--- name: perf-torch-sync-free-bf46757e5ba4 description: >- Identify and eliminate host-device synchronizations in PyTorch code. Detects sync points (.item(), .cpu(), boolean indexing, torch.tensor on CUDA), classifies false vs true dependencies, provides sync-free alternatives. Triggers: sync-free, synchronization, .item(), .cpu(), host-device sync, eliminate syncs, CPU stall, non_blocking, set_sync_debug_mode, cudaStreamSynchronize, cudaEventSynchronize, remove syncs, async GPU. tags: - synchronization - performance - pytorch license: Apache-2.0 metadata: author: NVIDIA Corporation --- # Writing Sync-Free PyTorch Code Sync-free code means the CPU continuously queues work to the GPU without waiting for GPU operations to complete. When host-device synchronizations are eliminated, the GPU works continuously without idle stalls. Every host-device synchronization ultimately calls one of three CUDA driver APIs that block the CPU thread: - `cuEventSynchronize` -- CPU waits until a specific GPU event completes - `cuStreamSynchronize` -- CPU waits until all work on a stream finishes - `cuCtxSynchronize` -- CPU waits until all work across all streams finishes ## When to Use Reach for this skill when you encounter: - **Triggers**: User wants to remove host-device synchronizations, eliminate CPU stalls from GPU waits, make code async/sync-free, remove `.item()` or `.cpu()` calls that block the CPU, or understand why specific PyTorch operations cause synchronization - **Symptoms**: Frequent `cudaStreamSynchronize` in nsys profiles, warnings from `torch.cuda.set_sync_debug_mode`, training throughput limited by CPU-GPU round-trips, `.item()` or `.cpu()` calls in hot loops - **Keywords**: "sync-free", "synchronization", ".item()", ".cpu()", "host-device sync", "eliminate syncs", "CPU stall", "non_blocking", "set_sync_debug_mode", "cudaStreamSynchronize", "cudaEventSynchronize", "remove syncs", "async GPU", "CPU waiting on GPU" Do NOT use this skill for: - Applying CUDA Graphs or reducing kernel launch overhead (use `perf-torch-cuda-graphs` instead) - Profiling GPU kernels, system timelines, or finding GPU idle time (use `perf-nsight-compute-analysis` or `perf-nsight-systems`) - Kernel optimization or code generation (use `kernel-triton-writing`) - Optimizing NCCL communication or distributed training collective operations - Reducing GPU memory usage or gradient checkpointing - General model compilation with `torch.compile` ## Requirements | Dependency | Version | Notes | |------------|---------|-------| | PyTorch | >=2.0 | With CUDA support | | NVIDIA GPU | Any | CUDA-capable | | Nsight Systems | Optional | For comprehensive sync detection via `nsys` | ## Workflow ### Step 1: Detect Synchronizations Use one or both methods to find sync points in the code. **Quick detection** -- PyTorch sync debug mode prints a warning with stack trace on every synchronization: ```python import torch # Enable at the start of the region you want to check torch.cuda.set_sync_debug_mode('warn') # prints warning + stack trace # torch.cuda.set_sync_debug_mode('error') # raises exception on sync # Run your training step / forward pass here train_step(model, batch) torch.cuda.set_sync_debug_mode(0) # disable ``` This mode only detects syncs going through PyTorch's wrapped `cuStreamSynchronize`. Third-party libraries calling CUDA sync APIs directly are not detected. **Comprehensive detection** -- Nsight Systems captures all sync calls including those from extensions and libraries: ```bash nsys profile --capture-range=cudaProfilerApi \ --python-sampling=true \ --backtrace=dwarf \ python your_script.py ``` In the Nsight Systems GUI, check the **CUDA API** timeline row and search for `cudaStreamSynchronize`, `cudaEventSynchronize`, or `cudaDeviceSynchronize`. The call stack panel shows which Python line triggered each sync. ### Step 2: Classify -- False vs True Dependencies After detecting syncs, classify each one before deciding how to fix it. **False dependencies** (avoidable) -- CPU does not actually need the GPU result. These can be eliminated without changing program logic: - Debug prints left in hot paths (`print(loss.item())`) - Unnecessary `.item()` calls for logging that could be deferred - Using `.cuda()` instead of `.to('cuda', non_blocking=True)` - Using `.type(torch.LongTensor)` instead of `.type(torch.long)` - Creating tensors from Python objects directly on CUDA **True dependencies** (require restructuring) -- CPU genuinely needs the GPU value to proceed: - **Control flow dependency**: `if loss.item() > threshold:` -- CPU branches on a GPU-computed value - **Dynamic memory allocation**: `output = x[mask]` -- output size depends on GPU computation - **CPU computation using GPU values**: computing statistics for logging, updating learning rates from metrics True dependencies require restructuring: move logic to GPU (`torch.where()`), delay to end of iteration, or accept that those parts stay outside any CUDA Graph capture region. ### Step 3: Eliminate Systematically Apply fixes in order of increasing difficulty. Start with easy wins. **1. Remove redundancy** -- Delete operations that do not need to happen: - Remove debug prints and logging from hot loops - Delete unnecessary `.item()` calls - Eliminate duplicate synchronizations **2. Use `non_blocking=True`** -- Make transfers async where CPU does not immediately use the result: ```python # Before (syncs) x_gpu = x_cpu.cuda() x_cpu = x_gpu.cpu() # After (async, no sync) x_gpu = x_cpu.to('cuda', non_blocking=True) x_cpu = x_gpu.to('cpu', non_blocking=True) # only if CPU does not use x_cpu immediately ``` Only use `non_blocking=True` for GPU-to-CPU when the CPU does not immediately read the result. Otherwise the CPU may operate on incomplete data. **3. Switch to sync-free API alternatives** -- See the Quick Reference Table below for a condensed mapping of common patterns. **4. Delay synchronization to end of iteration** -- Move logging and validation to after the optimizer step rather than mid-forward/backward: ```python # Before: sync mid-iteration loss = model(batch) print(f"Loss: {loss.item()}") # cuStreamSynchronize loss.backward() # After: delay to end of iteration loss = model(batch) loss.backward() optimizer.step() print(f"Loss: {loss.item()}") # sync is outside the hot path ``` **5. Coalesce multiple syncs into one** -- If you need several GPU values on CPU, gather them and transfer once: ```python # Before: 3 separate syncs loss_val = loss.item() # cuStreamSynchronize acc_val = accuracy.item() # cuStreamSynchronize gnorm_val = grad_norm.item() # cuStreamSynchronize # After: 1 sync metrics = torch.stack([loss, accuracy, grad_norm]) vals = metrics.cpu() # single cuStreamSynchronize loss_val, acc_val, gnorm_val = vals.tolist() ``` **6. Offload logic to GPU** -- Replace CPU-side logic with GPU-native ops: ```python # Before: CPU control flow (syncs) if loss.item() > threshold: result = a else: result = b # After: GPU-side selection (no sync) result = torch.where(loss > threshold, a, b) # Before: Python max (syncs) val = max(x_gpu[0, 0], x_gpu[0, 1]) # After: torch.max (no sync) val = torch.max(x_gpu[0, 0], x_gpu[0, 1]) ``` **7. Exclude unavoidable syncs from capture range** (last resort) -- If a sync cannot be eliminated, keep it outside the CUDA Graph capture region and graph only the sync-free sections. Partial graphing is better than no graphing. ### Step 4: Verify Re-run detection to confirm syncs are eliminated: ```python torch.cuda.set_sync_debug_mode('error') # will raise if any sync remains train_step(model, batch) torch.cuda.set_sync_debug_mode(0) ``` Or re-profile with Nsight Systems and confirm no `cudaStreamSynchronize` / `cudaEventSynchronize` / `cudaDeviceSynchronize` calls appear in the target region. ## Quick Reference Table | Sync-Inducing Pattern | Sync-Free Alternative | |----------------------|----------------------| | **Device Transfers** | | | `.cpu()` or `.to('cpu')` | `.to('cpu', non_blocking=True)` (fire-and-forget only) | | `.cuda()` or `.to('cuda')` | `.to('cuda', non_blocking=True)` | | `.type(torch.LongTensor)` | `.type(torch.long)` (dtype conversion, stays on GPU) | | **Tensor Creation** | | | `torch.tensor(obj, device='cuda')` | Create on CPU, then `.to('cuda', non_blocking=True)` | | `torch.tensor(0, device='cuda')` | `torch.zeros(1, device='cuda', dtype=...).squeeze()` | | `torch.as_tensor(arr, device='cuda')` | Create on CPU, then `.to('cuda', non_blocking=True)` | | `torch.cuda.BoolTensor(list)` | `torch.tensor(list, device='cpu').to('cuda', non_blocking=True)` | | **Control Flow** | | | `.item()` in conditionals | `torch.where()` or move outside critical region | | `if gpu_tensor:` | Keep logic on GPU with `torch.where()` | | Python `max(a, b)` on GPU tensors | `torch.max(a, b)` | | `torch.is_nonzero(t)` | Avoid; use GPU-side comparisons | | **Indexing** | | | `x_gpu[idx_cpu]` or `x_gpu[idx_list]` | `x_gpu[idx_gpu]` (keep indices on same device) | | `x_gpu[idx] = 0` (scalar assignment) | `x_gpu[idx] = zero_gpu` (GPU tensor value) | | `x[i:j]` with CUDA tensor bounds | `x[:, s]` with `s = torch.arange(i, j, device='cuda')` | | **Dynamic Shapes** | | | `x_gpu[mask_gpu]` (masked selection) | `torch.where(mask_gpu, x_gpu, 0)` (fixed shape) | | `torch.nonzero(mask)` | `torch.where()` or move outside critical region | | `torch.masked_select(x, mask)` | `torch.where(mask, x, 0)` | | `torch.unique(x)` | Avoid in hot path; precompute if possible | | `torch.repeat_interleave(x, r)` | Specify `output_size=N` if known | ## Finding More Information - **Tier 1 (this file)**: Workflow, classification, elimination strategies, and quick reference table - **Tier 2 (`references/sync-patterns.md`)**: Comprehensive pattern catalog with 9 categories, full code examples showing sync-inducing and sync-free versions, and the specific CUDA driver API triggered by each pattern --- Source attribution: NVIDIA Corporation Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/TensorRT-LLM/perf-torch-sync-free/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0 Copyright (c) 2026 NVIDIA Corporation This project is licensed under a dual license: - Apache License 2.0 (for code and configuration) - Creative Commons Attribution 4.0 International (CC BY 4.0) (for documentation) You may use the contents of this repository under the terms of either license, or both, as applicable to the type of content. ================================================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to the Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by the Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding any notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS ================================================================================ Creative Commons Attribution 4.0 International License (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ You are free to: Share — copy and redistribute the material in any medium or format for any purpose, even commercially. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material. For the full legal text, see: https://creativecommons.org/licenses/by/4.0/legalcode # Mega AI Hub resource metadata Creator / publisher: NVIDIA Corporation Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/TensorRT-LLM/perf-torch-sync-free/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Data & Analytics Roles: ML / AI Engineer, Data Scientist, Backend Developer Specialties: Backend Tools: Nsight Systems, Pytorch Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: df9778de1cfdffa91da4faa1b53f88b6ae39f10ab5ec433df876a214e8d8cafb