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LightGTS-huggingface/config.json

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+{
+  "act": "gelu",
+  "attn_dropout": 0.4,
+  "c_in": 1,
+  "context_points": 528,
+  "d_ff": 512,
+  "d_layers": 3,
+  "d_model": 256,
+  "dropout": 0.0,
+  "e_layers": 3,
+  "head_dropout": 0,
+  "head_type": "prediction",
+  "initializer_range": 0.02,
+  "mask_mode": "patch",
+  "mask_nums": 3,
+  "model_type": "LightGTS",
+  "n_heads": 16,
+  "num_patch": 11,
+  "patch_len": 48,
+  "shared_embedding": true,
+  "stride": 48,
+  "target_dim": 192,
+  "transformers_version": "4.30.2"
+}

LightGTS-huggingface/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac0de5227afaac3014d45de31e650b6e277fac7c7628dd897bd49c4a6f4dad91
+size 16018929

configuration_LightGTS.py

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+from transformers import PretrainedConfig
+from typing import Optional
+import math
+
+
+class LightGTSConfig(PretrainedConfig):
+
+    model_type = "LightGTS"
+
+
+    def __init__(self, context_points:int = 512, c_in:int = 1, target_dim:int = 96, patch_len:int = 32, stride:int = 32, mask_mode:str = 'patch',mask_nums:int = 3,
+                 e_layers:int=3, d_layers:int=3, d_model=256, n_heads=16, shared_embedding=True, d_ff:int=512, 
+                 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):
+
+        self.context_points = context_points
+        self.c_in = c_in
+        self.target_dim = target_dim
+        self.patch_len = patch_len
+        self.stride = stride
+        self.num_patch = (max(self.context_points, self.patch_len)-self.patch_len) // self.stride + 1
+        self.mask_mode = mask_mode
+        self.mask_nums = mask_nums
+        self.e_layers = e_layers
+        self.d_layers = d_layers
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.shared_embedding = shared_embedding
+        self.d_ff = d_ff
+        self.dropout = dropout
+        self.attn_dropout = attn_dropout
+        self.head_dropout = head_dropout
+        self.act = act
+        self.head_type = head_type
+        self.initializer_range = 0.02
+        super().__init__(**kwargs)

modeling_LightGTS.py

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+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 = LightGTS(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, patch_len=None, stride=None, target_dim=None):
+        
+        self.config.patch_len = patch_len
+        self.config.stride = stride
+        self.config.target_dim = target_dim
+
+        #切patch
+        batch_size,seq_len,n_vars = input.shape
+        num_patch = (max(seq_len, self.config.patch_len)-self.config.patch_len) // self.config.stride + 1
+        self.config.num_patch = num_patch
+        outputs = input.view(batch_size, num_patch, self.config.patch_len, n_vars)
+        outputs = outputs.transpose(2, 3)
+        outputs = self.model(outputs, target_dim=self.config.target_dim, patch_len=self.config.patch_len, stride=self.config.stride)
+        
+       
+        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 LightGTSForFinetune(LightGTSPreTrainedModel, TSGenerationMixin):
+    def __init__(self, config: LightGTSConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = LightGTS(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, patch_len=None, stride=None, target_dim=None):
+        
+        if patch_len is not None:
+            self.config.patch_len = patch_len
+        if stride is not None:
+            self.config.stride = stride
+        if target_dim is not None:
+            self.config.target_dim = target_dim
+
+        #切patch
+        batch_size,seq_len,n_vars = input.shape
+        num_patch = (max(seq_len, self.config.patch_len)-self.config.patch_len) // self.config.stride + 1
+        self.config.num_patch = num_patch
+        outputs = input.view(batch_size, num_patch, self.config.patch_len, n_vars)
+        outputs = outputs.transpose(2, 3)
+        outputs = self.model(outputs, target_dim=self.config.target_dim, patch_len=self.config.patch_len, stride=self.config.stride)
+        
+       
+        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.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)
+    
+    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, target_dim=None, patch_len=None, stride=None):                             
+        """
+        z: tensor [bs x num_patch x n_vars x patch_len]
+        """
+
+        if target_dim is not None:
+            self.target_dim = target_dim
+        if patch_len is not None:
+            self.patch_len = patch_len
+        if stride is not None:
+            self.stride = stride
+        self.out_patch_num = math.ceil(self.target_dim / self.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]
+        # 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[:,:,:,:], self.patch_len)   
+        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 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.d_model = d_model
+
+    def forward(self, x, patch_len):
+        """
+        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, patch_len, bias=False)
+        Linear.weight.data = resample_patchemb(old=self.linear.weight.data.T, new_patch_len=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)
+
+    # 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')

test_finetune.py

@@ -0,0 +1,101 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from tqdm import tqdm 
+import os
+from configuration_LightGTS import LightGTSConfig
+from modeling_LightGTS import LightGTSForFinetune
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, MODEL_MAPPING
+from transformers import AutoConfig
+os.environ['CUDA_VISIBLE_DEVICES']='6'
+class TimeSeriesDataset(Dataset):
+    def __init__(self, df, target_col="HUFL", lookback=528, pred_len=192):
+        self.lookback = lookback
+        self.pred_len = pred_len
+        
+        # 提取目标列
+        data = df[[target_col]].values
+        
+        # 强烈建议在微调时进行标准化
+        self.scaler = StandardScaler()
+        self.data = self.scaler.fit_transform(data)
+        
+    def __len__(self):
+        # 确保有足够的长度来切分一段 lookback + pred_len
+        return len(self.data) - self.lookback - self.pred_len + 1
+        
+    def __getitem__(self, idx):
+        # 获取输入窗口和目标预测窗口
+        x = self.data[idx : idx + self.lookback]
+        y = self.data[idx + self.lookback : idx + self.lookback + self.pred_len]
+        
+        return {
+            "input": torch.tensor(x, dtype=torch.float32), 
+            "labels": torch.tensor(y, dtype=torch.float32)
+        }
+
+if __name__=="__main__":
+    print("Loading data...")
+    df1 = pd.read_csv("/home/wlf/LightGTS/LightGTS/data/predict_datasets/ETTh1.csv")
+
+    lookback_length = 528 
+    target_length = 240
+
+    train_dataset = TimeSeriesDataset(df1, target_col="HUFL", lookback=lookback_length, pred_len=target_length)
+    train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)
+
+
+    print("Initializing model...")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    LightGTS_config = LightGTSConfig(context_points=528, c_in=1, target_dim=192, patch_len=48, stride=48)
+    LightGTS_config.save_pretrained("LightGTS-huggingface")
+
+    AutoConfig.register("LightGTS",LightGTSConfig)
+    AutoModelForCausalLM.register(LightGTSConfig, LightGTSForFinetune)
+
+    model = AutoModelForCausalLM.from_pretrained(
+        "./LightGTS-huggingface",
+        trust_remote_code=True
+    )
+
+    model.loss_fn = nn.MSELoss() 
+    model.to(device)
+    optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-2)
+
+    num_epochs = 1
+    print(f"Starting training on {device} for {num_epochs} epochs...")
+
+    for epoch in range(num_epochs):
+        model.train()
+        total_loss = 0.0
+        
+        progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch+1}/{num_epochs}")
+        
+        for batch in progress_bar:
+        
+            inputs = batch["input"].to(device)
+            labels = batch["labels"].to(device)
+            
+            optimizer.zero_grad()
+            
+            outputs = model(input=inputs, labels=labels, target_dim = 240, patch_len = 24, stride = 24)
+            loss = outputs["loss"]
+            
+            loss.backward()
+            optimizer.step()
+            
+            total_loss += loss.item()
+            
+            progress_bar.set_postfix({"loss": f"{loss.item():.4f}"})
+            
+        avg_loss = total_loss / len(train_dataloader)
+        print(f"Epoch [{epoch+1}/{num_epochs}] completed. Average Loss: {avg_loss:.4f}\n")
+
+    model.save_pretrained("./LightGTS-finetuned")
+    print("Training complete!")

test_zero-shot.py

@@ -0,0 +1,46 @@
+import argparse
+import torch
+import numpy as np
+import pandas as pd
+import os
+import random
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+from torch.utils.data import DataLoader
+from sklearn.preprocessing import StandardScaler
+from configuration_LightGTS import LightGTSConfig
+from modeling_LightGTS import LightGTSForPrediction
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, MODEL_MAPPING
+from transformers import AutoConfig
+
+if __name__ == "__main__":
+    LightGTS_config = LightGTSConfig(context_points=528, c_in=1, target_dim=192, patch_len=48, stride=48)
+    LightGTS_config.save_pretrained("LightGTS-huggingface")
+
+    AutoConfig.register("LightGTS",LightGTSConfig)
+    AutoModelForCausalLM.register(LightGTSConfig, LightGTSForPrediction)
+
+    model = AutoModelForCausalLM.from_pretrained(
+        "./LightGTS-huggingface",
+        trust_remote_code=True
+    )
+    df1 = pd.read_csv("/home/wlf/LightGTS/LightGTS/data/predict_datasets/ETTh1.csv")
+    df2 = pd.read_csv("/home/wlf/LightGTS/LightGTS/data/predict_datasets/ETTh2.csv")
+    print(df1,df2)
+
+    start = 300
+    lookback_length = 576 
+    lookback = torch.tensor(df1["HUFL"][start:start+lookback_length].values).unsqueeze(0).unsqueeze(-1).float()
+    all_length = 768
+    all = torch.tensor(df1["HUFL"][start:start+all_length].values).unsqueeze(0).unsqueeze(-1).float()
+
+    lookback2 = torch.tensor(df2["OT"][start:start+lookback_length].values).unsqueeze(0).unsqueeze(-1).float()
+    all2 = torch.tensor(df2["OT"][start:start+all_length].values).unsqueeze(0).unsqueeze(-1).float()
+    print(lookback.shape)
+
+        # zero-shot sample
+    outputs = model.generate(lookback, patch_len = 48, stride_len=48, max_output_length=192)
+    outputs2 = model.generate(lookback2, patch_len = 32, stride_len=32, max_output_length=192)
+    print(outputs2.shape)

ts_generation_mixin.py

@@ -0,0 +1,73 @@
+
+from typing import Any, Dict, List, Optional, Union, Callable
+import torch
+from transformers import GenerationMixin, LogitsProcessorList, StoppingCriteriaList
+from transformers.generation.utils import GenerationConfig, GenerateOutput
+from transformers.utils import ModelOutput
+
+
+class TSGenerationMixin(GenerationMixin):
+    @torch.no_grad()
+    def generate(self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+        synced_gpus: Optional[bool] = None,
+        assistant_model: Optional["PreTrainedModel"] = None,
+        streamer: Optional["BaseStreamer"] = None,
+        negative_prompt_ids: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        revin: Optional[bool] = True,
+        patch_len:Optional[int] = None,
+        stride_len:Optional[int]= None,
+        max_output_length:Optional[int] = None,
+        inference_patch_len: Optional[int] = None,
+
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.Tensor]:
+        
+
+        if len(inputs.shape) != 3:
+            raise ValueError('Input shape must be: [batch_size, seq_len, n_vars]')
+        
+        if revin:
+            means = inputs.mean(dim=1, keepdim=True)
+            stdev = inputs.std(dim=1, keepdim=True, unbiased=False) + 1e-5
+            inputs = (inputs - means) / stdev
+
+        
+
+        model_inputs = {
+            "input" : inputs,
+            "patch_len" : patch_len,
+            "stride" : stride_len,
+            "target_dim" : max_output_length
+        }
+
+
+        outputs = self(**model_inputs) #[batch_size,target_dim,n_vars]
+
+        outputs = outputs["prediction"]
+
+
+        if revin:
+
+            outputs = (outputs * stdev) + means
+
+        return outputs
+    
+    
+    def _update_model_kwargs_for_generation(
+            self,
+            outputs: ModelOutput,
+            model_kwargs: Dict[str, Any],
+            horizon_length: int = 1,
+            is_encoder_decoder: bool = False,
+            standardize_cache_format: bool = False,
+    ) -> Dict[str, Any]:
+        
+        return model_kwargs
+    
+