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ddd3436 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Any, Tuple, Optional
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VisionTransformerPretrainedModel,Qwen2_5_VLVisionBlock, Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLPreTrainedModel, Qwen2_5_VLCausalLMOutputWithPast
from transformers import AutoProcessor
from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
from model.TS_encoder.encoding_utils import CustomTransformerEncoder, RotaryEmbedding
from dataclasses import dataclass, field
from typing import Dict, Optional
@dataclass
class TimeSeriesConfig:
"""Configuration for time series encoder.
Attributes:
d_model: Dimension of model hidden states.
d_proj: Dimension of projection layer.
patch_size: Size of time series patches.
num_layers: Number of transformer layers.
num_heads: Number of attention heads.
d_ff_dropout: Dropout rate for feed-forward networks.
use_rope: Whether to use Rotary Position Embedding.
activation: Activation function name.
num_features: Number of input features.
"""
d_model: int = 512
d_proj: int = 256
patch_size: int = 14
num_query_tokens: int = 1
num_layers: int = 8
num_heads: int = 8
d_ff_dropout: float = 0.1
use_rope: bool = True
activation: str = "gelu"
num_features: int = 1
class TimeSeriesEncoder(nn.Module):
"""
Time Series Encoder with PatchTST-like patching, RoPE.
Args:
d_model (int): Model dimension
d_proj (int): Projection dimension
patch_size (int): Size of each patch
num_layers (int): Number of encoder layers
num_heads (int): Number of attention heads
d_ff_dropout (float): Dropout rate
max_total_tokens (int): Maximum sequence length
use_rope (bool): Use RoPE if True
num_features (int): Number of features in the time series
activation (str): "relu" or "gelu"
Inputs:
time_series (Tensor): Shape (batch_size, seq_len, num_features)
mask (Tensor): Shape (batch_size, seq_len)
Outputs:
local_embeddings (Tensor): Shape (batch_size, seq_len, num_features, d_proj)
"""
def __init__(self,d_model=512, d_proj=256, patch_size=14, num_layers=8, num_heads=8,
d_ff_dropout=0.1, max_total_tokens=8192, use_rope=True, num_features=1, #use_mv=False,
activation="gelu",**kwargs):
super().__init__()
self.patch_size = patch_size
self.d_model = d_model
self.d_proj = d_proj
self.num_layers = num_layers
self.num_heads = num_heads
self.d_ff_dropout = d_ff_dropout
self.max_total_tokens = max_total_tokens
self.use_rope = use_rope
self.num_features = num_features
self.activation = activation
# Patch embedding layer
self.embedding_layer = nn.Linear(patch_size, d_model)
if use_rope:
# Initialize RoPE and custom encoder
self.rope_embedder = RotaryEmbedding(d_model)
self.transformer_encoder = CustomTransformerEncoder(
d_model=d_model,
nhead=num_heads,
dim_feedforward=d_model * 4,
dropout=d_ff_dropout,
activation=activation,
num_layers=num_layers,
# use_mv=use_mv,
num_features=num_features
)
else:
# Standard encoder without RoPE
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=num_heads,
dim_feedforward=d_model * 4,
dropout=d_ff_dropout,
batch_first=True,
activation=activation
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
# Output projection layers
self.projection_layer = nn.Linear(d_model, patch_size * d_proj)
self._init_parameters()
def _init_parameters(self):
for name, param in self.named_parameters():
if 'weight' in name and 'linear' in name:
if self.activation == "relu":
nn.init.kaiming_uniform_(param, nonlinearity='relu')
elif self.activation == "gelu":
nn.init.kaiming_uniform_(param, nonlinearity='gelu')
elif 'bias' in name:
nn.init.constant_(param, 0.0)
def forward(self, time_series, mask):
"""Forward pass to generate local embeddings."""
if time_series.dim() == 2:
time_series = time_series.unsqueeze(-1)
device = time_series.device
B, seq_len, num_features = time_series.size()
assert num_features == self.num_features, f"Number of features mismatch with data: {num_features} vs param: {self.num_features}"
assert mask.size() == (B, seq_len), f"Mask shape mismatch: {mask.size()} vs {(B, seq_len)}"
# Pad sequence to be divisible by patch_size
padded_length = math.ceil(seq_len / self.patch_size) * self.patch_size
if padded_length > seq_len:
pad_amount = padded_length - seq_len
time_series = F.pad(time_series, (0, 0, 0, pad_amount), value=0)
mask = F.pad(mask, (0, pad_amount), value=0)
# Convert to patches
num_patches = padded_length // self.patch_size
total_length = num_patches * num_features
patches = time_series.view(B, num_patches, self.patch_size, num_features)
patches = patches.permute(0, 3, 1, 2).contiguous() # (B, num_features, num_patches, patch_size)
patches = patches.view(B, num_features * num_patches, self.patch_size) # (B, L, patch_size)
# Create feature IDs for patches
feature_id = torch.arange(num_features, device=device).repeat_interleave(
num_patches) # (num_features * num_patches = L,)
feature_id = feature_id.unsqueeze(0).expand(B, -1) # (B, L)
# Embed patches
embedded_patches = self.embedding_layer(patches) # (B, L, d_model)
# Create patch-level mask
mask = mask.view(B, num_patches, self.patch_size)
patch_mask = mask.sum(dim=-1) > 0 # (B, num_patches)
full_mask = patch_mask.unsqueeze(1).expand(-1, num_features, -1) # (B, num_features, num_patches)
full_mask = full_mask.reshape(B, num_features * num_patches) # (B, L)
# Generate RoPE frequencies if applicable
if self.use_rope:
freqs = self.rope_embedder(total_length).to(device)
else:
freqs = None
# Encode sequence
if num_features > 1:
output = self.transformer_encoder(
embedded_patches,
freqs=freqs,
src_id=feature_id,
attn_mask=full_mask
)
else:
output = self.transformer_encoder(
embedded_patches,
freqs=freqs,
attn_mask=full_mask
)
# Extract and project local embeddings
patch_embeddings = output # (B, L, d_model)
patch_proj = self.projection_layer(patch_embeddings) # (B, L, patch_size * d_proj)
local_embeddings = patch_proj.view(B, num_features, num_patches, self.patch_size, self.d_proj)
local_embeddings = local_embeddings.permute(0, 2, 3, 1, 4) # (B, num_patches, patch_size, num_features, d_proj)
local_embeddings = local_embeddings.view(B, -1, num_features, self.d_proj)[:, :seq_len, :, :] # (B, seq_len, num_features, d_proj)
return patch_embeddings,local_embeddings,full_mask
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