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f9b94bf | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 | """Hugging Face model implementation for MiniCPM-RobotTrack."""
from contextlib import contextmanager
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput
from .configuration_minicpm import MiniCPMConfig
from .configuration_robottrack import MiniCPMRobotTrackConfig
from .modeling_minicpm import MiniCPMModel
@contextmanager
def _default_dtype(dtype: torch.dtype):
previous = torch.get_default_dtype()
torch.set_default_dtype(dtype)
try:
yield
finally:
torch.set_default_dtype(previous)
def _dtype_from_name(name: str) -> torch.dtype:
try:
dtype = getattr(torch, name)
except AttributeError as exc:
raise ValueError(f"unsupported backbone_dtype={name!r}") from exc
if not isinstance(dtype, torch.dtype) or not dtype.is_floating_point:
raise ValueError(f"backbone_dtype must name a floating-point torch dtype: {name!r}")
return dtype
def _module_dtype(module: nn.Module) -> torch.dtype:
try:
return next(module.parameters()).dtype
except StopIteration:
return torch.float32
class VisionProjector(nn.Module):
"""Map concatenated DINOv3 and SigLIP features into MiniCPM space."""
def __init__(self, input_dim: int, hidden_dim: int) -> None:
super().__init__()
self.layers = nn.Sequential(
nn.LayerNorm(input_dim),
nn.Linear(input_dim, hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, hidden_dim),
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
return self.layers(features)
class TemporalMarkerEncoder(nn.Module):
"""Build one marker token for each frame represented in the sequence."""
def __init__(self, hidden_dim: int, max_time_steps: int) -> None:
super().__init__()
self.time_embedding = nn.Embedding(max_time_steps, hidden_dim)
self.stream_embedding = nn.Embedding(2, hidden_dim)
self.camera_embedding = nn.Embedding(1, hidden_dim)
def forward(self, time_step: int, stream_id: int, device: torch.device) -> torch.Tensor:
time = torch.tensor([time_step], dtype=torch.long, device=device)
stream = torch.tensor([stream_id], dtype=torch.long, device=device)
camera = torch.zeros(1, dtype=torch.long, device=device)
return (
self.time_embedding(time)
+ self.stream_embedding(stream)
+ self.camera_embedding(camera)
).squeeze(0)
class FunnelTrajectoryHead(nn.Module):
"""Six-layer funnel MLP that predicts a fixed waypoint trajectory."""
def __init__(
self,
hidden_dim: int,
num_waypoints: int,
action_dim: int,
dropout: float,
use_tanh: bool,
) -> None:
super().__init__()
output_dim = num_waypoints * action_dim
self.num_waypoints = num_waypoints
self.action_dim = action_dim
self.use_tanh = use_tanh
self.layers = nn.Sequential(
nn.LayerNorm(hidden_dim),
nn.Linear(hidden_dim, 4096),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(4096, 1024),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(1024, 512),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(512, 256),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(256, 128),
nn.GELU(),
nn.Dropout(dropout),
nn.LayerNorm(128),
nn.Linear(128, output_dim),
)
def forward(self, control_state: torch.Tensor) -> torch.Tensor:
trajectory = self.layers(control_state)
if self.use_tanh:
trajectory = torch.tanh(trajectory)
return trajectory.view(-1, self.num_waypoints, self.action_dim)
@dataclass
class MiniCPMRobotTrackOutput(ModelOutput):
"""Output of MiniCPM-RobotTrack."""
loss: Optional[torch.FloatTensor] = None
trajectories: Optional[torch.FloatTensor] = None
class MiniCPMRobotTrackModel(PreTrainedModel):
"""MiniCPM visual tracking policy with a funnel trajectory head."""
config_class = MiniCPMRobotTrackConfig
base_model_prefix = "backbone"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
_supports_sdpa = True
_supports_flash_attn_2 = True
_no_split_modules = ["MiniCPMDecoderLayer"]
def __init__(self, config: MiniCPMRobotTrackConfig) -> None:
super().__init__(config)
backbone_config = MiniCPMConfig(**config.backbone_config)
attention_implementation = getattr(config, "_attn_implementation", None)
if attention_implementation is not None:
backbone_config._attn_implementation = attention_implementation
backbone_config.use_cache = False
backbone_dtype = _dtype_from_name(config.backbone_dtype)
with _default_dtype(backbone_dtype):
self.backbone = MiniCPMModel(backbone_config)
hidden_dim = int(backbone_config.hidden_size)
self.vision_projector = VisionProjector(config.vision_feature_dim, hidden_dim)
self.temporal_markers = TemporalMarkerEncoder(hidden_dim, config.max_time_steps)
self.control_query = nn.Parameter(torch.empty(1, 1, hidden_dim))
nn.init.normal_(self.control_query, mean=0.0, std=0.02)
self.trajectory_head = FunnelTrajectoryHead(
hidden_dim=hidden_dim,
num_waypoints=config.num_waypoints,
action_dim=config.action_dim,
dropout=config.trajectory_dropout,
use_tanh=config.use_tanh_actions,
)
output_scale = torch.ones(1, 1, config.action_dim, dtype=torch.float32)
output_scale[..., :2] = config.xy_scale
self.register_buffer("output_scale", output_scale)
def get_input_embeddings(self) -> nn.Module:
return self.backbone.get_input_embeddings()
def set_input_embeddings(self, value: nn.Module) -> None:
self.backbone.set_input_embeddings(value)
def _insert_temporal_markers(
self,
tokens: torch.Tensor,
time_indices: torch.Tensor,
stream_id: int,
) -> torch.Tensor:
if tokens.ndim != 3 or time_indices.ndim != 2:
raise ValueError("visual tokens and time indices must have shapes [B, N, C] and [B, N]")
if tokens.shape[:2] != time_indices.shape:
raise ValueError("visual token and time-index shapes do not match")
if tokens.size(1) == 0:
return tokens
packed_rows: List[torch.Tensor] = []
time_rows = time_indices.detach().to("cpu").tolist()
for batch_index, time_row in enumerate(time_rows):
pieces: List[torch.Tensor] = []
start = 0
while start < len(time_row):
time_step = int(time_row[start])
if not 0 <= time_step < self.config.max_time_steps:
raise ValueError(f"time index {time_step} is outside the configured range")
end = start + 1
while end < len(time_row) and int(time_row[end]) == time_step:
end += 1
marker = self.temporal_markers(time_step, stream_id, tokens.device)
pieces.extend((marker.unsqueeze(0), tokens[batch_index, start:end]))
start = end
packed_rows.append(torch.cat(pieces, dim=0))
packed_lengths = {row.size(0) for row in packed_rows}
if len(packed_lengths) != 1:
raise ValueError("each batch item must contain the same number of represented frames")
return torch.stack(packed_rows, dim=0)
def _build_sequence(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor],
coarse_tokens: torch.Tensor,
coarse_time_indices: torch.Tensor,
fine_tokens: torch.Tensor,
fine_time_indices: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
device = self.control_query.device
input_ids = input_ids.to(device)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
else:
attention_mask = attention_mask.to(device)
batch_size = coarse_tokens.size(0)
if fine_tokens.size(0) != batch_size or input_ids.size(0) != batch_size:
raise ValueError("batch dimensions do not match")
projector_dtype = _module_dtype(self.vision_projector)
history = self.vision_projector(coarse_tokens.to(device=device, dtype=projector_dtype))
current = self.vision_projector(fine_tokens.to(device=device, dtype=projector_dtype))
history = self._insert_temporal_markers(
history, coarse_time_indices.to(device), stream_id=0
)
current = self._insert_temporal_markers(
current, fine_time_indices.to(device), stream_id=1
)
text = self.backbone.get_input_embeddings()(input_ids)
control_query = self.control_query.expand(batch_size, -1, -1)
sequence = torch.cat((text, history, current, control_query), dim=1)
sequence = sequence.to(dtype=_module_dtype(self.backbone))
full_attention_mask = torch.cat(
(
attention_mask,
torch.ones(batch_size, history.size(1), dtype=torch.long, device=device),
torch.ones(batch_size, current.size(1), dtype=torch.long, device=device),
torch.ones(batch_size, 1, dtype=torch.long, device=device),
),
dim=1,
)
return sequence, full_attention_mask
def normalize_trajectory(self, trajectory: torch.Tensor) -> torch.Tensor:
return trajectory / self.output_scale.to(device=trajectory.device, dtype=trajectory.dtype)
def forward(
self,
input_ids: torch.LongTensor,
coarse_tokens: torch.Tensor,
coarse_time_indices: torch.Tensor,
fine_tokens: torch.Tensor,
fine_time_indices: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
valid_mask: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[MiniCPMRobotTrackOutput, Tuple[torch.Tensor, ...]]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence, full_attention_mask = self._build_sequence(
input_ids=input_ids,
attention_mask=attention_mask,
coarse_tokens=coarse_tokens,
coarse_time_indices=coarse_time_indices,
fine_tokens=fine_tokens,
fine_time_indices=fine_time_indices,
)
output = self.backbone(
inputs_embeds=sequence,
attention_mask=full_attention_mask,
use_cache=False,
return_dict=True,
)
control_state = output.last_hidden_state[:, -1].to(
dtype=_module_dtype(self.trajectory_head)
)
normalized_trajectory = self.trajectory_head(control_state)
trajectories = normalized_trajectory * self.output_scale.to(
normalized_trajectory.dtype
)
loss = None
if labels is not None:
labels = labels.to(device=trajectories.device, dtype=trajectories.dtype)
if labels.shape != trajectories.shape:
raise ValueError("labels and predicted trajectories must have identical shapes")
normalized_labels = self.normalize_trajectory(labels)
squared_error = (normalized_trajectory - normalized_labels).square()
if valid_mask is None:
loss = squared_error.mean()
else:
mask = valid_mask.to(
device=trajectories.device, dtype=trajectories.dtype
).unsqueeze(-1)
denominator = mask.sum() * trajectories.size(-1)
loss = (
squared_error.mul(mask).sum() / denominator
if denominator.item() > 0
else trajectories.sum() * 0.0
)
if not return_dict:
return (trajectories,) if loss is None else (loss, trajectories)
return MiniCPMRobotTrackOutput(loss=loss, trajectories=trajectories)
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