Move torch.cond predicate non-persistent buffer to CPU #16378
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16378
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New Failures, 1 Unrelated FailureAs of commit ab861b9 with merge base c5d66a5 ( NEW FAILURES - The following jobs have failed:
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Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: ff22758 ghstack-comment-id: 3687889864 Pull-Request: #16378
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Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: 8d724ef ghstack-comment-id: 3687889864 Pull-Request: #16378
larryliu0820
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Dec 24, 2025
Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: 4714546 ghstack-comment-id: 3687889864 Pull-Request: #16378
larryliu0820
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Dec 24, 2025
Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: d813c68 ghstack-comment-id: 3687889864 Pull-Request: #16378
larryliu0820
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Dec 24, 2025
Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: efe08be ghstack-comment-id: 3687889864 Pull-Request: #16378
larryliu0820
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Dec 24, 2025
Avoid device-to-host memory copies when evaluating `torch.cond` predicates. When a GPU buffer (e.g., a KV cache `initialized` flag) is used as a predicate for `torch.cond`, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead. `MoveCondPredicateToCpuPass` moves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact. - Add `MoveCondPredicateToCpuPass` in `backends/cuda/passes/` - Add unit tests covering: - GPU buffer predicates moved to CPU - CPU buffer predicates unchanged - Computed predicates unaffected - Multiple `torch.cond` calls - Cross-attention cache pattern - Persistent buffers (state_dict) not moved - Add Python tests to `unittest-cuda` CI job in `cuda.yml` ghstack-source-id: 58e9268 ghstack-comment-id: 3687889864 Pull-Request: #16378
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Avoid device-to-host memory copies when evaluating
torch.condpredicates.When a GPU buffer (e.g., a KV cache
initializedflag) is used as a predicate fortorch.cond, the runtime must synchronize and copy the predicate value from GPU to CPU on every forward pass to evaluate the condition. This adds latency and synchronization overhead.MoveCondPredicateToCpuPassmoves non-persistent buffer predicates to CPU at export time, eliminating per-inference D2H transfers. The predicate is typically a small scalar (e.g., a boolean flag), so keeping it on CPU has negligible memory impact.MoveCondPredicateToCpuPassinbackends/cuda/passes/torch.condcallsunittest-cudaCI job incuda.yml