wehub-resource-sync
367 lines
12 KiB
Python
367 lines
12 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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import op_test
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from decorator_helper import prog_scope
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from op_test import (
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convert_float_to_uint16,
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convert_uint16_to_float,
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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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import paddle
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from paddle import base
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from paddle.base import Program, program_guard
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from paddle.base.backward import append_backward
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class TestAssignOp(op_test.OpTest):
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def setUp(self):
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self.python_api = paddle.assign
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self.public_python_api = paddle.assign
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self.op_type = "assign"
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self.prim_op_type = "prim"
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self.init_input_configs()
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x = np.random.random(size=self.shape).astype('float64')
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self.inputs = {'X': x}
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self.outputs = {'Out': x}
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def init_input_configs(self):
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self.shape = (100, 10)
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def test_forward(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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paddle.disable_static()
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def test_backward(self):
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paddle.enable_static()
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self.check_grad(
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['X'], 'Out', check_prim=True, check_pir=True, check_prim_pir=True
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)
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paddle.disable_static()
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class TestAssignOp_ZeroDim(TestAssignOp):
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def init_input_configs(self):
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self.shape = ()
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"FP16 test runs only on GPU",
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)
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class TestAssignFP16Op(op_test.OpTest):
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def setUp(self):
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self.python_api = paddle.assign
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self.public_python_api = paddle.assign
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self.op_type = "assign"
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self.prim_op_type = "prim"
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x = np.random.random(size=(100, 10)).astype('float16')
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self.inputs = {'X': x}
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self.outputs = {'Out': x}
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def test_forward(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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paddle.disable_static()
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def test_backward(self):
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paddle.enable_static()
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self.check_grad(
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['X'], 'Out', check_prim=True, check_pir=True, check_prim_pir=True
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)
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paddle.disable_static()
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm(),
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"BFP16 test runs only on CUDA",
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)
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class TestAssignBFP16Op(op_test.OpTest):
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def setUp(self):
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self.python_api = paddle.assign
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self.public_python_api = paddle.assign
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self.op_type = "assign"
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self.prim_op_type = "prim"
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x = np.random.uniform(0, 1, [100, 10]).astype(np.float32)
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x = convert_float_to_uint16(x)
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self.inputs = {'X': x}
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self.outputs = {'Out': x}
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def test_forward(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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paddle.disable_static()
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def test_backward(self):
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paddle.enable_static()
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self.check_grad(
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['X'], 'Out', check_prim=True, check_pir=True, check_prim_pir=True
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)
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paddle.disable_static()
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class TestAssignOpWithTensorArray(unittest.TestCase):
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def test_assign_tensor_array(self):
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name='x', shape=[100, 10], dtype='float32')
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x.stop_gradient = False
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y = paddle.tensor.fill_constant(
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shape=[100, 10], dtype='float32', value=1
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)
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z = paddle.add(x=x, y=y)
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i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
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init_array = paddle.tensor.array_write(x=z, i=i)
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array = paddle.assign(init_array)
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sums = paddle.tensor.array_read(array=init_array, i=i)
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mean = paddle.mean(sums)
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[(_, x_grad)] = append_backward(mean, parameter_list=[x])
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place = get_device_place()
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exe = paddle.static.Executor(place)
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feed_x = np.random.random(size=(100, 10)).astype('float32')
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ones = np.ones((100, 10)).astype('float32')
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feed_add = feed_x + ones
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res = exe.run(
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main_program,
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feed={'x': feed_x},
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fetch_list=[sums, x_grad],
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)
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np.testing.assert_allclose(res[0], feed_add, rtol=1e-05)
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np.testing.assert_allclose(res[1], ones / 1000.0, rtol=1e-05)
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paddle.disable_static()
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class TestAssignOpError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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# The type of input must be Variable or numpy.ndarray.
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x1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.assign, x1)
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# When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
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x2 = np.array([[2.5, 2.5]], dtype='uint8')
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self.assertRaises(TypeError, paddle.assign, x2)
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paddle.disable_static()
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class TestAssignOpApi(unittest.TestCase):
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def test_assign_numpy_array(self):
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for dtype in [np.bool_, np.float32, np.int32, np.int64]:
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with base.dygraph.guard():
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array = np.random.random(size=(100, 10)).astype(dtype)
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result1 = paddle.zeros(shape=[3, 3], dtype='float32')
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paddle.assign(array, result1)
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np.testing.assert_allclose(result1.numpy(), array, rtol=1e-05)
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def test_assign_List(self):
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l = [1, 2, 3]
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result = paddle.assign(l)
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np.testing.assert_allclose(result.numpy(), np.array(l), rtol=1e-05)
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def test_assign_BasicTypes(self):
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result1 = paddle.assign(2)
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result2 = paddle.assign(3.0)
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result3 = paddle.assign(True)
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np.testing.assert_allclose(result1.numpy(), np.array([2]), rtol=1e-05)
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np.testing.assert_allclose(result2.numpy(), np.array([3.0]), rtol=1e-05)
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np.testing.assert_allclose(result3.numpy(), np.array([1]), rtol=1e-05)
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def test_clone(self):
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self.python_api = paddle.clone
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x = paddle.ones([2])
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x.stop_gradient = False
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x.retain_grads()
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clone_x = paddle.clone(x)
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clone_x.retain_grads()
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y = clone_x**3
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y.backward()
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np.testing.assert_array_equal(x, [1, 1])
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np.testing.assert_array_equal(clone_x.grad.numpy(), [3, 3])
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np.testing.assert_array_equal(x.grad.numpy(), [3, 3])
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paddle.enable_static()
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with program_guard(Program(), Program()):
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x_np = np.random.randn(2, 3).astype('float32')
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x = paddle.static.data("X", shape=[2, 3])
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clone_x = paddle.clone(x)
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exe = paddle.static.Executor()
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y_np = exe.run(
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paddle.static.default_main_program(),
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feed={'X': x_np},
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fetch_list=[clone_x],
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)[0]
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np.testing.assert_array_equal(y_np, x_np)
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paddle.disable_static()
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"FP16 test runs only on GPU",
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)
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class TestAssignOpApiFP16(unittest.TestCase):
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def test_assign_fp16(self):
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x = np.random.uniform(0, 10, [3, 3]).astype(np.float16)
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x = paddle.to_tensor(x)
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result = paddle.zeros(shape=[3, 3], dtype='float16')
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paddle.assign(x, result)
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np.testing.assert_equal(result.numpy(), x.numpy())
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def test_assign_bfp16(self):
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x_f = np.random.uniform(0, 10, [3, 3]).astype(np.float32)
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x = convert_float_to_uint16(x_f)
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x = paddle.to_tensor(x)
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result = paddle.zeros(shape=[3, 3], dtype='bfloat16')
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paddle.assign(x, result)
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np.testing.assert_allclose(
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convert_uint16_to_float(result.numpy()), x_f, rtol=1e-02
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)
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np.testing.assert_equal(
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convert_uint16_to_float(result.numpy()), convert_uint16_to_float(x)
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)
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class TestAssignOut_(unittest.TestCase):
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def test_pir_assign_out_(self):
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with paddle.pir_utils.IrGuard():
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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out = paddle.tensor.fill_constant(
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[2, 2], dtype='float32', value=0.0
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)
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tmp = paddle.tensor.fill_constant(
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[2, 2], dtype='float32', value=1.0
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)
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tmp.stop_gradient = False
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x = paddle.add(tmp, tmp)
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paddle.assign(x, out)
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loss = paddle.mean(out)
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dx = paddle.autograd.ir_backward.grad(loss, tmp)
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exe = paddle.static.Executor()
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dx_out = exe.run(
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paddle.static.default_main_program(),
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feed={},
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fetch_list=[dx],
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)[0]
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np.testing.assert_array_equal(dx_out, 0.5 * np.ones((2, 2)))
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class TestAssignOpErrorApi(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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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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# The type of input must be Variable or numpy.ndarray.
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x1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.assign, x1)
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# When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
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x2 = np.array([[2.5, 2.5]], dtype='uint8')
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self.assertRaises(TypeError, paddle.assign, x2)
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paddle.disable_static()
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def test_type_error(self):
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paddle.enable_static()
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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.randn([3, 3]), paddle.randn([3, 3])]
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# not support to assign list(var)
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self.assertRaises(TypeError, paddle.assign, x)
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paddle.disable_static()
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class TestAssignDoubleGradCheck(unittest.TestCase):
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def assign_wrapper(self, x):
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return paddle.assign(x[0])
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [3, 4, 5], dtype)
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data.persistable = True
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out = paddle.assign(data)
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.double_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.assign_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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paddle.disable_static()
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class TestAssignTripleGradCheck(unittest.TestCase):
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def assign_wrapper(self, x):
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return paddle.assign(x[0])
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [3, 4, 5], dtype)
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data.persistable = True
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out = paddle.assign(data)
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.triple_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.triple_grad_check_for_dygraph(
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self.assign_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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paddle.disable_static()
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if __name__ == '__main__':
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unittest.main()
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