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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """Configuration class for Seq2seq."""
- import os
- import json
- import copy
- from typing import List
-
- import mindspore.common.dtype as mstype
-
-
- def _is_dataset_file(file: str):
- return "tfrecord" in file.lower() or "mindrecord" in file.lower()
-
-
- def _get_files_from_dir(folder: str):
- _files = []
- for file in os.listdir(folder):
- if _is_dataset_file(file):
- _files.append(os.path.join(folder, file))
- return _files
-
-
- def get_source_list(folder: str) -> List:
- """
- Get file list from a folder.
-
- Returns:
- list, file list.
- """
- _list = []
- if not folder:
- return _list
-
- if os.path.isdir(folder):
- _list = _get_files_from_dir(folder)
- else:
- if _is_dataset_file(folder):
- _list.append(folder)
- return _list
-
-
- PARAM_NODES = {"dataset_config",
- "model_config",
- "loss_scale_config",
- "learn_rate_config",
- "checkpoint_options"}
-
-
- class Seq2seqConfig:
- """
- Configuration for `seq2seq`.
-
- Args:
- random_seed (int): Random seed, it can be changed.
- epochs (int): Epoch number.
- batch_size (int): Batch size of input dataset.
- pre_train_dataset (str): Path of pre-training dataset file or folder.
- fine_tune_dataset (str): Path of fine-tune dataset file or folder.
- test_dataset (str): Path of test dataset file or folder.
- valid_dataset (str): Path of validation dataset file or folder.
- dataset_sink_mode (bool): Whether enable dataset sink mode.
- seq_length (int): Length of input sequence.
- vocab_size (int): The shape of each embedding vector.
- hidden_size (int): Size of embedding, attention, dim.
- num_hidden_layers (int): Encoder, Decoder layers.
- intermediate_size (int): Size of intermediate layer in the Transformer
- encoder/decoder cell.
- hidden_act (str): Activation function used in the Transformer encoder/decoder
- cell.
- hidden_dropout_prob (float): The dropout probability for hidden outputs.
- attention_dropout_prob (float): The dropout probability for Attention module.
- initializer_range (float): Initialization value of TruncatedNormal.
- label_smoothing (float): Label smoothing setting.
- beam_width (int): Beam width for beam search in inferring.
- length_penalty_weight (float): Penalty for sentence length.
- max_decode_length (int): Max decode length for inferring.
- input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
- dataset.
- init_loss_scale (int): Initialized loss scale.
- loss_scale_factor (int): Loss scale factor.
- scale_window (int): Window size of loss scale.
- lr_scheduler (str): Learning rate scheduler. Please see the Note as follow.
- optimizer (str): Optimizer for training, e.g. Adam, Lamb, momentum. Default: Adam.
- lr (float): Initial learning rate.
- min_lr (float): Minimum learning rate.
- decay_steps (int): Decay steps.
- lr_scheduler_power(float): A value used to calculate decayed learning rate.
- warmup_lr_remain_steps (int or float): Start decay at 'remain_steps' iteration.
- warmup_lr_decay_interval (int):interval between LR decay steps.
- decay_start_step (int): Step to decay.
- warmup_steps (int): Warm up steps.
- existed_ckpt (str): Using existed checkpoint to keep training or not.
- save_ckpt_steps (int): Interval of saving ckpt.
- keep_ckpt_max (int): Max ckpt files number.
- ckpt_prefix (str): Prefix of ckpt file.
- ckpt_path (str): Checkpoints save path.
- save_graphs (bool): Whether to save graphs, please set to True if mindinsight
- is wanted.
- dtype (mstype): Data type of the input.
-
- Note:
- There are three types of learning rate scheduler, square root scheduler, polynomial
- decay scheduler and warmup multistep learning rate scheduler.
- In square root scheduler, the following parameters can be used, lr, decay_start_step,
- warmup_steps and min_lr.
- In polynomial decay scheduler, the following parameters can be used, lr, min_lr, decay_steps,
- warmup_steps, lr_scheduler_power.
- In warmmup multistep learning rate scheduler, the following parameters can be used, lr, warmup_steps,
- warmup_lr_remain_steps, warmup_lr_decay_interval, decay_steps, lr_scheduler_power.
- """
-
- def __init__(self,
- random_seed=50,
- epochs=6, batch_size=128,
- pre_train_dataset: str = None,
- fine_tune_dataset: str = None,
- test_dataset: str = None,
- valid_dataset: str = None,
- dataset_sink_mode=True,
- seq_length=51, vocab_size=32320, hidden_size=1024,
- num_hidden_layers=4, intermediate_size=4096,
- hidden_act="tanh",
- hidden_dropout_prob=0.2, attention_dropout_prob=0.2,
- initializer_range=0.1,
- label_smoothing=0.1,
- beam_width=2,
- length_penalty_weight=0.6,
- max_decode_length=50,
- input_mask_from_dataset=False,
- init_loss_scale=65536,
- loss_scale_factor=2, scale_window=1000,
- lr_scheduler="WarmupMultiStepLR",
- optimizer="adam",
- lr=2e-3, min_lr=1e-6,
- decay_steps=4, lr_scheduler_power=0.5,
- warmup_lr_remain_steps=0.666, warmup_lr_decay_interval=-1,
- decay_start_step=-1, warmup_steps=200,
- existed_ckpt="", save_ckpt_steps=3452, keep_ckpt_max=6,
- ckpt_prefix="seq2seq", ckpt_path: str = None,
- save_graphs=False,
- dtype=mstype.float32):
-
- self.save_graphs = save_graphs
- self.random_seed = random_seed
- self.pre_train_dataset = get_source_list(pre_train_dataset) # type: List[str]
- self.fine_tune_dataset = get_source_list(fine_tune_dataset) # type: List[str]
- self.valid_dataset = get_source_list(valid_dataset) # type: List[str]
- self.test_dataset = get_source_list(test_dataset) # type: List[str]
-
- if not isinstance(epochs, int) and epochs < 0:
- raise ValueError("`epoch` must be type of int.")
-
- self.epochs = epochs
- self.dataset_sink_mode = dataset_sink_mode
-
- self.ckpt_path = ckpt_path
- self.keep_ckpt_max = keep_ckpt_max
- self.save_ckpt_steps = save_ckpt_steps
- self.ckpt_prefix = ckpt_prefix
- self.existed_ckpt = existed_ckpt
-
- self.batch_size = batch_size
- self.seq_length = seq_length
- self.vocab_size = vocab_size
- self.hidden_size = hidden_size
- self.num_hidden_layers = num_hidden_layers
- self.hidden_act = hidden_act
- self.intermediate_size = intermediate_size
- self.hidden_dropout_prob = hidden_dropout_prob
- self.attention_dropout_prob = attention_dropout_prob
-
- self.initializer_range = initializer_range
- self.label_smoothing = label_smoothing
-
- self.beam_width = beam_width
- self.length_penalty_weight = length_penalty_weight
- self.max_decode_length = max_decode_length
- self.input_mask_from_dataset = input_mask_from_dataset
- self.compute_type = mstype.float16
- self.dtype = dtype
-
- self.scale_window = scale_window
- self.loss_scale_factor = loss_scale_factor
- self.init_loss_scale = init_loss_scale
-
- self.optimizer = optimizer
- self.lr = lr
- self.lr_scheduler = lr_scheduler
- self.min_lr = min_lr
- self.lr_scheduler_power = lr_scheduler_power
- self.warmup_lr_remain_steps = warmup_lr_remain_steps
- self.warmup_lr_decay_interval = warmup_lr_decay_interval
- self.decay_steps = decay_steps
- self.decay_start_step = decay_start_step
- self.warmup_steps = warmup_steps
-
- @classmethod
- def from_dict(cls, json_object: dict):
- """Constructs a `TransformerConfig` from a Python dictionary of parameters."""
- _params = {}
- for node in PARAM_NODES:
- for key in json_object[node]:
- _params[key] = json_object[node][key]
- return cls(**_params)
-
- @classmethod
- def from_json_file(cls, json_file):
- """Constructs a `TransformerConfig` from a json file of parameters."""
- with open(json_file, "r") as reader:
- return cls.from_dict(json.load(reader))
-
- def to_dict(self):
- """Serializes this instance to a Python dictionary."""
- output = copy.deepcopy(self.__dict__)
- return output
-
- def to_json_string(self):
- """Serializes this instance to a JSON string."""
- return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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