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[Bugfix][ROCm] Use chunked_prefill_paged_decode as fallback for V1 attention on ROCm (#18093)

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Signed-off-by: kf <kuanfu.liu@embeddedllm.com>
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kliuae 2025-05-16 10:30:17 +08:00 committed by GitHub
parent 4e1c6a0264
commit ee659e3b60
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1 changed files with 76 additions and 31 deletions
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107
vllm/v1/attention/backends/triton_attn.py
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@ -7,6 +7,9 @@ import torch
from vllm import _custom_ops as ops from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl, from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionMetadata, AttentionType) AttentionMetadata, AttentionType)
from vllm.attention.ops.chunked_prefill_paged_decode import (
chunked_prefill_paged_decode)
from vllm.attention.ops.paged_attn import PagedAttention
from vllm.attention.ops.triton_unified_attention import unified_attention from vllm.attention.ops.triton_unified_attention import unified_attention
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.platforms import current_platform from vllm.platforms import current_platform
@ -162,19 +165,40 @@ class TritonAttentionImpl(AttentionImpl):
# Whenever making a change in this method, please benchmark the # Whenever making a change in this method, please benchmark the
# performance to make sure it does not introduce any overhead. # performance to make sure it does not introduce any overhead.
num_queries_per_kv = query.shape[1] // key.shape[1]
use_prefill_decode_attn = (num_queries_per_kv &
(num_queries_per_kv - 1)) != 0
num_actual_tokens = attn_metadata.num_actual_tokens num_actual_tokens = attn_metadata.num_actual_tokens
key_cache, value_cache = kv_cache.unbind(0) if use_prefill_decode_attn:
torch.ops._C_cache_ops.reshape_and_cache_flash( key_cache, value_cache = PagedAttention.split_kv_cache(
key, kv_cache, self.num_kv_heads, self.head_size)
value,
key_cache, # Reshape the input keys and values and store them in the cache.
value_cache, PagedAttention.write_to_paged_cache(
attn_metadata.slot_mapping, key,
self.kv_cache_dtype, value,
layer._k_scale, key_cache,
layer._v_scale, value_cache,
) attn_metadata.slot_mapping,
self.kv_cache_dtype,
layer._k_scale,
layer._v_scale,
)
else:
key_cache, value_cache = kv_cache.unbind(0)
torch.ops._C_cache_ops.reshape_and_cache_flash(
key,
value,
key_cache,
value_cache,
attn_metadata.slot_mapping,
self.kv_cache_dtype,
layer._k_scale,
layer._v_scale,
)
if self.kv_cache_dtype.startswith("fp8"): if self.kv_cache_dtype.startswith("fp8"):
key_cache = key_cache.view(self.fp8_dtype) key_cache = key_cache.view(self.fp8_dtype)
@ -209,26 +233,47 @@ class TritonAttentionImpl(AttentionImpl):
max_seqlen_k = attn_metadata.max_seq_len max_seqlen_k = attn_metadata.max_seq_len
block_table = attn_metadata.block_table block_table = attn_metadata.block_table
descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1]) if use_prefill_decode_attn:
# Compute attention and update output up to `num_actual_tokens`.
chunked_prefill_paged_decode(query=query[:num_actual_tokens],
key=key[:num_actual_tokens],
value=value[:num_actual_tokens],
output=output[:num_actual_tokens],
kv_cache_dtype=self.kv_cache_dtype,
key_cache=key_cache,
value_cache=value_cache,
block_table=block_table,
query_start_loc=cu_seqlens_q,
seq_lens=seqused_k,
max_seq_len=max_seqlen_k,
max_query_len=max_seqlen_q,
k_scale=layer._k_scale,
v_scale=layer._v_scale,
alibi_slopes=self.alibi_slopes,
sliding_window=self.sliding_window[0],
sm_scale=self.scale)
unified_attention( else:
q=query[:num_actual_tokens], descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
k=key_cache,
v=value_cache, unified_attention(
out=output[:num_actual_tokens], q=query[:num_actual_tokens],
cu_seqlens_q=cu_seqlens_q, k=key_cache,
max_seqlen_q=max_seqlen_q, v=value_cache,
seqused_k=seqused_k, out=output[:num_actual_tokens],
max_seqlen_k=max_seqlen_k, cu_seqlens_q=cu_seqlens_q,
softmax_scale=self.scale, max_seqlen_q=max_seqlen_q,
causal=True, seqused_k=seqused_k,
alibi_slopes=self.alibi_slopes, max_seqlen_k=max_seqlen_k,
window_size=self.sliding_window, softmax_scale=self.scale,
block_table=block_table, causal=True,
softcap=self.logits_soft_cap, alibi_slopes=self.alibi_slopes,
q_descale=None, # Not supported window_size=self.sliding_window,
k_descale=layer._k_scale.expand(descale_shape), block_table=block_table,
v_descale=layer._v_scale.expand(descale_shape), softcap=self.logits_soft_cap,
) q_descale=None, # Not supported
k_descale=layer._k_scale.expand(descale_shape),
v_descale=layer._v_scale.expand(descale_shape),
)
return output return output