
|
out = paddle.tensor_split(
|
|
paddle.to_tensor(x).astype("float32"),
|
|
indices_or_sections=2,
|
|
axis=2,
|
|
)
|
|
out_ref = np.array_split(x, 2, 2)
|
|
|
|
for n, p in zip(out_ref, out):
|
|
np.testing.assert_allclose(n, p.numpy(), rtol=RTOL, atol=ATOL)
|
|
|
|
# check grads for the first tensor
|
|
out = out[0]
|
|
|
|
for y in out:
|
|
y.stop_gradient = False
|
|
z = y * 123
|
|
grads = paddle.grad(z, y)
|
|
self.assertTrue(len(grads), 1)
|
|
self.assertEqual(grads[0].dtype, y.dtype)
|
|
self.assertEqual(grads[0].shape, y.shape)
|
|
|
|
def test_c(
|
|
self,
|
|
):
|
|
"""Test `dygraph`, and check grads"""
|
|
paddle.disable_static()
|
|
x = generate_data([4, 6, 3])["x"]
|
|
places = PLACES
|
|
for place in places:
|
|
out = paddle.tensor_split(
|
|
paddle.to_tensor(x).astype("float32"),
|
|
sections=2,
|
|
dim=2,
|
|
)
|
|
out_ref = np.array_split(x, 2, 2)
|
|
|
|
for n, p in zip(out_ref, out):
|
|
np.testing.assert_allclose(n, p.numpy(), rtol=RTOL, atol=ATOL)
|
|
|
|
# check grads for the first tensor
|
|
out = out[0]
|
|
|
|
for y in out:
|
|
y.stop_gradient = False
|
|
z = y * 123
|
|
grads = paddle.grad(z, y)
|
|
self.assertTrue(len(grads), 1)
|
|
self.assertEqual(grads[0].dtype, y.dtype)
|
|
self.assertEqual(grads[0].shape, y.shape)
|
|
|
|
def test_d(
|
|
self,
|
|
):
|
|
"""Test `dygraph`, and check grads"""
|
|
paddle.disable_static()
|
|
x = generate_data([4, 6, 3])["x"]
|
|
places = PLACES
|
|
for place in places:
|
|
out = paddle.tensor_split(
|
|
input=paddle.to_tensor(x).astype("float32"),
|
|
dim=1,
|
|
indices=[2, 4],
|
|
)
|
|
out_ref = np.array_split(x, [2, 4], 1)
|
|
|
|
for n, p in zip(out_ref, out):
|
|
np.testing.assert_allclose(n, p.numpy(), rtol=RTOL, atol=ATOL)
|
|
|
|
# check grads for the first tensor
|
|
out = out[0]
|
|
|
|
for y in out:
|
|
y.stop_gradient = False
|
|
z = y * 123
|
|
grads = paddle.grad(z, y)
|
|
self.assertTrue(len(grads), 1)
|
|
self.assertEqual(grads[0].dtype, y.dtype)
|
|
self.assertEqual(grads[0].shape, y.shape)
|
|
|
|
def test_e(
|
|
self,
|
|
):
|
|
"""Test `dygraph`, and check grads"""
|
|
paddle.disable_static()
|
|
x = generate_data([4, 6, 3])["x"]
|
|
places = PLACES
|
|
for place in places:
|
|
out = paddle.tensor_split(
|
|
indices=[2, 4],
|
|
dim=1,
|
|
input=paddle.to_tensor(x).astype("float32"),
|
|
)
|
|
out_ref = np.array_split(x, [2, 4], 1)
|
|
|
|
for n, p in zip(out_ref, out):
|
|
np.testing.assert_allclose(n, p.numpy(), rtol=RTOL, atol=ATOL)
|
|
|
|
# check grads for the first tensor
|
|
out = out[0]
|
|
|
|
for y in out:
|
|
y.stop_gradient = False
|
|
z = y * 123
|
|
grads = paddle.grad(z, y)
|
|
self.assertTrue(len(grads), 1)
|
|
self.assertEqual(grads[0].dtype, y.dtype)
|
|
self.assertEqual(grads[0].shape, y.shape)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|