wehub-resource-sync
1029 lines
33 KiB
Python
1029 lines
33 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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import unittest
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import gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op_test import (
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OpTest,
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check_cudnn_version_and_compute_capability,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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from utils import dygraph_guard, static_guard
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import paddle
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from paddle import base
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paddle.enable_static()
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class TestTransposeOp(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.inputs = {'X': np.random.random(self.shape).astype("float64")}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("float64"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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self.if_enable_cinn()
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def if_enable_cinn(self):
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pass
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def initTestCase(self):
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self.shape = (3, 40)
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self.axis = (1, 0)
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class TestTransposeOp_ZeroDim(TestTransposeOp):
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def initTestCase(self):
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self.shape = ()
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self.axis = ()
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestCase0(TestTransposeOp):
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def initTestCase(self):
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self.shape = (100,)
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self.axis = (0,)
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class TestCase1(TestTransposeOp):
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def initTestCase(self):
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self.shape = (3, 4, 10)
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self.axis = (0, 2, 1)
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class TestCase2(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5)
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self.axis = (0, 2, 3, 1)
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class TestCase3(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.axis = (4, 2, 3, 1, 0)
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class TestCase4(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6, 1)
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self.axis = (4, 2, 3, 1, 0, 5)
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class TestCase5(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 16, 96)
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self.axis = (0, 2, 1)
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class TestCase6(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 10, 12, 16)
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self.axis = (3, 1, 2, 0)
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class TestCase7(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 10, 2, 16)
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self.axis = (0, 1, 3, 2)
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class TestCase8(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
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self.axis = (0, 1, 3, 2, 4, 5, 6, 7)
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class TestCase9(TestTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
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self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
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class TestCase10(TestTransposeOp):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.inputs = {'X': np.random.random(self.shape).astype("float64")}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("float64"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def initTestCase(self):
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self.shape = (10, 8, 2)
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self.axis = (-1, 1, -3)
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class TestCase_ZeroDim(TestTransposeOp):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.enable_cinn = False
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self.inputs = {'X': np.random.random(self.shape).astype("float64")}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("float64"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def initTestCase(self):
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self.shape = ()
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self.axis = ()
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class TestAutoTuneTransposeOp(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.inputs = {'X': np.random.random(self.shape).astype("float64")}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("float64"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def initTestCase(self):
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base.core.set_autotune_range(0, 3)
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base.core.update_autotune_status()
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base.core.enable_autotune()
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self.shape = (1, 12, 256, 1)
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self.axis = (0, 3, 2, 1)
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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base.core.disable_autotune()
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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@unittest.skipIf(
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not check_cudnn_version_and_compute_capability(min_device_capability=9.0),
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"core is not compiled with CUDA or not support native fp8",
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)
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class TestFP8FastTranspose(unittest.TestCase):
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def setUp(self):
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self.dtype = paddle.float8_e4m3fn
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self.test_cases = [
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{"shape": (7168, 16384), "perm": [1, 0], "name": "2D(7168,16384)"},
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{
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"shape": (8, 7168, 4096),
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"perm": [0, 2, 1],
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"name": "3D(8,7168,4096)",
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},
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{
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"shape": (8, 2048, 7168),
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"perm": [0, 2, 1],
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"name": "3D(8,2048,7168)",
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},
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]
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def test_verify_transpose(self):
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paddle.disable_static()
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with paddle.no_grad():
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for case in self.test_cases:
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x = paddle.randn(case["shape"]).cast(self.dtype)
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np_data = x.numpy()
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gold = np.transpose(np_data, case["perm"])
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out = paddle.transpose(x, case["perm"]).contiguous()
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np.testing.assert_equal(out.numpy(), gold)
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paddle.enable_static()
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class TestAutoTuneTransposeFP16Op(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.dtype = np.float16
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype(self.dtype),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def initTestCase(self):
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base.core.set_autotune_range(0, 3)
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base.core.update_autotune_status()
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base.core.enable_autotune()
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self.shape = (1, 12, 256, 1)
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self.axis = (0, 3, 2, 1)
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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base.core.disable_autotune()
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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class TestAutoTuneTransposeBF16Op(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.dtype = np.uint16
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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self.prim_op_type = "prim"
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self.if_enable_cinn()
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x = np.random.random(self.shape).astype("float32")
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': convert_float_to_uint16(
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np.random.random(self.shape).astype("float32")
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),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def if_enable_cinn(self):
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self.enable_cinn = False
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def initTestCase(self):
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base.core.set_autotune_range(0, 3)
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base.core.update_autotune_status()
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base.core.enable_autotune()
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self.shape = (2, 8, 10)
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self.axis = (0, 2, 1)
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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base.core.disable_autotune()
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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class TestTransposeFP16Op(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.dtype = np.float16
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self.prim_op_type = "prim"
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self.if_enable_cinn()
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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x = np.random.random(self.shape).astype(self.dtype)
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self.inputs = {'X': x}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype(self.dtype),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def if_enable_cinn(self):
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pass
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def initTestCase(self):
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self.shape = (3, 40)
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self.axis = (1, 0)
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class TestTransposeBF16Op(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.dtype = np.uint16
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self.prim_op_type = "prim"
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self.enable_cinn = False
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self.python_api = paddle.transpose
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self.public_python_api = paddle.transpose
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x = np.random.random(self.shape).astype("float32")
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self.if_enable_cinn()
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {
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'axis': list(self.axis),
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'use_onednn': self.use_onednn,
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}
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self.outputs = {
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'XShape': convert_float_to_uint16(
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np.random.random(self.shape).astype("float32")
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),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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def if_enable_cinn(self):
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self.enable_cinn = False
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_onednn = False
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'], check_pir=True)
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def test_check_grad(self):
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pass
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def initTestCase(self):
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self.shape = (3, 2)
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self.axis = (1, 0)
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class TestTransposeOpBool(TestTransposeOp):
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def test_check_grad(self):
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pass
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class TestTransposeOpBool1D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (100,)
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self.axis = (0,)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool2D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (3, 40)
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self.axis = (1, 0)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool3D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (3, 4, 10)
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self.axis = (0, 2, 1)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool4D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5)
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self.axis = (0, 2, 3, 1)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool5D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.axis = (4, 2, 3, 1, 0)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool6D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6, 1)
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self.axis = (4, 2, 3, 1, 0, 5)
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self.inputs = {'X': np.random.random(self.shape).astype("bool")}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("bool"),
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'Out': self.inputs['X'].transpose(self.axis),
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}
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class TestTransposeOpBool7D(TestTransposeOpBool):
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def initTestCase(self):
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self.shape = (2, 3, 2, 3, 2, 4, 3)
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self.axis = (0, 1, 3, 2, 4, 5, 6)
|
|
self.inputs = {'X': np.random.random(self.shape).astype("bool")}
|
|
self.outputs = {
|
|
'XShape': np.random.random(self.shape).astype("bool"),
|
|
'Out': self.inputs['X'].transpose(self.axis),
|
|
}
|
|
|
|
|
|
class TestTransposeOpBool8D(TestTransposeOpBool):
|
|
def initTestCase(self):
|
|
self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
|
|
self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
|
|
self.inputs = {'X': np.random.random(self.shape).astype("bool")}
|
|
self.outputs = {
|
|
'XShape': np.random.random(self.shape).astype("bool"),
|
|
'Out': self.inputs['X'].transpose(self.axis),
|
|
}
|
|
|
|
|
|
class TestTransposeOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(
|
|
name='x', shape=[-1, 10, 5, 3], dtype='float64'
|
|
)
|
|
|
|
def test_x_Variable_check():
|
|
# the Input(x)'s type must be Variable
|
|
paddle.transpose("not_variable", perm=[1, 0, 2])
|
|
|
|
self.assertRaises(TypeError, test_x_Variable_check)
|
|
|
|
def test_perm_list_check():
|
|
# Input(perm)'s type must be list
|
|
paddle.transpose(x, perm="[1, 0, 2]")
|
|
|
|
self.assertRaises(TypeError, test_perm_list_check)
|
|
|
|
def test_perm_length_and_x_dim_check():
|
|
# Input(perm) is the permutation of dimensions of Input(input)
|
|
# its length should be equal to dimensions of Input(input)
|
|
paddle.transpose(x, perm=[1, 0, 2, 3, 4])
|
|
|
|
self.assertRaises(ValueError, test_perm_length_and_x_dim_check)
|
|
|
|
def test_each_elem_value_check():
|
|
# Each element in Input(perm) should be less than Input(x)'s dimension
|
|
paddle.transpose(x, perm=[3, 5, 7])
|
|
|
|
self.assertRaises(ValueError, test_each_elem_value_check)
|
|
|
|
|
|
class TestTransposeApi(unittest.TestCase):
|
|
def test_static_out(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name='x', shape=[2, 3, 4], dtype='float32')
|
|
x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
|
|
x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
x_np = np.random.random([2, 3, 4]).astype("float32")
|
|
result1, result2 = exe.run(
|
|
feed={"x": x_np}, fetch_list=[x_trans1, x_trans2]
|
|
)
|
|
expected_result1 = np.transpose(x_np, [1, 0, 2])
|
|
expected_result2 = np.transpose(x_np, (2, 1, 0))
|
|
|
|
np.testing.assert_array_equal(result1, expected_result1)
|
|
np.testing.assert_array_equal(result2, expected_result2)
|
|
|
|
def test_dygraph_out(self):
|
|
# This is an old test before 2.0 API so we need to disable static
|
|
# to trigger dygraph
|
|
paddle.disable_static()
|
|
x = paddle.randn([2, 3, 4])
|
|
x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
|
|
x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
|
|
x_np = x.numpy()
|
|
expected_result1 = np.transpose(x_np, [1, 0, 2])
|
|
expected_result2 = np.transpose(x_np, (2, 1, 0))
|
|
|
|
np.testing.assert_array_equal(x_trans1.numpy(), expected_result1)
|
|
np.testing.assert_array_equal(x_trans2.numpy(), expected_result2)
|
|
# This is an old test before 2.0 API so we enable static again after
|
|
# dygraph test
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestTAPI(unittest.TestCase):
|
|
def test_static_out(self):
|
|
with base.program_guard(base.Program()):
|
|
data = paddle.static.data(shape=[10], dtype="float64", name="data")
|
|
data_t = paddle.t(data)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
data_np = np.random.random([10]).astype("float64")
|
|
(result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
with base.program_guard(base.Program()):
|
|
data = paddle.static.data(
|
|
shape=[10, 5], dtype="float64", name="data"
|
|
)
|
|
data_t = paddle.t(data)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
data_np = np.random.random([10, 5]).astype("float64")
|
|
(result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
with base.program_guard(base.Program()):
|
|
data = paddle.static.data(
|
|
shape=[1, 5], dtype="float64", name="data"
|
|
)
|
|
data_t = paddle.t(data)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
data_np = np.random.random([1, 5]).astype("float64")
|
|
(result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
def test_dygraph_out(self):
|
|
with base.dygraph.guard():
|
|
np_x = np.random.random([10]).astype("float64")
|
|
data = paddle.to_tensor(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
with base.dygraph.guard():
|
|
np_x = np.random.random([10, 5]).astype("float64")
|
|
data = paddle.to_tensor(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
with base.dygraph.guard():
|
|
np_x = np.random.random([1, 5]).astype("float64")
|
|
data = paddle.to_tensor(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
def test_errors(self):
|
|
with base.program_guard(base.Program()):
|
|
x = paddle.static.data(name='x', shape=[10, 5, 3], dtype='float64')
|
|
|
|
def test_x_dimension_check():
|
|
paddle.t(x)
|
|
|
|
self.assertRaises(ValueError, test_x_dimension_check)
|
|
|
|
|
|
class TestMoveAxis(unittest.TestCase):
|
|
def test_static_moveaxis1(self):
|
|
x_np = np.random.randn(2, 3, 4, 5, 7)
|
|
expected = np.moveaxis(x_np, [0, 4, 3, 2], [1, 3, 2, 0])
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(base.Program()):
|
|
x = paddle.static.data("x", shape=[2, 3, 4, 5, 7], dtype='float64')
|
|
out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])
|
|
|
|
exe = paddle.static.Executor()
|
|
out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]
|
|
|
|
np.testing.assert_array_equal(out_np, expected)
|
|
|
|
def test_dygraph_moveaxis1(self):
|
|
x_np = np.random.randn(2, 3, 4, 5, 7)
|
|
expected = np.moveaxis(x_np, [0, 4, 3, 2], [1, 3, 2, 0])
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_np)
|
|
out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])
|
|
self.assertEqual(out.shape, [4, 2, 5, 7, 3])
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
paddle.enable_static()
|
|
|
|
def test_static_moveaxis2(self):
|
|
x_np = np.random.randn(2, 3, 5)
|
|
expected = np.moveaxis(x_np, -2, -1)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(base.Program()):
|
|
x = paddle.static.data("x", shape=[2, 3, 5], dtype='float64')
|
|
out = x.moveaxis(-2, -1)
|
|
|
|
exe = paddle.static.Executor()
|
|
out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]
|
|
|
|
np.testing.assert_array_equal(out_np, expected)
|
|
|
|
def test_dygraph_moveaxis2(self):
|
|
x_np = np.random.randn(2, 3, 5)
|
|
expected = np.moveaxis(x_np, -2, -1)
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_np)
|
|
out = x.moveaxis(-2, -1)
|
|
self.assertEqual(out.shape, [2, 5, 3])
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
paddle.enable_static()
|
|
|
|
def test_moveaxis3(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
[[1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j]]
|
|
)
|
|
out = x.moveaxis(0, 1)
|
|
self.assertEqual(out.shape, [2, 3])
|
|
paddle.enable_static()
|
|
|
|
def test_moveaxis_alias_movedim(self):
|
|
x_np = np.random.randn(2, 3, 5)
|
|
expected = np.moveaxis(x_np, -2, -1)
|
|
paddle.disable_static()
|
|
# tensor method
|
|
x = paddle.to_tensor(x_np)
|
|
out = x.movedim(-2, -1)
|
|
self.assertEqual(out.shape, [2, 5, 3])
|
|
# paddle method
|
|
out = paddle.movedim(x, -2, -1)
|
|
self.assertEqual(out.shape, [2, 5, 3])
|
|
# arg alias
|
|
out = paddle.movedim(input=x, source=-2, destination=-1)
|
|
self.assertEqual(out.shape, [2, 5, 3])
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
paddle.enable_static()
|
|
|
|
def test_error(self):
|
|
x = paddle.randn([2, 3, 4, 5])
|
|
# src must have the same number with dst
|
|
with self.assertRaises(AssertionError):
|
|
paddle.moveaxis(x, [1, 0], [2])
|
|
|
|
# each element of src must be unique
|
|
with self.assertRaises(ValueError):
|
|
paddle.moveaxis(x, [1, 1], [0, 2])
|
|
|
|
# each element of dst must be unique
|
|
with self.assertRaises(ValueError):
|
|
paddle.moveaxis(x, [0, 1], [2, 2])
|
|
|
|
# each element of src must be integer
|
|
with self.assertRaises(AssertionError):
|
|
paddle.moveaxis(x, [0.5], [1])
|
|
|
|
# each element of dst must be integer
|
|
with self.assertRaises(AssertionError):
|
|
paddle.moveaxis(x, [0], [1.5])
|
|
|
|
# each element of src must be in the range of [-4, 3)
|
|
with self.assertRaises(AssertionError):
|
|
paddle.moveaxis(x, [-10, 1], [2, 3])
|
|
|
|
# each element of dst must be in the range of [-4, 3)
|
|
with self.assertRaises(AssertionError):
|
|
paddle.moveaxis(x, [2, 1], [10, 3])
|
|
|
|
|
|
class TestTransposeDoubleGradCheck(unittest.TestCase):
|
|
def transpose_wrapper(self, x):
|
|
return paddle.transpose(x[0], [1, 0, 2])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 3, 4], dtype)
|
|
data.persistable = True
|
|
out = paddle.transpose(data, [1, 0, 2])
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.transpose_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestTransposeTripleGradCheck(unittest.TestCase):
|
|
def transpose_wrapper(self, x):
|
|
return paddle.transpose(x[0], [1, 0, 2])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 3, 4], dtype)
|
|
data.persistable = True
|
|
out = paddle.transpose(data, [1, 0, 2])
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.triple_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.transpose_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestTransposeAPI_ZeroDim(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
|
|
x = paddle.rand([])
|
|
x.stop_gradient = False
|
|
out = paddle.transpose(x, [])
|
|
if hasattr(out, 'retain_grads'):
|
|
out.retain_grads()
|
|
out.backward()
|
|
|
|
self.assertEqual(out.shape, [])
|
|
self.assertEqual(x.grad.shape, [])
|
|
self.assertEqual(out.grad.shape, [])
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestMatrixTransposeApi(unittest.TestCase):
|
|
def test_static_out(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name='x', shape=[2, 3, 4], dtype='float32')
|
|
x_trans1 = paddle.matrix_transpose(x)
|
|
x_trans2 = paddle.linalg.matrix_transpose(x)
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
x_np = np.random.random([2, 3, 4]).astype("float32")
|
|
result1, result2 = exe.run(
|
|
feed={"x": x_np}, fetch_list=[x_trans1, x_trans2]
|
|
)
|
|
expected_result = np.transpose(x_np, (0, 2, 1))
|
|
|
|
np.testing.assert_array_equal(result1, expected_result)
|
|
np.testing.assert_array_equal(result2, expected_result)
|
|
|
|
def test_dygraph_out(self):
|
|
paddle.disable_static()
|
|
x = paddle.randn([2, 3, 4])
|
|
x_trans1 = paddle.matrix_transpose(x)
|
|
x_trans2 = paddle.linalg.matrix_transpose(x)
|
|
x_np = x.numpy()
|
|
expected_result = np.transpose(x_np, (0, 2, 1))
|
|
|
|
np.testing.assert_array_equal(x_trans1.numpy(), expected_result)
|
|
np.testing.assert_array_equal(x_trans2.numpy(), expected_result)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestMatrixTransposeAPI_ZeroDim(unittest.TestCase):
|
|
def test_zero_dim_error(self):
|
|
paddle.disable_static()
|
|
x = paddle.rand([])
|
|
with self.assertRaises(ValueError) as context:
|
|
out = paddle.matrix_transpose(x)
|
|
self.assertIn(
|
|
"Tensor.ndim(0) is required to be greater than or equal to 2",
|
|
str(context.exception),
|
|
)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestMatrixTransposeApiFPPrecision(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def check_dtype_transpose(self, dtype):
|
|
x_np = np.random.random([2, 3, 4]).astype(dtype)
|
|
x = paddle.to_tensor(x_np)
|
|
out = paddle.matrix_transpose(x)
|
|
expected_result = np.transpose(x_np, (0, 2, 1))
|
|
np.testing.assert_array_equal(out.numpy(), expected_result)
|
|
|
|
def test_fp32(self):
|
|
self.check_dtype_transpose('float32')
|
|
|
|
def test_fp64(self):
|
|
self.check_dtype_transpose('float64')
|
|
|
|
def test_fp16(self):
|
|
if paddle.is_compiled_with_cuda() or is_custom_device():
|
|
self.check_dtype_transpose('float16')
|
|
|
|
def test_int8(self):
|
|
self.check_dtype_transpose('int8')
|
|
|
|
def test_int16(self):
|
|
self.check_dtype_transpose('int16')
|
|
|
|
def test_int32(self):
|
|
self.check_dtype_transpose('int32')
|
|
|
|
def test_int64(self):
|
|
self.check_dtype_transpose('int64')
|
|
|
|
def tearDown(self):
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestTransposeCompatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
self.places = [paddle.CPUPlace()]
|
|
if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
|
|
self.places.append(get_device_place())
|
|
self.func = paddle.transpose
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.shape = [4, 5, 6]
|
|
self.dtype = 'float32'
|
|
self.dim0 = 0
|
|
self.dim1 = 1
|
|
self.perm = [1, 0, 2]
|
|
|
|
self.np_input = np.random.rand(*self.shape).astype(self.dtype)
|
|
self.np_out = np.transpose(self.np_input, axes=self.perm)
|
|
|
|
def test_dygraph_compatibility(self):
|
|
with dygraph_guard():
|
|
for place in self.places:
|
|
paddle.device.set_device(place)
|
|
x = paddle.to_tensor(self.np_input)
|
|
outs = []
|
|
outs.append(paddle.transpose(x, perm=self.perm))
|
|
outs.append(paddle.transpose(x=x, perm=self.perm))
|
|
outs.append(paddle.transpose(input=x, perm=self.perm))
|
|
outs.append(paddle.transpose(x, self.dim0, self.dim1))
|
|
outs.append(
|
|
paddle.transpose(x=x, dim0=self.dim0, dim1=self.dim1)
|
|
)
|
|
outs.append(
|
|
paddle.transpose(input=x, dim0=self.dim0, dim1=self.dim1)
|
|
)
|
|
|
|
outs.append(x.transpose(self.perm))
|
|
outs.append(x.transpose(self.dim0, self.dim1))
|
|
outs.append(x.transpose(perm=self.perm))
|
|
outs.append(x.transpose(dim0=self.dim0, dim1=self.dim1))
|
|
outs.append(x.transpose(self.dim0, dim1=self.dim1))
|
|
|
|
for out in outs:
|
|
np.testing.assert_array_equal(self.np_out, out.numpy())
|
|
|
|
def test_static_compatibility(self):
|
|
with static_guard():
|
|
for place in self.places:
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with paddle.base.program_guard(main, startup):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.shape, dtype=self.dtype
|
|
)
|
|
outs = []
|
|
outs.append(paddle.transpose(x, perm=self.perm))
|
|
outs.append(paddle.transpose(x=x, perm=self.perm))
|
|
outs.append(paddle.transpose(input=x, perm=self.perm))
|
|
outs.append(paddle.transpose(x, self.dim0, self.dim1))
|
|
outs.append(
|
|
paddle.transpose(x=x, dim0=self.dim0, dim1=self.dim1)
|
|
)
|
|
outs.append(
|
|
paddle.transpose(
|
|
input=x, dim0=self.dim0, dim1=self.dim1
|
|
)
|
|
)
|
|
|
|
outs.append(x.transpose(self.perm))
|
|
outs.append(x.transpose(self.dim0, self.dim1))
|
|
outs.append(x.transpose(perm=self.perm))
|
|
outs.append(x.transpose(dim0=self.dim0, dim1=self.dim1))
|
|
outs.append(x.transpose(self.dim0, dim1=self.dim1))
|
|
|
|
exe = paddle.base.Executor(place)
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_input},
|
|
fetch_list=outs,
|
|
)
|
|
for out in fetches:
|
|
np.testing.assert_array_equal(self.np_out, out)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|