[ROCm][Kimi-K3][Perf] Fuse MLA decode KV-cache write and Q-prep via AITER (#57640)
Signed-off-by: Rita Brugarolas Brufau <rita.brugarolasbrufau@amd.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Andreas Karatzas <akaratza@amd.com>
This commit is contained in:
Rita Brugarolas
co-authored by
Claude
Andreas Karatzas
Claude
Andreas Karatzas
parent
d2fb35f66e
commit
8f24ab30a3
@@ -7,16 +7,47 @@ from typing import cast
|
||||
import torch
|
||||
|
||||
from vllm._aiter_ops import rocm_aiter_ops
|
||||
from vllm.model_executor.layers.attention.attention import get_attention_context
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.mla import MultiHeadLatentAttentionWrapper
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
_OPT_KV_LORA_RANK = 512
|
||||
_OPT_ROT_DIM = 64
|
||||
_OPT_MIN_SIZE = 2048
|
||||
|
||||
|
||||
class KimiK3MultiHeadLatentAttentionWrapper(MultiHeadLatentAttentionWrapper):
|
||||
"""Kimi-K3 MLA wrapper with eager AITER q/kv RMSNorm fusion."""
|
||||
"""Kimi-K3 MLA wrapper with eager AITER q/kv RMSNorm fusion and a fused
|
||||
decode Q-prep path."""
|
||||
|
||||
def __init__(self, *args, **kwargs) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
self._use_eager_qk_rmsnorm_fusion = bool(rocm_aiter_ops.is_enabled())
|
||||
self._fused_qk_prep = self._fused_qk_prep_supported()
|
||||
self._identity_rope: tuple[torch.Tensor, torch.Tensor] | None = None
|
||||
|
||||
def _fused_qk_prep_supported(self) -> bool:
|
||||
attn = self.mla_attn
|
||||
if not (
|
||||
rocm_aiter_ops.is_mla_enabled()
|
||||
and attn.kv_cache_dtype.startswith("fp8")
|
||||
and self.kv_lora_rank == _OPT_KV_LORA_RANK
|
||||
and self.qk_rope_head_dim == _OPT_ROT_DIM
|
||||
and self.kv_lora_rank * self.num_heads >= _OPT_MIN_SIZE
|
||||
):
|
||||
return False
|
||||
# The fused kernel writes q_out only for slot >= 0, so padded rows would
|
||||
# be garbage after a DCP query all-gather. PCP and HiSparse rewrite the
|
||||
# KV inputs/slots before the standalone write, which this path bypasses.
|
||||
if attn.impl.dcp_world_size > 1 or attn.use_pcp:
|
||||
return False
|
||||
if attn.hisparse_cache is not None or attn.q_pad_num_heads is not None:
|
||||
return False
|
||||
# Only the W_UK variants reproduced in _fused_decode are supported.
|
||||
return attn.is_aiter_triton_fp4_bmm_enabled or (
|
||||
not attn.is_aiter_triton_fp8_bmm_enabled and attn.W_UK_T is not None
|
||||
)
|
||||
|
||||
def _normalize_q_kv(
|
||||
self,
|
||||
@@ -38,6 +69,107 @@ class KimiK3MultiHeadLatentAttentionWrapper(MultiHeadLatentAttentionWrapper):
|
||||
|
||||
return q_layernorm(q_c), kv_layernorm(kv_c)
|
||||
|
||||
def _fused_decode(
|
||||
self,
|
||||
q: torch.Tensor,
|
||||
kv_c_normed: torch.Tensor,
|
||||
k_pe: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
output_shape: tuple[int, int],
|
||||
attn_metadata,
|
||||
layer,
|
||||
kv_cache: torch.Tensor,
|
||||
slot_mapping: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
"""Decode-only MLA through one fused AITER launch.
|
||||
|
||||
Replaces the fp8 KV-cache write plus the ``[ql_nope | q_pe]`` concat and
|
||||
that query's static fp8 quant with a single
|
||||
``fused_qk_rope_concat_and_cache_mla``. On an untouched upstream build
|
||||
the concat and quant already arrive as one inductor-fused kernel, so
|
||||
this collapses two launches into one.
|
||||
"""
|
||||
from aiter.ops.cache import fused_qk_rope_concat_and_cache_mla
|
||||
|
||||
attn = self.mla_attn
|
||||
q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
||||
# (B, N, P) -> (N, B, P)
|
||||
q_nope_t = q_nope.transpose(0, 1)
|
||||
|
||||
# Mirror MLAAttention.forward_impl's W_UK dispatch, *including its
|
||||
# precedence* -- fp4 is checked before fp8 there, and both flags can be
|
||||
# set at once. ql_nope must come out bit-identical to what the unfused
|
||||
# path feeds its concat+quant, because this only replaces what happens
|
||||
# after the bmm. Both variants return bf16, which the kernel consumes.
|
||||
if attn.is_aiter_triton_fp4_bmm_enabled:
|
||||
from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4
|
||||
|
||||
ql_nope = batched_gemm_a16wfp4(
|
||||
q_nope_t,
|
||||
attn.W_K,
|
||||
attn.W_K_scale,
|
||||
transpose_bm=True,
|
||||
prequant=True,
|
||||
y_scale=layer._q_scale,
|
||||
)
|
||||
else:
|
||||
assert attn.W_UK_T is not None # guaranteed by _fused_qk_prep_supported
|
||||
B, N = q_nope.shape[0], q_nope.shape[1]
|
||||
L = attn.W_UK_T.shape[-1]
|
||||
ql_nope = q_nope_t.new_empty((B, N, L))
|
||||
torch.bmm(q_nope_t, attn.W_UK_T, out=ql_nope.transpose(0, 1))
|
||||
|
||||
if self._identity_rope is None:
|
||||
# cos = 1, sin = 0 makes the kernel's RoPE the identity, which is
|
||||
# what a NoPE model needs. One row suffices because the `_opt`
|
||||
# kernel clamps `pos` into [0, cos_cache.size(0)); a full-length
|
||||
# cache would cost ~268 MB at K3's 1M max_position_embeddings.
|
||||
half = self.qk_rope_head_dim // 2
|
||||
opts = {"dtype": kv_c_normed.dtype, "device": kv_c_normed.device}
|
||||
self._identity_rope = (
|
||||
torch.ones(1, half, **opts),
|
||||
torch.zeros(1, half, **opts),
|
||||
)
|
||||
cos_cache, sin_cache = self._identity_rope
|
||||
|
||||
# An fp8 KV cache is allocated as uint8 and re-viewed as fp8 before use;
|
||||
# the AITER kernel rejects the raw uint8 dtype outright.
|
||||
fp8_dtype = current_platform.fp8_dtype()
|
||||
if kv_cache.dtype != fp8_dtype:
|
||||
kv_cache = kv_cache.view(fp8_dtype)
|
||||
|
||||
head_size = attn.kv_lora_rank + self.qk_rope_head_dim
|
||||
num_tokens, num_heads = ql_nope.shape[:2]
|
||||
q_out = torch.empty(
|
||||
(num_tokens, num_heads, head_size),
|
||||
dtype=fp8_dtype,
|
||||
device=ql_nope.device,
|
||||
)
|
||||
fused_qk_rope_concat_and_cache_mla(
|
||||
# the W_UK bmm hands back a transposed view; the kernel reads dense
|
||||
ql_nope.contiguous(),
|
||||
q_pe,
|
||||
kv_c_normed,
|
||||
k_pe.squeeze(1),
|
||||
kv_cache.view(kv_cache.shape[0], -1, head_size),
|
||||
q_out,
|
||||
slot_mapping.flatten(),
|
||||
layer._k_scale,
|
||||
layer._q_scale,
|
||||
positions,
|
||||
cos_cache,
|
||||
sin_cache,
|
||||
is_neox=True,
|
||||
is_nope_first=True,
|
||||
)
|
||||
|
||||
# forward_mqa is MLA-specific and not on AttentionImpl; MLAAttention
|
||||
# annotates its own call to it the same way.
|
||||
attn_out, _ = attn.impl.forward_mqa(q_out, kv_cache, attn_metadata, attn) # type: ignore[attr-defined]
|
||||
output = q.new_empty(output_shape)
|
||||
attn._v_up_proj(attn_out, out=output)
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
@@ -106,13 +238,43 @@ class KimiK3MultiHeadLatentAttentionWrapper(MultiHeadLatentAttentionWrapper):
|
||||
if self.dcp_q_replicate:
|
||||
q_dcp_replicated, q = q, q_proj_layer._local_view(q)
|
||||
|
||||
attn_out = self.mla_attn(
|
||||
q,
|
||||
kv_c_normed,
|
||||
k_pe,
|
||||
output_shape=(hidden_states.shape[0], self.num_heads * self.v_head_dim),
|
||||
q_dcp_replicated=q_dcp_replicated,
|
||||
)
|
||||
output_shape = (hidden_states.shape[0], self.num_heads * self.v_head_dim)
|
||||
|
||||
# The fused decode path covers only the MQA slice, so it is taken for
|
||||
# decode-only batches; anything else falls through to MLAAttention.
|
||||
attn_metadata = layer = kv_cache = slot_mapping = None
|
||||
fuse = self._fused_qk_prep and q_dcp_replicated is None
|
||||
if fuse:
|
||||
attn_metadata, layer, kv_cache, slot_mapping = get_attention_context(
|
||||
self.mla_attn.layer_name
|
||||
)
|
||||
fuse = (
|
||||
attn_metadata is not None
|
||||
and slot_mapping is not None
|
||||
and kv_cache.numel() > 0
|
||||
and attn_metadata.num_actual_tokens == attn_metadata.num_decode_tokens
|
||||
)
|
||||
|
||||
if fuse:
|
||||
attn_out = self._fused_decode(
|
||||
q,
|
||||
kv_c_normed,
|
||||
k_pe,
|
||||
positions,
|
||||
output_shape,
|
||||
attn_metadata,
|
||||
layer,
|
||||
kv_cache,
|
||||
slot_mapping,
|
||||
)
|
||||
else:
|
||||
attn_out = self.mla_attn(
|
||||
q,
|
||||
kv_c_normed,
|
||||
k_pe,
|
||||
output_shape=output_shape,
|
||||
q_dcp_replicated=q_dcp_replicated,
|
||||
)
|
||||
|
||||
if self.g_proj is not None:
|
||||
attn_out = attn_out * self.g_proj(hidden_states)[0].sigmoid()
|
||||
|
||||
Reference in New Issue
Block a user