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# Copyright 2023 The HuggingFace Team. All rights reserved. | |
# | |
# 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. | |
import math | |
import flax.linen as nn | |
import jax.numpy as jnp | |
def get_sinusoidal_embeddings( | |
timesteps: jnp.ndarray, | |
embedding_dim: int, | |
freq_shift: float = 1, | |
min_timescale: float = 1, | |
max_timescale: float = 1.0e4, | |
flip_sin_to_cos: bool = False, | |
scale: float = 1.0, | |
) -> jnp.ndarray: | |
"""Returns the positional encoding (same as Tensor2Tensor). | |
Args: | |
timesteps: a 1-D Tensor of N indices, one per batch element. | |
These may be fractional. | |
embedding_dim: The number of output channels. | |
min_timescale: The smallest time unit (should probably be 0.0). | |
max_timescale: The largest time unit. | |
Returns: | |
a Tensor of timing signals [N, num_channels] | |
""" | |
assert timesteps.ndim == 1, "Timesteps should be a 1d-array" | |
assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even" | |
num_timescales = float(embedding_dim // 2) | |
log_timescale_increment = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift) | |
inv_timescales = min_timescale * jnp.exp(jnp.arange(num_timescales, dtype=jnp.float32) * -log_timescale_increment) | |
emb = jnp.expand_dims(timesteps, 1) * jnp.expand_dims(inv_timescales, 0) | |
# scale embeddings | |
scaled_time = scale * emb | |
if flip_sin_to_cos: | |
signal = jnp.concatenate([jnp.cos(scaled_time), jnp.sin(scaled_time)], axis=1) | |
else: | |
signal = jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1) | |
signal = jnp.reshape(signal, [jnp.shape(timesteps)[0], embedding_dim]) | |
return signal | |
class FlaxTimestepEmbedding(nn.Module): | |
r""" | |
Time step Embedding Module. Learns embeddings for input time steps. | |
Args: | |
time_embed_dim (`int`, *optional*, defaults to `32`): | |
Time step embedding dimension | |
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
Parameters `dtype` | |
""" | |
time_embed_dim: int = 32 | |
dtype: jnp.dtype = jnp.float32 | |
def __call__(self, temb): | |
temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_1")(temb) | |
temb = nn.silu(temb) | |
temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_2")(temb) | |
return temb | |
class FlaxTimesteps(nn.Module): | |
r""" | |
Wrapper Module for sinusoidal Time step Embeddings as described in https://arxiv.org/abs/2006.11239 | |
Args: | |
dim (`int`, *optional*, defaults to `32`): | |
Time step embedding dimension | |
""" | |
dim: int = 32 | |
flip_sin_to_cos: bool = False | |
freq_shift: float = 1 | |
def __call__(self, timesteps): | |
return get_sinusoidal_embeddings( | |
timesteps, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift | |
) | |