Init the main branch for aiter

aiter/configs/untuned_gemm.csv

+M,N,K,bias,dtype,outdtype,scaleAB

aiter/dist/communication_op.py

+"""
+* Copyright (C) 2024-2025, The vLLM team.
+*
+* 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 typing import Any, Dict, Optional, Union
+
+import torch
+import torch.distributed
+
+from .parallel_state import get_tp_group
+
+
+def tensor_model_parallel_all_reduce(
+    input_: torch.Tensor, open_fp8_quant: bool = False
+) -> torch.Tensor:
+    """All-reduce the input tensor across model parallel group."""
+    return get_tp_group().all_reduce(input_, open_fp8_quant)
+
+
+def tensor_model_parallel_fused_allreduce_rmsnorm(
+    input_: torch.Tensor, residual_inp_: torch.Tensor, weight_: torch.Tensor, eps: float
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return get_tp_group().fused_allreduce_rmsnorm(input_, residual_inp_, weight_, eps)
+
+
+def tensor_model_parallel_custom_all_gather(input_: torch.Tensor) -> torch.Tensor:
+    return get_tp_group().custom_all_gather(input_)
+
+
+def tensor_model_parallel_all_gather(
+    input_: torch.Tensor, use_custom: bool = False, dim: int = -1
+) -> torch.Tensor:
+    """All-gather the input tensor across model parallel group."""
+    return get_tp_group().all_gather(input_, use_custom, dim)
+
+
+def tensor_model_parallel_gather(
+    input_: torch.Tensor, dst: int = 0, dim: int = -1
+) -> Optional[torch.Tensor]:
+    """Gather the input tensor across model parallel group."""
+    return get_tp_group().gather(input_, dst, dim)
+
+
+def broadcast_tensor_dict(
+    tensor_dict: Optional[Dict[Any, Union[torch.Tensor, Any]]] = None, src: int = 0
+):
+    if not torch.distributed.is_initialized():
+        return tensor_dict
+    return get_tp_group().broadcast_tensor_dict(tensor_dict, src)