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## To use these checkpoints, you need to use the following model structure for Transformer
### Import used packages
```python
import math
import torch
from torch import nn
```
### PositionalEncoding
```python
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000) -> None:
super().__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(
1
) # (max_len, 1)
div_term = torch.exp(
torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
)
pe[:, 0::2] = torch.sin(position * div_term) # (max_len, d_model // 2)
truncated_div_term = div_term[: d_model // 2]
pe[:, 1::2] = torch.cos(position * truncated_div_term) #
pe = pe.unsqueeze(0).transpose(0, 1) # (max_len, 1, d_model)
self.register_buffer("pe", pe)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.pe[: x.size(0), :, :]
return self.dropout(x)
```
### RPBClass
```python
class RelativePositionBiasV2(nn.Module):
def __init__(self, n_heads, num_buckets=32, max_distance=128, bidirectional=True):
super().__init__()
assert num_buckets % 2 == 0, "num_buckets should be even for bidirectional"
self.n_heads = n_heads
self.num_buckets = num_buckets
self.max_distance = max_distance
self.bidirectional = bidirectional
self.emb = nn.Embedding(num_buckets, n_heads)
def _relative_position_bucket(self, relative_position):
"""
relative_position: [Tq, Tk] = k - q
returns bucket ids in [0, num_buckets-1]
"""
num_buckets = self.num_buckets
max_distance = self.max_distance
ret = torch.zeros_like(relative_position, dtype=torch.long)
n = -relative_position # want smaller buckets for n > 0 (keys before queries)
if self.bidirectional:
half = num_buckets // 2
ret += (n < 0).long() * half
n = n.abs()
num_buckets = half # remaining buckets for non-negative distances
else:
n = torch.clamp(n, min=0)
# Now n >= 0
max_exact = num_buckets // 2
is_small = n < max_exact
# Avoid log(0) and division by zero; also ensure max_distance > max_exact
denom = max(1.0, math.log(max(max_distance, max_exact + 1) / max(1, max_exact)))
val_if_large = (
max_exact
+ (
(torch.log(n.float() / max(1, max_exact) + 1e-6) / denom)
* (num_buckets - max_exact)
).long()
)
val_if_large = torch.clamp(val_if_large, max=num_buckets - 1)
ret += torch.where(is_small, n.long(), val_if_large)
# Final clamp for absolute safety when bidirectional half-split was applied
return torch.clamp(ret, min=0, max=self.num_buckets - 1)
def forward(self, Tq, Tk, device=None):
device = device or torch.device("cpu")
qpos = torch.arange(Tq, device=device)[:, None]
kpos = torch.arange(Tk, device=device)[None, :]
buckets = self._relative_position_bucket(kpos - qpos) # [Tq, Tk]
bias = self.emb(buckets) # [Tq, Tk, H]
return bias.permute(2, 0, 1) # [H, Tq, Tk]
```
### Transformer Base Class
```python
class BaseTransformerComp(nn.Module):
"""Base class for transformer-based intra-stock components."""
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_layers: int,
num_heads: int,
dropout: float = 0.1,
mask_type: str = "none",
) -> None:
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.num_heads = num_heads
self.dropout_rate = dropout
self.mask_type = mask_type
def _reshape_input(self, x: torch.Tensor) -> tuple[torch.Tensor, int, int]:
"""
Reshape input from [batch, seq_len, n_stocks, n_feats] to [seq_len, batch*n_stocks, n_feats].
Returns reshaped tensor and original batch/n_stocks sizes for later reconstruction.
"""
batch, seq_len, n_stocks, n_feats = x.shape
if batch == 0 or seq_len == 0 or n_stocks == 0:
raise ValueError(
f"Invalid input dimensions: batch={batch}, seq_len={seq_len}, "
f"n_stocks={n_stocks}, n_feats={n_feats}"
)
x = x.permute(0, 2, 1, 3).contiguous()
x = x.reshape(batch * n_stocks, seq_len, n_feats) # [b * s, t, f]
x = x.permute(1, 0, 2).contiguous() # [t, b * s, f]
return x, batch, n_stocks
def _reshape_output(
self, x: torch.Tensor, batch: int, n_stocks: int
) -> torch.Tensor:
"""Reshape output from [seq_len, batch*n_stocks, hidden_dim] to [batch, n_stocks, hidden_dim]."""
output = x[-1] # Take last time step: [b * s, hidden_dim]
output = output.reshape(batch, n_stocks, -1) # [b, s, hidden_dim]
return output
def _generate_causal_mask(self, seq_len: int, device: torch.device) -> torch.Tensor:
"""Generate causal attention mask."""
mask = torch.triu(
torch.ones(seq_len, seq_len, device=device) * float("-inf"), diagonal=1
)
return mask
```
### Transformer Encoder Layer with RPB
```python
class TransformerEncoderLayerWithRPB(nn.Module):
def __init__(
self,
d_model: int,
nhead: int,
dim_feedforward: int,
dropout: float,
rbp,
):
super().__init__()
self.d_model = d_model
self.nhead = nhead
self.rbp = rbp
# QKV projections
self.qkv_proj = nn.Linear(d_model, 3 * d_model)
self.out_proj = nn.Linear(d_model, d_model)
# FFN layers
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
# Normalization and dropout
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = F.relu
def forward(
self,
src: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None,
is_causal: bool = False,
) -> torch.Tensor:
seq_len, batch_size, d_model = src.shape
head_dim = d_model // self.nhead
qkv = self.qkv_proj(src)
q, k, v = qkv.chunk(3, dim=-1)
q = q.reshape(seq_len, batch_size, self.nhead, head_dim).permute(1, 2, 0, 3)
k = k.reshape(seq_len, batch_size, self.nhead, head_dim).permute(1, 2, 0, 3)
v = v.reshape(seq_len, batch_size, self.nhead, head_dim).permute(1, 2, 0, 3)
attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(head_dim)
# Add RBP after QK^T
rbp_bias = self.rbp(
seq_len, seq_len, device=src.device
) # [nhead, seq_len, seq_len]
attn_weights = attn_weights + rbp_bias.unsqueeze(
0
) # [batch, nhead, seq_len, seq_len]
if src_mask is not None:
attn_weights = attn_weights + src_mask.unsqueeze(0).unsqueeze(0)
if src_key_padding_mask is not None:
attn_weights = attn_weights.masked_fill(
src_key_padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")
)
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = self.dropout1(attn_weights)
attn_output = torch.matmul(attn_weights, v) # [batch, nhead, seq_len, head_dim]
attn_output = attn_output.permute(2, 0, 1, 3).reshape(
seq_len, batch_size, d_model
)
attn_output = self.out_proj(attn_output)
src2 = src + self.dropout1(attn_output)
src2 = self.norm1(src2)
ffn_output = self.linear2(self.dropout(self.activation(self.linear1(src2))))
src3 = src2 + self.dropout2(ffn_output)
src3 = self.norm2(src3)
return src3
```
### RPB Components
```python
class TransformerRPBComp(BaseTransformerComp):
"""TransformerComp with Relative Bias Pooling."""
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_layers: int,
num_heads: int,
dropout: float = 0.1,
mask_type: str = "none",
) -> None:
super().__init__(input_dim, hidden_dim, num_layers, num_heads, dropout)
self.feature_layer = nn.Linear(input_dim, hidden_dim)
self.pe = PositionalEncoding(hidden_dim, dropout)
self.encoder_norm = nn.LayerNorm(hidden_dim)
self.mask_type = mask_type
self.rbp = RelativePositionBiasV2(n_heads=num_heads)
self.encoder_layers = nn.ModuleList(
[
TransformerEncoderLayerWithRPB(
d_model=hidden_dim,
nhead=num_heads,
dim_feedforward=hidden_dim * 4,
dropout=dropout,
rbp=self.rbp,
)
for _ in range(num_layers)
]
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""x.shape [batch, seq_len, n_stocks, n_feats]"""
x, batch, n_stocks = self._reshape_input(x)
seq_len = x.shape[0]
x = self.encoder_norm(self.pe(self.feature_layer(x))) # [t, b * s, d_model]
if self.mask_type == "causal":
mask = self._generate_causal_mask(seq_len, x.device).permute(1, 0)
else:
mask = None
for layer in self.encoder_layers:
x = layer(x, src_mask=mask)
return self._reshape_output(x, batch, n_stocks)
```
### Transformer Module
```python
class Transformer(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int = 1,
hidden_dim: int = 256,
num_layers: int = 2,
num_heads: int = 4,
dropout: float = 0.1,
tfm_type: str = "base",
mask_type: str = "none",
) -> None:
"""
tfm_type: "base", "rope", "rpb"
mask_type: "none", "alibi", "causal"
"""
super().__init__()
self.tfm_type = tfm_type
self.mask_type = mask_type
tfm_type_mapper = {
"base": TransformerComp,
"alibi": TransformerComp,
"rope": TransformerRoPEComp,
"rpb": TransformerRPBComp,
}
self.transformer_encoder = tfm_type_mapper[self.tfm_type](
input_dim=input_dim,
hidden_dim=hidden_dim,
num_layers=num_layers,
num_heads=num_heads,
dropout=dropout,
mask_type=mask_type,
)
self.fc_out = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim, bias=True),
nn.GELU(),
nn.Linear(hidden_dim, output_dim, bias=True),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
tfm_out = self.transformer_encoder(x) # [b, s, d_model]
final_out = self.fc_out(tfm_out).squeeze(-1) # [b, s]
return final_out
```
### Model Configuration
```yaml
input_dim: 8,
output_dim: 1,
hidden_dim: 64,
num_layers: 2,
num_heads: 4,
dropout: 0.0,
tfm_type: "rpb",
mask_type: "causal",
``` |