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| # Copyright 2022 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 | |
| ) | |