129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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import copy
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import torch
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import torch.nn.functional as F
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from xlayers import super_core
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from xlayers import trunc_normal_
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from models.xcore import get_model
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class LFNA_Meta(super_core.SuperModule):
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"""Learning to Forecast Neural Adaptation (Meta Model Design)."""
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def __init__(
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self,
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shape_container,
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layer_embeding,
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time_embedding,
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meta_timestamps,
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mha_depth: int = 2,
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dropout: float = 0.1,
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):
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super(LFNA_Meta, self).__init__()
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self._shape_container = shape_container
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self._num_layers = len(shape_container)
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self._numel_per_layer = []
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for ilayer in range(self._num_layers):
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self._numel_per_layer.append(shape_container[ilayer].numel())
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self._raw_meta_timestamps = meta_timestamps
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self.register_parameter(
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"_super_layer_embed",
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torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
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)
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self.register_parameter(
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"_super_meta_embed",
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torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)),
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)
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self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
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# build transformer
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layers = []
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for ilayer in range(mha_depth):
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layers.append(
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super_core.SuperTransformerEncoderLayer(
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time_embedding,
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4,
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True,
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4,
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dropout,
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norm_affine=False,
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order=super_core.LayerOrder.PostNorm,
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)
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)
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self.meta_corrector = super_core.SuperSequential(*layers)
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model_kwargs = dict(
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config=dict(model_type="dual_norm_mlp"),
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input_dim=layer_embeding + time_embedding,
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output_dim=max(self._numel_per_layer),
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hidden_dims=[(layer_embeding + time_embedding) * 2] * 3,
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act_cls="gelu",
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norm_cls="layer_norm_1d",
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dropout=dropout,
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)
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self._generator = get_model(**model_kwargs)
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# print("generator: {:}".format(self._generator))
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# unknown token
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self.register_parameter(
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"_unknown_token",
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torch.nn.Parameter(torch.Tensor(1, time_embedding)),
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)
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# initialization
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trunc_normal_(
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[self._super_layer_embed, self._super_meta_embed, self._unknown_token],
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std=0.02,
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)
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def forward_raw(self, timestamps):
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# timestamps is a batch of sequence of timestamps
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batch, seq = timestamps.shape
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timestamps = timestamps.unsqueeze(dim=-1)
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meta_timestamps = self._meta_timestamps.view(1, 1, -1)
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time_diffs = timestamps - meta_timestamps
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time_match_v, time_match_i = torch.min(torch.abs(time_diffs), dim=-1)
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# select corresponding meta-knowledge
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meta_match = torch.index_select(
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self._super_meta_embed, dim=0, index=time_match_i.view(-1)
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)
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meta_match = meta_match.view(batch, seq, -1)
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# create the probability
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time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
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time_probs[:, -1, :] = 0
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unknown_token = self._unknown_token.view(1, 1, -1)
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raw_meta_embed = time_probs * meta_match + (1 - time_probs) * unknown_token
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meta_embed = self.meta_corrector(raw_meta_embed)
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# create joint embed
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num_layer, _ = self._super_layer_embed.shape
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meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)
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layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand(
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batch, seq, -1, -1
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)
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joint_embed = torch.cat((meta_embed, layer_embed), dim=-1)
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batch_weights = self._generator(joint_embed)
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batch_containers = []
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for seq_weights in torch.split(batch_weights, 1):
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seq_containers = []
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for weights in torch.split(seq_weights.squeeze(0), 1):
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weights = torch.split(weights.squeeze(0), 1)
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seq_containers.append(self._shape_container.translate(weights))
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batch_containers.append(seq_containers)
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return batch_containers
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def forward_candidate(self, input):
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raise NotImplementedError
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def extra_repr(self) -> str:
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return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
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list(self._super_layer_embed.shape),
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list(self._super_meta_embed.shape),
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list(self._meta_timestamps.shape),
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)
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