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adapt to main2main for model runner v2 (#7578)

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### What this PR does / why we need it?
This PR aims to adapt to newest commit of vllm main branch for model
runner v2. please refer to
https://github.com/vllm-project/vllm-ascend/issues/5208
### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

- vLLM version: v0.18.0
- vLLM main:
ed359c497a

---------

Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
This commit is contained in:
Ronald
2026-03-25 09:08:44 +08:00
committed by GitHub
parent fc3ec100bc
commit d96440924a
16 changed files with 239 additions and 264 deletions
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203
vllm_ascend/worker/v2/aclgraph_utils.py
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@@ -16,128 +16,68 @@
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
from contextlib import contextmanager
from typing import Any
import numpy as np
import torch
import torch.nn as nn
import vllm
from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.forward_context import get_forward_context, set_forward_context
from vllm.logger import logger
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.worker.gpu.attn_utils import build_slot_mappings_by_layer
from vllm.v1.worker.gpu.block_table import BlockTables
from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
from vllm.v1.worker.gpu.cudagraph_utils import BatchExecutionDescriptor, ModelCudaGraphManager
from vllm.v1.worker.gpu.input_batch import InputBuffers
from vllm.v1.worker.gpu.model_states.interface import ModelState
from vllm.v1.worker.utils import AttentionGroup
from vllm_ascend.ascend_forward_context import _EXTRA_CTX
from vllm_ascend.compilation.acl_graph import set_graph_params, update_full_graph_params
from vllm_ascend.worker.v2.attn_utils import build_attn_metadata
from vllm_ascend.worker.v2.utils import torch_cuda_wrapper
class AclGraphManager(CudaGraphManager):
"""ACL Graph Manager for Ascend NPUs."""
class ModelAclGraphManager(ModelCudaGraphManager):
"""ACL Model Cuda Graph Manager for Ascend NPUs."""
def __init__(
self,
vllm_config: VllmConfig,
use_aux_hidden_state_outputs: bool,
device: torch.device,
model_runner: Any, # NPUModelRunner type, in case circular import, so we pass it as Any
cudagraph_mode: CUDAGraphMode,
decode_query_len: int,
model_runner: Any,
):
super().__init__(
vllm_config,
device,
cudagraph_mode,
decode_query_len,
)
# set model runner attribute, so we can access attributes model runner
# when call `run_fullgraph` method in CudaGraphManager,
# then we don't need to # copy `execute_model` method in `NPUModelRunner` class.
self.model_runner = model_runner
super().__init__(
vllm_config,
use_aux_hidden_state_outputs,
device,
)
# capture_sizes sorts in ascending order.
self.capture_sizes = sorted(self.compilation_config.cudagraph_capture_sizes)
# vllm-ascend need to update graph params of attention backend.
# so we need to set graph params before capture full graph.
if super().needs_capture():
set_graph_params(self.cudagraph_sizes)
set_graph_params(self.capture_sizes)
def _capture_full_graph(
self,
num_tokens: int,
num_reqs: int,
model: nn.Module,
input_ids: torch.Tensor,
positions: torch.Tensor,
inputs_embeds: torch.Tensor | None,
num_tokens_across_dp: torch.Tensor,
attn_metadata: dict[str, Any] | None,
slot_mappings: dict[str, torch.Tensor] | None,
has_lora: bool = False,
) -> None:
"""Override _capture_full_graph because we need to set capturing=True in forward context."""
# set capturing=True in before model forward.
model = ModelWithContext(model)
return super()._capture_full_graph(
num_tokens,
num_reqs,
model,
input_ids,
positions,
inputs_embeds,
num_tokens_across_dp,
attn_metadata,
slot_mappings,
has_lora,
)
def capture_graph(
self,
num_tokens: int,
capture_cg_mode: CUDAGraphMode,
model: nn.Module,
model_state: ModelState,
input_buffers: InputBuffers,
block_tables: BlockTables,
attn_groups: list[list[AttentionGroup]],
kv_cache_config: KVCacheConfig,
has_lora: bool = False,
uniform_decode: bool = False,
) -> None:
with torch_cuda_wrapper(), prepare_capture_inputs_wrapper():
super().capture_graph(
num_tokens,
capture_cg_mode,
model,
model_state,
input_buffers,
block_tables,
attn_groups,
kv_cache_config,
has_lora,
uniform_decode,
)
def run_fullgraph(self, num_tokens: int) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
def run_fullgraph(self, desc: BatchExecutionDescriptor) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
"""Override run_fullgraph to update full graph params in run_fullgraph."""
num_tokens = desc.num_tokens
logger.info_once(f"run_fullgraph with num_tokens={num_tokens}")
ret = super().run_fullgraph(num_tokens)
assert self.model_runner.cudagraph_and_dp_padding is not None
ret = super().run_fullgraph(desc)
positions = self.model_runner.input_buffers.positions[:num_tokens]
_num_tokens_after_padding, num_tokens_across_dp, synced_cudagraph_mode = (
self.model_runner.cudagraph_and_dp_padding
)
cudagraph_runtime_mode = CUDAGraphMode(synced_cudagraph_mode)
# refer to vllm.v1.worker.gpu.dp_utils.sync_cudagraph_and_dp_padding to
# calculate num_tokens_across_dp.
num_tokens_across_dp = torch.full([self.model_runner.dp_size], num_tokens, device=self.device)
with set_forward_context(
self.model_runner.input_batch.attn_metadata,
self.vllm_config,
num_tokens=num_tokens,
cudagraph_runtime_mode=cudagraph_runtime_mode,
cudagraph_runtime_mode=desc.cg_mode,
num_tokens_across_dp=num_tokens_across_dp,
batch_descriptor=None, # Full graph model don't need batch_descriptor
slot_mapping=self.model_runner.input_batch.slot_mappings,
@@ -155,79 +95,31 @@ class AclGraphManager(CudaGraphManager):
)
return ret
def is_uniform_decode(
def capture(
self,
num_reqs: int,
num_tokens: int,
max_query_len: int,
):
return (max_query_len == self.uniform_decode_query_len) and (num_tokens == max_query_len * num_reqs)
@contextmanager
def prepare_capture_inputs_wrapper():
"""Context manager to override input preparation for NPU graph capture."""
# TODO(Ronald1995): make prepare_inputs_to_capture as static method
# in CudaGraphManager.
ori = vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture
try:
vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture = prepare_inputs_to_capture
yield
finally:
vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture = ori
def prepare_inputs_to_capture(
num_reqs: int,
num_tokens: int,
input_buffers: InputBuffers,
block_tables: BlockTables,
attn_groups: list[list[AttentionGroup]],
max_model_len: int,
kv_cache_config: KVCacheConfig,
uniform_decode_query_len: int = 0,
) -> tuple[dict[str, Any], dict[str, torch.Tensor]]:
if uniform_decode_query_len > 0:
num_tokens_per_req = uniform_decode_query_len
else:
num_tokens_per_req = num_tokens // num_reqs
query_start_loc_np = np.arange(num_reqs + 1, dtype=np.int32) * num_tokens_per_req
query_start_loc_np[-1] = num_tokens
query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
input_buffers.query_start_loc[: num_reqs + 1] = query_start_loc_cpu
input_buffers.query_start_loc[num_reqs + 1 :] = num_tokens
query_start_loc = input_buffers.query_start_loc[: num_reqs + 1]
# HACK(woosuk): For faster warmup, we set seq_lens (GPU) to num_tokens
# rather than max_model_len.
input_buffers.seq_lens[:num_reqs] = num_tokens
input_buffers.seq_lens[num_reqs:] = 0
input_buffers.seq_lens_cpu[:num_reqs] = num_tokens
input_buffers.seq_lens_cpu[num_reqs:] = 0
input_buffers.dcp_local_seq_lens[:num_reqs] = num_tokens
input_buffers.dcp_local_seq_lens[num_reqs:] = 0
input_block_tables = [x[:num_reqs] for x in block_tables.input_block_tables]
slot_mappings = block_tables.slot_mappings[:, :num_tokens]
slot_mappings_by_layer = build_slot_mappings_by_layer(slot_mappings, kv_cache_config)
attn_metadata = build_attn_metadata(
attn_groups=attn_groups,
num_reqs=num_reqs,
num_tokens=num_tokens,
query_start_loc_gpu=query_start_loc,
query_start_loc_cpu=query_start_loc_cpu,
max_query_len=num_tokens_per_req,
seq_lens=input_buffers.seq_lens,
max_seq_len=max_model_len,
block_tables=input_block_tables,
slot_mappings=slot_mappings,
kv_cache_config=kv_cache_config,
seq_lens_np=input_buffers.seq_lens_np,
)
return attn_metadata, slot_mappings_by_layer
model: nn.Module,
model_state: ModelState,
input_buffers: InputBuffers,
block_tables: BlockTables,
attn_groups: list[list[AttentionGroup]],
kv_cache_config: KVCacheConfig,
has_lora: bool = False,
use_aux_hidden_state_outputs: bool = False,
progress_bar_desc: str = "Capturing CUDA graphs",
) -> None:
"""Capture CUDA graphs for model forward pass."""
model = ModelWithContext(model)
return super().capture(
model,
model_state,
input_buffers,
block_tables,
attn_groups,
kv_cache_config,
has_lora,
use_aux_hidden_state_outputs,
progress_bar_desc,
)
class ModelWithContext(nn.Module):
@@ -242,6 +134,7 @@ class ModelWithContext(nn.Module):
def forward(self, *args, **kwargs):
# In warmup phase, capturing=False by default.
# when capturing, we need to set capturing=True in forward context.
_EXTRA_CTX.capturing = True
if torch.npu.is_current_stream_capturing():
_EXTRA_CTX.capturing = True
return self.original_model(*args, **kwargs)