siriuz42
111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
# Copyright 2025 Google LLC
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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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"""Dense layers for TimesFM."""
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from flax import nnx
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import jax
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import jax.numpy as jnp
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import jaxtyping
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from .. import configs
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Array = jaxtyping.Array
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Bool = jaxtyping.Bool
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Float = jaxtyping.Float
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Integer = jaxtyping.Integer
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Num = jaxtyping.Num
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ResidualBlockConfig = configs.ResidualBlockConfig
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RandomFourierFeaturesConfig = configs.RandomFourierFeaturesConfig
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class ResidualBlock(nnx.Module):
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"""Residual block with two linear layers and a linear residual connection."""
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def __init__(self, config: ResidualBlockConfig, *, rngs=nnx.Rngs(42)):
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self.config = config
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self.hidden_layer = nnx.Linear(
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in_features=config.input_dims,
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out_features=config.hidden_dims,
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use_bias=config.use_bias,
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rngs=rngs,
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)
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self.output_layer = nnx.Linear(
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in_features=config.hidden_dims,
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out_features=config.output_dims,
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use_bias=config.use_bias,
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rngs=rngs,
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)
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self.residual_layer = nnx.Linear(
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in_features=config.input_dims,
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out_features=config.output_dims,
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use_bias=config.use_bias,
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rngs=rngs,
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)
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if config.activation == "relu":
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self.activation = jax.nn.relu
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elif config.activation == "swish":
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self.activation = jax.nn.swish
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elif config.activation == "none":
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self.activation = lambda x: x
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else:
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raise ValueError(f"Activation: {config.activation} not supported.")
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def __call__(self, x: Float[Array, "b ... i"]) -> Float[Array, "b ... o"]:
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return self.output_layer(
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self.activation(self.hidden_layer(x))
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) + self.residual_layer(x)
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class RandomFourierFeatures(nnx.Module):
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"""Random Fourier features layer."""
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__data__ = ("phrase_shifts",)
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def __init__(self, config: RandomFourierFeaturesConfig, *, rngs=nnx.Rngs(42)):
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self.config = config
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if config.output_dims % 4 != 0:
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raise ValueError(
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f"Output dims must be a multiple of 4: {config.output_dims} % 4 != 0."
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)
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num_projected_features = config.output_dims // 4
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self.phase_shifts = nnx.Param(jnp.zeros(shape=(2, num_projected_features)))
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self.projection_layer = nnx.Linear(
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in_features=config.input_dims,
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out_features=num_projected_features,
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use_bias=config.use_bias,
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rngs=rngs,
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)
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self.residual_layer = nnx.Linear(
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in_features=config.input_dims,
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out_features=config.output_dims,
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use_bias=config.use_bias,
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rngs=rngs,
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)
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def __call__(self, x: Float[Array, "b ... i"]) -> Float[Array, "b ... o"]:
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projected = self.projection_layer(x)
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cos_features = jnp.cos(projected)
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sin_features = jnp.sin(projected)
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sq_wave_1 = jnp.sign(jnp.sin(projected + self.phase_shifts[0, :]))
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sq_wave_2 = jnp.sign(jnp.sin(projected + self.phase_shifts[1, :]))
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fourier_features = jnp.concatenate(
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[cos_features, sin_features, sq_wave_1, sq_wave_2], axis=-1
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)
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residual = self.residual_layer(x)
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return fourier_features + residual
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