wehub-resource-sync
536 lines
16 KiB
Python
536 lines
16 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 numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import base, tensor
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class TestUnbind(unittest.TestCase):
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def setUp(self):
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self.init_dtype()
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self.input_1 = np.random.random([2, 3]).astype(self.dtype)
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if self.dtype == 'complex64' or self.dtype == 'complex128':
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self.input_1 = (
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np.random.random([2, 3]) + 1j * np.random.random([2, 3])
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).astype(self.dtype)
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def init_dtype(self):
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self.dtype = 'float32'
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def test_unbind(self):
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paddle.enable_static()
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self.init_dtype()
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main_program = paddle.base.Program()
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startup_program = paddle.base.Program()
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with paddle.base.program_guard(
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main_program=main_program, startup_program=startup_program
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):
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x_1 = paddle.static.data(shape=[2, 3], dtype=self.dtype, name='x_1')
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[out_0, out_1] = tensor.unbind(input=x_1, axis=0)
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axis = paddle.static.data(shape=[], dtype='int32', name='axis')
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exe = base.Executor(place=base.CPUPlace())
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[res_1, res_2] = exe.run(
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feed={"x_1": self.input_1, "axis": 0},
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fetch_list=[out_0, out_1],
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)
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np.testing.assert_array_equal(res_1, self.input_1[0, 0:100])
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np.testing.assert_array_equal(res_2, self.input_1[1, 0:100])
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def test_unbind_static_fp16_gpu(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input = np.random.random([2, 3]).astype("float16")
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x = paddle.static.data(name="x", shape=[2, 3], dtype="float16")
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y = paddle.unbind(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_array_equal(res[0], input[0, :])
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np.testing.assert_array_equal(res[1], input[1, :])
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def test_unbind_dygraph(self):
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with base.dygraph.guard():
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x = paddle.to_tensor(self.input_1)
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x.stop_gradient = False
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[res_1, res_2] = paddle.unbind(x, 0)
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np.testing.assert_array_equal(res_1, self.input_1[0, 0:100])
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np.testing.assert_array_equal(res_2, self.input_1[1, 0:100])
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out = paddle.add_n([res_1, res_2])
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np_grad = np.ones(x.shape, self.dtype)
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out.backward()
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np.testing.assert_array_equal(x.grad.numpy(False), np_grad)
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class TestUnbind_complex64(TestUnbind):
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def init_dtype(self):
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self.dtype = 'complex64'
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def test_unbind_static_fp16_gpu(self):
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pass
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class TestUnbind_complex128(TestUnbind):
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def init_dtype(self):
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self.dtype = 'complex128'
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def test_unbind_static_fp16_gpu(self):
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pass
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class TestLayersUnbind(unittest.TestCase):
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def setUp(self):
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self.init_dtype()
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self.input_1 = np.random.random([2, 3]).astype(self.dtype)
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if self.dtype == 'complex64' or self.dtype == 'complex128':
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self.input_1 = (
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np.random.random([2, 3]) + 1j * np.random.random([2, 3])
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).astype(self.dtype)
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def init_dtype(self):
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self.dtype = 'float32'
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def test_layers_unbind(self):
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paddle.enable_static()
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(prog, startup_prog):
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x_1 = paddle.static.data(shape=[2, 3], dtype=self.dtype, name='x_1')
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[out_0, out_1] = paddle.unbind(input=x_1, axis=0)
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axis = paddle.static.data(shape=[], dtype='int32', name='axis')
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exe = base.Executor(place=base.CPUPlace())
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[res_1, res_2] = exe.run(
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feed={"x_1": self.input_1, "axis": 0},
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fetch_list=[out_0, out_1],
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)
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np.testing.assert_array_equal(res_1, self.input_1[0, 0:100])
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np.testing.assert_array_equal(res_2, self.input_1[1, 0:100])
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class TestLayersUnbind_complex64(TestLayersUnbind):
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def init_dtype(self):
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self.dtype = 'complex64'
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class TestLayersUnbind_complex128(TestLayersUnbind):
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def init_dtype(self):
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self.dtype = 'complex128'
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class TestUnbindOp(OpTest):
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def initParameters(self):
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pass
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def outReshape(self):
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self.out[0] = self.out[0].reshape((2, 2))
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self.out[1] = self.out[1].reshape((2, 2))
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self.out[2] = self.out[2].reshape((2, 2))
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def setAxis(self):
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pass
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def setUp(self):
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self._set_op_type()
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self.prim_op_type = "comp"
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self.dtype = self.get_dtype()
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self.axis = 0
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self.num = 3
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self.initParameters()
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x = np.arange(12).reshape(3, 2, 2).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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x = (
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np.arange(12).reshape(3, 2, 2)
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+ 1j * np.arange(12).reshape(3, 2, 2)
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).astype(self.dtype)
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self.out = np.split(x, self.num, self.axis)
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self.outReshape()
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self.inputs = {'X': x}
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self.attrs = {'axis': self.axis}
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self.setAxis()
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self.outputs = {
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'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))]
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}
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self.python_api = paddle.unbind
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self.public_python_api = paddle.unbind
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self.python_out_sig = [f'out{i}' for i in range(len(self.out))]
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def get_dtype(self):
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return "float64"
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def _set_op_type(self):
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self.op_type = "unbind"
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'], ['out0', 'out1', 'out2'], check_pir=True, check_prim_pir=True
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)
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class TestUnbindOp1(TestUnbindOp):
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def initParameters(self):
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self.axis = 1
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self.num = 2
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def test_check_grad(self):
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self.check_grad(['X'], ['out0', 'out1'], check_pir=True)
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def outReshape(self):
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self.out[0] = self.out[0].reshape((3, 2))
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self.out[1] = self.out[1].reshape((3, 2))
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class TestUnbindOp2(TestUnbindOp):
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def initParameters(self):
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self.axis = 2
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self.num = 2
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def test_check_grad(self):
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self.check_grad(['X'], ['out0', 'out1'], check_pir=True)
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def outReshape(self):
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self.out[0] = self.out[0].reshape((3, 2))
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self.out[1] = self.out[1].reshape((3, 2))
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class TestUnbindOp3(TestUnbindOp):
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def initParameters(self):
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self.axis = 2
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self.num = 2
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def setAxis(self):
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self.attrs = {'axis': -1}
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def test_check_grad(self):
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self.check_grad(['X'], ['out0', 'out1'], check_pir=True)
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def outReshape(self):
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self.out[0] = self.out[0].reshape((3, 2))
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self.out[1] = self.out[1].reshape((3, 2))
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class TestUnbindOp4(TestUnbindOp):
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def initParameters(self):
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self.axis = 1
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self.num = 2
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def setAxis(self):
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self.attrs = {'axis': -2}
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def test_check_grad(self):
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self.check_grad(['X'], ['out0', 'out1'], check_pir=True)
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def outReshape(self):
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self.out[0] = self.out[0].reshape((3, 2))
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self.out[1] = self.out[1].reshape((3, 2))
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class TestUnbindOp1_Complex64(TestUnbindOp1):
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def get_dtype(self):
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return np.complex64
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp2_Complex64(TestUnbindOp2):
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def get_dtype(self):
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return np.complex64
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp3_Complex64(TestUnbindOp3):
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def get_dtype(self):
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return np.complex64
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp4_Complex64(TestUnbindOp4):
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def get_dtype(self):
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return np.complex64
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp1_Complex128(TestUnbindOp1):
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def get_dtype(self):
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return np.complex128
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp2_Complex128(TestUnbindOp2):
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def get_dtype(self):
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return np.complex128
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp3_Complex128(TestUnbindOp3):
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def get_dtype(self):
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return np.complex128
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindOp4_Complex128(TestUnbindOp4):
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def get_dtype(self):
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return np.complex128
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestUnbindFP16Op(OpTest):
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def setUp(self):
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paddle.disable_static()
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self.op_type = "unbind"
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self.prim_op_type = "comp"
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self.python_api = paddle.unbind
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self.public_python_api = paddle.unbind
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self.dtype = self.get_dtype()
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self.axis = 0
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self.num = 3
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x = np.arange(12).reshape(3, 2, 2).astype(self.dtype)
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self.out = np.split(x, self.num, self.axis)
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self.outReshape()
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self.inputs = {'X': x}
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self.attrs = {'axis': self.axis}
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self.outputs = {
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'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))]
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}
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self.python_out_sig = [f'out{i}' for i in range(len(self.out))]
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def outReshape(self):
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self.out[0] = self.out[0].reshape((2, 2))
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self.out[1] = self.out[1].reshape((2, 2))
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self.out[2] = self.out[2].reshape((2, 2))
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def get_dtype(self):
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return np.float16
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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class TestUnbindBF16Op(OpTest):
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def setUp(self):
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paddle.disable_static()
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self._set_op_type()
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self.prim_op_type = "comp"
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self.python_api = paddle.unbind
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self.public_python_api = paddle.unbind
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self.dtype = self.get_dtype()
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self.axis = 0
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self.num = 3
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x = np.arange(12).reshape(3, 2, 2).astype(self.dtype)
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self.out = np.split(x, self.num, self.axis)
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self.outReshape()
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {'axis': self.axis}
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self.outputs = {
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'Out': [
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(f'out{i}', convert_float_to_uint16(self.out[i]))
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for i in range(len(self.out))
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]
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}
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self.python_out_sig = [f'out{i}' for i in range(len(self.out))]
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def outReshape(self):
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self.out[0] = self.out[0].reshape((2, 2))
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self.out[1] = self.out[1].reshape((2, 2))
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self.out[2] = self.out[2].reshape((2, 2))
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def get_dtype(self):
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return np.uint16
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def _set_op_type(self):
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self.op_type = "unbind"
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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pass
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class TestUnbindAxisError(unittest.TestCase):
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def setUp(self):
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self.dtype = 'float32'
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def test_errors(self):
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paddle.enable_static()
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with paddle.base.program_guard(
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paddle.base.Program(), paddle.base.Program()
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):
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x = paddle.static.data(shape=[2, 3], dtype=self.dtype, name='x')
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def test_table_Variable():
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tensor.unbind(input=x, axis=2.0)
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self.assertRaises(TypeError, test_table_Variable)
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def test_invalid_axis():
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tensor.unbind(input=x, axis=2)
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self.assertRaises(ValueError, test_invalid_axis)
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class TestUnbindAxisError_complex64(TestUnbindAxisError):
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def setUp(self):
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self.dtype = 'complex64'
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class TestUnbindAxisError_complex128(TestUnbindAxisError):
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def setUp(self):
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self.dtype = 'complex128'
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class TestUnbindBool(unittest.TestCase):
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def test_bool(self):
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x = paddle.to_tensor([[True, True], [False, False]])
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xs = paddle.unbind(x, axis=0)
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self.assertEqual(len(xs), 2)
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np.testing.assert_array_equal(xs[0].numpy(False), [True, True])
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class TestUnbindGradOptionalInput(unittest.TestCase):
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def test_grad(self):
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a = paddle.zeros([3, 2, 3])
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a.stop_gradient = False
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x, y = a.unbind(-2)
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x.sum().backward() # y_grad is empty
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a_grad = a.detach()
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a_grad[:, 0, :] = 1
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np.testing.assert_array_equal(a.grad.numpy(False), a_grad.numpy(False))
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class TestUnbindAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.shape = [3, 4, 5]
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self.dtype = 'float32'
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self.axis = 0
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self.init_data()
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def init_data(self):
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self.np_input = np.random.rand(*self.shape).astype(self.dtype)
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self.np_out = np.split(
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self.np_input,
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indices_or_sections=self.np_input.shape[self.axis],
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axis=self.axis,
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)
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# Remove the extra dimension added by np.split
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self.np_out = [np.squeeze(arr, axis=self.axis) for arr in self.np_out]
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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paddle_dygraph_out = []
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# Positional args (args)
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out1 = paddle.unbind(x, self.axis)
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paddle_dygraph_out.append(out1)
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# Keyword args (kwargs)
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out2 = paddle.unbind(input=x, axis=self.axis)
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paddle_dygraph_out.append(out2)
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# Duplicate kwargs test (should be same as out2)
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out3 = paddle.unbind(input=x, dim=self.axis)
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paddle_dygraph_out.append(out3)
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# Default axis (axis=0)
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out4 = paddle.unbind(x)
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paddle_dygraph_out.append(out4)
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# Check all variants
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for out in paddle_dygraph_out:
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for i, array in enumerate(out):
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np.testing.assert_allclose(self.np_out[i], array.numpy())
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paddle.enable_static()
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def test_static_Compatibility(self):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
|
|
with paddle.static.program_guard(main, startup):
|
|
x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
|
|
|
|
# Positional args
|
|
out1 = paddle.unbind(x, self.axis)
|
|
|
|
# Keyword args
|
|
out2 = paddle.unbind(input=x, axis=self.axis)
|
|
|
|
out3 = paddle.unbind(input=x, dim=self.axis)
|
|
|
|
# Default axis
|
|
out4 = paddle.unbind(x)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_input},
|
|
fetch_list=[out1, out2, out3, out4],
|
|
)
|
|
|
|
paddle_static_out = [fetches[3 * i : 3 * (i + 1)] for i in range(4)]
|
|
for out in paddle_static_out:
|
|
for i, array in enumerate(out):
|
|
np.testing.assert_allclose(self.np_out[i], array)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|