wehub-resource-sync
622 lines
20 KiB
Python
622 lines
20 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
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paddle.enable_static()
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class TestStackOpBase(OpTest):
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def initDefaultParameters(self):
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self.num_inputs = 4
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self.input_dim = (5, 6, 7)
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self.axis = 0
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self.dtype = 'float64'
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def initParameters(self):
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pass
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def get_x_names(self):
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x_names = []
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for i in range(self.num_inputs):
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x_names.append(f'x{i}')
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return x_names
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def setUp(self):
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self.initDefaultParameters()
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self.initParameters()
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self.op_type = 'stack'
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self.prim_op_type = "comp"
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self.python_api = paddle.stack
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self.public_python_api = paddle.stack
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self.x = []
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for i in range(self.num_inputs):
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self.x.append(
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np.random.random(size=self.input_dim).astype(self.dtype)
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)
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tmp = []
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x_names = self.get_x_names()
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for i in range(self.num_inputs):
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tmp.append((x_names[i], self.x[i]))
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self.inputs = {'X': tmp}
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self.outputs = {'Y': np.stack(self.x, axis=self.axis)}
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self.attrs = {'axis': self.axis}
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def test_check_output(self):
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self.check_output(check_prim=True, 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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self.get_x_names(),
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'Y',
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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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class TestStackOp1(TestStackOpBase):
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def initParameters(self):
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self.num_inputs = 8
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class TestStackOp2(TestStackOpBase):
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def initParameters(self):
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self.num_inputs = 10
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class TestStackOp3(TestStackOpBase):
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def initParameters(self):
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self.axis = -1
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class TestStackOp4(TestStackOpBase):
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def initParameters(self):
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self.axis = -4
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class TestStackOp5(TestStackOpBase):
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def initParameters(self):
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self.axis = 1
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class TestStackOp6(TestStackOpBase):
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def initParameters(self):
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self.axis = 3
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class TestStackOp_ZeroDim(TestStackOpBase):
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def initParameters(self):
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self.input_dim = ()
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self.enable_cinn = False
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class TestStackFP16Op(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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class TestStackFP16Op1(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.num_inputs = 8
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class TestStackFP16Op2(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.num_inputs = 10
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class TestStackFP16Op3(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.axis = -1
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class TestStackFP16Op4(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.axis = -4
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class TestStackFP16Op5(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.axis = 1
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class TestStackFP16Op6(TestStackOpBase):
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def initParameters(self):
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self.dtype = np.float16
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self.axis = 3
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class TestStackBF16Op(OpTest):
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def initDefaultParameters(self):
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self.num_inputs = 4
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self.input_dim = (5, 6, 7)
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self.axis = 0
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self.dtype = np.uint16
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def initParameters(self):
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pass
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def get_x_names(self):
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x_names = []
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for i in range(self.num_inputs):
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x_names.append(f'x{i}')
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return x_names
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def setUp(self):
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self.initDefaultParameters()
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self.initParameters()
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self.op_type = 'stack'
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self.prim_op_type = "comp"
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self.python_api = paddle.stack
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self.public_python_api = paddle.stack
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self.x = []
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for i in range(self.num_inputs):
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self.x.append(
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np.random.random(size=self.input_dim).astype(np.float32)
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)
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out = np.stack(self.x, axis=self.axis)
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tmp = []
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x_names = self.get_x_names()
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for i in range(self.num_inputs):
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tmp.append((x_names[i], convert_float_to_uint16(self.x[i])))
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self.inputs = {'X': tmp}
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self.outputs = {'Y': convert_float_to_uint16(out)}
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self.attrs = {'axis': self.axis}
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def test_check_output(self):
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self.check_output(check_prim=True, 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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self.get_x_names(),
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'Y',
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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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class TestStackAPIWithDenseTensorArray(unittest.TestCase):
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"""
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Test stack api when the input(x) is a DenseTensorArray.
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"""
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def setUp(self):
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self.axis = 1
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self.iter_num = 3
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self.input_shape = [2, 3]
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self.x = np.random.random(self.input_shape).astype("float32")
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self.place = (
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get_device_place()
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if (base.is_compiled_with_cuda() or is_custom_device())
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else base.CPUPlace()
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)
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def test_case(self):
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self.program = paddle.static.Program()
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with paddle.static.program_guard(self.program):
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input = paddle.assign(self.x)
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tensor_array = paddle.tensor.create_array(dtype='float32')
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zero = paddle.tensor.fill_constant(
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shape=[1], value=0, dtype="int64"
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)
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for i in range(self.iter_num):
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paddle.tensor.array_write(input, zero + i, tensor_array)
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self.out_var = paddle.stack(tensor_array, axis=self.axis)
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self.assertTrue(self.out_var.shape[self.axis] == -1)
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exe = base.Executor(self.place)
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res = exe.run(self.program, fetch_list=self.out_var)
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np.testing.assert_array_equal(
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res[0], np.stack([self.x] * self.iter_num, axis=self.axis)
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)
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class TestTensorStackAPIWithDenseTensorArray(unittest.TestCase):
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"""
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Test stack api when the input(x) is a DenseTensorArray.
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"""
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def setUp(self):
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self.axis = 1
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self.iter_num = 3
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self.input_shape = [2, 3]
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self.x = np.random.random(self.input_shape).astype("float32")
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self.place = (
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get_device_place()
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if (base.is_compiled_with_cuda() or is_custom_device())
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else base.CPUPlace()
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)
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def test_case(self):
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self.program = paddle.static.Program()
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with paddle.static.program_guard(self.program):
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input = paddle.assign(self.x)
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tensor_array = paddle.tensor.create_array(dtype='float32')
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zero = paddle.tensor.fill_constant(
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shape=[1], value=0, dtype="int64"
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)
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for i in range(self.iter_num):
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paddle.tensor.array_write(input, zero + i, tensor_array)
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self.out_var = paddle.stack(tensor_array, axis=self.axis)
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self.assertTrue(self.out_var.shape[self.axis] == -1)
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exe = base.Executor(self.place)
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res = exe.run(self.program, fetch_list=self.out_var)
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np.testing.assert_array_equal(
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res[0], np.stack([self.x] * self.iter_num, axis=self.axis)
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)
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class API_test(unittest.TestCase):
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def test_out(self):
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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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data1 = paddle.static.data('data1', shape=[1, 2], dtype='float64')
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data2 = paddle.static.data('data2', shape=[1, 2], dtype='float64')
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data3 = paddle.static.data('data3', shape=[1, 2], dtype='float64')
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result_stack = paddle.stack([data1, data2, data3], axis=0)
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place = base.CPUPlace()
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exe = base.Executor(place)
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input1 = np.random.random([1, 2]).astype('float64')
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input2 = np.random.random([1, 2]).astype('float64')
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input3 = np.random.random([1, 2]).astype('float64')
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(result,) = exe.run(
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feed={"data1": input1, "data2": input2, "data3": input3},
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fetch_list=[result_stack],
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)
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expected_result = np.stack([input1, input2, input3], axis=0)
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np.testing.assert_allclose(expected_result, result, rtol=1e-05)
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def test_single_tensor_error(self):
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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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x = paddle.rand([2, 3])
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self.assertRaises(TypeError, paddle.stack, x)
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class API_DygraphTest(unittest.TestCase):
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def test_out(self):
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data1 = np.array([[1.0, 2.0]])
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data2 = np.array([[3.0, 4.0]])
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data3 = np.array([[5.0, 6.0]])
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with base.dygraph.guard():
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x1 = paddle.to_tensor(data1)
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x2 = paddle.to_tensor(data2)
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x3 = paddle.to_tensor(data3)
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result = paddle.stack([x1, x2, x3])
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result_np = result.numpy()
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expected_result = np.stack([data1, data2, data3])
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np.testing.assert_allclose(expected_result, result_np, rtol=1e-05)
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with base.dygraph.guard():
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y1 = paddle.to_tensor(data1)
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result = paddle.stack([y1], axis=0)
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result_np_2 = result.numpy()
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expected_result_2 = np.stack([data1], axis=0)
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np.testing.assert_allclose(expected_result_2, result_np_2, rtol=1e-05)
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def test_single_tensor_error(self):
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with base.dygraph.guard():
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x = paddle.to_tensor([1, 2, 3])
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self.assertRaisesRegex(
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ValueError,
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r"\(InvalidArgument\) stack\(\): argument 'x' \(position 0\) must be list of Tensors",
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paddle.stack,
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x,
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)
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class TestStackOpWithNegativeShape(unittest.TestCase):
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def test_out(self):
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main_prg, startup_prg = paddle.static.Program(), paddle.static.Program()
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with paddle.static.program_guard(main_prg, startup_prg):
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b = paddle.static.data(name='b', shape=[-1], dtype='int64')
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e = paddle.static.data(name='e', shape=[3], dtype='int64')
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k = paddle.stack([b, e], axis=0)
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exe = paddle.static.Executor()
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exe.run(startup_prg)
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out = exe.run(
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main_prg,
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feed={
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'b': np.ones(
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[
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3,
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]
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).astype("int64"),
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'e': np.zeros(
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[
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3,
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]
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).astype("int64"),
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},
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fetch_list=[k],
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)
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np.testing.assert_allclose(
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out[0], np.array([[1, 1, 1], [0, 0, 0]]), rtol=1e-05
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)
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class TestStackAPI_ZeroDim(unittest.TestCase):
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def test_dygraph(self):
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paddle.disable_static()
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x1 = paddle.rand([])
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x2 = paddle.rand([])
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x1.stop_gradient = False
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x2.stop_gradient = False
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out = paddle.stack([x1, x2])
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out.retain_grads()
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out.backward()
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self.assertEqual(out.shape, [2])
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self.assertEqual(x1.grad.shape, [])
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self.assertEqual(x2.grad.shape, [])
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self.assertEqual(out.grad.shape, [2])
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paddle.enable_static()
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class TestStackListOfSingleTensor(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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paddle.seed(2022)
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self.x = [paddle.randn((4, 2, 6), dtype="float32")]
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self.x[0].stop_gradient = False
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def test_list_single_tensor(self):
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expect = paddle.stack(self.x)
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paddle.base.core._set_prim_all_enabled(True)
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st_model = paddle.jit.to_static(
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paddle.stack,
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backend=None,
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full_graph=True,
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)
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actual = st_model(self.x)
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np.testing.assert_allclose(expect, actual)
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paddle.enable_static()
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class TestPrimStackGrad(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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paddle.seed(2022)
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self.x = [paddle.randn((4, 2, 6), dtype="float32") for _ in range(3)]
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for i in range(len(self.x)):
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self.x[i].stop_gradient = False
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def test_stack_double_grad(self):
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paddle.base.core.set_prim_eager_enabled(True)
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z = paddle.stack(self.x)
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z = paddle.tanh(z)
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grads_out = paddle.grad(z, self.x[1], create_graph=True)
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ggrads_out = paddle.grad(grads_out, self.x[1], create_graph=True)[0]
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zz = paddle.tanh(self.x[1])
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grads_expected = paddle.grad(zz, self.x[1], create_graph=True)
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ggrads_expected = paddle.grad(
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grads_expected, self.x[1], create_graph=False
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)[0]
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np.testing.assert_allclose(ggrads_out, ggrads_expected)
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paddle.enable_static()
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paddle.base.core.set_prim_eager_enabled(False)
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def test_stack_triple_grad(self):
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paddle.base.core.set_prim_eager_enabled(True)
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z = paddle.stack(self.x)
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z = paddle.tanh(z)
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grads_out = paddle.grad(z, self.x[1], create_graph=True)
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ggrads_out = paddle.grad(grads_out, self.x[1], create_graph=True)
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gggrads_out = paddle.grad(ggrads_out, self.x[1], create_graph=False)[0]
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zz = paddle.tanh(self.x[1])
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grads_expected = paddle.grad(zz, self.x[1], create_graph=True)
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ggrads_expected = paddle.grad(
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grads_expected, self.x[1], create_graph=True
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)
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gggrads_expected = paddle.grad(
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ggrads_expected, self.x[1], create_graph=True
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)[0]
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np.testing.assert_allclose(gggrads_out, gggrads_expected)
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paddle.enable_static()
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paddle.base.core.set_prim_eager_enabled(False)
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class TestStackAPI_ZeroSizedTensor(unittest.TestCase):
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def test_dygraph_cpu(self):
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place = base.CPUPlace()
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paddle.disable_static(place)
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x1 = paddle.ones([1, 0])
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x2 = paddle.ones([1, 0])
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x1.stop_gradient = False
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x2.stop_gradient = False
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out = paddle.stack([x1, x2])
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out.retain_grads()
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out.backward()
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np.testing.assert_equal(out.shape, [2, 1, 0])
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np.testing.assert_equal(x1.grad.shape, [1, 0])
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np.testing.assert_equal(x2.grad.shape, [1, 0])
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np.testing.assert_equal(out, np.ones([2, 1, 0]))
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paddle.enable_static()
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def test_dygraph_gpu(self):
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if base.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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paddle.disable_static(place)
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x1 = paddle.ones([1, 0])
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x2 = paddle.ones([1, 0])
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x1.stop_gradient = False
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x2.stop_gradient = False
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out = paddle.stack([x1, x2])
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out.retain_grads()
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out.backward()
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np.testing.assert_equal(out.shape, [2, 1, 0])
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np.testing.assert_equal(x1.grad.shape, [1, 0])
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np.testing.assert_equal(x2.grad.shape, [1, 0])
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np.testing.assert_equal(out, np.ones([2, 1, 0]))
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paddle.enable_static()
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def test_static_cpu(self):
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paddle.enable_static()
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place = base.CPUPlace()
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exe = base.Executor(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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data1 = paddle.static.data('data1', shape=[0, 2], dtype='float64')
|
|
data2 = paddle.static.data('data2', shape=[0, 2], dtype='float64')
|
|
data3 = paddle.static.data('data3', shape=[0, 2], dtype='float64')
|
|
result_stack = paddle.stack([data1, data2, data3], axis=0)
|
|
input1 = np.ones([0, 2]).astype('float64')
|
|
input2 = np.ones([0, 2]).astype('float64')
|
|
input3 = np.ones([0, 2]).astype('float64')
|
|
(result,) = exe.run(
|
|
feed={"data1": input1, "data2": input2, "data3": input3},
|
|
fetch_list=[result_stack],
|
|
)
|
|
expected_result = np.stack([input1, input2, input3], axis=0)
|
|
np.testing.assert_equal(expected_result, result)
|
|
|
|
def test_static_gpu(self):
|
|
if base.is_compiled_with_cuda() or is_custom_device():
|
|
paddle.enable_static()
|
|
place = get_device_place()
|
|
exe = base.Executor(place)
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
data1 = paddle.static.data(
|
|
'data1', shape=[0, 2], dtype='float64'
|
|
)
|
|
data2 = paddle.static.data(
|
|
'data2', shape=[0, 2], dtype='float64'
|
|
)
|
|
data3 = paddle.static.data(
|
|
'data3', shape=[0, 2], dtype='float64'
|
|
)
|
|
result_stack = paddle.stack([data1, data2, data3], axis=0)
|
|
input1 = np.ones([0, 2]).astype('float64')
|
|
input2 = np.ones([0, 2]).astype('float64')
|
|
input3 = np.ones([0, 2]).astype('float64')
|
|
(result,) = exe.run(
|
|
feed={"data1": input1, "data2": input2, "data3": input3},
|
|
fetch_list=[result_stack],
|
|
)
|
|
expected_result = np.stack([input1, input2, input3], axis=0)
|
|
np.testing.assert_equal(expected_result, result)
|
|
|
|
|
|
class TestStackOutAndParamDecorator(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.inputs_np = [
|
|
np.random.rand(2, 3).astype(np.float32) for _ in range(3)
|
|
]
|
|
self.test_types = [
|
|
"decorator_tensors",
|
|
"decorator_dim",
|
|
"decorator_both",
|
|
"out",
|
|
"out_decorator",
|
|
]
|
|
|
|
def do_test(self, test_type):
|
|
inputs = [
|
|
paddle.to_tensor(x, stop_gradient=False) for x in self.inputs_np
|
|
]
|
|
|
|
if test_type == 'raw':
|
|
result = paddle.stack(inputs, axis=1)
|
|
result.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return result, grads
|
|
elif test_type == 'decorator_tensors':
|
|
result = paddle.stack(tensors=inputs, axis=1)
|
|
result.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return result, grads
|
|
elif test_type == 'decorator_dim':
|
|
result = paddle.stack(inputs, dim=1)
|
|
result.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return result, grads
|
|
elif test_type == 'decorator_both':
|
|
result = paddle.stack(tensors=inputs, dim=1)
|
|
result.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return result, grads
|
|
elif test_type == 'out':
|
|
out = paddle.empty((2, 3, 3), dtype='float32')
|
|
out.stop_gradient = False
|
|
paddle.stack(inputs, axis=1, out=out)
|
|
out.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return out, grads
|
|
elif test_type == 'out_decorator':
|
|
out = paddle.empty((2, 3, 3), dtype='float32')
|
|
out.stop_gradient = False
|
|
paddle.stack(tensors=inputs, dim=1, out=out)
|
|
out.mean().backward()
|
|
grads = [x.grad for x in inputs]
|
|
return out, grads
|
|
else:
|
|
raise ValueError(f"Unknown test type: {test_type}")
|
|
|
|
def test_all(self):
|
|
out_std, grads_std = self.do_test('raw')
|
|
for test_type in self.test_types:
|
|
out, grads = self.do_test(test_type)
|
|
np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-20)
|
|
for g, g_std in zip(grads, grads_std):
|
|
np.testing.assert_allclose(g.numpy(), g_std.numpy(), rtol=1e-20)
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|