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233 lines
9.1 KiB
233 lines
9.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Configuration class for Transformer."""
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import os
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import json
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import copy
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from typing import List
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import mindspore.common.dtype as mstype
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def _is_dataset_file(file: str):
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return "tfrecord" in file.lower() or "mindrecord" in file.lower()
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def _get_files_from_dir(folder: str):
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_files = []
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for file in os.listdir(folder):
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if _is_dataset_file(file):
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_files.append(os.path.join(folder, file))
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return _files
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def get_source_list(folder: str) -> List:
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"""
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Get file list from a folder.
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Returns:
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list, file list.
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"""
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_list = []
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if not folder:
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return _list
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if os.path.isdir(folder):
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_list = _get_files_from_dir(folder)
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else:
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if _is_dataset_file(folder):
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_list.append(folder)
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return _list
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PARAM_NODES = {"dataset_config",
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"model_config",
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"loss_scale_config",
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"learn_rate_config",
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"checkpoint_options"}
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class TransformerConfig:
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"""
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Configuration for `Transformer`.
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Args:
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random_seed (int): Random seed.
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batch_size (int): Batch size of input dataset.
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epochs (int): Epoch number.
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dataset_sink_mode (bool): Whether enable dataset sink mode.
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dataset_sink_step (int): Dataset sink step.
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lr_scheduler (str): Whether use lr_scheduler, only support "ISR" now.
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lr (float): Initial learning rate.
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min_lr (float): Minimum learning rate.
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decay_start_step (int): Step to decay.
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warmup_steps (int): Warm up steps.
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dataset_schema (str): Path of dataset schema file.
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pre_train_dataset (str): Path of pre-training dataset file or folder.
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fine_tune_dataset (str): Path of fine-tune dataset file or folder.
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test_dataset (str): Path of test dataset file or folder.
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valid_dataset (str): Path of validation dataset file or folder.
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ckpt_path (str): Checkpoints save path.
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save_ckpt_steps (int): Interval of saving ckpt.
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ckpt_prefix (str): Prefix of ckpt file.
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keep_ckpt_max (int): Max ckpt files number.
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seq_length (int): Length of input sequence. Default: 64.
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vocab_size (int): The shape of each embedding vector. Default: 46192.
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hidden_size (int): Size of embedding, attention, dim. Default: 512.
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num_hidden_layers (int): Encoder, Decoder layers.
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num_attention_heads (int): Number of hidden layers in the Transformer encoder/decoder
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cell. Default: 6.
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intermediate_size (int): Size of intermediate layer in the Transformer
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encoder/decoder cell. Default: 4096.
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hidden_act (str): Activation function used in the Transformer encoder/decoder
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cell. Default: "relu".
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init_loss_scale (int): Initialized loss scale.
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loss_scale_factor (int): Loss scale factor.
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scale_window (int): Window size of loss scale.
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beam_width (int): Beam width for beam search in inferring. Default: 4.
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length_penalty_weight (float): Penalty for sentence length. Default: 1.0.
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label_smoothing (float): Label smoothing setting. Default: 0.1.
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input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
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dataset. Default: True.
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save_graphs (bool): Whether to save graphs, please set to True if mindinsight
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is wanted.
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dtype (mstype): Data type of the input. Default: mstype.float32.
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max_decode_length (int): Max decode length for inferring. Default: 64.
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hidden_dropout_prob (float): The dropout probability for hidden outputs. Default: 0.1.
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attention_dropout_prob (float): The dropout probability for
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Multi-head Self-Attention. Default: 0.1.
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max_position_embeddings (int): Maximum length of sequences used in this
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model. Default: 512.
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initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
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"""
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def __init__(self,
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random_seed=74,
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batch_size=64, epochs=1,
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dataset_sink_mode=True, dataset_sink_step=1,
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lr_scheduler="", optimizer="adam",
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lr=1e-4, min_lr=1e-6,
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decay_steps=10000, poly_lr_scheduler_power=1,
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decay_start_step=-1, warmup_steps=2000,
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pre_train_dataset: str = None,
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fine_tune_dataset: str = None,
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test_dataset: str = None,
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valid_dataset: str = None,
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ckpt_path: str = None,
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save_ckpt_steps=2000,
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ckpt_prefix="CKPT",
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existed_ckpt="",
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keep_ckpt_max=20,
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seq_length=128,
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vocab_size=46192,
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hidden_size=512,
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num_hidden_layers=6,
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num_attention_heads=8,
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intermediate_size=4096,
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hidden_act="relu",
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hidden_dropout_prob=0.1,
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attention_dropout_prob=0.1,
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max_position_embeddings=64,
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initializer_range=0.02,
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init_loss_scale=2 ** 10,
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loss_scale_factor=2, scale_window=2000,
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beam_width=5,
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length_penalty_weight=1.0,
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label_smoothing=0.1,
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input_mask_from_dataset=True,
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save_graphs=False,
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dtype=mstype.float32,
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max_decode_length=64):
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self.save_graphs = save_graphs
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self.random_seed = random_seed
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self.pre_train_dataset = get_source_list(pre_train_dataset) # type: List[str]
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self.fine_tune_dataset = get_source_list(fine_tune_dataset) # type: List[str]
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self.valid_dataset = get_source_list(valid_dataset) # type: List[str]
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self.test_dataset = get_source_list(test_dataset) # type: List[str]
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if not isinstance(epochs, int) and epochs < 0:
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raise ValueError("`epoch` must be type of int.")
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self.epochs = epochs
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self.dataset_sink_mode = dataset_sink_mode
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self.dataset_sink_step = dataset_sink_step
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self.ckpt_path = ckpt_path
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self.keep_ckpt_max = keep_ckpt_max
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self.save_ckpt_steps = save_ckpt_steps
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self.ckpt_prefix = ckpt_prefix
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self.existed_ckpt = existed_ckpt
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_dropout_prob = attention_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.label_smoothing = label_smoothing
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self.beam_width = beam_width
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self.length_penalty_weight = length_penalty_weight
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self.max_decode_length = max_decode_length
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self.input_mask_from_dataset = input_mask_from_dataset
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self.compute_type = mstype.float16
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self.dtype = dtype
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self.scale_window = scale_window
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self.loss_scale_factor = loss_scale_factor
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self.init_loss_scale = init_loss_scale
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self.optimizer = optimizer
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self.lr = lr
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self.lr_scheduler = lr_scheduler
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self.min_lr = min_lr
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self.poly_lr_scheduler_power = poly_lr_scheduler_power
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self.decay_steps = decay_steps
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self.decay_start_step = decay_start_step
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self.warmup_steps = warmup_steps
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self.train_url = ""
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@classmethod
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def from_dict(cls, json_object: dict):
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"""Constructs a `TransformerConfig` from a Python dictionary of parameters."""
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_params = {}
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for node in PARAM_NODES:
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for key in json_object[node]:
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_params[key] = json_object[node][key]
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return cls(**_params)
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `TransformerConfig` from a json file of parameters."""
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with open(json_file, "r") as reader:
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return cls.from_dict(json.load(reader))
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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