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modeling_autoformer.py
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modeling_autoformer.py
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# coding=utf-8
# Copyright (c) 2021 THUML @ Tsinghua University
# Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Copyright 2023 The HuggingFace Inc. 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.
""" PyTorch Autoformer model."""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
ModelOutput,
SampleTSPredictionOutput,
Seq2SeqTSPredictionOutput,
)
from ...modeling_utils import PreTrainedModel
from ...time_series_utils import NegativeBinomialOutput, NormalOutput, StudentTOutput
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_autoformer import AutoformerConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "AutoformerConfig"
@dataclass
class AutoFormerDecoderOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
trend (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Trend tensor for each time series.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
trend: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class AutoformerModelOutput(ModelOutput):
"""
Autoformer model output that contains the additional trend output.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
trend (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Trend tensor for each time series.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Shift values of each time series' context window which is used to give the model inputs of the same
magnitude and then used to shift back to the original magnitude.
scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Scaling values of each time series' context window which is used to give the model inputs of the same
magnitude and then used to rescale back to the original magnitude.
static_features: (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
Static features of each time series' in a batch which are copied to the covariates at inference time.
"""
last_hidden_state: torch.FloatTensor = None
trend: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
loc: Optional[torch.FloatTensor] = None
scale: Optional[torch.FloatTensor] = None
static_features: Optional[torch.FloatTensor] = None
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"huggingface/autoformer-tourism-monthly",
# See all Autoformer models at https://huggingface.co/models?filter=autoformer
]
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesFeatureEmbedder with TimeSeries->Autoformer
class AutoformerFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
"""
def __init__(self, cardinalities: List[int], embedding_dims: List[int]) -> None:
super().__init__()
self.num_features = len(cardinalities)
self.embedders = nn.ModuleList([nn.Embedding(c, d) for c, d in zip(cardinalities, embedding_dims)])
def forward(self, features: torch.Tensor) -> torch.Tensor:
if self.num_features > 1:
# we slice the last dimension, giving an array of length
# self.num_features with shape (N,T) or (N)
cat_feature_slices = torch.chunk(features, self.num_features, dim=-1)
else:
cat_feature_slices = [features]
return torch.cat(
[
embed(cat_feature_slice.squeeze(-1))
for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices)
],
dim=-1,
)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
class AutoformerStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-5
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
Calculating the scale on the observed indicator.
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim)
denominator = denominator.clamp_min(1.0)
loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator
variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
scale = torch.sqrt(variance + self.minimum_scale)
return (data - loc) / scale, loc, scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
class AutoformerMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
self.default_scale = config.default_scale if hasattr(config, "default_scale") else None
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
Calculating the scale on the observed indicator.
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True)
num_observed = observed_indicator.sum(self.dim, keepdim=True)
scale = ts_sum / torch.clamp(num_observed, min=1)
# If `default_scale` is provided, we use it, otherwise we use the scale
# of the batch.
if self.default_scale is None:
batch_sum = ts_sum.sum(dim=0)
batch_observations = torch.clamp(num_observed.sum(0), min=1)
default_scale = torch.squeeze(batch_sum / batch_observations)
else:
default_scale = self.default_scale * torch.ones_like(scale)
# apply default scale where there are no observations
scale = torch.where(num_observed > 0, scale, default_scale)
# ensure the scale is at least `self.minimum_scale`
scale = torch.clamp(scale, min=self.minimum_scale)
scaled_data = data / scale
if not self.keepdim:
scale = scale.squeeze(dim=self.dim)
return scaled_data, torch.zeros_like(scale), scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
class AutoformerNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
return data, loc, scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.weighted_average
def weighted_average(input_tensor: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None) -> torch.Tensor:
"""
Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero,
meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`.
Args:
input_tensor (`torch.FloatTensor`):
Input tensor, of which the average must be computed.
weights (`torch.FloatTensor`, *optional*):
Weights tensor, of the same shape as `input_tensor`.
dim (`int`, *optional*):
The dim along which to average `input_tensor`.
Returns:
`torch.FloatTensor`: The tensor with values averaged along the specified `dim`.
"""
if weights is not None:
weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor))
sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0)
return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights
else:
return input_tensor.mean(dim=dim)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.nll
def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor:
"""
Computes the negative log likelihood loss from input distribution with respect to target.
"""
return -input.log_prob(target)
# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Autoformer
class AutoformerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(out: nn.Parameter) -> nn.Parameter:
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
position_enc = np.array(
[[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
)
out.requires_grad = False # set early to avoid an error in pytorch-1.8+
sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
return out
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesValueEmbedding with TimeSeries->Autoformer
class AutoformerValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x)
# Class based on
# https://github.com/thuml/Autoformer/blob/c6a0694ff484753f2d986cc0bb1f99ee850fc1a8/layers/Autoformer_EncDec.py#L39
# where AutoformerSeriesDecompositionLayer is series_decomp + moving_average
class AutoformerSeriesDecompositionLayer(nn.Module):
"""
Returns the trend and the seasonal parts of the time series. Calculated as:
x_trend = AvgPool(Padding(X)) and x_seasonal = X - x_trend
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.kernel_size = config.moving_average
self.avg = nn.AvgPool1d(kernel_size=self.kernel_size, stride=1, padding=0)
def forward(self, x):
"""Input shape: Batch x Time x EMBED_DIM"""
# padding on the both ends of time series
num_of_pads = (self.kernel_size - 1) // 2
front = x[:, 0:1, :].repeat(1, num_of_pads, 1)
end = x[:, -1:, :].repeat(1, num_of_pads, 1)
x_padded = torch.cat([front, x, end], dim=1)
# calculate the trend and seasonal part of the series
x_trend = self.avg(x_padded.permute(0, 2, 1)).permute(0, 2, 1)
x_seasonal = x - x_trend
return x_seasonal, x_trend
# Class based on
# https://github.com/thuml/Autoformer/blob/c6a0694ff484753f2d986cc0bb1f99ee850fc1a8/layers/Autoformer_EncDec.py#L6
# where AutoformerLayernorm is my_Layernorm
class AutoformerLayernorm(nn.Module):
"""
Special designed layer normalization for the seasonal part, calculated as: AutoformerLayernorm(x) = nn.LayerNorm(x)
- torch.mean(nn.LayerNorm(x))
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.layernorm = nn.LayerNorm(config.d_model)
def forward(self, x):
x_hat = self.layernorm(x)
bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)
return x_hat - bias
class AutoformerAttention(nn.Module):
"""
AutoCorrelation Mechanism with the following two phases:
(1) period-based dependencies discovery (2) time delay aggregation
This block replace the canonical self-attention mechanism.
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
autocorrelation_factor: int = 3,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.autocorrelation_factor = autocorrelation_factor
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
# (1) period-based dependencies discovery
# Resize (truncation or zero filling)
queries_time_length = query_states.size(1)
values_time_length = value_states.size(1)
if queries_time_length > values_time_length:
query_states = query_states[:, : (queries_time_length - values_time_length), :]
zeros = torch.zeros_like(query_states).float()
value_states = torch.cat([value_states, zeros], dim=1)
key_states = torch.cat([key_states, zeros], dim=1)
else:
value_states = value_states[:, :queries_time_length, :]
key_states = key_states[:, :queries_time_length, :]
query_states_fft = torch.fft.rfft(query_states, n=tgt_len, dim=1)
key_states_fft = torch.fft.rfft(key_states, n=tgt_len, dim=1)
attn_weights = query_states_fft * torch.conj(key_states_fft)
attn_weights = torch.fft.irfft(attn_weights, n=tgt_len, dim=1) # Autocorrelation(Q,K)
src_len = key_states.size(1)
channel = key_states.size(2)
if attn_weights.size() != (bsz * self.num_heads, tgt_len, channel):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, channel)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, channel)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, channel)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, channel)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, channel)
else:
attn_weights_reshaped = None
# time delay aggregation
time_length = value_states.size(1)
autocorrelations = attn_weights.view(bsz, self.num_heads, tgt_len, channel)
# find top k autocorrelations delays
top_k = int(self.autocorrelation_factor * math.log(time_length))
autocorrelations_mean_on_head_channel = torch.mean(autocorrelations, dim=(1, -1)) # bsz x tgt_len
if self.training:
autocorrelations_mean_on_bsz = torch.mean(autocorrelations_mean_on_head_channel, dim=0)
_, top_k_delays_index = torch.topk(autocorrelations_mean_on_bsz, top_k)
top_k_autocorrelations = torch.stack(
[autocorrelations_mean_on_head_channel[:, top_k_delays_index[i]] for i in range(top_k)], dim=-1
)
else:
top_k_autocorrelations, top_k_delays_index = torch.topk(
autocorrelations_mean_on_head_channel, top_k, dim=1
)
top_k_autocorrelations = torch.softmax(top_k_autocorrelations, dim=-1) # bsz x top_k
# compute aggregation: value_states.roll(delay) * top_k_autocorrelations(delay)
if not self.training:
# used for compute values_states.roll(delay) in inference
tmp_values = value_states.repeat(1, 2, 1)
init_index = (
torch.arange(time_length)
.view(1, -1, 1)
.repeat(bsz * self.num_heads, 1, channel)
.to(value_states.device)
)
delays_agg = torch.zeros_like(value_states).float() # bsz x time_length x channel
for i in range(top_k):
# compute value_states roll delay
if not self.training:
tmp_delay = init_index + top_k_delays_index[:, i].view(-1, 1, 1).repeat(
self.num_heads, tgt_len, channel
)
value_states_roll_delay = torch.gather(tmp_values, dim=1, index=tmp_delay)
else:
value_states_roll_delay = value_states.roll(shifts=-int(top_k_delays_index[i]), dims=1)
# aggregation
top_k_autocorrelations_at_delay = (
top_k_autocorrelations[:, i].view(-1, 1, 1).repeat(self.num_heads, tgt_len, channel)
)
delays_agg += value_states_roll_delay * top_k_autocorrelations_at_delay
attn_output = delays_agg.contiguous()
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class AutoformerEncoderLayer(nn.Module):
def __init__(self, config: AutoformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = AutoformerAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
autocorrelation_factor=config.autocorrelation_factor,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = AutoformerLayernorm(config)
self.decomp1 = AutoformerSeriesDecompositionLayer(config)
self.decomp2 = AutoformerSeriesDecompositionLayer(config)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# added layer norm here as an improvement
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, _ = self.decomp1(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states, _ = self.decomp2(hidden_states)
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class AutoformerDecoderLayer(nn.Module):
def __init__(self, config: AutoformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = AutoformerAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
autocorrelation_factor=config.autocorrelation_factor,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = AutoformerAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
autocorrelation_factor=config.autocorrelation_factor,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = AutoformerLayernorm(config)
self.decomp1 = AutoformerSeriesDecompositionLayer(config)
self.decomp2 = AutoformerSeriesDecompositionLayer(config)
self.decomp3 = AutoformerSeriesDecompositionLayer(config)
# source: https://github.com/thuml/Autoformer/blob/e6371e24f2ae2dd53e472edefdd5814c5176f864/layers/Autoformer_EncDec.py#L128
self.trend_projection = nn.Conv1d(
in_channels=self.embed_dim,
out_channels=config.feature_size,
kernel_size=3,
stride=1,
padding=1,
padding_mode="circular",
bias=False,
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache: (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the `present_key_value` state to be used for subsequent
decoding.
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states, trend1 = self.decomp1(hidden_states)
# added layer norm here as an improvement
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states, trend2 = self.decomp2(hidden_states)
# added layer norm here as an improvement
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states, trend3 = self.decomp3(hidden_states)
hidden_states = self.final_layer_norm(hidden_states)
if encoder_hidden_states is not None:
residual_trend = trend1 + trend2 + trend3
else:
residual_trend = trend1 + trend3
residual_trend = self.trend_projection(residual_trend.permute(0, 2, 1)).transpose(1, 2)
outputs = ((hidden_states, residual_trend),)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class AutoformerPreTrainedModel(PreTrainedModel):
config_class = AutoformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, AutoformerSinusoidalPositionalEmbedding):
pass
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
AUTOFORMER_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`AutoformerConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
AUTOFORMER_INPUTS_DOCSTRING = r"""
Args:
past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Past values of the time series, that serve as context in order to predict the future. These values may
contain lags, i.e. additional values from the past which are added in order to serve as "extra context".
The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as
`static_categorical_features`, `static_real_features`, `past_time_features`).
The sequence length here is equal to `context_length` + `max(config.lags_sequence)`.
Missing values need to be replaced with zeros.
past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`, *optional*):
Optional time features, which the model internally will add to `past_values`. These could be things like
"month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These
could also be so-called "age" features, which basically help the model know "at which point in life" a
time-series is. Age features have small values for distant past time steps and increase monotonically the
more we approach the current time step.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where
the position encodings are learned from scratch internally as parameters of the model, the Time Series
Transformer requires to provide additional time features.
The Autoformer only learns additional embeddings for `static_categorical_features`.
past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in
`[0, 1]`:
- 1 for values that are **observed**,
- 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
static_categorical_features (`torch.LongTensor` of shape `(batch_size, number of static categorical features)`, *optional*):
Optional static categorical features for which the model will learn an embedding, which it will add to the
values of the time series.
Static categorical features are features which have the same value for all time steps (static over time).
A typical example of a static categorical feature is a time series ID.
static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*):
Optional static real features which the model will add to the values of the time series.
Static real features are features which have the same value for all time steps (static over time).
A typical example of a static real feature is promotion information.
future_values (`torch.FloatTensor` of shape `(batch_size, prediction_length)`):
Future values of the time series, that serve as labels for the model. The `future_values` is what the
Transformer needs to learn to output, given the `past_values`.
See the demo notebook and code snippets for details.
Missing values need to be replaced with zeros.
future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`, *optional*):
Optional time features, which the model internally will add to `future_values`. These could be things like
"month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These
could also be so-called "age" features, which basically help the model know "at which point in life" a
time-series is. Age features have small values for distant past time steps and increase monotonically the
more we approach the current time step.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where
the position encodings are learned from scratch internally as parameters of the model, the Time Series
Transformer requires to provide additional features.