261 lines
12 KiB
Python
261 lines
12 KiB
Python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. 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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"""PatchTST model configuration"""
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from typing import List, Optional, Union
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class PatchTSTConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`PatchTSTModel`]. It is used to instantiate an
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PatchTST model according to the specified arguments, defining the model architecture.
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[ibm/patchtst](https://huggingface.co/ibm/patchtst) architecture.
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Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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num_input_channels (`int`, *optional*, defaults to 1):
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The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of
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multivariate targets.
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context_length (`int`, *optional*, defaults to 32):
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The context length of the input sequence.
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distribution_output (`str`, *optional*, defaults to `"student_t"`):
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The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or
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"negative_binomial".
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loss (`str`, *optional*, defaults to `"mse"`):
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The loss function for the model corresponding to the `distribution_output` head. For parametric
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distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared
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error "mse".
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patch_length (`int`, *optional*, defaults to 1):
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Define the patch length of the patchification process.
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patch_stride (`int`, *optional*, defaults to 1):
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Define the stride of the patchification process.
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num_hidden_layers (`int`, *optional*, defaults to 3):
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Number of hidden layers.
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d_model (`int`, *optional*, defaults to 128):
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Dimensionality of the transformer layers.
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num_attention_heads (`int`, *optional*, defaults to 4):
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Number of attention heads for each attention layer in the Transformer encoder.
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share_embedding (`bool`, *optional*, defaults to `True`):
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Sharing the input embedding across all channels.
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channel_attention (`bool`, *optional*, defaults to `False`):
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Activate channel attention block in the Transformer to allow channels to attend each other.
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ffn_dim (`int`, *optional*, defaults to 512):
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Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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norm_type (`str` , *optional*, defaults to `"batchnorm"`):
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Normalization at each Transformer layer. Can be `"batchnorm"` or `"layernorm"`.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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A value added to the denominator for numerical stability of normalization.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for the attention probabilities.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the Transformer.
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positional_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability in the positional embedding layer.
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path_dropout (`float`, *optional*, defaults to 0.0):
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The dropout path in the residual block.
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ff_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability used between the two layers of the feed-forward networks.
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bias (`bool`, *optional*, defaults to `True`):
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Whether to add bias in the feed-forward networks.
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activation_function (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (string) in the Transformer.`"gelu"` and `"relu"` are supported.
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pre_norm (`bool`, *optional*, defaults to `True`):
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Normalization is applied before self-attention if pre_norm is set to `True`. Otherwise, normalization is
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applied after residual block.
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positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
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Positional encodings. Options `"random"` and `"sincos"` are supported.
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use_cls_token (`bool`, *optional*, defaults to `False`):
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Whether cls token is used.
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init_std (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated normal weight initialization distribution.
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share_projection (`bool`, *optional*, defaults to `True`):
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Sharing the projection layer across different channels in the forecast head.
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scaling (`Union`, *optional*, defaults to `"std"`):
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Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
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scaler is set to "mean".
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do_mask_input (`bool`, *optional*):
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Apply masking during the pretraining.
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mask_type (`str`, *optional*, defaults to `"random"`):
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Masking type. Only `"random"` and `"forecast"` are currently supported.
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random_mask_ratio (`float`, *optional*, defaults to 0.5):
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Masking ratio applied to mask the input data during random pretraining.
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num_forecast_mask_patches (`int` or `list`, *optional*, defaults to `[2]`):
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Number of patches to be masked at the end of each batch sample. If it is an integer,
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all the samples in the batch will have the same number of masked patches. If it is a list,
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samples in the batch will be randomly masked by numbers defined in the list. This argument is only used
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for forecast pretraining.
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channel_consistent_masking (`bool`, *optional*, defaults to `False`):
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If channel consistent masking is True, all the channels will have the same masking pattern.
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unmasked_channel_indices (`list`, *optional*):
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Indices of channels that are not masked during pretraining. Values in the list are number between 1 and
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`num_input_channels`
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mask_value (`int`, *optional*, defaults to 0):
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Values in the masked patches will be filled by `mask_value`.
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pooling_type (`str`, *optional*, defaults to `"mean"`):
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Pooling of the embedding. `"mean"`, `"max"` and `None` are supported.
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head_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for head.
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prediction_length (`int`, *optional*, defaults to 24):
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The prediction horizon that the model will output.
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num_targets (`int`, *optional*, defaults to 1):
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Number of targets for regression and classification tasks. For classification, it is the number of
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classes.
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output_range (`list`, *optional*):
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Output range for regression task. The range of output values can be set to enforce the model to produce
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values within a range.
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num_parallel_samples (`int`, *optional*, defaults to 100):
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The number of samples is generated in parallel for probabilistic prediction.
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```python
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>>> from transformers import PatchTSTConfig, PatchTSTModel
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>>> # Initializing an PatchTST configuration with 12 time steps for prediction
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>>> configuration = PatchTSTConfig(prediction_length=12)
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>>> # Randomly initializing a model (with random weights) from the configuration
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>>> model = PatchTSTModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "patchtst"
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attribute_map = {
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"hidden_size": "d_model",
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"num_attention_heads": "num_attention_heads",
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"num_hidden_layers": "num_hidden_layers",
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}
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def __init__(
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self,
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# time series specific configuration
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num_input_channels: int = 1,
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context_length: int = 32,
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distribution_output: str = "student_t",
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loss: str = "mse",
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# PatchTST arguments
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patch_length: int = 1,
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patch_stride: int = 1,
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# Transformer architecture configuration
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num_hidden_layers: int = 3,
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d_model: int = 128,
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num_attention_heads: int = 4,
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share_embedding: bool = True,
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channel_attention: bool = False,
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ffn_dim: int = 512,
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norm_type: str = "batchnorm",
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norm_eps: float = 1e-05,
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attention_dropout: float = 0.0,
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dropout: float = 0.0,
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positional_dropout: float = 0.0,
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path_dropout: float = 0.0,
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ff_dropout: float = 0.0,
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bias: bool = True,
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activation_function: str = "gelu",
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pre_norm: bool = True,
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positional_encoding_type: str = "sincos",
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use_cls_token: bool = False,
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init_std: float = 0.02,
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share_projection: bool = True,
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scaling: Optional[Union[str, bool]] = "std",
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# mask pretraining
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do_mask_input: Optional[bool] = None,
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mask_type: str = "random",
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random_mask_ratio: float = 0.5,
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num_forecast_mask_patches: Optional[Union[List[int], int]] = [2],
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channel_consistent_masking: Optional[bool] = False,
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unmasked_channel_indices: Optional[List[int]] = None,
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mask_value: int = 0,
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# head
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pooling_type: str = "mean",
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head_dropout: float = 0.0,
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prediction_length: int = 24,
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num_targets: int = 1,
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output_range: Optional[List] = None,
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# distribution head
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num_parallel_samples: int = 100,
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**kwargs,
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):
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# time series specific configuration
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self.context_length = context_length
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self.num_input_channels = num_input_channels # n_vars
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self.loss = loss
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self.distribution_output = distribution_output
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self.num_parallel_samples = num_parallel_samples
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# Transformer architecture configuration
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self.d_model = d_model
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self.num_attention_heads = num_attention_heads
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self.ffn_dim = ffn_dim
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self.num_hidden_layers = num_hidden_layers
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.share_embedding = share_embedding
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self.channel_attention = channel_attention
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self.norm_type = norm_type
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self.norm_eps = norm_eps
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self.positional_dropout = positional_dropout
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self.path_dropout = path_dropout
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self.ff_dropout = ff_dropout
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self.bias = bias
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self.activation_function = activation_function
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self.pre_norm = pre_norm
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self.positional_encoding_type = positional_encoding_type
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self.use_cls_token = use_cls_token
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self.init_std = init_std
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self.scaling = scaling
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# PatchTST parameters
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self.patch_length = patch_length
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self.patch_stride = patch_stride
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# Mask pretraining
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self.do_mask_input = do_mask_input
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self.mask_type = mask_type
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self.random_mask_ratio = random_mask_ratio # for random masking
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self.num_forecast_mask_patches = num_forecast_mask_patches # for forecast masking
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self.channel_consistent_masking = channel_consistent_masking
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self.unmasked_channel_indices = unmasked_channel_indices
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self.mask_value = mask_value
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# general head params
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self.pooling_type = pooling_type
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self.head_dropout = head_dropout
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# For prediction head
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self.share_projection = share_projection
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self.prediction_length = prediction_length
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# For prediction and regression head
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self.num_parallel_samples = num_parallel_samples
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# Regression
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self.num_targets = num_targets
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self.output_range = output_range
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super().__init__(**kwargs)
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