Alex Blank
526 lines
22 KiB
Python
Executable File
526 lines
22 KiB
Python
Executable File
# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
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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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# Modified by Alexander Blank, 2025.
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# Modifications:
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# - added support for multiple outputs
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# - added support for mode configurable targets
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# - added support for single dimension, non-quantile outputs
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# - added support for target agnostic predictions, for cases, where the target does not become known after prediction
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from torch.nn.parameter import UninitializedParameter
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from typing import Dict, Tuple, Optional, List
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MAKE_CONVERT_COMPATIBLE = os.environ.get("TFT_SCRIPTING", None) is not None
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from torch.nn import LayerNorm
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class MaybeLayerNorm(nn.Module):
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def __init__(self, output_size, hidden_size, eps):
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super().__init__()
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if output_size and output_size == 1:
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self.ln = nn.Identity()
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else:
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self.ln = LayerNorm(output_size if output_size else hidden_size, eps=eps)
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def forward(self, x):
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return self.ln(x)
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class GLU(nn.Module):
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def __init__(self, hidden_size, output_size):
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super().__init__()
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self.lin = nn.Linear(hidden_size, output_size * 2)
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def forward(self, x: Tensor) -> Tensor:
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x = self.lin(x)
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x = F.glu(x)
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return x
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class GRN(nn.Module):
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def __init__(self,
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input_size,
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hidden_size,
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output_size=None,
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context_hidden_size=None,
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dropout=0.0, ):
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super().__init__()
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self.layer_norm = MaybeLayerNorm(output_size, hidden_size, eps=1e-3)
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self.lin_a = nn.Linear(input_size, hidden_size)
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if context_hidden_size is not None:
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self.lin_c = nn.Linear(context_hidden_size, hidden_size, bias=False)
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else:
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self.lin_c = nn.Identity()
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self.lin_i = nn.Linear(hidden_size, hidden_size)
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self.glu = GLU(hidden_size, output_size if output_size else hidden_size)
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self.dropout = nn.Dropout(dropout)
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self.out_proj = nn.Linear(input_size, output_size) if output_size else None
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def forward(self, a: Tensor, c: Optional[Tensor] = None):
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x = self.lin_a(a)
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if c is not None:
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x = x + self.lin_c(c).unsqueeze(1)
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x = F.elu(x)
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x = self.lin_i(x)
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x = self.dropout(x)
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x = self.glu(x)
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y = a if self.out_proj is None else self.out_proj(a)
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x = x + y
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return self.layer_norm(x)
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# @torch.jit.script #Currently broken with autocast
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def fused_pointwise_linear_v1(x, a, b):
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out = torch.mul(x.unsqueeze(-1), a)
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out = out + b
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return out
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@torch.jit.script
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def fused_pointwise_linear_v2(x, a, b):
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out = x.unsqueeze(3) * a
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out = out + b
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return out
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class TFTEmbedding(nn.Module):
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def __init__(self, config, initialize_cont_params=True):
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# initialize_cont_params=False prevents form initializing parameters inside this class
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# so they can be lazily initialized in LazyEmbedding module
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super().__init__()
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self.s_cat_inp_lens = config.static_categorical_inp_lens
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self.t_cat_k_inp_lens = config.temporal_known_categorical_inp_lens
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self.t_cat_o_inp_lens = config.temporal_observed_categorical_inp_lens
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self.s_cont_inp_size = config.static_continuous_inp_size
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self.t_cont_k_inp_size = config.temporal_known_continuous_inp_size
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self.t_cont_o_inp_size = config.temporal_observed_continuous_inp_size
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self.t_tgt_size = config.temporal_target_size
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self.hidden_size = config.hidden_size
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# There are 7 types of input:
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# 1. Static categorical
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# 2. Static continuous
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# 3. Temporal known a priori categorical
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# 4. Temporal known a priori continuous
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# 5. Temporal observed categorical
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# 6. Temporal observed continuous
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# 7. Temporal observed targets (time series obseved so far)
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self.s_cat_embed = nn.ModuleList([
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nn.Embedding(n, self.hidden_size) for n in self.s_cat_inp_lens]) if self.s_cat_inp_lens else None
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self.t_cat_k_embed = nn.ModuleList([
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nn.Embedding(n, self.hidden_size) for n in self.t_cat_k_inp_lens]) if self.t_cat_k_inp_lens else None
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self.t_cat_o_embed = nn.ModuleList([
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nn.Embedding(n, self.hidden_size) for n in self.t_cat_o_inp_lens]) if self.t_cat_o_inp_lens else None
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if initialize_cont_params:
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self.s_cont_embedding_vectors = nn.Parameter(
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torch.Tensor(self.s_cont_inp_size, self.hidden_size)) if self.s_cont_inp_size else None
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self.t_cont_k_embedding_vectors = nn.Parameter(
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torch.Tensor(self.t_cont_k_inp_size, self.hidden_size)) if self.t_cont_k_inp_size else None
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self.t_cont_o_embedding_vectors = nn.Parameter(
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torch.Tensor(self.t_cont_o_inp_size, self.hidden_size)) if self.t_cont_o_inp_size else None
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self.t_tgt_embedding_vectors = nn.Parameter(torch.Tensor(self.t_tgt_size, self.hidden_size))
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self.s_cont_embedding_bias = nn.Parameter(
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torch.zeros(self.s_cont_inp_size, self.hidden_size)) if self.s_cont_inp_size else None
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self.t_cont_k_embedding_bias = nn.Parameter(
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torch.zeros(self.t_cont_k_inp_size, self.hidden_size)) if self.t_cont_k_inp_size else None
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self.t_cont_o_embedding_bias = nn.Parameter(
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torch.zeros(self.t_cont_o_inp_size, self.hidden_size)) if self.t_cont_o_inp_size else None
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self.t_tgt_embedding_bias = nn.Parameter(torch.zeros(self.t_tgt_size, self.hidden_size))
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self.reset_parameters()
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def reset_parameters(self):
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if self.s_cont_embedding_vectors is not None:
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torch.nn.init.xavier_normal_(self.s_cont_embedding_vectors)
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torch.nn.init.zeros_(self.s_cont_embedding_bias)
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if self.t_cont_k_embedding_vectors is not None:
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torch.nn.init.xavier_normal_(self.t_cont_k_embedding_vectors)
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torch.nn.init.zeros_(self.t_cont_k_embedding_bias)
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if self.t_cont_o_embedding_vectors is not None:
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torch.nn.init.xavier_normal_(self.t_cont_o_embedding_vectors)
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torch.nn.init.zeros_(self.t_cont_o_embedding_bias)
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if self.t_tgt_embedding_vectors is not None:
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torch.nn.init.xavier_normal_(self.t_tgt_embedding_vectors)
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torch.nn.init.zeros_(self.t_tgt_embedding_bias)
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if self.s_cat_embed is not None:
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for module in self.s_cat_embed:
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module.reset_parameters()
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if self.t_cat_k_embed is not None:
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for module in self.t_cat_k_embed:
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module.reset_parameters()
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if self.t_cat_o_embed is not None:
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for module in self.t_cat_o_embed:
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module.reset_parameters()
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def _apply_embedding(self,
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cat: Optional[Tensor],
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cont: Optional[Tensor],
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cat_emb: Optional[nn.ModuleList],
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cont_emb: Tensor,
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cont_bias: Tensor,
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) -> Tuple[Optional[Tensor], Optional[Tensor]]:
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e_cat = torch.stack([embed(cat[..., i]) for i, embed in enumerate(cat_emb)],
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dim=-2) if cat is not None else None
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if cont is not None:
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# the line below is equivalent to following einsums
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# e_cont = torch.einsum('btf,fh->bthf', cont, cont_emb)
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# e_cont = torch.einsum('bf,fh->bhf', cont, cont_emb)
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if MAKE_CONVERT_COMPATIBLE:
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e_cont = torch.mul(cont.unsqueeze(-1), cont_emb)
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e_cont = e_cont + cont_bias
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else:
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e_cont = fused_pointwise_linear_v1(cont, cont_emb, cont_bias)
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else:
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e_cont = None
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if e_cat is not None and e_cont is not None:
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return torch.cat([e_cat, e_cont], dim=-2)
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elif e_cat is not None:
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return e_cat
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elif e_cont is not None:
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return e_cont
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else:
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return None
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def forward(self, x: Dict[str, Tensor], use_target: bool = False):
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# Extract inputs
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s_cat_inp = x.get('s_cat', None)
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s_cont_inp = x.get('s_cont', None)
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t_cat_k_inp = x.get('k_cat', None)
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t_cont_k_inp = x.get('k_cont', None)
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t_cat_o_inp = x.get('o_cat', None)
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t_cont_o_inp = x.get('o_cont', None)
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# Only use target if teacher forcing is enabled.
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# When disabled, we ignore target values.
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if use_target:
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t_tgt_obs = x['target'] # Must be present when using teacher forcing
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else:
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t_tgt_obs = None
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# For static inputs, take the first timestep
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s_cat_inp = s_cat_inp[:, 0, :] if s_cat_inp is not None else None
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s_cont_inp = s_cont_inp[:, 0, :] if s_cont_inp is not None else None
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# Apply embeddings for static and known/observed temporal features
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s_inp = self._apply_embedding(s_cat_inp,
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s_cont_inp,
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self.s_cat_embed,
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self.s_cont_embedding_vectors,
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self.s_cont_embedding_bias)
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t_known_inp = self._apply_embedding(t_cat_k_inp,
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t_cont_k_inp,
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self.t_cat_k_embed,
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self.t_cont_k_embedding_vectors,
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self.t_cont_k_embedding_bias)
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t_observed_inp = self._apply_embedding(t_cat_o_inp,
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t_cont_o_inp,
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self.t_cat_o_embed,
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self.t_cont_o_embedding_vectors,
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self.t_cont_o_embedding_bias)
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# Compute the target embedding only if teacher forcing is enabled.
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if use_target and t_tgt_obs is not None:
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if MAKE_CONVERT_COMPATIBLE:
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t_observed_tgt = torch.matmul(t_tgt_obs.unsqueeze(3).unsqueeze(4),
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self.t_tgt_embedding_vectors.unsqueeze(1)).squeeze(3)
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t_observed_tgt = t_observed_tgt + self.t_tgt_embedding_bias
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else:
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t_observed_tgt = fused_pointwise_linear_v2(t_tgt_obs,
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self.t_tgt_embedding_vectors,
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self.t_tgt_embedding_bias)
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else:
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t_observed_tgt = None
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return s_inp, t_known_inp, t_observed_inp, t_observed_tgt
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class LazyEmbedding(nn.modules.lazy.LazyModuleMixin, TFTEmbedding):
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cls_to_become = TFTEmbedding
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def __init__(self, config):
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super().__init__(config, initialize_cont_params=False)
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if config.static_continuous_inp_size:
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self.s_cont_embedding_vectors = UninitializedParameter()
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self.s_cont_embedding_bias = UninitializedParameter()
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else:
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self.s_cont_embedding_vectors = None
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self.s_cont_embedding_bias = None
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if config.temporal_known_continuous_inp_size:
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self.t_cont_k_embedding_vectors = UninitializedParameter()
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self.t_cont_k_embedding_bias = UninitializedParameter()
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else:
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self.t_cont_k_embedding_vectors = None
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self.t_cont_k_embedding_bias = None
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if config.temporal_observed_continuous_inp_size:
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self.t_cont_o_embedding_vectors = UninitializedParameter()
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self.t_cont_o_embedding_bias = UninitializedParameter()
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else:
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self.t_cont_o_embedding_vectors = None
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self.t_cont_o_embedding_bias = None
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self.t_tgt_embedding_vectors = UninitializedParameter()
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self.t_tgt_embedding_bias = UninitializedParameter()
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def initialize_parameters(self, x):
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if self.has_uninitialized_params():
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s_cont_inp = x.get('s_cont', None)
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t_cont_k_inp = x.get('k_cont', None)
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t_cont_o_inp = x.get('o_cont', None)
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t_tgt_obs = x['target'] # Has to be present
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if s_cont_inp is not None:
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self.s_cont_embedding_vectors.materialize((s_cont_inp.shape[-1], self.hidden_size))
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self.s_cont_embedding_bias.materialize((s_cont_inp.shape[-1], self.hidden_size))
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if t_cont_k_inp is not None:
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self.t_cont_k_embedding_vectors.materialize((t_cont_k_inp.shape[-1], self.hidden_size))
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self.t_cont_k_embedding_bias.materialize((t_cont_k_inp.shape[-1], self.hidden_size))
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if t_cont_o_inp is not None:
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self.t_cont_o_embedding_vectors.materialize((t_cont_o_inp.shape[-1], self.hidden_size))
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self.t_cont_o_embedding_bias.materialize((t_cont_o_inp.shape[-1], self.hidden_size))
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self.t_tgt_embedding_vectors.materialize((t_tgt_obs.shape[-1], self.hidden_size))
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self.t_tgt_embedding_bias.materialize((t_tgt_obs.shape[-1], self.hidden_size))
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self.reset_parameters()
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# def forward(self, x: Dict[str, Tensor], use_target: bool = True):
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# return super().forward(x, use_target=use_target)
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class VariableSelectionNetwork(nn.Module):
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def __init__(self, config, num_inputs):
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super().__init__()
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self.joint_grn = GRN(config.hidden_size * num_inputs, config.hidden_size, output_size=num_inputs,
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context_hidden_size=config.hidden_size)
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self.var_grns = nn.ModuleList(
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[GRN(config.hidden_size, config.hidden_size, dropout=config.dropout) for _ in range(num_inputs)])
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def forward(self, x: Tensor, context: Optional[Tensor] = None):
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Xi = torch.flatten(x, start_dim=-2)
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grn_outputs = self.joint_grn(Xi, c=context)
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sparse_weights = F.softmax(grn_outputs, dim=-1)
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transformed_embed_list = [m(x[..., i, :]) for i, m in enumerate(self.var_grns)]
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transformed_embed = torch.stack(transformed_embed_list, dim=-1)
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# the line below performs batched matrix vector multiplication
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# for temporal features it's bthf,btf->bth
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# for static features it's bhf,bf->bh
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variable_ctx = torch.matmul(transformed_embed, sparse_weights.unsqueeze(-1)).squeeze(-1)
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return variable_ctx, sparse_weights
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class StaticCovariateEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.vsn = VariableSelectionNetwork(config, config.num_static_vars)
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self.context_grns = nn.ModuleList(
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[GRN(config.hidden_size, config.hidden_size, dropout=config.dropout) for _ in range(4)])
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def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
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variable_ctx, sparse_weights = self.vsn(x)
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# Context vectors:
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# variable selection context
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# enrichment context
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# state_c context
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# state_h context
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cs, ce, ch, cc = [m(variable_ctx) for m in self.context_grns]
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return cs, ce, ch, cc
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class InterpretableMultiHeadAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.n_head = config.n_head
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assert config.hidden_size % config.n_head == 0
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self.d_head = config.hidden_size // config.n_head
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self.qkv_linears = nn.Linear(config.hidden_size, (2 * self.n_head + 1) * self.d_head, bias=False)
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self.out_proj = nn.Linear(self.d_head, config.hidden_size, bias=False)
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self.attn_dropout = nn.Dropout(config.attn_dropout)
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self.out_dropout = nn.Dropout(config.dropout)
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self.scale = self.d_head ** -0.5
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self.register_buffer("_mask",
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torch.triu(torch.full((config.example_length, config.example_length), float('-inf')),
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1).unsqueeze(0))
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def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
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bs, t, h_size = x.shape
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qkv = self.qkv_linears(x)
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q, k, v = qkv.split((self.n_head * self.d_head, self.n_head * self.d_head, self.d_head), dim=-1)
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q = q.view(bs, t, self.n_head, self.d_head)
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k = k.view(bs, t, self.n_head, self.d_head)
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v = v.view(bs, t, self.d_head)
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# attn_score = torch.einsum('bind,bjnd->bnij', q, k)
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attn_score = torch.matmul(q.permute((0, 2, 1, 3)), k.permute((0, 2, 3, 1)))
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attn_score.mul_(self.scale)
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attn_score = attn_score + self._mask
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attn_prob = F.softmax(attn_score, dim=3)
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attn_prob = self.attn_dropout(attn_prob)
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# attn_vec = torch.einsum('bnij,bjd->bnid', attn_prob, v)
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attn_vec = torch.matmul(attn_prob, v.unsqueeze(1))
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m_attn_vec = torch.mean(attn_vec, dim=1)
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out = self.out_proj(m_attn_vec)
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out = self.out_dropout(out)
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return out, attn_prob
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class TFTBack(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.encoder_length = config.encoder_length
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self.history_vsn = VariableSelectionNetwork(config, config.num_historic_vars)
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self.history_encoder = nn.LSTM(config.hidden_size, config.hidden_size, batch_first=True)
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self.future_vsn = VariableSelectionNetwork(config, config.num_future_vars)
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self.future_encoder = nn.LSTM(config.hidden_size, config.hidden_size, batch_first=True)
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self.input_gate = GLU(config.hidden_size, config.hidden_size)
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self.input_gate_ln = LayerNorm(config.hidden_size, eps=1e-3)
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self.enrichment_grn = GRN(config.hidden_size,
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config.hidden_size,
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context_hidden_size=config.hidden_size,
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dropout=config.dropout)
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self.attention = InterpretableMultiHeadAttention(config)
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self.attention_gate = GLU(config.hidden_size, config.hidden_size)
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self.attention_ln = LayerNorm(config.hidden_size, eps=1e-3)
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self.positionwise_grn = GRN(config.hidden_size,
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config.hidden_size,
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dropout=config.dropout)
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self.decoder_gate = GLU(config.hidden_size, config.hidden_size)
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self.decoder_ln = LayerNorm(config.hidden_size, eps=1e-3)
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self.quantiles = config.quantiles
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self.target_size = config.target_size
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if self.quantiles is not None:
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self.output = nn.Linear(config.hidden_size, len(config.quantiles) * config.target_size)
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else:
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self.output = nn.Linear(config.hidden_size, config.target_size)
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def forward(self, historical_inputs, cs, ch, cc, ce, future_inputs):
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historical_features, _ = self.history_vsn(historical_inputs, cs)
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history, state = self.history_encoder(historical_features, (ch, cc))
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future_features, _ = self.future_vsn(future_inputs, cs)
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future, _ = self.future_encoder(future_features, state)
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torch.cuda.synchronize()
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|
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# skip connection
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input_embedding = torch.cat([historical_features, future_features], dim=1)
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temporal_features = torch.cat([history, future], dim=1)
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temporal_features = self.input_gate(temporal_features)
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temporal_features = temporal_features + input_embedding
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temporal_features = self.input_gate_ln(temporal_features)
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|
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# Static enrichment
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enriched = self.enrichment_grn(temporal_features, c=ce)
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|
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# Temporal self attention
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x, _ = self.attention(enriched)
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|
|
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# Don't compute hictorical quantiles
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x = x[:, self.encoder_length:, :]
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temporal_features = temporal_features[:, self.encoder_length:, :]
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enriched = enriched[:, self.encoder_length:, :]
|
|
|
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x = self.attention_gate(x)
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|
x = x + enriched
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|
x = self.attention_ln(x)
|
|
|
|
# Position-wise feed-forward
|
|
x = self.positionwise_grn(x)
|
|
|
|
# Final skip connection
|
|
x = self.decoder_gate(x)
|
|
x = x + temporal_features
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|
x = self.decoder_ln(x)
|
|
|
|
out = self.output(x)
|
|
if self.quantiles is not None:
|
|
# Reshape to [batch, time, target_size, n_quantiles]
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|
out = out.view(out.size(0), out.size(1), self.target_size, len(self.quantiles))
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|
else:
|
|
# Reshape to [batch, time, target_size]
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|
out = out.view(out.size(0), out.size(1), self.target_size)
|
|
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|
return out
|
|
|
|
|
|
class TemporalFusionTransformer(nn.Module):
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|
"""
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|
Implementation of https://arxiv.org/abs/1912.09363
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|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
if hasattr(config, 'model'):
|
|
config = config.model
|
|
|
|
self.encoder_length = config.encoder_length # this determines from how distant past we want to use data from
|
|
|
|
# self.embedding = LazyEmbedding(config)
|
|
self.embedding = TFTEmbedding(config)
|
|
self.static_encoder = StaticCovariateEncoder(config)
|
|
# if MAKE_CONVERT_COMPATIBLE:
|
|
self.TFTpart2 = TFTBack(config)
|
|
# else:
|
|
# self.TFTpart2 = torch.jit.script(TFTBack(config))
|
|
|
|
def forward(self, x: Dict[str, Tensor]) -> Tensor:
|
|
# Call embedding with use_target=False to skip target features entirely.
|
|
s_inp, t_known_inp, t_observed_inp, t_observed_tgt = self.embedding(x, use_target=False)
|
|
|
|
# Compute static context
|
|
cs, ce, ch, cc = self.static_encoder(s_inp)
|
|
ch, cc = ch.unsqueeze(0), cc.unsqueeze(0) # Initialize LSTM states
|
|
|
|
# Build historical inputs without teacher-forced targets.
|
|
# Include observed features if available, and the known inputs.
|
|
historical_inputs = []
|
|
if t_observed_inp is not None:
|
|
historical_inputs.append(t_observed_inp[:, :self.encoder_length, :])
|
|
historical_inputs.append(t_known_inp[:, :self.encoder_length, :])
|
|
historical_inputs = torch.cat(historical_inputs, dim=-2)
|
|
|
|
# Future inputs remain the same
|
|
future_inputs = t_known_inp[:, self.encoder_length:]
|
|
return self.TFTpart2(historical_inputs, cs, ch, cc, ce, future_inputs)
|