wehub-resource-sync
150 lines
5.0 KiB
Python
150 lines
5.0 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 get_device_place
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import paddle
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from paddle import base
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from paddle.framework import in_pir_mode
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from paddle.nn import functional
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from paddle.nn.functional.input import embedding_renorm_
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class EmbeddingStatic(unittest.TestCase):
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def test_1(self):
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prog = base.Program()
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with base.program_guard(prog):
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def test_bad_x():
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initializer = paddle.nn.initializer.Assign(
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np.random.random(size=(128, 100))
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)
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param_attr = base.ParamAttr(
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name="emb_weight",
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learning_rate=0.5,
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initializer=initializer,
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trainable=True,
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)
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if in_pir_mode():
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weight = paddle.pir.core.create_parameter(
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shape=(128, 100),
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dtype="float32",
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**param_attr._to_kwargs(with_initializer=True),
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)
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else:
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weight = prog.global_block().create_parameter(
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(128, 100), attr=param_attr, dtype="float32"
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)
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label = paddle.static.data(
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name="label",
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shape=[-1, 4],
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dtype="int64",
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)
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emb = functional.embedding(
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x=label, weight=weight, sparse=True, name="embedding"
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)
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test_bad_x()
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def test_2(self):
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prog = base.Program()
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with base.program_guard(prog):
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def test_bad_x():
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initializer = paddle.nn.initializer.Assign(
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np.random.random(size=(128, 100))
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)
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param_attr = base.ParamAttr(
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name="emb_weight",
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learning_rate=0.5,
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initializer=initializer,
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trainable=True,
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)
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if in_pir_mode():
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weight = paddle.pir.core.create_parameter(
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shape=(128, 100),
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dtype="float32",
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**param_attr._to_kwargs(with_initializer=True),
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)
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else:
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weight = prog.global_block().create_parameter(
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(128, 100), attr=param_attr, dtype="float32"
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)
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label = paddle.static.data(
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name="label",
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shape=[-1, 4],
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dtype="int32",
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)
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emb = functional.embedding(
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x=label,
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weight=weight,
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padding_idx=129,
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sparse=True,
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name="embedding",
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)
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with self.assertRaises(ValueError):
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test_bad_x()
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def test_3_renorm(self):
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x_np = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.int64)
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weight_np = np.random.random((10, 4)).astype(np.float32) * 10
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max_norm = 5.0
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norm_type = 2.0
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y_ref = self.ref_embedding_renorm_(x_np, weight_np, max_norm, norm_type)
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place = get_device_place()
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.static.data(name="x", shape=[-1, 3], dtype="int64")
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weight = paddle.static.data(
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name="weight", shape=[10, 4], dtype="float32"
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)
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res = embedding_renorm_(
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x=x, weight=weight, max_norm=max_norm, norm_type=norm_type
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)
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exe = paddle.static.Executor(place)
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res_val = exe.run(
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prog, feed={"x": x_np, "weight": weight_np}, fetch_list=[res]
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)
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paddle_result = res_val[0]
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np.testing.assert_allclose(paddle_result, y_ref, atol=1e-5)
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def ref_embedding_renorm_(self, x, weight, max_norm, norm_type=2.0):
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x = np.reshape(x, (-1,))
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x = np.unique(x)
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x = np.sort(x)
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for i in range(len(x)):
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norm = np.linalg.norm(
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weight[int(x[i])], ord=norm_type, axis=0, keepdims=False
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)
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if norm > max_norm:
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weight[int(x[i])] *= max_norm / (norm + 1e-7)
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return weight
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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