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9f818c5 | 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 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
from __future__ import annotations
import torch
import torch.distributed as dist
import wandb
from cosmos_framework.model._base import ImaginaireModel
from cosmos_framework.utils import distributed
from cosmos_framework.utils.callback import Callback
from cosmos_framework.callbacks.wandb_log import _LossRecord
from cosmos_framework.data.vfm.action.domain_utils import EMBODIMENT_TO_DOMAIN_ID
# Build inverse mapping: domain_id -> embodiment_type. First occurrence wins when multiple embodiment names share the
# same domain id.
DOMAIN_ID_TO_EMBODIMENT: dict[int, str] = {}
for _k, _v in EMBODIMENT_TO_DOMAIN_ID.items():
DOMAIN_ID_TO_EMBODIMENT.setdefault(_v, _k)
class TrainingStatsCallback(Callback):
"""Callback for tracking and logging training mode and embodiment statistics to wandb."""
def __init__(self, log_freq: int = 100):
super().__init__()
self.log_freq = log_freq
self._mode_counts: dict[str, int] = {}
self._mode_total_count: int = 0
self._embodiment_counts: dict[str, int] = {}
self._embodiment_total_count: int = 0
self._per_embodiment_loss: dict[str, _LossRecord] = {}
self._per_embodiment_sub_loss: dict[str, dict[str, _LossRecord]] = {}
def _accumulate_mode_counts(self, data_batch: dict[str, torch.Tensor]) -> None:
modes = data_batch.get("mode", None)
if modes is None:
return
if isinstance(modes, str):
modes_list = [modes]
elif isinstance(modes, (list, tuple)):
modes_list = [str(m) for m in modes]
elif isinstance(modes, torch.Tensor):
# Defensive: support cases where mode might be encoded numerically.
modes_list = [str(m) for m in modes.detach().cpu().tolist()]
else:
modes_list = [str(modes)]
for mode in modes_list:
self._mode_total_count += 1
self._mode_counts[mode] = self._mode_counts.get(mode, 0) + 1
def _accumulate_embodiment_counts(self, data_batch: dict[str, torch.Tensor]) -> None:
domain_ids = data_batch.get("domain_id", None)
if domain_ids is None:
return
if isinstance(domain_ids, int):
domain_id_list = [domain_ids]
elif isinstance(domain_ids, (list, tuple)):
domain_id_list = [int(d) for d in domain_ids if d is not None]
elif isinstance(domain_ids, torch.Tensor):
# Flatten to handle any shape (scalar, 1D, or 2D with trailing dim)
domain_id_list = [int(d) for d in domain_ids.detach().cpu().flatten().tolist()]
else:
domain_id_list = [int(domain_ids)]
for domain_id in domain_id_list:
embodiment = DOMAIN_ID_TO_EMBODIMENT.get(domain_id, f"unknown_{domain_id}")
self._embodiment_total_count += 1
self._embodiment_counts[embodiment] = self._embodiment_counts.get(embodiment, 0) + 1
def _gather_global_mode_counts(self) -> tuple[int, dict[str, int]]:
"""
Returns (global_total, global_mode_counts) aggregated across all ranks.
"""
local: dict[str, int] = dict(self._mode_counts)
local["__total__"] = int(self._mode_total_count)
if dist.is_available() and dist.is_initialized():
world_size = int(dist.get_world_size())
gathered: list[dict[str, int] | None] = [None for _ in range(world_size)]
dist.all_gather_object(gathered, local)
else:
gathered = [local]
global_total = 0
global_counts: dict[str, int] = {}
for item in gathered:
if not item:
continue
global_total += int(item.get("__total__", 0))
for k, v in item.items():
if k == "__total__":
continue
global_counts[k] = global_counts.get(k, 0) + int(v)
return global_total, global_counts
def _gather_global_embodiment_counts(self) -> tuple[int, dict[str, int]]:
"""
Returns (global_total, global_embodiment_counts) aggregated across all ranks.
"""
local: dict[str, int] = dict(self._embodiment_counts)
local["__total__"] = int(self._embodiment_total_count)
if dist.is_available() and dist.is_initialized():
world_size = int(dist.get_world_size())
gathered: list[dict[str, int] | None] = [None for _ in range(world_size)]
dist.all_gather_object(gathered, local)
else:
gathered = [local]
global_total = 0
global_counts: dict[str, int] = {}
for item in gathered:
if not item:
continue
global_total += int(item.get("__total__", 0))
for k, v in item.items():
if k == "__total__":
continue
global_counts[k] = global_counts.get(k, 0) + int(v)
return global_total, global_counts
def _build_mode_log_dict(
self, *, log_prefix: str, global_total: int, global_counts: dict[str, int]
) -> dict[str, float]:
info: dict[str, float] = {}
denom = float(global_total) if global_total > 0 else 0.0
for mode in sorted(global_counts.keys()):
count = float(global_counts.get(mode, 0))
pct = (100.0 * count / denom) if denom > 0 else 0.0
info[f"{log_prefix}_stats_mode/{mode}"] = pct
return info
def _build_embodiment_log_dict(
self, *, log_prefix: str, global_total: int, global_counts: dict[str, int]
) -> dict[str, float]:
info: dict[str, float] = {}
denom = float(global_total) if global_total > 0 else 0.0
for embodiment in sorted(global_counts.keys()):
count = float(global_counts.get(embodiment, 0))
pct = (100.0 * count / denom) if denom > 0 else 0.0
info[f"{log_prefix}_stats_embodiment/{embodiment}"] = pct
return info
def _get_batch_embodiment(self, data_batch: dict[str, torch.Tensor]) -> str | None:
"""Extract the embodiment name from the first non-None sample's domain_id."""
domain_ids = data_batch.get("domain_id", None)
if domain_ids is None:
return None
if isinstance(domain_ids, torch.Tensor):
if domain_ids.numel() == 0:
return None
domain_id = int(domain_ids.flatten()[0].item())
elif isinstance(domain_ids, (list, tuple)):
first = next((d for d in domain_ids if d is not None), None)
if first is None:
return None
domain_id = int(first)
else:
domain_id = int(domain_ids)
return DOMAIN_ID_TO_EMBODIMENT.get(domain_id, f"unknown_{domain_id}")
def _accumulate_per_embodiment_loss(
self,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
) -> None:
embodiment = self._get_batch_embodiment(data_batch)
if embodiment is None:
return
if embodiment not in self._per_embodiment_loss:
self._per_embodiment_loss[embodiment] = _LossRecord()
self._per_embodiment_loss[embodiment].loss += loss.detach().float()
self._per_embodiment_loss[embodiment].iter_count += 1
if embodiment not in self._per_embodiment_sub_loss:
self._per_embodiment_sub_loss[embodiment] = {}
for key in output_batch:
if "loss" in key and "per_instance" not in key:
if key not in self._per_embodiment_sub_loss[embodiment]:
self._per_embodiment_sub_loss[embodiment][key] = _LossRecord()
self._per_embodiment_sub_loss[embodiment][key].loss += output_batch[key].detach().float()
self._per_embodiment_sub_loss[embodiment][key].iter_count += 1
def _compute_per_embodiment_loss_stats(self, log_prefix: str) -> dict[str, float]:
"""Compute per-embodiment loss averages across all ranks.
All ranks must call this method (contains collective operations).
Returns the log dict (only meaningful on rank 0).
"""
dist_available = dist.is_available() and dist.is_initialized()
world_size = int(dist.get_world_size()) if dist_available else 1
# Step 1: gather union of embodiment names across ranks
local_embodiments = sorted(self._per_embodiment_loss.keys())
if dist_available:
all_embodiments: list[list[str] | None] = [None for _ in range(world_size)]
dist.all_gather_object(all_embodiments, local_embodiments)
else:
all_embodiments = [local_embodiments]
union_embodiments = sorted({e for el in all_embodiments for e in el})
# Step 2: gather union of sub-loss keys across ranks
local_sub_keys = sorted({k for d in self._per_embodiment_sub_loss.values() for k in d})
if dist_available:
all_sub_keys: list[list[str] | None] = [None for _ in range(world_size)]
dist.all_gather_object(all_sub_keys, local_sub_keys)
else:
all_sub_keys = [local_sub_keys]
union_sub_keys = sorted({k for kl in all_sub_keys for k in kl})
# Step 3: insert NaN dummy _LossRecord for missing embodiment/key combos
for emb in union_embodiments:
if emb not in self._per_embodiment_loss:
dummy = _LossRecord()
dummy.loss += torch.tensor([float("nan")], device="cuda")
dummy.iter_count += 1
self._per_embodiment_loss[emb] = dummy
if emb not in self._per_embodiment_sub_loss:
self._per_embodiment_sub_loss[emb] = {}
for key in union_sub_keys:
if key not in self._per_embodiment_sub_loss[emb]:
dummy = _LossRecord()
dummy.loss += torch.tensor([float("nan")], device="cuda")
dummy.iter_count += 1
self._per_embodiment_sub_loss[emb][key] = dummy
# Step 4: compute distributed averages (all ranks participate in all_reduce)
log_dict: dict[str, float] = {}
for emb in union_embodiments:
avg, valid = self._per_embodiment_loss[emb].get_stat(return_valid_mask_sum=True)
if valid > 0:
log_dict[f"{log_prefix}_stats_loss/{emb}"] = avg
for emb in union_embodiments:
for key in union_sub_keys:
avg, valid = self._per_embodiment_sub_loss[emb][key].get_stat(return_valid_mask_sum=True)
if valid > 0:
log_dict[f"{log_prefix}_stats_loss_detail/{emb}_{key}"] = avg
# Step 5: reset accumulators
self._per_embodiment_loss = {}
self._per_embodiment_sub_loss = {}
return log_dict
@torch.no_grad()
def on_training_step_end(
self,
model: ImaginaireModel,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
iteration: int = 0,
) -> None:
self._accumulate_mode_counts(data_batch)
self._accumulate_embodiment_counts(data_batch)
self._accumulate_per_embodiment_loss(data_batch, output_batch, loss)
if iteration % self.log_freq != 0:
return
# All ranks must participate in collective operations below.
mode_total, mode_counts = self._gather_global_mode_counts()
embodiment_total, embodiment_counts = self._gather_global_embodiment_counts()
per_embodiment_loss_dict = self._compute_per_embodiment_loss_stats(log_prefix="train")
if not distributed.is_rank0():
return
if wandb.run is None:
return
log_dict: dict[str, float] = {}
log_dict.update(
self._build_mode_log_dict(log_prefix="train", global_total=mode_total, global_counts=mode_counts)
)
log_dict.update(
self._build_embodiment_log_dict(
log_prefix="train", global_total=embodiment_total, global_counts=embodiment_counts
)
)
log_dict.update(per_embodiment_loss_dict)
wandb.log({k: float(v) for k, v in log_dict.items()}, step=iteration)
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