##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 # ##################################################### import copy import torch import torch.nn.functional as F from xautodl.xlayers import super_core from xautodl.xlayers import trunc_normal_ from xautodl.models.xcore import get_model class LFNA_Meta(super_core.SuperModule): """Learning to Forecast Neural Adaptation (Meta Model Design).""" def __init__( self, shape_container, layer_embedding, time_embedding, meta_timestamps, mha_depth: int = 1, dropout: float = 0.1, ): super(LFNA_Meta, self).__init__() self._shape_container = shape_container self._num_layers = len(shape_container) self._numel_per_layer = [] for ilayer in range(self._num_layers): self._numel_per_layer.append(shape_container[ilayer].numel()) self._raw_meta_timestamps = meta_timestamps self.register_parameter( "_super_layer_embed", torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embedding)), ) self.register_parameter( "_super_meta_embed", torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)), ) self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps)) self._time_embed_dim = time_embedding self._append_meta_embed = dict(fixed=None, learnt=None) self._append_meta_timestamps = dict(fixed=None, learnt=None) self._tscalar_embed = super_core.SuperDynamicPositionE( time_embedding, scale=100 ) # build transformer self._trans_att = super_core.SuperQKVAttention( time_embedding, time_embedding, time_embedding, time_embedding, 4, True, attn_drop=None, proj_drop=dropout, ) layers = [] for ilayer in range(mha_depth): layers.append( super_core.SuperTransformerEncoderLayer( time_embedding, 4, True, 4, dropout, norm_affine=False, order=super_core.LayerOrder.PostNorm, ) ) layers.append(super_core.SuperLinear(time_embedding, time_embedding)) self.meta_corrector = super_core.SuperSequential(*layers) model_kwargs = dict( config=dict(model_type="dual_norm_mlp"), input_dim=layer_embedding + time_embedding, output_dim=max(self._numel_per_layer), hidden_dims=[(layer_embedding + time_embedding) * 2] * 3, act_cls="gelu", norm_cls="layer_norm_1d", dropout=dropout, ) self._generator = get_model(**model_kwargs) # print("generator: {:}".format(self._generator)) # initialization trunc_normal_( [self._super_layer_embed, self._super_meta_embed], std=0.02, ) @property def meta_timestamps(self): meta_timestamps = [self._meta_timestamps] for key in ("fixed", "learnt"): if self._append_meta_timestamps[key] is not None: meta_timestamps.append(self._append_meta_timestamps[key]) return torch.cat(meta_timestamps) @property def super_meta_embed(self): meta_embed = [self._super_meta_embed] for key in ("fixed", "learnt"): if self._append_meta_embed[key] is not None: meta_embed.append(self._append_meta_embed[key]) return torch.cat(meta_embed) def create_meta_embed(self): param = torch.Tensor(1, self._time_embed_dim) trunc_normal_(param, std=0.02) param = param.to(self._super_meta_embed.device) param = torch.nn.Parameter(param, True) return param def get_closest_meta_distance(self, timestamp): with torch.no_grad(): distances = torch.abs(self.meta_timestamps - timestamp) return torch.min(distances).item() def replace_append_learnt(self, timestamp, meta_embed): self._append_meta_timestamps["learnt"] = timestamp self._append_meta_embed["learnt"] = meta_embed def append_fixed(self, timestamp, meta_embed): with torch.no_grad(): device = self._super_meta_embed.device timestamp = timestamp.detach().clone().to(device) meta_embed = meta_embed.detach().clone().to(device) if self._append_meta_timestamps["fixed"] is None: self._append_meta_timestamps["fixed"] = timestamp else: self._append_meta_timestamps["fixed"] = torch.cat( (self._append_meta_timestamps["fixed"], timestamp), dim=0 ) if self._append_meta_embed["fixed"] is None: self._append_meta_embed["fixed"] = meta_embed else: self._append_meta_embed["fixed"] = torch.cat( (self._append_meta_embed["fixed"], meta_embed), dim=0 ) def _obtain_time_embed(self, timestamps): # timestamps is a batch of sequence of timestamps batch, seq = timestamps.shape timestamp_q_embed = self._tscalar_embed(timestamps) timestamp_k_embed = self._tscalar_embed(self.meta_timestamps.view(1, -1)) timestamp_v_embed = self.super_meta_embed.unsqueeze(dim=0) timestamp_embeds = self._trans_att( timestamp_q_embed, timestamp_k_embed, timestamp_v_embed ) corrected_embeds = self.meta_corrector(timestamp_embeds) return corrected_embeds def forward_raw(self, timestamps): batch, seq = timestamps.shape meta_embed = self._obtain_time_embed(timestamps) # create joint embed num_layer, _ = self._super_layer_embed.shape meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1) layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand( batch, seq, -1, -1 ) joint_embed = torch.cat((meta_embed, layer_embed), dim=-1) batch_weights = self._generator(joint_embed) batch_containers = [] for seq_weights in torch.split(batch_weights, 1): seq_containers = [] for weights in torch.split(seq_weights.squeeze(0), 1): weights = torch.split(weights.squeeze(0), 1) seq_containers.append(self._shape_container.translate(weights)) batch_containers.append(seq_containers) return batch_containers def forward_candidate(self, input): raise NotImplementedError def extra_repr(self) -> str: return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format( list(self._super_layer_embed.shape), list(self._super_meta_embed.shape), list(self._meta_timestamps.shape), )