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support aclgraph (#426)

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### What this PR does / why we need it?
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This PR supports the access of vllm-acend to the piecewise_graph feature
provided by the v1 engine.

1. register unifiled_ascend_attention_with_output for piecewise_graph to
split graph.
2. support NPUGraph to accelerate kernel launch.

### Does this PR introduce _any_ user-facing change?
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support npugraph to default， Users can disenable the npugraph feature by
configuring enforce_eager.

This has corresponding requirements for the versions of torch_npu and
CANN, and they need to support graph capture.

### How was this patch tested?
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it turn to default

---------

Signed-off-by: Bug Hunter Yan <yanpq@zju.edu.cn>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
This commit is contained in:
Bug Hunter Yan
2025-04-23 20:56:24 +08:00
committed by GitHub
parent 5c6d05a59e
commit 05bdcbeae4
15 changed files with 454 additions and 119 deletions
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228
vllm_ascend/attention/attention_v1.py
View File

@@ -24,6 +24,8 @@ import torch_npu
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer, AttentionType)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.utils import direct_register_custom_op
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.worker.gpu_input_batch import InputBatch
@@ -31,6 +33,7 @@ from vllm_ascend.ops.attention import vanilla_chunked_prefill
class AscendAttentionBackend(AttentionBackend):
accept_output_buffer: bool = True
@staticmethod
def get_name() -> str:
@@ -198,6 +201,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
kv_cache: torch.Tensor,
attn_metadata: AscendMetadata,
output: Optional[torch.Tensor] = None,
trace_flag: bool = True,
) -> torch.Tensor:
"""Forward pass with Ascend attention.
Args:
@@ -215,98 +219,150 @@ class AscendAttentionBackendImpl(AttentionImpl):
shape = [batch_size * seq_len, num_heads, head_size]
"""
num_tokens = query.shape[0]
output = torch.empty(num_tokens,
self.num_heads,
self.head_size,
dtype=query.dtype,
device=query.device)
if attn_metadata is None:
# Profiling run.
return output.view(num_tokens, self.hidden_size)
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
attn_type = self.attn_type
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"PallasAttentionBackendImpl")
# View q k v to BSH.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
# TODO: Remove this contiguous in the future.
value = value.contiguous()
if kv_cache.numel() > 0:
if self.key_cache is None:
self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
slots = attn_metadata.slot_mapping
torch_npu._npu_reshape_and_cache(key=key,
value=value,
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slots)
if hasattr(layer, 'quant_method'):
# TODO: Add attr (num_prefills, prefill_metadata, decode_metadata) to AscendMetadata
pass
# V0-Style scheduler situation.
elif attn_metadata.attn_state == AscendAttentionState.PrefillOnly:
assert attn_metadata is not None
assert attn_metadata.attn_mask is not None
mask = attn_metadata.attn_mask
torch_npu._npu_flash_attention(query=query,
key=key,
value=value,
mask=mask,
seq_len=attn_metadata.seq_lens,
scale_value=self.scale,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
out=output)
elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
block_tables = attn_metadata.block_tables
torch_npu._npu_paged_attention(query=query,
key_cache=self.key_cache,
value_cache=self.value_cache,
num_kv_heads=self.num_kv_heads,
num_heads=self.num_heads,
scale_value=self.scale,
block_table=block_tables,
context_lens=attn_metadata.seq_lens,
out=output)
# Normal V1 situation.
if output is None:
output = torch.empty(num_tokens,
self.num_heads,
self.head_size,
dtype=query.dtype,
device=query.device)
if trace_flag:
torch.ops.vllm.unified_ascend_attention_with_output(
query=query,
key=key,
value=value,
output=output,
layer_name=layer.layer_name)
else:
# use chunked prefill for head size 192 scenario, like deepseek
# paged_attention_splitfuse maybe crash at such scenario
# TODO: vanilla path will be removed after the kernel support
# head_size 192 scenario
if self.head_size == 192:
cu_seqlen_q = [0] + attn_metadata.query_lens.tolist()
cu_seqlen_k = [0] + attn_metadata.seq_lens.tolist()
cu_seqlen_q = torch.tensor(cu_seqlen_q, device="npu")
cu_seqlen_k = torch.tensor(cu_seqlen_k, device="npu")
cu_seqlen_q = torch.cumsum(cu_seqlen_q, dim=0)
cu_seqlen_k = torch.cumsum(cu_seqlen_k, dim=0)
max_seqlen_q = torch.max(attn_metadata.query_lens)
max_seqlen_k = torch.max(attn_metadata.seq_lens)
vanilla_chunked_prefill(output, query, self.key_cache,
self.value_cache,
attn_metadata.block_tables,
cu_seqlen_q, cu_seqlen_k, max_seqlen_q,
max_seqlen_k, self.scale, None, True)
else:
torch_npu._npu_paged_attention_splitfuse(
num_tokens = query.shape[0]
if attn_metadata is None:
return output.view(num_tokens, self.hidden_size)
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
attn_type = self.attn_type
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"PallasAttentionBackendImpl")
# View q k v to BSH.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
# TODO: Remove this contiguous in the future.
value = value.contiguous()
if kv_cache.numel() > 0:
if self.key_cache is None:
self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
slots = attn_metadata.slot_mapping
torch_npu._npu_reshape_and_cache(key=key,
value=value,
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slots)
if hasattr(layer, 'quant_method'):
# TODO: Add attr (num_prefills, prefill_metadata, decode_metadata) to AscendMetadata
pass
# V0-Style scheduler situation.
elif attn_metadata.attn_state == AscendAttentionState.PrefillOnly:
assert attn_metadata is not None
assert attn_metadata.attn_mask is not None
mask = attn_metadata.attn_mask
torch_npu._npu_flash_attention(query=query,
key=key,
value=value,
mask=mask,
seq_len=attn_metadata.seq_lens,
scale_value=self.scale,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
out=output)
elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
block_tables = attn_metadata.block_tables
torch_npu._npu_paged_attention(
query=query,
key_cache=self.key_cache,
value_cache=self.value_cache,
mask=attn_metadata.attn_mask,
block_table=attn_metadata.block_tables,
seq_len=attn_metadata.query_lens,
context_lens=attn_metadata.seq_lens,
num_kv_heads=self.num_kv_heads,
num_heads=self.num_heads,
scale_value=self.scale,
block_table=block_tables,
context_lens=attn_metadata.seq_lens,
out=output)
# Normal V1 situation.
else:
# use chunked prefill for head size 192 scenario, like deepseek
# paged_attention_splitfuse maybe crash at such scenario
# TODO: vanilla path will be removed after the kernel support
# head_size 192 scenario
if self.head_size == 192:
cu_seqlen_q = [0] + attn_metadata.query_lens.tolist()
cu_seqlen_k = [0] + attn_metadata.seq_lens.tolist()
cu_seqlen_q = torch.tensor(cu_seqlen_q, device="npu")
cu_seqlen_k = torch.tensor(cu_seqlen_k, device="npu")
cu_seqlen_q = torch.cumsum(cu_seqlen_q, dim=0)
cu_seqlen_k = torch.cumsum(cu_seqlen_k, dim=0)
max_seqlen_q = torch.max(attn_metadata.query_lens)
max_seqlen_k = torch.max(attn_metadata.seq_lens)
vanilla_chunked_prefill(output, query, self.key_cache,
self.value_cache,
attn_metadata.block_tables,
cu_seqlen_q, cu_seqlen_k,
max_seqlen_q, max_seqlen_k,
self.scale, None, True)
else:
# use paged attention
torch_npu._npu_paged_attention_splitfuse(
query=query,
key_cache=self.key_cache,
value_cache=self.value_cache,
mask=attn_metadata.attn_mask,
block_table=attn_metadata.block_tables,
seq_len=attn_metadata.query_lens,
context_lens=attn_metadata.seq_lens,
num_kv_heads=self.num_kv_heads,
num_heads=self.num_heads,
scale_value=self.scale,
out=output)
return output.view(num_tokens, self.hidden_size)
def unified_ascend_attention_with_output(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
output: torch.Tensor,
layer_name: str,
) -> None:
forward_context: ForwardContext = get_forward_context()
attn_metadata = forward_context.attn_metadata
self = forward_context.no_compile_layers[layer_name]
kv_cache = self.kv_cache[forward_context.virtual_engine]
self.impl.forward(self,
query,
key,
value,
kv_cache,
attn_metadata,
output,
trace_flag=False)
return
def unified_attention_with_output_fake(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
output: torch.Tensor,
layer_name: str,
) -> None:
return
direct_register_custom_op(
op_name="unified_ascend_attention_with_output",
op_func=unified_ascend_attention_with_output,
mutates_args=["output"],
fake_impl=unified_attention_with_output_fake,
dispatch_key="PrivateUse1",
)