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# 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.
import argparse
import os
import torch
from cosmos_predict1.autoregressive.configs.base.model_config import create_video2world_model_config
from cosmos_predict1.autoregressive.utils.checkpoint import merge_tensor_parallel_state_dicts
from cosmos_predict1.utils import log
def merge_sharded_checkpoints(checkpoint_path, output_path, tensor_parallel_size, model_size, model_family):
assert checkpoint_path.endswith(".pt"), "Checkpoint path must end with .pt"
assert model_family == "cosmos", "Only cosmos model family is currently supported"
assert model_size == "4b", "Only 4B model size is currently supported"
model_config, _ = create_video2world_model_config(
model_ckpt_path=checkpoint_path,
model_family=model_family,
model_size=model_size,
tensor_model_parallel_size=tensor_parallel_size,
tokenizer_ckpt_path="checkpoints/Cosmos-Tokenize1-DV8x16x16-720p/ema.jit",
)
log.info(f"Merging sharded checkpoints from {checkpoint_path.replace('.pt', '_model_mp_*.pt')} into {output_path}")
checkpoint_paths = [checkpoint_path.replace(".pt", f"_model_mp_{rank}.pt") for rank in range(tensor_parallel_size)]
for path in checkpoint_paths:
assert os.path.exists(path), f"Checkpoint path {path} does not exist"
log.info(f"Found checkpoint {path}")
sharded_state_dicts = [torch.load(path, map_location="cpu") for path in checkpoint_paths]
merged_state_dict = merge_tensor_parallel_state_dicts(sharded_state_dicts, model_config)
torch.save(merged_state_dict, output_path)
log.info(f"Merged checkpoint saved to {output_path}")
def parse_args():
parser = argparse.ArgumentParser(description="Merge Cosmos-Predict1-4B autoregressive checkpoints")
parser.add_argument(
"--checkpoint_path",
"-c",
type=str,
required=True,
help="Path to the checkpoint to merge. Must end with .pt and be colocated with the sharded checkpoints ending in _model_mp_{rank}.pt",
)
parser.add_argument("--output_path", "-o", type=str, required=True, help="Path to the output merged checkpoint")
parser.add_argument("--tensor_parallel_size", "-t", type=int, required=True, help="Tensor parallel size")
parser.add_argument("--model_size", "-s", type=str, required=True, help="Model size")
parser.add_argument("--model_family", "-f", type=str, required=False, default="cosmos", help="Model family")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
merge_sharded_checkpoints(
args.checkpoint_path, args.output_path, args.tensor_parallel_size, args.model_size, args.model_family
)
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