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Running on L40S
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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 | # 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 torch.utils.data
import wandb
from cosmos_framework.model._base import ImaginaireModel
from cosmos_framework.utils import distributed
from cosmos_framework.utils.callback import Callback
class DataStatsCallback(Callback):
def __init__(
self,
logging_iter_multipler: int = 1,
save_s3: bool = False,
) -> None:
super().__init__()
self.logging_iter_multipler = logging_iter_multipler
assert self.logging_iter_multipler > 0, "logging_iter_multipler should be greater than 0"
self.save_s3 = save_s3
self.wandb_extra_tag = f"@{logging_iter_multipler}" if logging_iter_multipler > 1 else ""
self.name = "data_stats" + self.wandb_extra_tag
self.data_freq_current = {}
self.data_freq_acc = {}
self.avg_num_assistant_tokens = []
self.avg_num_real_tokens = []
self.max_num_real_tokens = []
self.min_num_real_tokens = []
# Per-dataset token length tracking
self.dataset_token_lengths = {} # dataset_name -> list of avg_num_real_tokens
self.dataset_assistant_tokens = {} # dataset_name -> list of avg_num_assistant_tokens
self.num_log_current = 0
self.total_count_acc = {}
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.num_log_current += 1
dataset_name = data_batch.get("dataset_name", "default")
# Handle case where dataset_name gets batched into a list
if isinstance(dataset_name, list):
assert len(dataset_name) == 1, "dataset_name should be a list of 1"
dataset_name = dataset_name[0]
if dataset_name in ["default"] and "__url__" in data_batch:
# try to get the name from url
dataset_name = "/".join(data_batch["__url__"][0].split("/")[:-1])
if dataset_name not in self.data_freq_current:
self.data_freq_current[dataset_name] = torch.tensor(0, device="cuda") # []
self.data_freq_current[dataset_name] += 1
if dataset_name not in self.data_freq_acc:
self.data_freq_acc[dataset_name] = torch.tensor(0, device="cuda") # []
self.data_freq_acc[dataset_name] += 1
if "avg_num_assistant_tokens" in output_batch:
self.avg_num_assistant_tokens.append(output_batch["avg_num_assistant_tokens"])
# Track per-dataset assistant tokens
if dataset_name not in self.dataset_assistant_tokens:
self.dataset_assistant_tokens[dataset_name] = []
self.dataset_assistant_tokens[dataset_name].append(output_batch["avg_num_assistant_tokens"])
if "avg_num_real_tokens" in output_batch:
self.avg_num_real_tokens.append(output_batch["avg_num_real_tokens"])
# Track per-dataset token lengths
if dataset_name not in self.dataset_token_lengths:
self.dataset_token_lengths[dataset_name] = []
self.dataset_token_lengths[dataset_name].append(output_batch["avg_num_real_tokens"])
if "max_num_real_tokens" in output_batch:
self.max_num_real_tokens.append(output_batch["max_num_real_tokens"])
if "min_num_real_tokens" in output_batch:
self.min_num_real_tokens.append(output_batch["min_num_real_tokens"])
if iteration % (self.config.trainer.logging_iter * self.logging_iter_multipler) == 0:
# Step 1: Gather all dataset names across ranks
local_dataset_names = list(self.data_freq_current.keys())
all_dataset_names = [None for _ in range(dist.get_world_size())]
dist.all_gather_object(all_dataset_names, local_dataset_names)
# Step 2: Create the union of all dataset names
union_dataset_names = set()
for names in all_dataset_names:
union_dataset_names.update(names)
union_dataset_names = sorted(list(union_dataset_names))
# Step 3: For any missing dataset name, add dummy _LossRecord with NaN loss
for dataset_name in union_dataset_names:
if dataset_name not in self.data_freq_acc:
self.data_freq_acc[dataset_name] = torch.tensor(0, device="cuda") # []
if dataset_name not in self.data_freq_current:
self.data_freq_current[dataset_name] = torch.tensor(0, device="cuda") # []
# Step 4: Calculate the total count of each dataset across all ranks
total_count_current = {}
for dataset_name in union_dataset_names:
acc_tensor = self.data_freq_acc[dataset_name].clone()
current_tensor = self.data_freq_current[dataset_name].clone()
dist.all_reduce(acc_tensor, op=dist.ReduceOp.SUM)
dist.all_reduce(current_tensor, op=dist.ReduceOp.SUM)
self.total_count_acc[dataset_name] = acc_tensor.item()
total_count_current[dataset_name] = current_tensor.item()
if distributed.is_rank0() and wandb.run is not None:
info = {}
if len(self.avg_num_assistant_tokens) > 0:
info["data_stats_tokens/avg_num_assistant_tokens"] = sum(self.avg_num_assistant_tokens) / len(
self.avg_num_assistant_tokens
)
self.avg_num_assistant_tokens = []
if len(self.avg_num_real_tokens) > 0:
info["data_stats_tokens/avg_num_real_tokens"] = sum(self.avg_num_real_tokens) / len(
self.avg_num_real_tokens
)
self.avg_num_real_tokens = []
if len(self.max_num_real_tokens) > 0:
info["data_stats_tokens/max_num_real_tokens"] = max(self.max_num_real_tokens)
self.max_num_real_tokens = []
if len(self.min_num_real_tokens) > 0:
info["data_stats_tokens/min_num_real_tokens"] = min(self.min_num_real_tokens)
self.min_num_real_tokens = []
# Log per-dataset average token lengths
for dataset_name in union_dataset_names:
if dataset_name in self.dataset_token_lengths and len(self.dataset_token_lengths[dataset_name]) > 0:
avg_token_length = sum(self.dataset_token_lengths[dataset_name]) / len(
self.dataset_token_lengths[dataset_name]
)
info[f"data_stats_avg_tokens_per_dataset/{dataset_name}"] = avg_token_length
if (
dataset_name in self.dataset_assistant_tokens
and len(self.dataset_assistant_tokens[dataset_name]) > 0
):
avg_assistant_tokens = sum(self.dataset_assistant_tokens[dataset_name]) / len(
self.dataset_assistant_tokens[dataset_name]
)
info[f"data_stats_avg_assistant_tokens_per_dataset/{dataset_name}"] = avg_assistant_tokens
# Reset per-dataset token lengths after logging
self.dataset_token_lengths = {}
self.dataset_assistant_tokens = {}
# Log the valid count per dataset
for dataset_name in union_dataset_names:
info[f"data_stats_count_acc/{dataset_name}"] = self.total_count_acc[dataset_name]
info[f"data_stats_count_current/{dataset_name}"] = total_count_current[dataset_name]
self.num_log_current = 0
wandb.log(info, step=iteration)
# Create a table of the data stats, columns: Dataset, Accumulated frequency, Current frequency, Accumulated Count, Current Count
table_html = "<table><tr><th>Dataset</th><th>Accumulated frequency</th><th>Current frequency</th><th>Accumulated Count</th><th>Current Count</th></tr>"
total_count_acc_sum = sum(self.total_count_acc.values())
total_count_current_sum = sum(total_count_current.values())
# Sort union_dataset_names by total_count_acc, from highest to lowest
union_dataset_names = sorted(union_dataset_names, key=lambda x: self.total_count_acc[x], reverse=True)
acc_freq_list = []
current_freq_list = []
for name in union_dataset_names:
acc_freq = self.total_count_acc[name] / total_count_acc_sum
acc_freq_list.append(acc_freq)
current_freq = total_count_current[name] / total_count_current_sum
current_freq_list.append(current_freq)
table_html += f"<tr><td>{name}</td><td>{acc_freq}</td><td>{current_freq}</td><td>{self.total_count_acc[name]}</td><td>{total_count_current[name]}</td></tr>"
# Sum over all dataset for each column
acc_freq_sum = sum(acc_freq_list)
current_freq_sum = sum(current_freq_list)
table_html += f"<tr><td>Total ({len(union_dataset_names)})</td><td>{acc_freq_sum}</td><td>{current_freq_sum}</td><td>{total_count_acc_sum}</td><td>{total_count_current_sum}</td></tr>"
table_html += "</table>"
wandb.log({"table_data_stats/html": wandb.Html(table_html)}, step=iteration)
# Reset self.data_freq_current
self.data_freq_current = {k: v * 0 for k, v in self.data_freq_current.items()}
if (
distributed.is_rank0()
and wandb.run is not None
and iteration in [100, 1000, 2000, 5000, 15000, 30000]
and len(self.total_count_acc)
):
# log a table of the total_count_acc
# Sort self.total_count_acc by value, from highest to lowest
sorted_total_count_acc = sorted(self.total_count_acc.items(), key=lambda x: x[1], reverse=True)
table = wandb.Table(data=[[k, v] for k, v in sorted_total_count_acc], columns=["Dataset", "Count"])
wandb.log(
{
f"data_counts_bar_{iteration:09d}": wandb.plot.bar(
table, "Dataset", "Count", title=f"Count per Dataset iter {iteration:09d}"
)
},
step=iteration,
)
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