import random from typing import Optional, Tuple, Union import math import torch import torch.nn.functional as F from einops import rearrange from torch import nn from transformers import PreTrainedModel from transformers.activations import ACT2FN from transformers.modeling_outputs import MoeModelOutputWithPast, MoeCausalLMOutputWithPast from .configuration_aurora import AuroraConfig from .flow_loss import FlowLoss from .modality_connector import ModalityConnector, VisionEncoder, TextEncoder from .prototype_retriever import PrototypeRetriever from .ts_generation_mixin import TSGenerationMixin from .util_functions import resample, Transpose, causal_attention_mask, RoPE_decoder class AuroraPatchEmbedding(nn.Module): def __init__(self, config: AuroraConfig): super().__init__() self.proj_layer = nn.Linear(config.token_len, config.hidden_size, bias=False) self.token_len = config.token_len self.threshold_ratio = config.threshold_ratio self.mask_ratio = config.mask_ratio def _freq_masking(self, x): x_fft = torch.fft.rfft(x, dim=-1) x_ifft_list = [] for ratio in self.threshold_ratio: temp = x_fft.clone() truncation = int(temp.shape[-1] * ratio) if random.random() > self.mask_ratio: temp[:, :truncation] = 0 else: temp[:, truncation:] = 0 x_ifft = torch.fft.irfft(temp, dim=-1) x_ifft_list.append(x_ifft) x_ifft = torch.stack(x_ifft_list, dim=0) return rearrange(x_ifft, 's b l -> (s b) l') def _predict(self, x, inference_token_len=48): input_length = x.shape[-1] padding_length = (inference_token_len - (input_length % inference_token_len)) % inference_token_len x = F.pad(x, (padding_length, 0)) x = x.unfold(dimension=-1, size=inference_token_len, step=inference_token_len) resampled_weight = resample(old=self.proj_layer.weight.data, new_patch_len=inference_token_len) output = F.linear(x, resampled_weight) return output, None def forward(self, x, inference_token_len=48): if not self.training: return self._predict(x, inference_token_len) input_length = x.shape[-1] padding_length = (self.token_len - (input_length % self.token_len)) % self.token_len x = F.pad(x, (padding_length, 0)) x_masked = self._freq_masking(x) x_origin = x.unfold(dimension=-1, size=self.token_len, step=self.token_len) output_origin = self.proj_layer(x_origin) x_masked = x_masked.unfold(dimension=-1, size=self.token_len, step=self.token_len) output_masked = self.proj_layer(x_masked) return output_origin, output_masked class AuroraAttention(nn.Module): def __init__(self, config: AuroraConfig, layer_idx: Optional[int] = None, rope: bool = False): super().__init__() self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.attention_dropout = config.dropout_rate self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.rope = rope def _scaled_dot_product_attention(self, Q, K, V, bias=None, attn_mask=None): attn_scores = torch.matmul(Q, K.transpose(-2, -1)) attn_scores = attn_scores / math.sqrt(Q.size(-1)) if attn_mask is not None: if attn_mask.dtype == torch.bool: attn_scores = attn_scores.masked_fill(attn_mask, float('-inf')) else: attn_scores = attn_scores + attn_mask if bias is not None: if attn_scores.shape[0] > bias.shape[0]: bias = bias.repeat(attn_scores.shape[0] // bias.shape[0], 1, 1, 1) attn_scores += bias attn_weights = F.softmax(attn_scores, dim=-1) if self.attention_dropout > 0.0 and self.training: attn_weights = F.dropout(attn_weights, p=self.attention_dropout) attn_output = torch.matmul(attn_weights, V) return attn_output, attn_scores def forward( self, hidden_states: torch.Tensor, key_embedding: torch.Tensor = None, value_embedding: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, bias: torch.Tensor = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: bsz, q_len, _ = hidden_states.size() if key_embedding is None: key_embedding = hidden_states if value_embedding is None: value_embedding = hidden_states _, k_len, _ = key_embedding.size() _, v_len, _ = value_embedding.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(key_embedding) value_states = self.v_proj(value_embedding) query_states = query_states.view( bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view( bsz, k_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view( bsz, v_len, self.num_heads, self.head_dim).transpose(1, 2) if self.rope: query_states, key_states = RoPE_decoder(query_states, key_states) attn_output, attn_scores = self._scaled_dot_product_attention( Q=query_states, K=key_states, V=value_states, bias=bias, attn_mask=attention_mask) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) if not output_attentions: attn_scores = None return attn_output, attn_scores class AuroraFFN(nn.Module): def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str): super().__init__() self.ffn = nn.Sequential(nn.Linear(hidden_size, intermediate_size), ACT2FN[hidden_act], nn.Linear(intermediate_size, hidden_size)) def forward(self, hidden_state): return self.ffn(hidden_state) class AuroraDecoderLayer(nn.Module): def __init__(self, config: AuroraConfig, layer_idx: int): super().__init__() self.self_attn = AuroraAttention(config, layer_idx, rope=False) self.cross_attn = AuroraAttention(config, layer_idx, rope=True) self.ffn_layer = AuroraFFN( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act ) if config.norm_mode == 'batch': self.norm1 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) self.norm2 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) self.norm3 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) else: self.norm1 = torch.nn.LayerNorm(config.hidden_size) self.norm2 = torch.nn.LayerNorm(config.hidden_size) self.norm3 = torch.nn.LayerNorm(config.hidden_size) def forward( self, hidden_states: torch.Tensor, cross_states: torch.Tensor, output_attentions: Optional[bool] = False, **kwargs, ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: residual = hidden_states num_token = hidden_states.shape[1] attention_mask = causal_attention_mask(num_token).to(hidden_states.device) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, ) x_attn = residual + self.norm1(hidden_states) x_cross, cross_attn_weights = self.cross_attn(hidden_states=x_attn, key_embedding=cross_states, value_embedding=cross_states) x_cross = self.norm2(x_cross) + x_attn # Fully Connected output_states = self.ffn_layer(x_cross) output_states = self.norm3(output_states) + x_cross if not output_attentions: self_attn_weights = None cross_attn_weights = None return output_states, self_attn_weights, cross_attn_weights class AuroraEncoderLayer(nn.Module): def __init__(self, config: AuroraConfig, layer_idx: int): super().__init__() self.self_attn = AuroraAttention(config, layer_idx, rope=False) self.ffn_layer = AuroraFFN( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act ) if config.norm_mode == 'batch': self.norm1 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) self.norm2 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) else: self.norm1 = torch.nn.LayerNorm(config.hidden_size) self.norm2 = torch.nn.LayerNorm(config.hidden_size) self.dropout_1 = nn.Dropout(config.dropout_rate) self.dropout_2 = nn.Dropout(config.dropout_rate) def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, bias: torch.Tensor = None, **kwargs ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, output_attentions=output_attentions, bias=bias ) x_attn = self.norm1(residual + self.dropout_1(hidden_states)) # Fully Connected output_states = self.ffn_layer(x_attn) output_states = self.norm2(self.dropout_2(output_states) + x_attn) if not output_attentions: self_attn_weights = None return output_states, self_attn_weights class AuroraPredictHead(nn.Module): def __init__(self, config: AuroraConfig): super().__init__() self.output_proj = nn.Linear(config.hidden_size, config.token_len, bias=False) self.dropout = nn.Dropout(config.dropout_rate) def _predict(self, hidden_states: torch.Tensor, inference_token_len=48): resampled_weight = resample(old=self.output_proj.weight.data.T, new_patch_len=inference_token_len).T output = F.linear(hidden_states, resampled_weight) return output def forward( self, hidden_states: torch.Tensor, inference_token_len: int = 48, **kwargs ) -> torch.FloatTensor: if not self.training: return self._predict(hidden_states, inference_token_len) return self.output_proj(self.dropout(hidden_states)) class AuroraPreTrainedModel(PreTrainedModel): config_class = AuroraConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["AuroraEncoderLayer", "AuroraDecoderLayer"] _supports_flash_attn_2 = True _supports_sdpa = False _supports_cache_class = False class AuroraModel(nn.Module): def __init__(self, config: AuroraConfig): super().__init__() self.embed_layer = AuroraPatchEmbedding(config) self.enc_layers = nn.ModuleList( [AuroraEncoderLayer(config, layer_idx) for layer_idx in range(config.num_enc_layers)] ) self.dec_layers = nn.ModuleList( [AuroraDecoderLayer(config, layer_idx) for layer_idx in range(config.num_dec_layers)] ) self.mask_num = len(config.threshold_ratio) self.gradient_checkpointing = False self.VisionEncoder = VisionEncoder(config) self.TextEncoder = TextEncoder(config) self.ModalityConnector = ModalityConnector(config) self.VisionGuider = AuroraAttention(config) self.TextGuider = AuroraAttention(config) self.W = nn.Parameter(torch.eye(config.num_distill)) self.fuse = nn.Linear(config.hidden_size, config.hidden_size) def forward( self, input_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, text_input_ids: Optional[torch.FloatTensor] = None, text_attention_mask: Optional[torch.FloatTensor] = None, text_token_type_ids: Optional[torch.FloatTensor] = None, vision_ids: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, predict_token_num: Optional[int] = None, inference_token_len: Optional[int] = None, ) -> Union[Tuple, MoeModelOutputWithPast]: # input_ids is the input of time series, its shape is [batch_size, seq_len] output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError( "You have to specify either decoder_input_ids or decoder_inputs_embeds") if inference_token_len is None: inference_token_len = self.config.token_len masked_embeds = None if inputs_embeds is None: inputs_embeds, masked_embeds = self.embed_layer(input_ids, inference_token_len) if masked_embeds is None: x_enc = inputs_embeds else: x_enc = torch.concat([inputs_embeds, masked_embeds], dim=0) if vision_ids is not None: vision_features = self.VisionEncoder(vision_ids, type='real') else: vision_features = self.VisionEncoder(input_ids, type='pseudo') _, attn_vision = self.VisionGuider( inputs_embeds, vision_features, vision_features, output_attentions=True ) if text_input_ids is not None: text_features = self.TextEncoder({'input_ids': text_input_ids, 'attention_mask': text_attention_mask, 'token_type_ids': text_token_type_ids}) _, attn_text = self.TextGuider( inputs_embeds, text_features, text_features, output_attentions=True ) else: text_features = None attn_text = None if attn_text is not None: guided_bias = torch.einsum("bhik,kl,bhjl->bhij", attn_vision, self.W, attn_text) else: guided_bias = None # encoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for encoder_layer in self.enc_layers: if output_hidden_states: all_hidden_states += (x_enc,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, x_enc, output_attentions, guided_bias ) else: layer_outputs = encoder_layer( x_enc, output_attentions=output_attentions, bias=guided_bias ) x_enc = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if x_enc.shape[0] > batch_size: x_enc, x_rec = torch.split(x_enc, [batch_size, x_enc.shape[0] - batch_size], dim=0) x_rec = rearrange(x_rec, '(s b) n d -> s b n d', s=self.mask_num) x_rec = x_rec.mean(0) else: x_rec = None decay_weights = 0.5 ** torch.arange(predict_token_num) decay_weights = decay_weights.unsqueeze(0).unsqueeze(-1).to(x_enc.device) from_text, from_vision = self.ModalityConnector(x_enc, text_features, vision_features) if from_text is not None: x_enc = x_enc + self.fuse(from_vision + from_text) else: x_enc = x_enc + self.fuse(from_vision) last_token = x_enc[:, -1:, :] x_dec = decay_weights * last_token.repeat(1, predict_token_num, 1) # decoder layers for decoder_layer in self.dec_layers: if output_hidden_states: all_hidden_states += (x_dec,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, x_dec, x_enc, output_attentions=output_attentions, ) else: layer_outputs = decoder_layer( x_dec, x_enc, output_attentions=output_attentions ) x_dec = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (x_dec,) if not return_dict: return tuple( v for v in [x_dec, all_hidden_states, all_self_attns] if v is not None ) output_states = (x_rec, x_dec, from_text, from_vision) return MoeModelOutputWithPast( last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_self_attns, ) class AuroraForPrediction(AuroraPreTrainedModel, TSGenerationMixin): def __init__(self, config: AuroraConfig): super().__init__(config) self.config = config self.model = AuroraModel(config) self.point_loss = torch.nn.MSELoss(reduction='none') self.flow_match = FlowLoss(config.token_len, config.hidden_size, config.flow_loss_depth, config.hidden_size, config.num_sampling_steps) self.linear_head = AuroraPredictHead(config) self.retriever = PrototypeRetriever(config) def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def forward( self, input_ids: torch.FloatTensor = None, text_input_ids: torch.FloatTensor = None, text_attention_mask: torch.FloatTensor = None, text_token_type_ids: torch.FloatTensor = None, vision_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.FloatTensor] = None, loss_masks: Optional[torch.FloatTensor] = None, mask_y: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, max_output_length: Optional[int] = None, revin: Optional[bool] = True, num_samples: Optional[int] = 1, inference_token_len: Optional[int] = 48, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if max_output_length is None: max_output_length = labels.shape[1] predict_token_num = math.ceil(max_output_length / self.config.token_len) else: predict_token_num = math.ceil(max_output_length / inference_token_len) if revin: means = input_ids.mean(1, keepdim=True).detach() stdev = input_ids.std(dim=1, keepdim=True, unbiased=False).detach() + 1e-5 input_ids = (input_ids - means) / stdev outputs = self.model( input_ids=input_ids, inputs_embeds=inputs_embeds, text_input_ids=text_input_ids, text_attention_mask=text_attention_mask, text_token_type_ids=text_token_type_ids, vision_ids=vision_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, predict_token_num=predict_token_num, inference_token_len=inference_token_len ) hidden_states = outputs[0] if not return_dict else outputs.last_hidden_state x_rec, x_dec, from_text, from_vision = hidden_states if from_text is not None: generated_prototypes = self.retriever(from_text + from_vision, predict_token_num) else: generated_prototypes = self.retriever(from_vision, predict_token_num) loss = None predictions = None eps = 1e2 mask = None if labels is not None: if revin: origin_labels = labels labels = (labels - means) / stdev origin_length = labels.shape[-1] target_length = predict_token_num * self.config.token_len if origin_length < target_length: pad_length = target_length - origin_length labels = F.pad(labels, (0, pad_length)) mask = torch.tensor([1] * origin_length + [0] * pad_length, device=labels.device) mask = mask.unsqueeze(0) reco = rearrange(self.linear_head(x_rec), 'b n p -> b (n p)') fore = rearrange(self.linear_head(x_dec), 'b n p -> b (n p)') if revin: fore = fore * stdev + means reco_loss = self.point_loss(reco[:, :input_ids.shape[-1]], input_ids) fore_loss = self.point_loss(fore[:, :origin_length], origin_labels) reco_loss = reco_loss[reco_loss < eps] fore_loss = fore_loss[fore_loss < eps] point_loss = reco_loss.mean() + fore_loss.mean() shift_labels = labels.unfold( dimension=-1, size=self.config.token_len, step=self.config.token_len) bsz, L, _ = shift_labels.shape shift_labels = shift_labels.reshape( bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) x_dec = x_dec.reshape( bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) protos = generated_prototypes.reshape(bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) flow_loss = self.flow_match(target=shift_labels, z=x_dec.detach(), prototype=protos, eps=eps, mask=mask) loss = point_loss + flow_loss else: predictions = self.flow_match.sample(z=rearrange(x_dec, 'b n d -> (b n) d'), prototype=rearrange(generated_prototypes, 'b n p -> (b n) p'), num_samples=num_samples, inference_token_len=inference_token_len) predictions = rearrange(predictions, '(b n) s p -> b s (n p)', n=predict_token_num)[:, :, :max_output_length] if revin: stdev = stdev.unsqueeze(1).repeat(1, num_samples, 1) means = means.unsqueeze(1).repeat(1, num_samples, 1) predictions = (predictions * stdev) + means return MoeCausalLMOutputWithPast( loss=loss, logits=predictions, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )