Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,024 Bytes
246c106 |
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 |
from torch import nn, Tensor
from einops import rearrange
import torch
from genie.attention import SelfAttention
import numpy as np
from typing import Optional
class Mlp(nn.Module):
def __init__(
self,
d_model: int,
mlp_ratio: float = 4.0,
mlp_bias: bool = True,
mlp_drop: float = 0.0,
) -> None:
super().__init__()
hidden_dim = int(d_model * mlp_ratio)
self.fc1 = nn.Linear(d_model, hidden_dim, bias=mlp_bias)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_dim, d_model, bias=mlp_bias)
self.drop = nn.Dropout(mlp_drop)
def forward(self, x: Tensor) -> Tensor:
x = self.drop(self.act(self.fc1(x)))
x = self.drop(self.fc2(x))
return x
class STBlock(nn.Module):
# See Figure 4 of https://arxiv.org/pdf/2402.15391.pdf
def __init__(
self,
num_heads: int,
d_model: int,
qkv_bias: bool = False,
proj_bias: bool = True,
qk_norm: bool = True,
use_mup: bool = True,
attn_drop: float = 0.05, # add dropout
mlp_ratio: float = 4.0,
mlp_bias: bool = True,
mlp_drop: float = 0.05,
# action relevant
action_processing: str = "mlp",
jointly_predict_actions: bool = False,
mask_token_id: int = 0
) -> None:
super().__init__()
self.norm1 = nn.Identity() if qk_norm else nn.LayerNorm(d_model, eps=1e-05)
# sequence dim is over each frame's 16x16 patch tokens
self.spatial_attn = SelfAttention(
num_heads=num_heads,
d_model=d_model,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
qk_norm=qk_norm,
use_mup=use_mup,
attn_drop=attn_drop,
)
# sequence dim is over time sequence (16)
self.temporal_attn = SelfAttention(
num_heads=num_heads,
d_model=d_model,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
qk_norm=qk_norm,
use_mup=use_mup,
attn_drop=attn_drop,
)
self.action_prediction = jointly_predict_actions
self.action_processing = action_processing
self.norm2 = nn.Identity() if qk_norm else nn.LayerNorm(d_model, eps=1e-05)
self.mlp = Mlp(d_model=d_model, mlp_ratio=mlp_ratio, mlp_bias=mlp_bias, mlp_drop=mlp_drop)
self.action_projectors = None # set at run-time
def forward(self, x_TSC: Tensor, action_ids: Tensor = None, domain = None) -> Tensor:
"""
The main forward pass of the STBlock. It does action conditioning (with options),
(bidrectional) spatial attention, (causal) temporal attention, and action masking.
"""
T, S = x_TSC.size(1), x_TSC.size(2)
x_SC = rearrange(x_TSC, 'B T S C -> (B T) S C')
x_SC = x_SC + self.spatial_attn(self.norm1(x_SC))
# Process attention temporally
x_TC = rearrange(x_SC, '(B T) S C -> (B S) T C', T=T)
if action_ids is not None and domain is not None and self.action_projectors is not None:
# action_ids: [B, T, D]. Only apply to video parts
if "mlp" in self.action_processing:
action_ids = self.action_projectors[domain](action_ids) # does not depend on x_TC
x_TC = rearrange(x_TC, '(B S) T C -> B S T C', S=S)
x_TC = x_TC + action_ids[:, None, :x_TC.shape[2]] # expand across spatial
x_TC = rearrange(x_TC, 'B S T C -> (B S) T C', S=S)
elif "cross_attention" in self.action_processing:
x_TC = x_TC + self.action_projectors[domain](x_TC, action_ids, action_ids)
elif "modulate" in self.action_processing:
try:
x_TC = x_TC + self.action_projectors[domain](x_TC, action_ids)
except:
import IPython; IPython.embed()
# Apply the Causal Transformer
x_TC = x_TC + self.temporal_attn(x_TC, causal=True) # [256, 16, 256]
x_TC = x_TC + self.mlp(self.norm2(x_TC))
x_TSC = rearrange(x_TC, '(B S) T C -> B T S C', S=S)
return x_TSC
class STTransformerDecoder(nn.Module):
def __init__(
self,
num_layers: int,
num_heads: int,
d_model: int,
qkv_bias: bool = False,
proj_bias: bool = True,
qk_norm: bool = True,
use_mup: bool = True,
attn_drop: float = 0.0,
mlp_ratio: float = 4.0,
mlp_bias: bool = True,
mlp_drop: float = 0.0,
# action relevant
action_processing: str = "mlp",
jointly_predict_actions: bool = False,
random_dummy_action: bool = True,
mask_token_id: int = 0,
):
super().__init__()
self.layers = nn.ModuleList([STBlock(
num_heads=num_heads,
d_model=d_model,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
qk_norm=qk_norm,
use_mup=use_mup,
attn_drop=attn_drop,
mlp_ratio=mlp_ratio,
mlp_bias=mlp_bias,
mlp_drop=mlp_drop,
action_processing=action_processing,
jointly_predict_actions=jointly_predict_actions,
mask_token_id=mask_token_id
) for _ in range(num_layers)])
self.apply(self._init_weights)
def _init_weights(self, m):
"""
Weight initialization for transformer
"""
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=0.1)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, tgt, action_ids=None, domain=""):
x = tgt
for layer in self.layers:
x = layer(x, action_ids=action_ids, domain=domain)
return x
|