wehub-resource-sync
102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
# Copyright (c) 2023 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 sys
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import paddle
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from paddlenlp.utils.log import logger
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from .model_base import BenchmarkBase
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)))
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from bigru_crf.data import create_data_loader # noqa: E402
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from bigru_crf.model import BiGruCrf # noqa: E402
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class BiGruCrfBenchmark(BenchmarkBase):
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def __init__(self):
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super().__init__()
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@staticmethod
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def add_args(args, parser):
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parser.add_argument(
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"--base_lr", type=float, default=0.001, help="The basic learning rate that affects the entire network."
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)
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parser.add_argument(
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"--crf_lr", type=float, default=0.2, help="The learning rate ratio that affects CRF layers."
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)
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parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
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parser.add_argument(
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"--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer."
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)
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return parser
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def create_data_loader(self, args, **kwargs):
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self.word_vocab, self.label_vocab, train_loader, test_loader = create_data_loader(args)
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self.num_batch = len(train_loader)
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return train_loader, test_loader
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def build_model(self, args, **kwargs):
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model = BiGruCrf(
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args.emb_dim, args.hidden_size, len(self.word_vocab), len(self.label_vocab), crf_lr=args.crf_lr
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)
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return model
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def forward(self, model, args, input_data=None, **kwargs):
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(token_ids, length, label_ids) = input_data
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loss = model(token_ids, length, label_ids)
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avg_loss = paddle.mean(loss)
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return avg_loss, args.batch_size
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def logger(
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self,
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args,
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step_id=None,
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pass_id=None,
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batch_id=None,
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loss=None,
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batch_cost=None,
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reader_cost=None,
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num_samples=None,
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ips=None,
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**kwargs
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):
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max_mem_reserved_msg = ""
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max_mem_allocated_msg = ""
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if paddle.device.is_compiled_with_cuda():
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max_mem_reserved_msg = f"max_mem_reserved: {paddle.device.cuda.max_memory_reserved() // (1024 ** 2)} MB,"
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max_mem_allocated_msg = f"max_mem_allocated: {paddle.device.cuda.max_memory_allocated() // (1024 ** 2)} MB"
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logger.info(
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"global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, "
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"avg_samples: %.5f, ips: %.5f sequences/sec, %s %s"
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% (
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step_id,
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args.epoch * self.num_batch,
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loss,
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reader_cost,
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batch_cost,
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num_samples,
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ips,
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max_mem_reserved_msg,
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max_mem_allocated_msg,
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)
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)
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