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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 torch
from cosmos_predict1.autoregressive.configs.base.model_config import create_video2world_model_config
from cosmos_predict1.autoregressive.utils.checkpoint import obtain_tensor_parallel_state_dict
from cosmos_predict1.utils import log
def shard_checkpoint(checkpoint_path, tensor_parallel_size, model_size, model_family, target_backend="pytorch"):
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"Sharding checkpoint {checkpoint_path} with {tensor_parallel_size} ranks")
checkpoint = torch.load(checkpoint_path, map_location="cpu", mmap=True)
for tensor_parallel_rank in range(tensor_parallel_size):
shard = obtain_tensor_parallel_state_dict(
checkpoint, tensor_parallel_size, tensor_parallel_rank, model_config, target_backend=target_backend
)
shard_path = checkpoint_path.replace(".pt", f"_model_mp_{tensor_parallel_rank}.pt")
log.info(f"Saving shard {shard_path}")
torch.save(shard, shard_path)
def parse_args():
parser = argparse.ArgumentParser(description="Shard NVIDIA Cosmos Predict1 autoregressive models")
parser.add_argument(
"--checkpoint_path",
"-c",
type=str,
required=True,
default="checkpoints/Cosmos-Predict1-4B/model.pt",
help="Path to the checkpoint to shard",
)
parser.add_argument("--tensor_parallel_size", "-t", type=int, required=True, help="Number of tensor parallel ranks")
parser.add_argument("--target_backend", "-b", type=str, required=False, default="pytorch", help="Target backend")
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()
shard_checkpoint(
args.checkpoint_path, args.tensor_parallel_size, args.model_size, args.model_family, args.target_backend
)
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