DecisionIntelligence
/

LightGTS

@@ -1,862 +0,0 @@
-from transformers import PreTrainedModel
-from configuration_LightGTS import LightGTSConfig
-from ts_generation_mixin import TSGenerationMixin
-import torch
-from torch import nn
-from torch import Tensor
-from typing import Callable, Optional
-import math
-import torch.nn.functional as F
-import numpy as np
-class LightGTSPreTrainedModel(PreTrainedModel):
-    config_class = LightGTSConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["TSTEncoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = False
-    _supports_cache_class = True
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, torch.nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, torch.nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-class LightGTSForPrediction(LightGTSPreTrainedModel, TSGenerationMixin):
-    def __init__(self, config: LightGTSConfig):
-        super().__init__(config)
-        self.config = config
-        self.model = LightGTSForZeroShot(c_in=config.c_in,
-                target_dim=config.target_dim,
-                patch_len=config.patch_len,
-                stride=config.stride,
-                num_patch=config.num_patch,
-                e_layers=config.e_layers,
-                d_layers=config.d_layers,
-                n_heads=config.n_heads,
-                d_model=config.d_model,
-                shared_embedding=True,
-                d_ff=config.d_ff,
-                dropout=config.dropout,
-                attn_dropout=config.attn_dropout,
-                head_dropout=config.head_dropout,
-                act='relu',
-                head_type=config.head_type,
-                res_attention=False,
-                learn_pe=False
-                )
-    def forward(self, input, labels=None):
-        outputs = self.model(input)
-        loss = None
-        if labels is not None:
-            if outputs.shape != labels.shape:
-                outputs = outputs.view(labels.shape)
-            loss = self.loss_fn(outputs, labels)
-        return {"prediction": outputs, "loss": loss}
-class LightGTS(nn.Module):
-    """
-    Output dimension:
-         [bs x target_dim x nvars] for prediction
-         [bs x target_dim] for regression
-         [bs x target_dim] for classification
-         [bs x num_patch x n_vars x patch_len] for pretrain
-    """
-    def __init__(self, c_in:int, target_dim:int, patch_len:int, stride:int, num_patch:int, mask_mode:str = 'patch',mask_nums:int = 3,
-                 e_layers:int=3, d_layers:int=3, d_model=128, n_heads=16, shared_embedding=True, d_ff:int=256,
-                 norm:str='BatchNorm', attn_dropout:float=0.4, dropout:float=0., act:str="gelu",
-                 res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,
-                 pe:str='sincos', learn_pe:bool=False, head_dropout = 0,
-                 head_type = "prediction", individual = False,
-                 y_range:Optional[tuple]=None, verbose:bool=False, **kwargs):
-        super().__init__()
-        assert head_type in ['pretrain', 'prediction', 'regression', 'classification'], 'head type should be either pretrain, prediction, or regression'
-        # Basic
-        self.num_patch = num_patch
-        self.target_dim=target_dim
-        self.out_patch_num = math.ceil(target_dim / patch_len)
-        self.target_patch_len = 48
-        # Embedding
-        self.embedding = nn.Linear(self.target_patch_len, d_model)
-        self.cls_embedding = nn.Parameter(torch.randn(1, 1, 1, d_model),requires_grad=True)
-        # Encoder
-        self.encoder = TSTEncoder(d_model, n_heads, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout,
-                                   pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=e_layers,
-                                    store_attn=store_attn)
-        # Decoder
-        self.decoder = Decoder(d_layers, patch_len=patch_len, d_model=d_model, n_heads=n_heads, d_ff=d_ff,attn_dropout= attn_dropout, dropout=dropout)
-        # Head
-        self.n_vars = c_in
-        self.head_type = head_type
-        self.mask_mode = mask_mode
-        self.mask_nums = mask_nums
-        self.d_model  = d_model
-        self.patch_len = patch_len
-        if head_type == "pretrain":
-            self.head = PretrainHead(d_model, patch_len, head_dropout) # custom head passed as a partial func with all its kwargs
-        elif head_type == "prediction":
-            self.head = decoder_PredictHead(d_model, self.patch_len, self.target_patch_len, head_dropout)
-    # def get_dynamic_weights(self, n_preds):
-    #     """
-    #     Generate dynamic weights for the replicated tokens. This example uses a linearly decreasing weight.
-    #     You can modify this to use other schemes like exponential decay, sine/cosine, etc.
-    #     """
-    #     # Linearly decreasing weights from 1.0 to 0.5 (as an example)
-    #     weights = torch.linspace(1.0, 0.5, n_preds)
-    #     return weights
-    def get_dynamic_weights(self, n_preds, decay_rate=0.5):
-        """
-        Generate dynamic weights for the replicated tokens using an exponential decay scheme.
-        Args:
-        - n_preds (int): Number of predictions to generate weights for.
-        - decay_rate (float): The base of the exponential decay. Lower values decay faster (default: 0.9).
-        Returns:
-        - torch.Tensor: A tensor of weights with exponential decay.
-        """
-        # Exponential decay weights
-        weights = decay_rate ** torch.arange(n_preds)
-        return weights
-    def decoder_predict(self, bs, n_vars, dec_cross):
-        """
-        dec_cross: tensor [bs x  n_vars x num_patch x d_model]
-        """
-        # dec_in = self.decoder_embedding.e xpand(bs, self.n_vars, self.out_patch_num, -1)
-        # dec_in = self.embedding(self.decoder_len).expand(bs, -1, -1, -1)
-        # dec_in = self.decoder_embedding.expand(bs, n_vars, self.out_patch_num, -1)
-        # dec_in = dec_cross.mean(2).unsqueeze(2).expand(-1,-1,self.out_patch_num,-1)
-        dec_in = dec_cross[:,:,-1,:].unsqueeze(2).expand(-1,-1,self.out_patch_num,-1)
-        # dec_in = torch.ones_like(dec_in)
-        weights = self.get_dynamic_weights(self.out_patch_num).to(dec_in.device)
-        dec_in = dec_in * weights.unsqueeze(0).unsqueeze(0).unsqueeze(-1)
-        # dec_in = torch.cat((dec_in, self.sep_tokens), dim=2)
-        # dec_in = dec_cross[:,:,-self.out_patch_num:,:]
-        # dec_in = torch.ones([bs, n_vars, self.out_patch_num, self.d_model]).to(dec_cross.device)
-        # dec_in = dec_in + self.pos[-self.out_patch_num:,:]
-        decoder_output = self.decoder(dec_in, dec_cross)
-        decoder_output = decoder_output.transpose(2,3)
-        return decoder_output
-    def forward(self, z):
-        """
-        z: tensor [bs x num_patch x n_vars x patch_len]
-        """
-        bs, num_patch, n_vars, patch_len = z.shape
-        # tokenizer
-        cls_tokens = self.cls_embedding.expand(bs, n_vars, -1, -1)
-        embedding = nn.Linear(patch_len, self.d_model, bias=False)
-        embedding.weight.data = resample_patchemb(old=self.embedding.weight.data, new_patch_len=self.patch_len)
-        z = embedding(z).permute(0,2,1,3) # [bs x n_vars x num_patch x d_model]
-        z = torch.cat((cls_tokens, z), dim=2)  # [bs x n_vars x (1 + num_patch) x d_model]
-        # encoder
-        z = torch.reshape(z, (-1, 1 + num_patch, self.d_model)) # [bs*n_vars x num_patch x d_model]
-        z = self.encoder(z)
-        z = torch.reshape(z, (-1, n_vars, 1 + num_patch, self.d_model)) # [bs, n_vars x num_patch x d_model]
-        # decoder
-        z = self.decoder_predict(bs, n_vars, z[:,:,:,:])
-        # predict
-        z = self.head(z[:,:,:,:])
-        z = z[:,:self.target_dim, :]
-        # z: [bs x target_dim x nvars] for prediction
-        #    [bs x target_dim] for regression
-        #    [bs x target_dim] for classification
-        #    [bs x num_patch x n_vars x patch_len] for pretrain
-        return z
-class LightGTSForZeroShot(nn.Module):
-    """
-    Output dimension:
-         [bs x target_dim x nvars] for prediction
-         [bs x target_dim] for regression
-         [bs x target_dim] for classification
-         [bs x num_patch x n_vars x patch_len] for pretrain
-    """
-    def __init__(self, c_in:int, target_dim:int, patch_len:int, stride:int, num_patch:int, mask_mode:str = 'patch',mask_nums:int = 3,
-                 e_layers:int=3, d_layers:int=3, d_model=128, n_heads=16, shared_embedding=True, d_ff:int=256,
-                 norm:str='BatchNorm', attn_dropout:float=0.4, dropout:float=0., act:str="gelu",
-                 res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,
-                 pe:str='sincos', learn_pe:bool=False, head_dropout = 0,
-                 head_type = "prediction", individual = False,
-                 y_range:Optional[tuple]=None, verbose:bool=False, **kwargs):
-        super().__init__()
-        assert head_type in ['pretrain', 'prediction', 'regression', 'classification'], 'head type should be either pretrain, prediction, or regression'
-        # Basic
-        self.num_patch = num_patch
-        self.target_dim=target_dim
-        self.out_patch_num = math.ceil(target_dim / patch_len)
-        self.target_patch_len = 48
-        # Embedding
-        self.embedding = nn.Linear(self.target_patch_len, d_model)
-        # self.decoder_embedding = nn.Parameter(torch.randn(1, 1,1, d_model),requires_grad=True)
-        self.cls_embedding = nn.Parameter(torch.randn(1, 1, 1, d_model),requires_grad=True)
-        # self.sep_embedding = nn.Parameter(torch.randn(1, 1, 1, d_model),requires_grad=True)
-        # Position Embedding
-        # self.pos = positional_encoding(pe, learn_pe, 1 + num_patch + self.out_patch_num, d_model)
-        # self.drop_out = nn.Dropout(dropout)
-        # Encoder
-        self.encoder = TSTEncoder(d_model, n_heads, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout,
-                                   pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=e_layers,
-                                    store_attn=store_attn)
-        # Decoder
-        self.decoder = Decoder(d_layers, patch_len=patch_len, d_model=d_model, n_heads=n_heads, d_ff=d_ff,attn_dropout= attn_dropout, dropout=dropout)
-        # Head
-        self.n_vars = c_in
-        self.head_type = head_type
-        self.mask_mode = mask_mode
-        self.mask_nums = mask_nums
-        self.d_model  = d_model
-        self.patch_len = patch_len
-        if head_type == "pretrain":
-            self.head = PretrainHead(d_model, patch_len, head_dropout) # custom head passed as a partial func with all its kwargs
-        elif head_type == "prediction":
-            self.head = decoder_PredictHead(d_model, self.patch_len, self.target_patch_len, head_dropout)
-        # self.apply(self._init_weights)
-    # def get_dynamic_weights(self, n_preds):
-    #     """
-    #     Generate dynamic weights for the replicated tokens. This example uses a linearly decreasing weight.
-    #     You can modify this to use other schemes like exponential decay, sine/cosine, etc.
-    #     """
-    #     # Linearly decreasing weights from 1.0 to 0.5 (as an example)
-    #     weights = torch.linspace(1.0, 0.5, n_preds)
-    #     return weights
-    def get_dynamic_weights(self, n_preds, decay_rate=0.5):
-        """
-        Generate dynamic weights for the replicated tokens using an exponential decay scheme.
-        Args:
-        - n_preds (int): Number of predictions to generate weights for.
-        - decay_rate (float): The base of the exponential decay. Lower values decay faster (default: 0.9).
-        Returns:
-        - torch.Tensor: A tensor of weights with exponential decay.
-        """
-        # Exponential decay weights
-        weights = decay_rate ** torch.arange(n_preds)
-        return weights
-    def decoder_predict(self, bs, n_vars, dec_cross):
-        """
-        dec_cross: tensor [bs x  n_vars x num_patch x d_model]
-        """
-        # dec_in = self.decoder_embedding.expand(bs, self.n_vars, self.out_patch_num, -1)
-        # dec_in = self.embedding(self.decoder_len).expand(bs, -1, -1, -1)
-        # dec_in = self.decoder_embedding.expand(bs, n_vars, self.out_patch_num, -1)
-        # dec_in = dec_cross.mean(2).unsqueeze(2).expand(-1,-1,self.out_patch_num,-1)
-        dec_in = dec_cross[:,:,-1,:].unsqueeze(2).expand(-1,-1,self.out_patch_num,-1)
-        weights = self.get_dynamic_weights(self.out_patch_num).to(dec_in.device)
-        dec_in = dec_in * weights.unsqueeze(0).unsqueeze(0).unsqueeze(-1)
-        # dec_in = torch.cat((dec_in, self.sep_tokens), dim=2)
-        # dec_in = dec_cross[:,:,-self.out_patch_num:,:]
-        # dec_in = torch.ones([bs, n_vars, self.out_patch_num, self.d_model]).to(dec_cross.device)
-        # dec_in = dec_in + self.pos[-self.out_patch_num:,:]
-        decoder_output = self.decoder(dec_in, dec_cross)
-        decoder_output = decoder_output.transpose(2,3)
-        return decoder_output
-    def forward(self, z):
-        """
-        z: tensor [bs x num_patch x n_vars x patch_len]
-        """
-        bs, num_patch, n_vars, patch_len = z.shape
-        z = resize(z, target_patch_len=self.target_patch_len)
-        # tokenizer
-        cls_tokens = self.cls_embedding.expand(bs, n_vars, -1, -1)
-        z = self.embedding(z).permute(0,2,1,3) # [bs x n_vars x num_patch x d_model]
-        z = torch.cat((cls_tokens, z), dim=2)  # [bs x n_vars x (1 + num_patch) x d_model]
-        # z = self.drop_out(z + self.pos[:1 + self.num_patch, :])
-        # encoder
-        z = torch.reshape(z, (-1, 1 + num_patch, self.d_model)) # [bs*n_vars x num_patch x d_model]
-        z = self.encoder(z)
-        z = torch.reshape(z, (-1, n_vars, 1 + num_patch, self.d_model)) # [bs, n_vars x num_patch x d_model]
-        # decoder
-        z = self.decoder_predict(bs, n_vars, z[:,:,:,:])
-        # predict
-        z = self.head(z[:,:,:,:])
-        z = z[:,:self.target_dim, :]
-        # z: [bs x target_dim x nvars] for prediction
-        #    [bs x target_dim] for regression
-        #    [bs x target_dim] for classification
-        #    [bs x num_patch x n_vars x patch_len] for pretrain
-        return z
-def resize(x, target_patch_len):
-    '''
-    x: tensor [bs x num_patch x n_vars x patch_len]]
-    '''
-    bs, num_patch, n_vars, patch_len = x.shape
-    x = x.reshape(bs*num_patch, n_vars, patch_len)
-    x = F.interpolate(x, size=target_patch_len, mode='linear', align_corners=False)
-    return x.reshape(bs, num_patch, n_vars, target_patch_len)
-class TSTEncoder(nn.Module):
-    def __init__(self, d_model, n_heads, d_ff=None,
-                        norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu',
-                        res_attention=False, n_layers=1, pre_norm=False, store_attn=False):
-        super().__init__()
-        self.layers = nn.ModuleList([TSTEncoderLayer(d_model, n_heads=n_heads, d_ff=d_ff, norm=norm,
-                                                      attn_dropout=attn_dropout, dropout=dropout,
-                                                      activation=activation, res_attention=res_attention,
-                                                      pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)])
-        self.res_attention = res_attention
-    def forward(self, src:Tensor):
-        """
-        src: tensor [bs x q_len x d_model]
-        """
-        output = src
-        scores = None
-        if self.res_attention:
-            for mod in self.layers: output, scores = mod(output, prev=scores)
-            return output
-        else:
-            for mod in self.layers: output = mod(output)
-            return output
-class TSTEncoderLayer(nn.Module):
-    def __init__(self, d_model, n_heads, d_ff=256, store_attn=False,
-                 norm='LayerNorm', attn_dropout=0, dropout=0., bias=True,
-                activation="gelu", res_attention=False, pre_norm=False):
-        super().__init__()
-        assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})"
-        d_k = d_model // n_heads
-        d_v = d_model // n_heads
-        # Multi-Head attention
-        self.res_attention = res_attention
-        self.self_attn = MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention)
-        # Add & Norm
-        self.dropout_attn = nn.Dropout(dropout)
-        if "batch" in norm.lower():
-            self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
-        else:
-            self.norm_attn = nn.LayerNorm(d_model)
-        # Position-wise Feed-Forward
-        self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias),
-                                get_activation_fn(activation),
-                                nn.Dropout(dropout),
-                                nn.Linear(d_ff, d_model, bias=bias))
-        # Add & Norm
-        self.dropout_ffn = nn.Dropout(dropout)
-        if "batch" in norm.lower():
-            self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
-        else:
-            self.norm_ffn = nn.LayerNorm(d_model)
-        self.pre_norm = pre_norm
-        self.store_attn = store_attn
-        # # se block
-        # self.SE = SE_Block(inchannel=7)
-    def forward(self, src:Tensor, prev:Optional[Tensor]=None):
-        """
-        src: tensor [bs x q_len x d_model]
-        """
-        # Multi-Head attention sublayer
-        if self.pre_norm:
-            src = self.norm_attn(src)
-        ## Multi-Head attention
-        if self.res_attention:
-            src2, attn, scores = self.self_attn(src, src, src, prev)
-        else:
-            # attention_mask = causal_attention_mask(src.shape[1]).to(src.device)
-            # src2, attn = self.self_attn(src, src, src, attn_mask=attention_mask)
-            src2, attn = self.self_attn(src, src, src)
-        if self.store_attn:
-            self.attn = attn
-        # total, num_patch, d_model = src2.size()
-        # bs = int(total/7)
-        # src2 = self.SE(src2.reshape(bs, 7, num_patch, -1)).reshape(total, num_patch, -1)
-        ## Add & Norm
-        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout
-        if not self.pre_norm:
-            src = self.norm_attn(src)
-        # Feed-forward sublayer
-        if self.pre_norm:
-            src = self.norm_ffn(src)
-        ## Position-wise Feed-Forward
-        src2 = self.ff(src)
-        ## Add & Norm
-        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout
-        if not self.pre_norm:
-            src = self.norm_ffn(src)
-        if self.res_attention:
-            return src, scores
-        else:
-            return src
-class Decoder(nn.Module):
-    def __init__(self, d_layers, patch_len, d_model, n_heads, d_ff=None, attn_dropout=0.2, dropout=0.1):
-        super(Decoder, self).__init__()
-        self.decoder_layers = nn.ModuleList()
-        for i in range(d_layers):
-            self.decoder_layers.append(DecoderLayer(patch_len, d_model, n_heads, d_ff, attn_dropout, dropout))
-    def forward(self, x, cross):
-        output = x
-        for layer in self.decoder_layers:
-            output = layer(output, cross)
-        return output
-class DecoderLayer(nn.Module):
-    def __init__(self, patch_len, d_model, n_heads, d_ff=None, attn_dropout = 0.2, dropout=0.5, norm="BatchNorm"):
-        super(DecoderLayer, self).__init__()
-        self.self_attention = MultiheadAttention(d_model, n_heads, res_attention=False, attn_dropout=attn_dropout)
-        self.cross_attention = MultiheadAttention(d_model, n_heads, attn_dropout=attn_dropout, rope_type=True)
-        # self.pos_embed = nn.Conv1d(d_model, d_model, kernel_size=3, padding=1, groups=d_model)
-        if 'batch' in norm.lower():
-            self.norm1 = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
-            self.norm2 = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
-            self.norm3 = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
-        else:
-            self.norm1 = nn.LayerNorm(d_model)
-            self.norm2 = nn.LayerNorm(d_model)
-            self.norm3 = nn.LayerNorm(d_model)
-        self.dropout = nn.Dropout(dropout)
-        self.MLP1 = CMlp(in_features = d_model, hidden_features = d_ff, out_features = d_model, drop=dropout)
-    def forward(self, x, cross):
-        batch, n_vars, num_patch, d_model = x.shape
-        x = x.reshape(batch*n_vars, num_patch, d_model)
-        # x = x.permute(0,2,1)
-        # x = x + self.pos_embed(x)
-        # x = x.permute(0,2,1)
-        cross = cross.reshape(batch*n_vars, -1, d_model)
-        attention_mask = causal_attention_mask(num_patch).to(x.device)
-        x_attn , _= self.self_attention(x, attn_mask=attention_mask)
-        x_attn = self.norm1(x_attn) + x
-        x_cross , _ = self.cross_attention(x_attn, cross, cross)
-        x_cross = self.dropout(self.norm2(x_cross)) + x_attn
-        x_ff = self.MLP1(x_cross)
-        x_ff = self.norm3(x_ff) + x_cross
-        x_ff = x_ff.reshape(batch, n_vars, num_patch, d_model)
-        return x_ff
-def causal_attention_mask(seq_length):
-    """
-    创建一个因果注意力掩码。掩码中的每个位置 (i, j)
-    表示在计算第i个位置的attention时, 第j个位置是否可以被看见。
-    如果j <= i, 这个位置被设为1(可见), 否则设为0(不可见)。
-    Args:
-        seq_length (int): 序列的长度
-    Returns:
-        torch.Tensor: 因果注意力掩码，大小为 (seq_length, seq_length)
-    """
-    mask = torch.triu(torch.ones(seq_length, seq_length) * float('-inf'), diagonal=1)
-    return mask
-class CMlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Conv1d(in_features, hidden_features, 1)
-        self.act = act_layer()
-        self.fc2 = nn.Conv1d(hidden_features, out_features, 1)
-        self.drop = nn.Dropout(drop)
-    def forward(self, x):
-        x = x.permute(0,2,1)
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        x = x.permute(0,2,1)
-        return x
-class Transpose(nn.Module):
-    def __init__(self, *dims, contiguous=False):
-        super().__init__()
-        self.dims, self.contiguous = dims, contiguous
-    def forward(self, x):
-        if self.contiguous: return x.transpose(*self.dims).contiguous()
-        else: return x.transpose(*self.dims)
-class MultiheadAttention(nn.Module):
-    def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True, lsa=False, rope_type=False):
-        """Multi Head Attention Layer
-        Input shape:
-            Q:       [batch_size (bs) x max_q_len x d_model]
-            K, V:    [batch_size (bs) x q_len x d_model]
-            mask:    [q_len x q_len]
-        """
-        super().__init__()
-        d_k = d_model // n_heads if d_k is None else d_k
-        d_v = d_model // n_heads if d_v is None else d_v
-        self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v
-        self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
-        self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
-        self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias)
-        # Scaled Dot-Product Attention (multiple heads)
-        self.res_attention = res_attention
-        self.sdp_attn = ScaledDotProductAttention(d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa, rope_type=rope_type)
-        # Poject output
-        self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout))
-    def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None,
-                key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
-        bs = Q.size(0)
-        if K is None: K = Q
-        if V is None: V = Q
-        # Linear (+ split in multiple heads)
-        q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2)       # q_s    : [bs x n_heads x max_q_len x d_k]
-        k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1)     # k_s    : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3)
-        v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2)       # v_s    : [bs x n_heads x q_len x d_v]
-        # Apply Scaled Dot-Product Attention (multiple heads)
-        if self.res_attention:
-            output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
-        else:
-            output, attn_weights = self.sdp_attn(q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
-        # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len]
-        # back to the original inputs dimensions
-        output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # output: [bs x q_len x n_heads * d_v]
-        output = self.to_out(output)
-        if self.res_attention: return output, attn_weights, attn_scores
-        else: return output, attn_weights
-class ScaledDotProductAttention(nn.Module):
-    r"""Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with optional residual attention from previous layer
-    (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets
-    by Lee et al, 2021)"""
-    def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False, lsa=False, rope_type=False):
-        super().__init__()
-        self.attn_dropout = nn.Dropout(attn_dropout)
-        self.res_attention = res_attention
-        head_dim = d_model // n_heads
-        self.scale = nn.Parameter(torch.tensor(head_dim ** -0.5), requires_grad=lsa)
-        self.lsa = lsa
-        self.rope_type = rope_type
-    def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
-        '''
-        Input shape:
-            q               : [bs x n_heads x max_q_len x d_k]
-            k               : [bs x n_heads x d_k x seq_len]
-            v               : [bs x n_heads x seq_len x d_v]
-            prev            : [bs x n_heads x q_len x seq_len]
-            key_padding_mask: [bs x seq_len]
-            attn_mask       : [1 x seq_len x seq_len]
-        Output shape:
-            output:  [bs x n_heads x q_len x d_v]
-            attn   : [bs x n_heads x q_len x seq_len]
-            scores : [bs x n_heads x q_len x seq_len]
-        '''
-        # using RoPE
-        if self.rope_type:
-            q, k = RoPE_decoder(q, k.permute(0,1,3,2))
-        else:
-            q, k = RoPE(q, k.permute(0,1,3,2))
-        k = k.permute(0,1,3,2)
-        # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence
-        attn_scores = torch.matmul(q, k) * self.scale      # attn_scores : [bs x n_heads x max_q_len x q_len]
-        # Add pre-softmax attention scores from the previous layer (optional)
-        if prev is not None: attn_scores = attn_scores + prev
-        # Attention mask (optional)
-        if attn_mask is not None:                                     # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len
-            if attn_mask.dtype == torch.bool:
-                attn_scores.masked_fill_(attn_mask, -np.inf)
-            else:
-                attn_scores += attn_mask
-        # Key padding mask (optional)
-        if key_padding_mask is not None:                              # mask with shape [bs x q_len] (only when max_w_len == q_len)
-            attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf)
-        # normalize the attention weights
-        attn_weights = F.softmax(attn_scores, dim=-1)                 # attn_weights   : [bs x n_heads x max_q_len x q_len]
-        attn_weights = self.attn_dropout(attn_weights)
-        # compute the new values given the attention weights
-        output = torch.matmul(attn_weights, v)                        # output: [bs x n_heads x max_q_len x d_v]
-        if self.res_attention: return output, attn_weights, attn_scores
-        else: return output, attn_weights
-def RoPE(q, k):
-    # q,k: (bs, head, max_len, output_dim)
-    batch_size = q.shape[0]
-    nums_head = q.shape[1]
-    max_len = q.shape[2]
-    output_dim = q.shape[-1]
-    # (bs, head, max_len, output_dim)
-    pos_emb = sinusoidal_position_embedding(batch_size, nums_head, max_len, output_dim, q.device, factor=1)
-    # cos_pos,sin_pos: (bs, head, max_len, output_dim)
-    # 看rope公式可知，相邻cos，sin之间是相同的，所以复制一遍。如(1,2,3)变成(1,1,2,2,3,3)
-    cos_pos = pos_emb[...,  1::2].repeat_interleave(2, dim=-1)  # 将奇数列信息抽取出来也就是cos 拿出来并复制
-    sin_pos = pos_emb[..., ::2].repeat_interleave(2, dim=-1)  # 将偶数列信息抽取出来也就是sin 拿出来并复制
-    # q,k: (bs, head, max_len, output_dim)
-    q2 = torch.stack([-q[..., 1::2], q[..., ::2]], dim=-1)
-    q2 = q2.reshape(q.shape)  # reshape后就是正负交替了
-    # 更新qw, *对应位置相乘
-    q = q * cos_pos + q2 * sin_pos
-    k2 = torch.stack([-k[..., 1::2], k[..., ::2]], dim=-1)
-    k2 = k2.reshape(k.shape)
-    # 更新kw, *对应位置相乘
-    k = k * cos_pos + k2 * sin_pos
-    return q, k
-def RoPE_decoder(q, k):
-    # q,k: (bs, head, max_len, output_dim)
-    batch_size = q.shape[0]
-    nums_head = q.shape[1]
-    q_max_len = q.shape[2]
-    k_max_len = k.shape[2]
-    output_dim = q.shape[-1]
-    # (bs, head, max_len, output_dim)
-    pos_emb = sinusoidal_position_embedding(batch_size, nums_head, k_max_len + q_max_len, output_dim, q.device, factor=1)
-    # cos_pos,sin_pos: (bs, head, max_len, output_dim)
-    # 看rope公式可知，相邻cos，sin之间是相同的，所以复制一遍。如(1,2,3)变成(1,1,2,2,3,3)
-    cos_pos = pos_emb[...,  1::2].repeat_interleave(2, dim=-1)  # 将奇数列信息抽取出来也就是cos 拿出来并复制
-    sin_pos = pos_emb[..., ::2].repeat_interleave(2, dim=-1)  # 将偶数列信息抽取出来也就是sin 拿出来并复制
-    # q,k: (bs, head, max_len, output_dim)
-    q2 = torch.stack([-q[..., 1::2], q[..., ::2]], dim=-1)
-    q2 = q2.reshape(q.shape)  # reshape后就是正负交替了
-    # 更新qw, *对应位置相乘
-    q = q * cos_pos[:,:,-q_max_len:,:] + q2 * sin_pos[:,:,-q_max_len:,:]
-    k2 = torch.stack([-k[..., 1::2], k[..., ::2]], dim=-1)
-    k2 = k2.reshape(k.shape)
-    # 更新kw, *对应位置相乘
-    k = k * cos_pos[:,:,:k_max_len,:] + k2 * sin_pos[:,:,:k_max_len,:]
-    return q, k
-def sinusoidal_position_embedding(batch_size, nums_head, max_len, output_dim, device, factor=1.0):
-    # (max_len * factor, 1)
-    position = torch.arange(0, max_len * factor, 1 / factor, dtype=torch.float).unsqueeze(-1)
-    # (output_dim//2)
-    ids = torch.arange(0, output_dim // 2, dtype=torch.float)  # i 范围是 [0, d/2]
-    theta = torch.pow(10000, -2 * ids / output_dim)
-    # (max_len * factor, output_dim//2)
-    embeddings = position * theta
-    # (max_len * factor, output_dim//2, 2)
-    embeddings = torch.stack([torch.sin(embeddings), torch.cos(embeddings)], dim=-1)
-    # (bs, head, max_len * factor, output_dim//2, 2)
-    embeddings = embeddings.repeat((batch_size, nums_head, *([1] * len(embeddings.shape))))
-    # (bs, head, max_len * factor, output_dim)
-    embeddings = torch.reshape(embeddings, (batch_size, nums_head, -1, output_dim))
-    embeddings = embeddings.to(device)
-    # 如果 factor > 1, 使用插值位置来生成更细粒度的嵌入
-    if factor > 1.0:
-        interpolation_indices = torch.linspace(0, embeddings.shape[2] - 1, max_len).long()
-        embeddings = embeddings[:, :, interpolation_indices, :]
-    return embeddings
-class PretrainHead(nn.Module):
-    def __init__(self, d_model, patch_len, dropout):
-        super().__init__()
-        self.dropout = nn.Dropout(dropout)
-        self.linear = nn.Linear(d_model, patch_len)
-    def forward(self, x):
-        """
-        x: tensor [bs x nvars x d_model x num_patch]
-        output: tensor [bs x nvars x num_patch x patch_len]
-        """
-        x = x.transpose(2,3)                     # [bs x nvars x num_patch x d_model]
-        x = self.linear( self.dropout(x) )      # [bs x nvars x num_patch x patch_len]
-        x = x.permute(0,2,1,3)                  # [bs x num_patch x nvars x patch_len]
-        return x
-class decoder_PredictHead(nn.Module):
-    def __init__(self, d_model, patch_len, target_patch_len, dropout):
-        super().__init__()
-        self.dropout = nn.Dropout(dropout)
-        self.linear = nn.Linear(d_model, target_patch_len)
-        self.patch_len = patch_len
-        self.d_model = d_model
-    def forward(self, x):
-        """
-        x: tensor [bs x nvars x d_model x num_patch]
-        output: tensor [bs x nvars x num_patch x patch_len]
-        """
-        Linear = nn.Linear(self.d_model, self.patch_len, bias=False)
-        Linear.weight.data = resample_patchemb(old=self.linear.weight.data.T, new_patch_len=self.patch_len).T
-        x = x.transpose(2,3)                     # [bs x nvars x num_patch x d_model]
-        x = Linear( self.dropout(x) )      # [bs x nvars x num_patch x patch_len]
-        x = x.permute(0,2,3,1)                  # [bs x num_patch x  x patch_len x nvars]
-        return x.reshape(x.shape[0],-1,x.shape[3])
-def resample_patchemb(old: torch.Tensor, new_patch_len: int):
-    assert old.dim() == 2, "输入张量应为2D (d_model, patch_size)"
-    if old.size(1) == new_patch_len:
-        return old
-    old = old.T
-    old_shape = old.size(0)
-    factor = new_patch_len/old_shape
-    # 定义辅助函数：批量resize
-    def resize(x_tensor, new_shape):
-        return F.interpolate(x_tensor.unsqueeze(0), size=new_shape, mode='linear').squeeze(0)
-    # 构造缩放矩阵
-    basis_vectors = torch.eye(old_shape, dtype=torch.float32, device=old.device)
-    resize_mat = resize(basis_vectors, new_patch_len).T
-    # 计算伪逆
-    resize_mat_pinv = torch.linalg.pinv(resize_mat.T)
-    # z_inverse = z @ resize_mat_pinv
-    # z_inverse_var = z_inverse.var(dim=-1).mean(dim=1).mean()
-    # z_var = z.var(dim=-1).mean(dim=1).mean()
-    # z_interpolate = z_inverse @ resize_mat.T
-    # z_interpolate_var = z_interpolate.var(dim=-1).mean(dim=1).mean()
-    # print(z_inverse_var)
-    # print(z_var)
-    # print(z_interpolate_var/z_inverse_var)
-    # 直接矩阵操作完成重采样
-    resampled_kernels = resize_mat_pinv @ old * math.sqrt(factor)
-    return resampled_kernels.T
-def get_activation_fn(activation):
-    if callable(activation): return activation()
-    elif activation.lower() == "relu": return nn.ReLU()
-    elif activation.lower() == "gelu": return nn.GELU()
-    raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable')