wehub-resource-sync
109 lines
3.6 KiB
Python
109 lines
3.6 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(
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os.path.abspath(
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os.path.join(
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os.path.dirname(__file__), os.pardir, os.pardir, os.pardir, os.pardir, "examples", "machine_translation"
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)
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)
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)
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from seq2seq.data import create_train_loader # noqa: E402
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from seq2seq.seq2seq_attn import CrossEntropyCriterion, Seq2SeqAttnModel # noqa: E402
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class Seq2SeqBenchmark(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("--num_layers", type=int, default=2, help="Number of layers. ")
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parser.add_argument("--hidden_size", type=int, default=512, help="Hidden size. ")
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parser.add_argument("--dropout", type=float, default=0.2, help="Dropout rate. ")
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parser.add_argument("--init_scale", type=float, default=0.1, help="Initial scale. ")
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parser.add_argument("--max_len", type=int, default=args.max_seq_len, help="Number of layers. ")
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def create_data_loader(self, args, **kwargs):
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(train_loader, eval_loader, self.src_vocab_size, self.tgt_vocab_size, self.eos_id) = create_train_loader(args)
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self.num_batch = len(train_loader)
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return train_loader, eval_loader
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def build_model(self, args, **kwargs):
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model = Seq2SeqAttnModel(
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self.src_vocab_size,
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self.tgt_vocab_size,
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args.hidden_size,
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args.hidden_size,
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args.num_layers,
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args.dropout,
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self.eos_id,
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)
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self.criterion = CrossEntropyCriterion()
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return model
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def forward(self, model, args, input_data=None, **kwargs):
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(src, src_length, trg, label, trg_mask) = input_data
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predict = model(src, src_length, trg)
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loss = self.criterion(predict, label, trg_mask)
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return loss, paddle.sum(trg_mask).numpy()
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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: %.6f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, "
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"avg_samples: %.5f, ips: %.5f words/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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