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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 | |
| ) | |