wehub-resource-sync
124 lines
4.2 KiB
Python
124 lines
4.2 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.transformers.xlnet.modeling import XLNetForSequenceClassification
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from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
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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", "language_model"
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)
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)
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)
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from xlnet.run_glue import create_data_loader # noqa: E402
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class XLNetBenchmark(BenchmarkBase):
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def __init__(self):
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self.label_list = None
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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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"--model_name_or_path",
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type=str,
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default="xlnet-base-cased",
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help="Model name. Defaults to xlnet-base-cased. ",
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)
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parser.add_argument("--task_name", type=str, default="SST-2", help="Task name. Defaults to sst-2. ")
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parser.add_argument("--max_seq_length", type=int, default=args.max_seq_len, help="Maximum sequence length. ")
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def create_data_loader(self, args, **kwargs):
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args.task_name = args.task_name.lower()
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tokenizer = XLNetTokenizer.from_pretrained(args.model_name_or_path)
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if args.task_name == "mnli":
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(
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train_data_loader,
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dev_data_loader_matched,
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dev_data_loader_mismatched,
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train_ds,
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_,
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_,
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) = create_data_loader(args, tokenizer)
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else:
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train_loader, dev_loader, train_ds, _ = create_data_loader(args, tokenizer)
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self.num_batch = len(train_loader)
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self.label_list = train_ds.label_list
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if args.task_name == "mnli":
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return train_data_loader, (dev_data_loader_matched, dev_data_loader_mismatched)
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else:
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return train_loader, dev_loader
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def build_model(self, args, **kwargs):
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num_classes = 1 if self.label_list is None else len(self.label_list)
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model = XLNetForSequenceClassification.from_pretrained(args.model_name_or_path, num_classes=num_classes)
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self.loss_fct = paddle.nn.loss.CrossEntropyLoss() if self.label_list else paddle.nn.loss.MSELoss()
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return model
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def forward(self, model, args, input_data=None, **kwargs):
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input_ids, token_type_ids, attention_mask, labels = input_data
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logits = model(input_ids, token_type_ids, attention_mask)
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loss = self.loss_fct(logits, labels)
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return 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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