Spaces:
Build error
Build error
File size: 7,087 Bytes
b6af722 |
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 |
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from hydra.core.config_store import ConfigStore
from megatron.core import parallel_state
from torch.utils.data import DataLoader, DistributedSampler
from cosmos_predict1.diffusion.training.callbacks.iter_speed import IterSpeed
from cosmos_predict1.diffusion.training.callbacks.low_precision import LowPrecisionCallback
from cosmos_predict1.diffusion.training.datasets.dataset_multiview import Dataset
from cosmos_predict1.diffusion.training.models.extend_model_multiview import FSDPExtendDiffusionModel
from cosmos_predict1.diffusion.training.networks.general_dit_lvg_multiview import VideoExtendMultiviewGeneralDIT
from cosmos_predict1.utils import log
from cosmos_predict1.utils.callbacks.grad_clip import GradClip
from cosmos_predict1.utils.lazy_config import PLACEHOLDER
from cosmos_predict1.utils.lazy_config import LazyCall as L
from cosmos_predict1.utils.lazy_config import LazyDict
def get_sampler(dataset):
return DistributedSampler(
dataset,
num_replicas=parallel_state.get_data_parallel_world_size(),
rank=parallel_state.get_data_parallel_rank(),
shuffle=True,
seed=0,
)
cs = ConfigStore.instance()
num_frames = 57
num_views = 5
view_keys = ["pinhole_front_left", "pinhole_front", "pinhole_front_right", "pinhole_side_left", "pinhole_side_right"]
example_multiview_dataset_waymo = L(Dataset)(
dataset_dir="datasets/waymo",
sequence_interval=1,
num_frames=num_frames,
view_keys=view_keys,
video_size=(480, 848),
)
video2world_multiview_7b_example_waymo = LazyDict(
dict(
defaults=[
{"override /net": "faditv2_7b"},
{"override /conditioner": "video_cond"},
{"override /ckpt_klass": "fsdp"},
{"override /checkpoint": "local"},
{"override /vae": "cosmos_diffusion_tokenizer_comp8x8x8"},
"_self_",
],
job=dict(
project="posttraining",
group="diffusion_video2world",
name="video2world_multiview_7b_example_waymo",
),
optimizer=dict(
lr=2 ** (-14.3), # 2**(-14.3) approx 5e-5
weight_decay=0.1,
betas=[0.9, 0.99],
eps=1e-10,
),
checkpoint=dict(
save_iter=200,
# broadcast_via_filesystem=True,
broadcast_via_filesystem=False,
load_path="checkpoints/Cosmos-Predict1-7B-Video2World-Sample-AV-Multiview/model.pt",
load_training_state=False,
strict_resume=False,
keys_not_to_resume=[],
),
trainer=dict(
max_iter=2000,
distributed_parallelism="fsdp",
logging_iter=200,
callbacks=dict(
grad_clip=L(GradClip)(
model_key="model",
fsdp_enabled=True,
),
low_prec=L(LowPrecisionCallback)(config=PLACEHOLDER, trainer=PLACEHOLDER, update_iter=1),
iter_speed=L(IterSpeed)(
every_n=200,
hit_thres=5,
),
),
),
model_parallel=dict(
sequence_parallel=False,
tensor_model_parallel_size=1,
context_parallel_size=1,
),
model=dict(
n_views=num_views,
# Use 16x16x32x40 latent shape for training
latent_shape=[
16, # Latent channel dim
16, # Latent temporal dim
88, # Latent height dim
160, # Latent width dim
],
loss_reduce="mean",
ema=dict(
enabled=True,
),
fsdp_enabled=True,
fsdp=dict(
policy="block",
checkpoint=True,
min_num_params=1024,
sharding_group_size=32,
sharding_strategy="hybrid",
),
net=L(VideoExtendMultiviewGeneralDIT)(
rope_h_extrapolation_ratio=1,
rope_w_extrapolation_ratio=1,
rope_t_extrapolation_ratio=2,
n_views=num_views,
),
conditioner=dict(
video_cond_bool=dict(
condition_location="first_random_n",
cfg_unconditional_type="zero_condition_region_condition_mask",
apply_corruption_to_condition_region="noise_with_sigma",
condition_on_augment_sigma=False,
dropout_rate=0.0, # No dropout
first_random_n_num_condition_t_max=2,
normalize_condition_latent=False,
# Let the augment sigma mostly fall in the range of 0 to 0.3
augment_sigma_sample_p_mean=-3.0,
augment_sigma_sample_p_std=2.0,
augment_sigma_sample_multiplier=1.0,
)
),
vae=dict(pixel_chunk_duration=num_frames),
),
model_obj=L(FSDPExtendDiffusionModel)(
config=PLACEHOLDER,
fsdp_checkpointer=PLACEHOLDER,
),
# warming up for first 2500 steps~(when resume from 310000)
scheduler=dict(
warm_up_steps=[2500],
cycle_lengths=[10000000000000],
f_start=[1.0e-6],
f_max=[1.0],
f_min=[1.0],
),
dataloader_train=L(DataLoader)(
dataset=example_multiview_dataset_waymo,
sampler=L(get_sampler)(dataset=example_multiview_dataset_waymo),
batch_size=1,
drop_last=True,
pin_memory=True,
num_workers=8,
),
dataloader_val=L(DataLoader)(
dataset=example_multiview_dataset_waymo,
sampler=L(get_sampler)(dataset=example_multiview_dataset_waymo),
batch_size=1,
drop_last=True,
pin_memory=True,
num_workers=8,
),
)
)
def register_experiments(cs):
# Register the experiments
for _item in [
video2world_multiview_7b_example_waymo,
]:
experiment_name = _item["job"]["name"]
log.info(f"Registering experiment: {experiment_name}")
cs.store(
group="experiment",
package="_global_",
name=experiment_name,
node=_item,
)
|