wehub-resource-sync
293 lines
10 KiB
Python
293 lines
10 KiB
Python
# Copyright (c) 2020 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 convert_float_to_uint16, convert_uint16_to_float
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle import _legacy_C_ops, base
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from paddle.base import Program, program_guard
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paddle.enable_static()
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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class XPUTestDropoutOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'dropout'
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self.use_dynamic_create_class = False
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class TestDropoutOp(XPUOpTest):
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def setUp(self):
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self.init_inputs_shape()
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self.init_attrs()
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self.dtype = self.in_type
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self.op_type = 'dropout'
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if self.dtype == np.uint16:
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x = np.random.random(self.shape).astype('float32')
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x = convert_float_to_uint16(x)
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self.inputs = {'X': x}
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else:
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self.inputs = {
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'X': np.random.random(self.shape).astype(self.dtype)
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}
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self.attrs = {
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'dropout_prob': self.dropout_prob,
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'fix_seed': self.fix_seed,
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'is_test': self.is_test,
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'dropout_implementation': self.dropout_implementation,
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}
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if self.dtype == np.uint16:
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x_float32 = convert_uint16_to_float(self.inputs['X'])
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out = x_float32 * (1.0 - self.dropout_prob)
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out = convert_float_to_uint16(out)
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else:
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out = self.inputs['X'] * (1.0 - self.dropout_prob)
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if not self.is_test:
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mask = None
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if self.dropout_prob == 0.0:
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mask = np.ones(self.shape)
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elif self.dropout_prob == 1.0:
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mask = np.zeros(self.shape)
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if self.dtype == np.uint16:
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mask = convert_float_to_uint16(mask.astype('float32'))
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else:
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mask = mask.astype(self.dtype)
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self.outputs = {'Out': out, 'Mask': mask}
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else:
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self.outputs = {'Out': out}
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def init_inputs_shape(self):
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self.shape = [32, 64]
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def init_attrs(self):
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self.__class__.no_need_check_grad = False
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self.dropout_prob = 0.0
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self.fix_seed = True
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self.is_test = False
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self.dropout_implementation = "upscale_in_train"
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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if (
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hasattr(self.__class__, "no_need_check_grad")
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and self.__class__.no_need_check_grad
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):
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return
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self.check_grad(['X'], 'Out')
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class TestDropoutOpInput1d(TestDropoutOp):
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def init_inputs_shape(self):
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self.shape = [2000]
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class TestDropoutOp2(TestDropoutOp):
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def init_inputs_shape(self):
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self.shape = [32, 64]
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def init_attrs(self):
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self.dropout_prob = 1.0
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self.fix_seed = True
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self.is_test = False
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self.dropout_implementation = "upscale_in_train"
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class TestDropoutOp3(TestDropoutOp):
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def init_inputs_shape(self):
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self.shape = [32, 64, 2]
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class TestDropoutOp4(TestDropoutOp):
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def init_attrs(self):
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self.__class__.no_need_check_grad = True
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self.dropout_prob = 0.35
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self.fix_seed = True
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self.is_test = True
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self.dropout_implementation = "downgrade_in_infer"
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class TestDropoutOp5(TestDropoutOp):
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def init_inputs_shape(self):
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self.shape = [32, 64, 3]
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def init_attrs(self):
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self.__class__.no_need_check_grad = True
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self.dropout_prob = 0.75
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self.fix_seed = True
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self.is_test = True
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self.dropout_implementation = "downgrade_in_infer"
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class TestDropoutOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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def test_Variable():
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# the input of dropout must be Variable.
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x1 = base.create_lod_tensor(
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np.array([-1, 3, 5, 5]),
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[[1, 1, 1, 1]],
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base.CPUPlace(),
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)
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paddle.nn.functional.dropout(x1, p=0.5)
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self.assertRaises(TypeError, test_Variable)
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def test_dtype():
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# the input dtype of dropout must be float16 or float32 or float64
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# float16 only can be set on GPU place
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x2 = paddle.static.data(
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name='x2', shape=[-1, 3, 4, 5, 6], dtype="int32"
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)
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paddle.nn.functional.dropout(x2, p=0.5)
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self.assertRaises(TypeError, test_dtype)
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class TestDropoutCAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = [base.CPUPlace()]
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self.places.append(base.XPUPlace(0))
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([40, 40]).astype(self.in_type)
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result_np = input_np
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input = paddle.to_tensor(input_np)
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m = paddle.nn.Dropout(p=0.0)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(result.numpy(), result_np)
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class TestDropoutBackward(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = [base.CPUPlace()]
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self.places.append(base.XPUPlace(0))
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def cal_grad_upscale_train(self, mask, prob):
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if self.in_type == np.uint16:
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return convert_float_to_uint16(
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mask.astype("float32") / (1 - prob)
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)
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else:
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return mask.astype(self.in_type) / (1 - prob)
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def cal_grad_downscale_in_infer(self, mask):
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if self.in_type == np.uint16:
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return convert_float_to_uint16(mask.astype("float32"))
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else:
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return mask.astype(self.in_type)
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def test_backward_downscale_in_infer(self):
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for place in self.places:
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with base.dygraph.guard(place):
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prob = 0.1
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if self.in_type == np.uint16:
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input = paddle.uniform([100, 40], dtype="bfloat16")
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else:
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input = paddle.uniform([100, 40], dtype=self.in_type)
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input.stop_gradient = False
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out, mask = _legacy_C_ops.dropout(
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input, 'dropout_prob', prob
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)
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nonzero = paddle.count_nonzero(out)
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np.testing.assert_allclose(
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prob, 1 - nonzero / 4000, atol=0.02
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)
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out.backward()
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if self.in_type == np.uint16:
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np.testing.assert_allclose(
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input.gradient(),
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self.cal_grad_downscale_in_infer(mask.numpy()),
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)
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else:
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np.testing.assert_allclose(
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input.gradient(),
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self.cal_grad_downscale_in_infer(mask.numpy()),
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)
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def test_backward_upscale_train(self):
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for place in self.places:
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with base.dygraph.guard(place):
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prob = 0.5
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if self.in_type == np.uint16:
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input = paddle.uniform([100, 40], dtype="bfloat16")
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else:
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input = paddle.uniform([100, 40], dtype=self.in_type)
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input.stop_gradient = False
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out, mask = _legacy_C_ops.dropout(
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input,
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'dropout_prob',
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prob,
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"dropout_implementation",
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"upscale_in_train",
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)
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nonzero = paddle.count_nonzero(out)
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np.testing.assert_allclose(
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prob, 1 - nonzero / 4000, atol=0.02
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)
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out.backward()
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np.testing.assert_allclose(
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input.gradient(),
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self.cal_grad_upscale_train(mask.numpy(), prob),
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)
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def test_backward_upscale_train_2(self):
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for place in self.places:
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with base.dygraph.guard(place):
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prob = 0.2
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if self.in_type == np.uint16:
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input = paddle.uniform([100, 40], dtype="bfloat16")
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else:
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input = paddle.uniform([100, 40], dtype=self.in_type)
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input.stop_gradient = False
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out, mask = _legacy_C_ops.dropout(
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input,
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'dropout_prob',
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prob,
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"dropout_implementation",
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"upscale_in_train",
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)
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nonzero = paddle.count_nonzero(out)
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np.testing.assert_allclose(
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prob, 1 - nonzero / 4000, atol=0.02
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)
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out.backward()
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np.testing.assert_allclose(
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input.gradient(),
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self.cal_grad_upscale_train(mask.numpy(), prob),
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)
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support_types = get_xpu_op_support_types('dropout')
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for stype in support_types:
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create_test_class(globals(), XPUTestDropoutOp, stype)
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if __name__ == '__main__':
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unittest.main()
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