File size: 2,485 Bytes
0ec2598 | 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 | import math
import torch
import torch.nn as nn
# ============================================================================
# ActionFFNEncoder
# ============================================================================
class ActionFFNEncoder(nn.Module):
"""MLP encoder: (B, T, action_dim) -> (B, T, embed_dim).
Output tokens replace the text context in DiT cross-attention.
"""
def __init__(
self,
action_dim: int,
embed_dim: int,
num_layers: int = 2,
max_timesteps: int = 16,
):
super().__init__()
layers = [nn.Linear(action_dim, embed_dim), nn.GELU()]
for _ in range(max(0, num_layers - 2)):
layers += [nn.Linear(embed_dim, embed_dim), nn.GELU()]
self.mlp = nn.Sequential(*layers)
self.norm = nn.LayerNorm(embed_dim)
pe = self._sinusoidal_pe(max_timesteps, embed_dim)
self.temporal_pe = nn.Parameter(pe) # (max_timesteps, embed_dim)
@staticmethod
def _sinusoidal_pe(length: int, dim: int) -> torch.Tensor:
pos = torch.arange(length).unsqueeze(1).float()
div = torch.exp(
torch.arange(0, dim, 2).float() * (-math.log(10000.0) / dim)
)
pe = torch.zeros(length, dim)
pe[:, 0::2] = torch.sin(pos * div)
pe[:, 1::2] = torch.cos(pos * div)
return pe
def forward(self, actions: torch.Tensor) -> torch.Tensor:
"""
Args:
actions: (B, T, action_dim)
Returns:
(B, T, embed_dim)
"""
x = self.mlp(actions)
T = actions.shape[1]
x = x + self.temporal_pe[:T].to(
dtype=x.dtype,
device=x.device,
)
return self.norm(x)
# ============================================================================
# Main
# ============================================================================
def main():
# Hyperparameters
batch_size = 2
timesteps = 16
action_dim = 7
embed_dim = 512
# Random input
actions = torch.randn(batch_size, timesteps, action_dim)
# Model
model = ActionFFNEncoder(
action_dim=action_dim,
embed_dim=embed_dim,
num_layers=2,
max_timesteps=32,
)
# Forward
outputs = model(actions)
print(f"Input shape : {actions.shape}")
print(f"Output shape: {outputs.shape}")
print(f"Output dtype: {outputs.dtype}")
if __name__ == "__main__":
main() |