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Scripts for encoding time series into TS-MLLM Format.
"""
from einops import rearrange
import numpy as np
from typing import *
from jaxtyping import Float, Int
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Any, Tuple, Optional
class RMSNorm(nn.Module):
"""Root Mean Square Normalization layer."""
def __init__(self, size: int, dim: int = -1, eps: float = 1e-5) -> None:
super().__init__()
self.scale = nn.Parameter(torch.ones(size))
self.eps = eps
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm_x = x.to(torch.float32).pow(2).mean(dim=self.dim, keepdim=True)
x_normed = x * torch.rsqrt(norm_x + self.eps)
return (self.scale * x_normed).type_as(x)
class RotaryEmbedding(nn.Module):
"""Rotary Positional Embedding for injecting positional information."""
def __init__(self, dim):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
def forward(self, seq_len):
t = torch.arange(seq_len, device=self.inv_freq.device).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
return freqs # Shape: (seq_len, dim // 2)
class BinaryAttentionBias(nn.Module):
"""Binary Variate Attention for time series data."""
def __init__(self,
num_heads: Int):
super().__init__()
self.num_heads = num_heads
self.emd = nn.Embedding(2, num_heads)
def forward(self,
query_id: Int[torch.Tensor, "batch_size q_len"],
kv_id: Int[torch.Tensor, "batch_size kv_len"],
) -> Float[torch.Tensor, "batch_size num_heads q_len kv_len"]:
ind = torch.eq(query_id.unsqueeze(-1), kv_id.unsqueeze(-2))
ind = ind.unsqueeze(1) # (batch_size, 1, q_len, kv_len)
weight = rearrange(self.emd.weight, "two num_heads -> two num_heads 1 1") # (2, num_heads, 1, 1)
bias = ~ind * weight[:1] + ind * weight[1:] # (batch_size, num_heads, q_len, kv_len)
return bias
class LlamaMLP(nn.Module):
def __init__(self, d_model, dim_feedforward=2048):
super().__init__()
self.hidden_size = d_model
self.intermediate_size = dim_feedforward
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=True)
self.act_fn = F.gelu
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
class CustomTransformerEncoder(nn.Module):
"""Stack of Transformer Encoder Layers."""
def __init__(self, d_model, nhead, dim_feedforward, dropout, activation, num_layers, num_features):
super().__init__()
self.layers = nn.ModuleList([
TransformerEncoderLayerWithRoPE(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
activation=activation,
num_features=num_features
) for _ in range(num_layers)
])
def forward(self, src, freqs, src_id=None, attn_mask=None):
output = src
for layer in self.layers:
output = layer(output, freqs, src_id, attn_mask=attn_mask)
return output
class TransformerEncoderLayerWithRoPE(nn.Module):
"""Transformer Encoder Layer with RoPE and RMSNorm."""
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", num_features=1):
super().__init__()
self.self_attn = MultiheadAttentionWithRoPE(d_model, nhead, num_features)
self.dropout = nn.Dropout(dropout)
self.input_norm = RMSNorm(d_model)
self.output_norm = RMSNorm(d_model)
self.mlp = LlamaMLP(d_model, dim_feedforward)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
def forward(self, src, freqs, src_id=None, attn_mask=None):
residual = src
src = self.input_norm(src)
src = self.self_attn(src, src, src, freqs, src_id, src_id, attn_mask=attn_mask)
src = src + residual
residual = src
src = self.output_norm(src)
src = self.mlp(src)
src = residual + self.dropout2(src)
return src
class MultiheadAttentionWithRoPE(nn.Module):
"""Multi-head Attention with Rotary Positional Encoding (RoPE), non-causal by default."""
"========== NOtice that this applies BinaryAttentionBias ==========="
def __init__(self, embed_dim, num_heads, num_features):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.num_features = num_features
assert self.head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"
# Linear projections for Q, K, V, and output
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False)
# Binary attention bias for time series
if num_features > 1:
self.binary_attention_bias = BinaryAttentionBias(num_heads)
def apply_rope(self, x, freqs):
"""Apply Rotary Positional Encoding to the input tensor."""
B, seq_len, embed_dim = x.shape
assert embed_dim == self.embed_dim, "Embedding dimension mismatch"
assert freqs.shape == (seq_len, embed_dim // 2), "freqs shape mismatch"
# Reshape for rotation: split embed_dim into pairs
x_ = x.view(B, seq_len, embed_dim // 2, 2)
cos = freqs.cos().unsqueeze(0) # (1, seq_len, embed_dim // 2, 1)
sin = freqs.sin().unsqueeze(0) # (1, seq_len, embed_dim // 2, 1)
# Apply rotation to each pair
x_rot = torch.stack(
[
x_[..., 0] * cos - x_[..., 1] * sin,
x_[..., 0] * sin + x_[..., 1] * cos,
],
dim=-1
)
return x_rot.view(B, seq_len, embed_dim)
def forward(self, query, key, value, freqs, query_id=None, kv_id=None, attn_mask=None):
"""
Forward pass for multi-head attention with RoPE.
Args:
query (Tensor): Shape (B, T, C)
key (Tensor): Shape (B, T, C)
value (Tensor): Shape (B, T, C)
freqs (Tensor): RoPE frequencies, shape (T, embed_dim // 2)
query_id (Tensor, optional): Shape (B, q_len), feature IDs for query
kv_id (Tensor, optional): Shape (B, kv_len), feature IDs for key/value
attn_mask (Tensor, optional): Shape (B, T), True for valid positions, False for padding.
Returns:
Tensor: Attention output, shape (B, T, C)
"""
B, T, C = query.shape
assert key.shape == (B, T, C) and value.shape == (B, T, C), "query, key, value shapes must match"
# Project inputs to Q, K, V
Q = self.q_proj(query)
K = self.k_proj(key)
V = self.v_proj(value)
# Apply RoPE to Q and K
Q_rot = self.apply_rope(Q, freqs)
K_rot = self.apply_rope(K, freqs)
# Reshape for multi-head attention
Q_rot = Q_rot.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B, nh, T, hs)
K_rot = K_rot.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B, nh, T, hs)
V = V.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B, nh, T, hs)
# Prepare attention mask for padding
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(1).unsqueeze(2) # (B, 1, 1, T)
else:
attn_mask = None
if query_id is not None and kv_id is not None:
# Add binary attention bias
attn_bias = self.binary_attention_bias(query_id, kv_id) # (B, num_heads, q_len, kv_len)
scores = torch.matmul(Q_rot, K_rot.transpose(-2, -1)) / math.sqrt(
self.head_dim) # (B, num_heads, q_len, kv_len)
scores += attn_bias
if attn_mask is not None:
scores = scores.masked_fill(~attn_mask, float('-inf'))
attn_weights = F.softmax(scores, dim=-1) # (B, num_heads, q_len, kv_len)
y = torch.matmul(attn_weights, V) # (B, num_heads, q_len, hs)
else:
# Compute scaled dot-product attention (non-causal) without binary bias
# for param in self.binary_attention_bias.parameters():
# param.requires_grad = False
y = F.scaled_dot_product_attention(
Q_rot, K_rot, V,
attn_mask=attn_mask,
is_causal=False # Non-causal attention for encoder
) # (B, nh, T, hs)
# Reshape and project output
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.out_proj(y)
return y
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