[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

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[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

As of commit 1b7fdc1 with merge base 28a7fac ():

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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

As of commit 1b7fdc1 with merge base 28a7fac ():

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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

As of commit 1b7fdc1 with merge base 28a7fac ():

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• pull / test-models-linux (add_mul, 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
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• pull / test-models-linux (mobilebert, xnnpack-quantization-delegation, linux.2xlarge) / linux-job (gh)
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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

As of commit 1b7fdc1 with merge base 28a7fac ():

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[github-actions]

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[SS-JIA]

Review automatically exported from Phabricator review in Meta.

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Lift FastVLM dispatch limits and harden graph construction #21602
• [ExecuTorch][WebGPU] Cover Conv1d routes and dynamic manifests #21601
• [ExecuTorch][WebGPU] Support general groups=1 Conv1d #21600
• [ExecuTorch][WebGPU] Test byte-packed BOOL tails #21599
• [ExecuTorch][WebGPU] Support byte-packed BOOL storage #21598
• [ExecuTorch][Vulkan] Preserve persistent buffer mutations #21597
• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• ->[ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Llama prefill drives the ordinary quantized-linear projections at a small set
of fixed batch-row counts, and the generic Steel schedule leaves throughput on
the table for them. This adds a frozen BK64 Steel quantized-GEMM route for the
exact accepted Llama ordinary-projection shapes at live M128, M508, and M512,
selected only when the capability and dynamic-route guards all pass. M511 and
other prefill sizes stay on the generic Steel schedule and M1 stays on bicol
decode, so the route fails closed outside its accepted shapes. Mirrors Vulkan
xplat/executorch/backends/vulkan/runtime/graph/ops/glsl/q4gsw_linear_gemm__w_4x8.glsl
(the 4-bit grouped-symmetric-weight GEMM), specialized here for the BK64 tile.

Key changes:

• runtime/ops/quantized_linear/q4gsw_steel_bk64.wgsl (+ generated header): the
  BK64-tiled q4gsw prefill GEMM kernel.
• QuantizedLinear.cpp, WebGPUUtils.h: exact-shape predicate, capability guard,
  and dynamic re-entry into and out of the BK64 route.
  @exported-using-ghexport

Differential Revision: D113171739

Differential Revision: D113171739

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21130

• 📄 Preview Python docs built from this PR

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

❌ 37 New Failures, 30 Pending

As of commit 1b7fdc1 with merge base 28a7fac ():

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• pull / test-qnn-wheel-packages-linux (3.10) / linux-job (gh)
  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID A1B0:3BE4F5:82B6351:1B7B6909:6A760B76 and timestamp 2026-08-07 16:44:38 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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  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID 8C3C:AA848:8636526:1C347EE7:6A760B7A and timestamp 2026-08-07 16:44:42 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-qnn-wheel-packages-linux (3.12) / 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-qnn-wheel-packages-linux (3.13) / linux-job (gh)
  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID 8D82:2ABE79:4C6C676:10294BDA:6A760B80 and timestamp 2026-08-07 16:44:48 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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  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID 9544:37AD1A:820FDA4:1B4ECDA0:6A760BCD and timestamp 2026-08-07 16:46:05 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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  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID D7AC:3C7861:846DAD2:1BD0BB2B:6A760B87 and timestamp 2026-08-07 16:44:55 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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  ##[error]API rate limit exceeded for installation. If you reach out to GitHub Support for help, please include the request ID 9FCA:2C3D6C:4C4541A:1023DAF5:6A760B4C and timestamp 2026-08-07 16:43:56 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 / unittest / macos / macos-job (gh)
  ##[error]Can't find 'action.yml', 'action.yaml' or 'Dockerfile' under '/Users/ec2-user/runner/_work/executorch/executorch/test-infra/.github/actions/check-disk-space'. Did you forget to run actions/checkout before running your local action?
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• pull / unittest-editable / macos / macos-job (gh)
  ##[error]Can't find 'action.yml', 'action.yaml' or 'Dockerfile' under '/Users/ec2-user/runner/_work/executorch/executorch/test-infra/.github/actions/check-disk-space'. Did you forget to run actions/checkout before running your local action?

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