| |
| |
| """PatchTST-FM model configuration""" |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
| PATCHTSTFM_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
|
|
|
|
| class PatchTSTFMConfig(PretrainedConfig): |
| model_type = "patchtst_fm" |
| attribute_map = { |
| "hidden_size": "d_model", |
| "num_hidden_layers": "n_layer", |
| } |
|
|
| |
| def __init__( |
| self, |
| context_length: int = 8192, |
| prediction_length: int = 64, |
| d_patch: int = 16, |
| d_model: int = 384, |
| n_head: int = 6, |
| n_layer: int = 6, |
| norm_first: bool = True, |
| pretrain_mask_ratio: float = 0.4, |
| pretrain_mask_cont: int = 8, |
| num_quantile: int = 99, |
| dropout: float = 0.1, |
| **kwargs, |
| ): |
| self.context_length = context_length |
| self.prediction_length = prediction_length |
| self.d_patch = d_patch |
| self.n_patch = int(context_length // d_patch) |
| self.d_model = d_model |
| self.n_head = n_head |
| self.n_layer = n_layer |
| self.norm_first = norm_first |
| self.pretrain_mask_ratio = pretrain_mask_ratio |
| self.pretrain_mask_cont = pretrain_mask_cont |
| self.num_quantile = num_quantile |
| if not 0.0 <= dropout < 1.0: |
| raise ValueError( |
| f"dropout must satisfy 0 <= dropout < 1, got {dropout}." |
| ) |
| self.dropout = float(dropout) |
| if num_quantile % 9 == 0: |
| quantiles = [i / (self.num_quantile + 1) for i in range(1, self.num_quantile + 1)] |
| else: |
| quantiles = [i / (self.num_quantile - 1) for i in range(1, self.num_quantile - 1)] |
| quantiles = [0.01] + quantiles + [0.99] |
| self.quantile_levels = quantiles |
| |
| super().__init__(**kwargs) |
|
|
