wehub-resource-sync
382 lines
12 KiB
Python
382 lines
12 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 os
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle.base import core
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from paddle.base.framework import convert_nptype_to_datatype_or_vartype
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from paddle.jit.dy2static.utils import _compatible_non_tensor_spec
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from paddle.static import InputSpec
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class TestInputSpec(unittest.TestCase):
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def test_default(self):
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tensor_spec = InputSpec([3, 4])
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self.assertEqual(
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tensor_spec.dtype, convert_nptype_to_datatype_or_vartype('float32')
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)
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self.assertIsNone(tensor_spec.name)
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def test_from_tensor(self):
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if paddle.framework.use_pir_api():
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x_bool = paddle.pir.core.create_parameter(
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dtype='float32',
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shape=[1],
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name='xx',
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initializer=paddle.nn.initializer.Uniform(),
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)
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else:
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x_bool = paddle.tensor.fill_constant(
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shape=[1], dtype='bool', value=True
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)
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bool_spec = InputSpec.from_tensor(x_bool)
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self.assertEqual(bool_spec.dtype, x_bool.dtype)
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self.assertEqual(list(bool_spec.shape), list(x_bool.shape))
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self.assertEqual(bool_spec.name, x_bool.name)
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bool_spec2 = InputSpec.from_tensor(x_bool, name='bool_spec')
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self.assertEqual(bool_spec2.name, bool_spec2.name)
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def test_from_numpy(self):
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x_numpy = np.ones([10, 12])
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x_np_spec = InputSpec.from_numpy(x_numpy)
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self.assertEqual(
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x_np_spec.dtype,
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convert_nptype_to_datatype_or_vartype(x_numpy.dtype),
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)
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self.assertEqual(x_np_spec.shape, x_numpy.shape)
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self.assertIsNone(x_np_spec.name)
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x_numpy2 = np.array([1, 2, 3, 4]).astype('int64')
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x_np_spec2 = InputSpec.from_numpy(x_numpy2, name='x_np_int64')
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self.assertEqual(
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x_np_spec2.dtype,
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convert_nptype_to_datatype_or_vartype(x_numpy2.dtype),
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)
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self.assertEqual(x_np_spec2.shape, x_numpy2.shape)
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self.assertEqual(x_np_spec2.name, 'x_np_int64')
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def test_shape_with_none(self):
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tensor_spec = InputSpec([None, 4, None], dtype='int8', name='x_spec')
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self.assertEqual(
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tensor_spec.dtype, convert_nptype_to_datatype_or_vartype('int8')
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)
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self.assertEqual(tensor_spec.name, 'x_spec')
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self.assertEqual(tensor_spec.shape, (-1, 4, -1))
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def test_shape_raise_error(self):
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# 1. shape should only contain int and None.
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with self.assertRaises(ValueError):
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tensor_spec = InputSpec(['None', 4, None], dtype='int8')
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# 2. shape should be type `list` or `tuple`
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with self.assertRaises(TypeError):
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tensor_spec = InputSpec(4, dtype='int8')
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def test_batch_and_unbatch(self):
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tensor_spec = InputSpec([10])
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# insert batch_size
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batch_tensor_spec = tensor_spec.batch(16)
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self.assertEqual(batch_tensor_spec.shape, (16, 10))
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# unbatch
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unbatch_spec = batch_tensor_spec.unbatch()
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self.assertEqual(unbatch_spec.shape, (10,))
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# 1. `batch` requires len(batch_size) == 1
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with self.assertRaises(ValueError):
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tensor_spec.batch([16, 12])
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# 2. `batch` requires type(batch_size) == int
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with self.assertRaises(TypeError):
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tensor_spec.batch('16')
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def test_eq_and_hash(self):
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tensor_spec_1 = InputSpec([10, 16], dtype='float32')
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tensor_spec_2 = InputSpec([10, 16], dtype='float32')
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tensor_spec_3 = InputSpec([10, 16], dtype='float32', name='x')
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tensor_spec_4 = InputSpec([16], dtype='float32', name='x')
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# override ``__eq__`` according to [shape, dtype, name]
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self.assertTrue(tensor_spec_1 == tensor_spec_2)
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self.assertTrue(tensor_spec_1 != tensor_spec_3) # different name
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self.assertTrue(tensor_spec_3 != tensor_spec_4) # different shape
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# override ``__hash__`` according to [shape, dtype]
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self.assertTrue(hash(tensor_spec_1) == hash(tensor_spec_2))
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self.assertTrue(hash(tensor_spec_1) == hash(tensor_spec_3))
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self.assertTrue(hash(tensor_spec_3) != hash(tensor_spec_4))
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class NetWithNonTensorSpec(paddle.nn.Layer):
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def __init__(self, in_num, out_num):
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super().__init__()
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self.linear_1 = paddle.nn.Linear(in_num, out_num)
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self.bn_1 = paddle.nn.BatchNorm1D(out_num)
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self.linear_2 = paddle.nn.Linear(in_num, out_num)
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self.bn_2 = paddle.nn.BatchNorm1D(out_num)
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self.linear_3 = paddle.nn.Linear(in_num, out_num)
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self.bn_3 = paddle.nn.BatchNorm1D(out_num)
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def forward(self, x, bool_v=False, str_v="bn", int_v=1, list_v=None):
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x = self.linear_1(x)
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if 'bn' in str_v:
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x = self.bn_1(x)
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if bool_v:
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x = self.linear_2(x)
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x = self.bn_2(x)
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config = {"int_v": int_v, 'other_key': "value"}
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if list_v and list_v[-1] > 2:
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x = self.linear_3(x)
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x = self.another_func(x, config)
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out = paddle.mean(x)
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return out
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def another_func(self, x, config=None):
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# config is a dict actually
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use_bn = config['int_v'] > 0
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x = self.linear_1(x)
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if use_bn:
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x = self.bn_3(x)
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return x
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class TestNetWithNonTensorSpec(unittest.TestCase):
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def setUp(self):
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self.in_num = 16
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self.out_num = 16
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self.x_spec = paddle.static.InputSpec([-1, 16], name='x')
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self.x = paddle.randn([4, 16])
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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@classmethod
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def setUpClass(cls):
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paddle.disable_static()
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def test_non_tensor_bool(self):
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specs = [self.x_spec, False]
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self.check_result(specs, 'bool')
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def test_non_tensor_str(self):
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specs = [self.x_spec, True, "xxx"]
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self.check_result(specs, 'str')
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def test_non_tensor_int(self):
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specs = [self.x_spec, True, "bn", 10]
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self.check_result(specs, 'int')
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def test_non_tensor_list(self):
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specs = [self.x_spec, False, "bn", -10, [4]]
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self.check_result(specs, 'list')
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def check_result(self, specs, path):
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path = os.path.join(self.temp_dir.name, './net_non_tensor_', path)
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net = NetWithNonTensorSpec(self.in_num, self.out_num)
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net.eval()
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# dygraph out
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dy_out = net(self.x, *specs[1:])
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# jit.save directly
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paddle.jit.save(net, path + '_direct', input_spec=specs)
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load_net = paddle.jit.load(path + '_direct')
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load_net.eval()
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pred_out = load_net(self.x)
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np.testing.assert_allclose(dy_out, pred_out, rtol=1e-05)
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# @to_static by InputSpec
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net = paddle.jit.to_static(net, input_spec=specs, full_graph=True)
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st_out = net(self.x, *specs[1:])
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np.testing.assert_allclose(dy_out, st_out, rtol=1e-05)
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# jit.save and jit.load
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paddle.jit.save(net, path)
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load_net = paddle.jit.load(path)
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load_net.eval()
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load_out = load_net(self.x)
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np.testing.assert_allclose(st_out, load_out, rtol=1e-05)
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def test_spec_compatible(self):
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net = NetWithNonTensorSpec(self.in_num, self.out_num)
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specs = [self.x_spec, False, "bn", -10]
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net = paddle.jit.to_static(net, input_spec=specs, full_graph=True)
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net.eval()
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path = os.path.join(self.temp_dir.name, './net_twice')
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# NOTE: check input_specs_compatible
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new_specs = [self.x_spec, True, "bn", 10]
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with self.assertRaises(ValueError):
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paddle.jit.save(net, path, input_spec=new_specs)
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dy_out = net(self.x)
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paddle.jit.save(net, path, [self.x_spec, False, "bn"])
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load_net = paddle.jit.load(path)
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load_net.eval()
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pred_out = load_net(self.x)
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np.testing.assert_allclose(dy_out, pred_out, rtol=1e-05)
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class NetWithNonTensorSpecPrune(paddle.nn.Layer):
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def __init__(self, in_num, out_num):
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super().__init__()
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self.linear_1 = paddle.nn.Linear(in_num, out_num)
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self.bn_1 = paddle.nn.BatchNorm1D(out_num)
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def forward(self, x, y, use_bn=False):
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x = self.linear_1(x)
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if use_bn:
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x = self.bn_1(x)
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out = paddle.mean(x)
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if y is not None:
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loss = paddle.mean(y) + out
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return out, loss
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class TestNetWithNonTensorSpecWithPrune(unittest.TestCase):
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def setUp(self):
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self.in_num = 16
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self.out_num = 16
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self.x_spec = paddle.static.InputSpec([-1, 16], name='x')
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self.y_spec = paddle.static.InputSpec([16], name='y')
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self.x = paddle.randn([4, 16])
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self.y = paddle.randn([16])
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self.temp_dir = tempfile.TemporaryDirectory()
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@classmethod
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def setUpClass(cls):
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paddle.disable_static()
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def test_non_tensor_with_prune(self):
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specs = [self.x_spec, self.y_spec, True]
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path = os.path.join(self.temp_dir.name, './net_non_tensor_prune_')
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net = NetWithNonTensorSpecPrune(self.in_num, self.out_num)
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net.eval()
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# dygraph out
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dy_out, _ = net(self.x, self.y, *specs[2:])
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# jit.save directly
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paddle.jit.save(net, path + '_direct', input_spec=specs)
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load_net = paddle.jit.load(path + '_direct')
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load_net.eval()
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pred_out, _ = load_net(self.x, self.y)
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np.testing.assert_allclose(dy_out, pred_out, rtol=1e-05)
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# @to_static by InputSpec
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net = paddle.jit.to_static(net, input_spec=specs, full_graph=True)
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st_out, _ = net(self.x, self.y, *specs[2:])
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np.testing.assert_allclose(dy_out, st_out, rtol=1e-05)
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# jit.save and jit.load with prune y and loss
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prune_specs = [self.x_spec, True]
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if paddle.framework.use_pir_api():
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output_spec = [0]
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else:
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output_spec = [st_out]
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paddle.jit.save(
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net,
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path,
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prune_specs,
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output_spec=output_spec,
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input_names_after_prune=[self.x_spec.name],
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)
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load_net = paddle.jit.load(path)
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load_net.eval()
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load_out = load_net(self.x) # no y and no loss
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np.testing.assert_allclose(st_out, load_out, rtol=1e-05)
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class UnHashableObject:
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def __init__(self, val):
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self.val = val
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def __hash__(self):
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raise TypeError("Unsupported to call hash()")
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class TestCompatibleNonTensorSpec(unittest.TestCase):
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def test_case(self):
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self.assertTrue(_compatible_non_tensor_spec([1, 2, 3], [1, 2, 3]))
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self.assertFalse(_compatible_non_tensor_spec([1, 2, 3], [1, 2]))
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self.assertFalse(_compatible_non_tensor_spec([1, 2, 3], [1, 3, 2]))
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# not supported unhashable object.
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self.assertTrue(
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_compatible_non_tensor_spec(
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UnHashableObject(1), UnHashableObject(1)
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)
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)
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class NegSpecNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(10, 5)
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def forward(self, x):
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return self.linear(x)
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class TestNegSpecWithPrim(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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core._set_prim_all_enabled(True)
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def tearDown(self):
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core._set_prim_all_enabled(False)
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def test_run(self):
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net = NegSpecNet()
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net = paddle.jit.to_static(
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net,
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input_spec=[paddle.static.InputSpec(shape=[-1, 10])],
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full_graph=True,
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)
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x = paddle.randn([2, 10])
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out = net(x)
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np.testing.assert_equal(out.shape, [2, 5])
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if __name__ == '__main__':
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unittest.main()
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