127 lines
5.6 KiB
Python
127 lines
5.6 KiB
Python
# coding=utf-8
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# Copyright 2025 Google LLC and HuggingFace Inc. team.
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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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"""TimesFM model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class TimesFmConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`TimesFmModelForPrediction`] or a [`TFTimesFmModel`]. It is used to
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instantiate a TimesFM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the TimesFM
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[google/timesfm-2.0-500m-pytorch](https://huggingface.co/google/timesfm-2.0-500m-pytorch) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Arguments:
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patch_length (`int`, *optional*, defaults to 32):
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The length of one patch in the input sequence.
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context_length (`int`, *optional*, defaults to 512):
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The length of the input context.
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horizon_length (`int`, *optional*, defaults to 128):
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The length of the prediction horizon.
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freq_size (`int`, *optional*, defaults to 3):
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The number of frequency embeddings.
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num_hidden_layers (`int`, *optional*, defaults to 50):
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Number of Transformer layers.
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hidden_size (`int`, *optional*, defaults to 1280):
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Size of the hidden layers in the feed-forward networks.
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intermediate_size (`int`, *optional*, defaults to 1280):
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Dimension of the MLP representations.
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head_dim (`int`, *optional*, defaults to 80):
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Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will
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be defined as `num_attention_heads * head_dim`.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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tolerance (`float`, *optional*, defaults to 1e-06):
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The tolerance for the quantile loss.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the RMS normalization layers.
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quantiles (`list[float]`, *optional*, defaults to `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`):
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The quantiles to predict.
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pad_val (`float`, *optional*, defaults to 1123581321.0):
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The value used to pad the predictions.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for the attention scores.
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use_positional_embedding (`bool`, *optional*, defaults to `False`):
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Whether to add positional embeddings.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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min_timescale (`int`, *optional*, defaults to 1):
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The start of the geometric positional index. Determines the periodicity of
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the added signal.
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max_timescale (`int`, *optional*, defaults to 10000):
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The end of the geometric positional index. Determines the frequency of the
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added signal.
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"""
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model_type = "timesfm"
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keys_to_ignore_at_inference = []
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is_encoder_decoder = False
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def __init__(
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self,
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patch_length: int = 32,
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context_length: int = 512,
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horizon_length: int = 128,
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freq_size: int = 3,
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num_hidden_layers: int = 50,
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hidden_size: int = 1280,
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intermediate_size: int = 1280,
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head_dim: int = 80,
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num_attention_heads: int = 16,
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tolerance: float = 1e-6,
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rms_norm_eps: float = 1e-6,
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quantiles: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
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pad_val: float = 1123581321.0,
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attention_dropout: float = 0.0,
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use_positional_embedding: bool = False,
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initializer_range: float = 0.02,
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min_timescale: int = 1,
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max_timescale: int = 10_000,
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**kwargs,
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):
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self.patch_length = patch_length
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self.context_length = context_length
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self.horizon_length = horizon_length
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self.quantiles = quantiles
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self.pad_val = pad_val
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self.freq_size = freq_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.head_dim = head_dim
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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.tolerance = tolerance
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self.rms_norm_eps = rms_norm_eps
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self.attention_dropout = attention_dropout
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self.use_positional_embedding = use_positional_embedding
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self.initializer_range = initializer_range
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self.min_timescale = min_timescale
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self.max_timescale = max_timescale
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super().__init__(
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is_encoder_decoder=self.is_encoder_decoder,
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**kwargs,
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)
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__all__ = ["TimesFmConfig"]
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