c980e68d40
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
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.ascend_forward_context import _EXTRA_CTX
|
|
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 = _EXTRA_CTX.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, _EXTRA_CTX.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: torch.Tensor | None = 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)
|