[ExecuTorch][WebGPU] Add Llama K16 online causal attention - #21132

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[ExecuTorch][WebGPU] Add Llama K16 online causal attention#21132
meta-codesync[bot] merged 11 commits into
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@JCNTH

@JCNTHJCNTH commented Jul 22, 2026•
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Stack from ghstack (oldest at bottom):

Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.

Key changes:

  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    handling, and fallback to the materialized route.
    @exported-using-ghexport

Differential Revision: D113171738

Differential Revision: D113171738

@JCNTH
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pytorch-botBot commented Jul 22, 2026•
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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132

Note: Links to docs will display an error until the docs builds have been completed.

❌ 48 New Failures, 28 Pending

As of commit 2a2acde with merge base 28a7fac (image):

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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit that referenced this pull request Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit that referenced this pull request Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit that referenced this pull request Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit that referenced this pull request Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention - #21132
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Contributor
Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
[ghstack-poisoned]
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
As of commit 2a2acde with merge base 28a7fac (image):
NEW FAILURES - The following jobs have failed:
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  • pull / test-arm-backend-no-driver (test_pytest_ops_no_target) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-arm-backend-no-driver (test_pytest_ops_tosa) / linux-job (gh)
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  • pull / test-arm-backend-no-driver (test_run_tosa) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-arm-cortex-m-size-test (bare_metal) / linux-job (gh)
    Error: Input required and not supplied: path
  • pull / test-arm-cortex-m-size-test (zephyr-preset) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
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    ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID B798:F25C:37BAAEE:BD4C352:6A760B2F and timestamp 2026-08-07 16:43:27 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) - https://docs.github.com/en/rest/using-the-rest-api/getting-started-with-the-rest-api#rate-limiting
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  • pull / test-models-linux (emformer_transcribe, portable, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-models-linux (emformer_transcribe, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (linear, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (mv2, portable, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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Contributor
Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention - #21132
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Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
[ghstack-poisoned]
@pytorch-bot
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
As of commit 2a2acde with merge base 28a7fac (image):
NEW FAILURES - The following jobs have failed:
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  • pull / test-arm-backend-no-driver (test_pytest_ops_no_target) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-arm-backend-no-driver (test_pytest_ops_tosa) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-arm-backend-no-driver (test_run_tosa) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-arm-cortex-m-size-test (bare_metal) / linux-job (gh)
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  • pull / test-arm-cortex-m-size-test (zephyr-preset) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-binary-size-linux / linux-job (gh)
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  • pull / test-custom-ops-linux / linux-job (gh)
    ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID B798:F25C:37BAAEE:BD4C352:6A760B2F and timestamp 2026-08-07 16:43:27 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) - https://docs.github.com/en/rest/using-the-rest-api/getting-started-with-the-rest-api#rate-limiting
  • pull / test-llama-runner-qnn-linux (fp32, qnn_16a16w, qnn) / linux-job (gh)
    ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID CC30:37837C:8300DD0:1B7CBF24:6A760B84 and timestamp 2026-08-07 16:44:52 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) - https://docs.github.com/en/rest/using-the-rest-api/getting-started-with-the-rest-api#rate-limiting
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  • pull / test-models-linux (add_mul, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (add, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (add, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
    ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID CB94:957DF:4FA5958:110A0219:6A760B52 and timestamp 2026-08-07 16:44:02 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) - https://docs.github.com/en/rest/using-the-rest-api/getting-started-with-the-rest-api#rate-limiting
  • pull / test-models-linux (emformer_transcribe, portable, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-models-linux (emformer_transcribe, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
    ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID 83B4:3FD34A:864D9D5:1C0CA3F0:6A760B5C and timestamp 2026-08-07 16:44:13 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) - https://docs.github.com/en/rest/using-the-rest-api/getting-started-with-the-rest-api#rate-limiting
  • pull / test-models-linux (ic3, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (ic3, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (linear, portable, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-models-linux (linear, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (mobilebert, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (mobilebert, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (mv2, portable, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-models-linux (mv2, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (resnet18, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux (resnet18, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
  • pull / test-models-linux (resnet50, portable, linux.2xlarge) / linux-job (gh)
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  • pull / test-models-linux-basic (mv3, portable, cmake, linux.2xlarge, executorch-ubuntu-22.04-clang12) / linux-job (gh)
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  • pull / test-models-linux-basic (mv3, xnnpack-quantization-delegation, buck2, linux.2xlarge, executorch-u... / linux-job (gh)
    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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Contributor
Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
[ghstack-poisoned]
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
As of commit 2a2acde with merge base 28a7fac (image):
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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@JCNTH@psiddh

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[ExecuTorch][WebGPU] Add Llama K16 online causal attention - #21132
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Aug 7, 2026
Merged
[ExecuTorch][WebGPU] Add Llama K16 online causal attention#21132
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@JCNTH
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Jul 22, 2026•

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Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
[ghstack-poisoned]
@pytorch-bot
pytorch-botBot
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Jul 22, 2026•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
As of commit 2a2acde with merge base 28a7fac (image):
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention - #21132
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Contributor
Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
Update
5f3c4f6
[ghstack-poisoned]
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
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    ##[error]An error occurred trying to start process '/usr/bin/bash' with working directory '/home/ec2-user/actions-runner/_work/executorch/executorch/pytorch/executorch'. No such file or directory
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  • pull / test-models-linux (emformer_transcribe, portable, linux.2xlarge) / linux-job (gh)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention#21132
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Stack from ghstack (oldest at bottom):
Materializing the full attention-weight matrix for Llama prefill is memory- and

bandwidth-heavy and does not scale to longer sequences. This adds a single-pass

online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64

geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill

shapes through S512 never materialize attention weights. S1 keeps the existing

FlashDecoding path, and unsupported geometry, storage, or capabilities fall back

to the materialized implementation. No Vulkan analogue (WebGPU-specific): the

Vulkan backend has only a materialized attention (compute the weights, then a

separate multi-pass softmax), with no online-softmax kernel.
Key changes:
  • runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
    
header): the online-softmax single-pass causal kernel.
  • Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
    
handling, and fallback to the materialized route.
    
@exported-using-ghexport
Differential Revision: D113171738
Differential Revision: D113171738
@JCNTH
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21132
Note: Links to docs will display an error until the docs builds have been completed.
❌ 48 New Failures, 28 Pending
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[ExecuTorch][WebGPU] Add Llama K16 online causal attention
2bd9d69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
8f138b6
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
c4b7230
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
90ef517
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
JCNTH added a commit
that referenced
this pull request
Aug 7, 2026
@JCNTH
[ExecuTorch][WebGPU] Add Llama K16 online causal attention
7ed6f69
Pull Request resolved: #21132
Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.
Key changes:
- runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
header): the online-softmax single-pass causal kernel.
- Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
handling, and fallback to the materialized route.
ghstack-source-id: 411961446
@exported-using-ghexport
Differential Revision: [D113171738](https://our.internmc.facebook.com/intern/diff/D113171738/)
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