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@@ -17,12 +17,11 @@ 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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-if os.environ.get("TFT_SCRIPTING", False):
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- from torch.nn import LayerNorm
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-else:
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- from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
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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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@@ -46,21 +45,20 @@ class GLU(nn.Module):
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x = F.glu(x)
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return x
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-
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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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+ hidden_size,
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output_size=None,
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context_hidden_size=None,
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- dropout=0):
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+ dropout=0.0,):
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super().__init__()
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-
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-
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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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@@ -74,13 +72,28 @@ class GRN(nn.Module):
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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 not self.out_proj else self.out_proj(a)
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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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- x = self.layer_norm(x)
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- return x
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+ return self.layer_norm(x)
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+
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+
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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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+
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[email protected]
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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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+
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class TFTEmbedding(nn.Module):
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- def __init__(self, config):
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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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@@ -108,23 +121,43 @@ class TFTEmbedding(nn.Module):
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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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- self.s_cont_embedding_vectors = nn.Parameter(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(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(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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+ if initialize_cont_params:
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+ self.s_cont_embedding_vectors = nn.Parameter(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(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(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(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(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(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.s_cont_embedding_bias = nn.Parameter(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(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(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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+
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+
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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.xavier_normal_(self.t_tgt_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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+
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def _apply_embedding(self,
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cat: Optional[Tensor],
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@@ -138,8 +171,11 @@ class TFTEmbedding(nn.Module):
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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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- e_cont = torch.mul(cont.unsqueeze(-1), cont_emb)
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- e_cont = e_cont + cont_bias
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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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@@ -185,11 +221,68 @@ class TFTEmbedding(nn.Module):
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# Temporal observed targets
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# t_observed_tgt = torch.einsum('btf,fh->btfh', t_tgt_obs, self.t_tgt_embedding_vectors)
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- t_observed_tgt = torch.matmul(t_tgt_obs.unsqueeze(3).unsqueeze(4), 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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+ if MAKE_CONVERT_COMPATIBLE:
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+ t_observed_tgt = torch.matmul(t_tgt_obs.unsqueeze(3).unsqueeze(4), 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, self.t_tgt_embedding_vectors, self.t_tgt_embedding_bias)
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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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+
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+ def __init__(self, config):
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+ super().__init__(config, initialize_cont_params=False)
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+
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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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+
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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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+
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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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+
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+ self.t_tgt_embedding_vectors = UninitializedParameter()
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+ self.t_tgt_embedding_bias = UninitializedParameter()
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ self.reset_parameters()
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+
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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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@@ -197,7 +290,7 @@ class VariableSelectionNetwork(nn.Module):
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self.var_grns = nn.ModuleList([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 = x.reshape(*x.shape[:-2], -1)
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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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@@ -223,7 +316,7 @@ class StaticCovariateEncoder(nn.Module):
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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 = tuple(m(variable_ctx) for m in self.context_grns)
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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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@@ -241,7 +334,7 @@ class InterpretableMultiHeadAttention(nn.Module):
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self.scale = self.d_head**-0.5
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self.register_buffer("_mask", torch.triu(torch.full((config.example_length, config.example_length), float('-inf')), 1).unsqueeze(0))
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- def forward(self, x: Tensor, mask_future_timesteps: bool = True) -> Tuple[Tensor, Tensor]:
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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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@@ -253,8 +346,7 @@ class InterpretableMultiHeadAttention(nn.Module):
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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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- if mask_future_timesteps:
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- attn_score = attn_score + self._mask
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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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@@ -267,24 +359,12 @@ class InterpretableMultiHeadAttention(nn.Module):
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return out, attn_prob
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-
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-
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-class TemporalFusionTransformer(nn.Module):
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- """
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- Implementation of https://arxiv.org/abs/1912.09363
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- """
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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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- if hasattr(config, 'model'):
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- config = config.model
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-
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- self.encoder_length = config.encoder_length #this determines from how distant past we want to use data from
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-
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- self.embedding = TFTEmbedding(config)
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- self.static_encoder = StaticCovariateEncoder(config)
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-
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- self.history_vsn = VariableSelectionNetwork(config, config.num_historic_vars)
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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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@@ -309,28 +389,13 @@ class TemporalFusionTransformer(nn.Module):
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self.decoder_ln = LayerNorm(config.hidden_size, eps=1e-3)
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self.quantile_proj = nn.Linear(config.hidden_size, len(config.quantiles))
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-
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- def forward(self, x: Dict[str, Tensor]) -> Tensor:
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- s_inp, t_known_inp, t_observed_inp, t_observed_tgt = self.embedding(x)
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-
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- # Static context
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- cs, ce, ch, cc = self.static_encoder(s_inp)
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- ch, cc = ch.unsqueeze(0), cc.unsqueeze(0) #lstm initial states
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-
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- # Temporal input
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- _historical_inputs = [t_known_inp[:,:self.encoder_length,:], t_observed_tgt[:,:self.encoder_length,:]]
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- if t_observed_inp is not None:
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- _historical_inputs.insert(0,t_observed_inp[:,:self.encoder_length,:])
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-
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- historical_inputs = torch.cat(_historical_inputs, dim=-2)
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- future_inputs = t_known_inp[:, self.encoder_length:]
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-
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- # Encoders
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+
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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() # this call gives perf boost for unknown reasons
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+ torch.cuda.synchronize()
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# skip connection
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input_embedding = torch.cat([historical_features, future_features], dim=1)
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@@ -343,7 +408,7 @@ class TemporalFusionTransformer(nn.Module):
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enriched = self.enrichment_grn(temporal_features, c=ce)
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# Temporal self attention
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- x, _ = self.attention(enriched, mask_future_timesteps=True)
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+ x, _ = self.attention(enriched)
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# Don't compute hictorical quantiles
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x = x[:, self.encoder_length:, :]
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@@ -365,3 +430,39 @@ class TemporalFusionTransformer(nn.Module):
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out = self.quantile_proj(x)
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return out
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+
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+
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+class TemporalFusionTransformer(nn.Module):
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+ """
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+ Implementation of https://arxiv.org/abs/1912.09363
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+ """
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+ def __init__(self, config):
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+ super().__init__()
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+
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+ if hasattr(config, 'model'):
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+ config = config.model
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+
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+ self.encoder_length = config.encoder_length #this determines from how distant past we want to use data from
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+
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+ self.embedding = LazyEmbedding(config)
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+ self.static_encoder = StaticCovariateEncoder(config)
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+ if MAKE_CONVERT_COMPATIBLE:
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+ self.TFTpart2 = TFTBack(config)
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+ else:
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+ self.TFTpart2 = torch.jit.script(TFTBack(config))
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+
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+ def forward(self, x: Dict[str, Tensor]) -> Tensor:
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+ s_inp, t_known_inp, t_observed_inp, t_observed_tgt = self.embedding(x)
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+
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+ # Static context
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+ cs, ce, ch, cc = self.static_encoder(s_inp)
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+ ch, cc = ch.unsqueeze(0), cc.unsqueeze(0) #lstm initial states
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+
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+ # Temporal input
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+ _historical_inputs = [t_known_inp[:,:self.encoder_length,:], t_observed_tgt[:,:self.encoder_length,:]]
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+ if t_observed_inp is not None:
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+ _historical_inputs.insert(0,t_observed_inp[:,:self.encoder_length,:])
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+
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+ historical_inputs = torch.cat(_historical_inputs, dim=-2)
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+ future_inputs = t_known_inp[:, self.encoder_length:]
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+ return self.TFTpart2(historical_inputs, cs, ch, cc, ce, future_inputs)
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Izzy Putterman