295018ec0f
### 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>
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
from typing import Optional
|
|
|
|
import torch
|
|
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
|
|
|
|
|
|
def fc3_all_gather_and_maybe_unpad_impl(x: torch.Tensor, ) -> torch.Tensor:
|
|
try:
|
|
forward_context = get_forward_context()
|
|
except AssertionError:
|
|
return x
|
|
x = get_fc3_quant_x_group().all_gather(x, 0)
|
|
dp_metadata = forward_context.dp_metadata
|
|
if dp_metadata is None:
|
|
pad_size = forward_context.pad_size
|
|
if pad_size > 0:
|
|
x = x[:-pad_size]
|
|
else:
|
|
# unpad
|
|
num_tokens_across_dp_cpu = dp_metadata.num_tokens_across_dp_cpu
|
|
result = torch.empty((num_tokens_across_dp_cpu.sum(), *x.shape[1:]),
|
|
device=x.device,
|
|
dtype=x.dtype)
|
|
dp_size = get_dp_group().world_size
|
|
x = x.view(dp_size, forward_context.padded_length, *x.shape[1:])
|
|
offset = 0
|
|
for idx in range(dp_size):
|
|
num_tokens_dp = num_tokens_across_dp_cpu[idx]
|
|
result[offset:offset + num_tokens_dp] = x[idx, :num_tokens_dp]
|
|
offset += num_tokens_dp
|
|
x = result
|
|
|
|
return x
|
|
|
|
|
|
def all_gather_async(input: torch.Tensor,
|
|
group: GroupCoordinator,
|
|
output: Optional[torch.Tensor] = None,
|
|
async_op: bool = True):
|
|
if group.world_size == 1:
|
|
return input, None
|
|
if output is None:
|
|
input_size = input.size()
|
|
output_size = (input_size[0] * group.world_size, ) + input_size[1:]
|
|
output = torch.empty(output_size,
|
|
dtype=input.dtype,
|
|
device=input.device)
|
|
return output, dist.all_gather_into_tensor(output,
|
|
input,
|
|
group=group.device_group,
|
|
async_op=async_op) |