wehub-resource-sync
151 lines
4.0 KiB
Python
151 lines
4.0 KiB
Python
# Copyright (c) 2021 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 numpy as np
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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runtime_main,
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)
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import paddle
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import paddle.nn.functional as F
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paddle.seed(123)
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np.random.seed(2021)
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, hidden_size, vocab_size, is_sparse=False):
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super().__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.embedding = paddle.nn.Embedding(
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self.vocab_size,
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self.hidden_size,
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sparse=is_sparse,
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)
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self.lin_a = paddle.nn.Linear(self.hidden_size, self.vocab_size)
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self.lin_b = paddle.nn.Linear(self.vocab_size, 1)
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self.unused_net = paddle.nn.Linear(5, 3)
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self.phony = self.create_parameter(shape=[1], dtype="float32")
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def forward(self, input, label, conf):
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x_emb = self.embedding(input)
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fc = self.lin_a(x_emb)
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mask = conf > 0
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mask = paddle.cast(mask, dtype="int64")
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mask.stop_gradient = True
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emb_mask = mask.max(1).flatten()
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emb_mask_inds = paddle.nonzero(emb_mask > 0).flatten()
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emb_mask_inds.stop_gradient = True
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if emb_mask_inds.numel() == 0:
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loss_box = self.phony * 0
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else:
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projection = self.lin_b(fc)
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projection = paddle.reshape(projection, shape=[-1, 1])
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output = paddle.gather(projection, emb_mask_inds)
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target = paddle.gather(label, emb_mask_inds)
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loss_box = F.smooth_l1_loss(
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output, target, reduction='sum', delta=1.0
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)
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loss_box = loss_box / len(conf)
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return loss_box
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# global configs
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batch_size = 4
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batch_num = 2000
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hidden_size = 5
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vocab_size = 100
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conf_dataset = [
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[0],
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[0],
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[0],
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[0],
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[1],
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[0],
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[1],
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[0],
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[0],
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[1],
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[0],
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[1],
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[1],
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[1],
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[1],
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[1],
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[1],
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[1],
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[1],
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[1],
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[1],
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[0],
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[0],
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[1],
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]
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def fake_sample_reader():
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def __reader__():
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for i in range(batch_num):
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x_data = np.random.randint(0, vocab_size)
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y_data = np.random.random_sample((1,)).astype('float32')
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conf_data = np.array(conf_dataset[i % len(conf_dataset)]).astype(
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'int64'
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)
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yield x_data, y_data, conf_data
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return __reader__
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class TestSimpleNet(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = SimpleNet(
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hidden_size=hidden_size, vocab_size=vocab_size, is_sparse=False
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)
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train_reader = paddle.batch(
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fake_sample_reader(), batch_size=batch_size, drop_last=True
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=model.parameters()
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)
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return model, train_reader, optimizer
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def run_one_loop(self, model, optimizer, batch):
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x_data = np.array([x[0] for x in batch]).astype('int64')
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y_data = np.array([x[1] for x in batch]).astype('float32')
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conf_data = np.array([x[2] for x in batch]).astype('int64')
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x_data = x_data.reshape((-1, 1))
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y_data = y_data.reshape((-1, 1))
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conf_data = conf_data.reshape((-1, 1))
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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conf = paddle.to_tensor(conf_data)
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loss = model(x, y, conf)
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return loss
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if __name__ == "__main__":
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runtime_main(TestSimpleNet)
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