[Perf] Fuse RoPE + KV cache update for MLA backends (#35879): eliminate extra Inductor copy + enable on ROCm#163
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Follow-up to the cache+rope fusion in PR vllm-project#35879. Eliminates an extra Triton copy kernel that Inductor inserts on every MLA layer when the fused custom op mutates a strided view of `k_pe` that is also consumed by a second opaque custom op in a downstream partition. Problem: with the fusion enabled, the post-fusion FX graph consumes a strided slice of the qkv_a_proj output in both `fused_concat_and_cache_mla_rope` (which mutates it in-place) and the downstream `unified_mla_attention_with_output`. Inductor partitions at the second op and, because `k_pe` is a strided alias mutated then re-read across the partition boundary by an opaque consumer, it materialises a fresh contiguous buffer to keep aliasing safe. That materialisation shows up once per MLA layer (~4.7us * 200 steps on Kimi-K2-Thinking-MXFP4, MI355x), nearly doubling the per-layer fused-op cost. Fix: make the C++ kernel write the rotated `k_pe` into a fresh contiguous output tensor `k_pe_out` (shape `[num_tokens, 1, rot_dim]`) instead of mutating the strided input view. The kv_cache write inside the kernel still sees the rotated value (rotation stays in registers; only the final store target changes). Changes: * C++ kernel + ABI: `concat_and_cache_mla_rope_fused` gains a trailing `Tensor! k_pe_out` output. * Python op wrapper (`vllm/_custom_ops.py`): allocates `k_pe_out` contiguous like `k_pe` and returns it; drops `k_pe` from `mutates_args`. * Fusion pass (`kv_cache_mla_rope_fusion.py`): rewires downstream consumers to the new contiguous `k_pe_out`. * `fix_functionalization.py`: updated to match the new op signature (3 outputs; only the third - `k_pe_out` - is wired to the new contiguous tensor; the first two get rank-1 `new_empty(0)` slots). Based on AMD-MLPerf/workloads-inference@ee50ce9. Made-with: Cursor Signed-off-by: Rita Brugarolas Brufau <rita.brugarolasbrufau@amd.com>
Signed-off-by: Rita Brugarolas Brufau <rita.brugarolasbrufau@amd.com>
rbrugaro-amd
requested review from
ProExpertProg,
mgoin,
robertgshaw2-redhat,
tlrmchlsmth,
yewentao256 and
youkaichao
as code owners
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@rbrugaro-amd can you provide more information on the rematerialization? We should file a pytorch issue with a minimal repro if we can. |
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In
What Inductor does with it After fusion, the post-grad FX graph has this per-layer pattern (layer 0 shown): # fused op mutates k_pe (unsqueeze_1) in-place, returns bf16[0]
fused_concat_and_cache_mla_rope_default_60: "bf16[0]" = torch.ops.vllm.fused_concat_and_cache_mla_rope.default(
arg9_1, slice_tensor_60, unsqueeze_1, getitem_1523, ...)
# attention op reads the SAME strided view across the Inductor partition boundary
auto_functionalized = torch.ops.higher_order.auto_functionalized(
torch.ops.vllm.unified_mla_attention_with_output.default,
q=reshape_default_60, kv_c_normed=getitem_1523, k_pe=unsqueeze_1, ...)Inductor partitions at @triton.jit
def triton_poi_fused_fused_concat_and_cache_mla_rope_slice_split_with_sizes_unsqueeze_view_10(
in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = (xindex % 64) # rot_dim index
x1 = xindex // 64 # token index
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2048 + x0 + 2112*x1), xmask).to(tl.float32)
tl.store(out_ptr0 + (2048 + x0 + 2112*x1), tmp0, xmask) # identity write-back (no-op)
tl.store(out_ptr1 + (x2), tmp0, xmask) # strided → contiguous copyThe stride pattern How this PR fixes it The fused op now returns a fresh contiguous # fused op returns contiguous k_pe_out
fused_concat_and_cache_mla_rope_default_60: "bf16[s72, 1, 64]" = torch.ops.vllm.fused_concat_and_cache_mla_rope.default(
arg9_1, slice_tensor_60, unsqueeze_1, getitem_1523, ...)
# attention op receives the contiguous output — no alias conflict
auto_functionalized = torch.ops.higher_order.auto_functionalized(
torch.ops.vllm.unified_mla_attention_with_output.default,
q=reshape_default_60, kv_c_normed=getitem_1523, k_pe=fused_concat_and_cache_mla_rope_default_60, ...)No strided alias crosses the partition boundary, so Inductor has nothing to |
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Thank you for the detailed description. cc @zou3519 @BoyuanFeng @eellison is this a fundamental limitation of Inductor, or is this something we can address? |
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Great fix, I'm looking forward to the benchmark results! 😄 |
As i understand, this is a known limitation of inductor with |
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@attila-dusnoki-htec but we have the custom |
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@ElizaWszola @rbrugaro-amd let me look at this a bit more closely to understand if we can avoid the fix altogether |
I had to double check it and i was wrong. |
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@attila-dusnoki-htec can we just remove the |
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Just a brief update, I was able to reproduce this with Full command: Graph: Turn into the following partitioned code: For some reason |
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Without inductor partition (just Triton kernel: |
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cc @ProExpertProg would it be possible to share either the fx graph runnable or the tlparse ? that would help make it easier to investigate |
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I'll send one over once I'm back at my laptop, repro should also be easy from the commands above on the base branch of this PR |
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I tried to add the Kernel signature + meta triton_meta={'signature': {
'in_ptr0': '*bf16',
'out_ptr0': '*bf16',
'out_ptr1': '*bf16', # <- contiguous staging buffer
'xnumel': 'i32', 'XBLOCK': 'constexpr'}, ...},
inductor_meta={..., 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], ...},
def triton_poi_fused_..._1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK):With flexible_layout (kernel_74.py): triton_meta={'signature': {
'in_ptr0': '*bf16',
'out_ptr0': '*bf16', # <- only two buffers now
'xnumel': 'i32', 'XBLOCK': 'constexpr'}, ...},
inductor_meta={..., 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], ...},
def triton_poi_fused_..._1(in_ptr0, out_ptr0, xnumel, XBLOCK):Orchestrator call site Baseline: buf13 = torch.ops.vllm.fused_concat_and_cache_mla_rope.default(
arg9_1,
reinterpret_tensor(buf11, (s72, 16, 64), (3072, 192, 1), 128),
reinterpret_tensor(buf6, (s72, 1, 64), (2112, 64, 1), 2048),
buf9, ...)
buf18 = empty_strided_cuda((s72, 1, 64), (64, 64, 1), torch.bfloat16) # <- per-layer alloc
triton_poi_fused_..._1.run(buf6, buf6, buf18, ..., stream=stream0)
return (buf2, buf9, buf11, buf12, buf16, buf18, ...) # <- buf18 crosses partition
# next partition
torch.ops.vllm.unified_mla_attention_with_output.default(..., buf18, ...) # <- contiguous k_peWith flexible_layout: buf13 = torch.ops.vllm.fused_concat_and_cache_mla_rope.default(
arg9_1,
reinterpret_tensor(buf11, (s72, 16, 64), (3072, 192, 1), 128),
reinterpret_tensor(buf6, (s72, 1, 64), (2112, 64, 1), 2048),
buf9, ...)
# <- no alloc
triton_poi_fused_..._1.run(buf6, buf6, ..., stream=stream0)
return (buf2, buf6, buf9, buf11, buf12, buf16, ...) # <- buf6 itself crosses
# next partition
torch.ops.vllm.unified_mla_attention_with_output.default(
...,
reinterpret_tensor(buf6, (s72, 1, 64), (2112, 0, 1), 2048), # <- strided view, free
...)Kernel Baseline (load + store-back + store-to-contiguous): tmp0 = tl.load (in_ptr0 + (2048 + x0 + 2112*x1), xmask)
tl.store(out_ptr0 + (2048 + x0 + 2112*x1), tmp0, xmask) # buf6 -> buf6
tl.store(out_ptr1 + (x2), tmp0, xmask) # -> contiguous buf18With flexible_layout (load + store-back only, out_ptr0 == in_ptr0 == buf6): tmp0 = tl.load (in_ptr0 + (2048 + x0 + 2112*x1), xmask)
tl.store(out_ptr0 + (2048 + x0 + 2112*x1), tmp0, xmask) # buf6 -> buf6 (self-copy)Inductor honored the tag, so the kernel is no longer there for layout reasons it seems, but the same-named realize-marker survives :/ |
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@attila-dusnoki-htec could you collect a tlparse with and without the tag change? Also with and without inductor partition would be good (without we can use splitting_ops=[]) |
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I'm actually not seeing the identity copy, and the extra kernel is succesfully eliminated for me with
All were run with the base command: Dynamo partition ( Inductor partition ( Dynamo partition ( Inductor partition ( |
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With these 2 changes, i'm also observing the absence of the triton kernel: diff --git a/vllm/model_executor/layers/attention/mla_attention.py b/vllm/model_executor/layers/attention/mla_attention.py
--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -1009,6 +1009,7 @@ direct_register_custom_op(
op_name="unified_mla_attention_with_output",
op_func=unified_mla_attention_with_output,
mutates_args=["output", "output_block_scale"],
fake_impl=unified_mla_attention_with_output_fake,
dispatch_key=current_platform.dispatch_key,
+ tags=(torch.Tag.flexible_layout,),
)diff --git a/vllm/compilation/passes/fusion/kv_cache_mla_rope_fusion.py b/vllm/compilation/passes/fusion/kv_cache_mla_rope_fusion.py
--- a/vllm/compilation/passes/fusion/kv_cache_mla_rope_fusion.py
+++ b/vllm/compilation/passes/fusion/kv_cache_mla_rope_fusion.py
@@ -86,7 +86,6 @@ direct_register_custom_op(
op_name="fused_concat_and_cache_mla_rope",
op_func=fused_concat_and_cache_mla_rope_impl,
fake_impl=fused_concat_and_cache_mla_rope_fake,
- mutates_args=["q_pe", "k_pe"],
)Only adding the tag was not enough. The accuracy seems correct on my use case. |
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@attila-dusnoki-htec the mutates_args change is probably not correct :/ |
Of course not, a better solution would the actual PR change. |
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Looks like inductor partition + |
6 participants
Follow-up to vllm-project#35879. Two changes on top of the fused RoPE + KV cache update for MLA backends:
1. Return contiguous
k_pefrom fused kernel (eliminate extra Triton copy)Problem: With the fusion enabled, the post-fusion FX graph has a strided slice of the
qkv_a_projoutput consumed back-to-back by two opaque custom ops:Because
k_peis a strided alias mutated by the first op and then re-read across the Inductor partition boundary by the second, Inductor materialises it into a fresh contiguous buffer to keep aliasing safe. That materialisation shows up as:once per MLA layer (~4.7 µs × 200 steps on Kimi-K2-Thinking-MXFP4/MI355x), nearly doubling the per-layer fused-op cost.
Fix: Make the C++ kernel write the rotated
k_peinto a fresh contiguous output tensork_pe_out([num_tokens, 1, rot_dim]) instead of mutating the strided input view. The kv_cache write inside the kernel still sees the rotated value (rotation stays in registers; only the final store target changes).Changes:
concat_and_cache_mla_rope_fusedgains a trailingTensor! k_pe_outoutput.vllm/_custom_ops.py): allocatesk_pe_outcontiguous likek_peand returns it; dropsk_pefrommutates_args.kv_cache_mla_rope_fusion.py): rewires downstream consumers to the new contiguousk_pe_out.fix_functionalization.py: updated to match the new op signature (3 outputs; only the third —k_pe_out— is wired to the new contiguous tensor; the first two get rank-1new_empty(0)slots).2. Enable fusion on ROCm (CUDA-alike platforms)
The config guard in
PassConfig.__post_init__disabledenable_cache_mla_rope_fusionwhencurrent_platform.is_cuda()returnedFalse, which silently prevents the fusion on ROCm even though the pass itself is imported under anis_cuda_alike()gate inpass_manager.py. Relaxed the guard tois_cuda_alike()so ROCm (HIP) gets the same fused path.before and after this fusion
before (PR35789)
after (this PR)
Accuracy validation
Tested with Kimi-K2-Thinking-MXFP4 on MI355x with fusion enabled (
enable_cache_mla_rope_fusion: True):Test plan
Acknowledgements
Thanks @attila-dusnoki-htec for contiguous
k_pe_outkernel change