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venv/Lib/site-packages/transformers/models/timesfm/configuration_timesfm.py

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