[Refactor]Refactor of vllm_ascend/distributed module (#5719)

### What this PR does / why we need it?
Based on the RFC:https://github.com/vllm-project/vllm-ascend/issues/5604

This PR is a refactoring of vllm_ascend/distributed, moving all
kv_transfer realtaed codes into a dedicated folder, which has already
been done in vLLM

### Does this PR introduce _any_ user-facing change?
NA

### How was this patch tested?


- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: lty <linhebiwen@gmail.com>

vllm_ascend/distributed/utils.py

@@ -1,4 +1,3 @@
-import os
 from typing import Optional
 
 import torch
@@ -6,63 +5,7 @@ import torch.distributed as dist
 from vllm.distributed.parallel_state import GroupCoordinator, get_dp_group
 from vllm.forward_context import get_forward_context
 
-from vllm_ascend.distributed.parallel_state import (get_fc3_quant_x_group,
-                                                    get_p_tp_group)
-
-
-def kv_alltoall_and_rearrange(pd_tp_ratio: int, key: torch.Tensor,
-                              value: torch.TensorType):
-    if pd_tp_ratio <= 1:
-        return None, None
-    elif key is None or value is None:
-        raise ValueError("key or value is None")
-    k_output = alltoall_and_rearrange(pd_tp_ratio, key)
-    v_output = alltoall_and_rearrange(pd_tp_ratio, value)
-    return k_output, v_output
-
-
-def alltoall_and_rearrange(tp_ratio: int, input_tensor: torch.Tensor):
-    num_kv_heads = input_tensor.size(1)
-    output_tensor = torch.zeros_like(input_tensor)
-    dist.all_to_all_single(output_tensor,
-                           input_tensor,
-                           group=get_p_tp_group().device_group)
-    input_tensor = 0
-    result = rearrange_output(output_tensor, tp_ratio, num_kv_heads)
-    output_tensor = 0
-    return result
-
-
-def rearrange_output(base_output: torch.Tensor, cut_num: int,
-                     num_kv_heads: int):
-    size_0 = base_output.size(0)
-    if size_0 % cut_num != 0:
-        raise ValueError(
-            f"The size of dim 0 [{size_0}] must be divisible by the cut_num [{cut_num}]"
-        )
-    chunk_size = size_0 // cut_num
-    reshaped = base_output.view(cut_num, chunk_size, -1)
-    transposed = reshaped.transpose(0, 1)
-    return transposed.contiguous().view(size_0, num_kv_heads, -1)
-
-
-def align_memory(tensor: torch.Tensor, alignment: int) -> torch.Tensor:
-    data_ptr = tensor.data_ptr()
-    aligned_addr = (data_ptr + alignment - 1) // alignment * alignment
-    offset = (aligned_addr - data_ptr) // tensor.element_size()
-    return tensor[int(offset):]
-
-
-def get_transfer_timeout_value():
-    ascend_transfer_timeout = os.getenv("ASCEND_TRANSFER_TIMEOUT", "")
-    if len(ascend_transfer_timeout) > 0:
-        return int(ascend_transfer_timeout)
-    hccl_rdma_timeout = int(os.getenv('HCCL_RDMA_TIMEOUT',
-                                      '20'))  # type: ignore
-    hccl_rdma_retry_cnt = int(os.getenv('HCCL_RDMA_RETRY_CNT',
-                                        '7'))  # type: ignore
-    return int((4.096 * (2**hccl_rdma_timeout)) * hccl_rdma_retry_cnt // 1000 +
-               3000)
+from vllm_ascend.distributed.parallel_state import get_fc3_quant_x_group
 
 
 def fc3_all_gather_and_maybe_unpad_impl(x: torch.Tensor, ) -> torch.Tensor:
@@ -90,6 +33,7 @@ def fc3_all_gather_and_maybe_unpad_impl(x: torch.Tensor, ) -> torch.Tensor:
             result[offset:offset + num_tokens_dp] = x[idx, :num_tokens_dp]
             offset += num_tokens_dp
         x = result
+
     return x