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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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 torch
from torch import Tensor
from torch.distributed import ProcessGroup, all_gather, get_world_size
def cat_outputs_cp(x: Tensor, seq_dim: int, cp_group: ProcessGroup) -> Tensor:
"""
Concatenates tensors from multiple processes along a specified dimension.
This function gathers tensors from all processes in the given process group
and concatenates them along the specified dimension.
Args:
x (Tensor): The input tensor to be gathered and concatenated.
seq_dim (int): The dimension along which to concatenate the gathered tensors.
cp_group (ProcessGroup): The process group containing all the processes involved in the gathering.
Returns:
Tensor: A tensor resulting from the concatenation of tensors from all processes.
Raises:
RuntimeError: If the gathering of tensors fails.
"""
# Number of processes in the group
world_size = get_world_size(cp_group)
# List to hold tensors from each rank
gathered_tensors = [torch.zeros_like(x) for _ in range(world_size)]
# Attempt to gather tensors from all ranks
try:
all_gather(gathered_tensors, x, group=cp_group)
except RuntimeError as e:
raise RuntimeError(f"Gathering failed: {e}")
# Concatenate tensors along the specified dimension
return torch.cat(gathered_tensors, dim=seq_dim)
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