How does Atlas handle PyTorch device placement bugs from stack traces?

Atlas reads the stack trace frames, then uses `grep` to find error messages like "Tensor on CPU, expected CUDA". It then uses `lsp` to find references to `tensor.to(device)` calls or `nn.Module` definitions, helping identify where a tensor is incorrectly placed, causing device syncs or errors.

Can Atlas fix PyTorch DataLoader worker count issues?

Yes, Atlas can trace `DataLoader` related errors from stack traces. It can identify problematic worker counts or data loading logic by analyzing relevant code sections and propose `edit` changes to your `DataLoader` instantiation or dataset processing.

What PyTorch tools does Atlas integrate with for debugging?

Atlas integrates deeply with the PyTorch ecosystem. It understands `pyproject.toml` for `uv` managed dependencies, uses `pytest` for running regression tests, and applies `ruff format` for code style, all while analyzing your `nn.Module` definitions and `torch.to(device)` calls.

How does Atlas ensure safety when modifying PyTorch code?

Atlas ensures safety through permission-gated tool calls, a read-only plan agent, and presenting all proposed `edit` changes as unified diffs for explicit developer approval. It also uses Git integration to snapshot changes for easy rollback.

Can Atlas debug PyTorch autograd issues from a stack trace?

Yes, Atlas can help debug PyTorch autograd issues. By analyzing the stack trace and using `grep` for autograd-related errors, then `lsp` to trace back through `torch.no_grad` blocks or custom `autograd.Function` implementations, Atlas can pinpoint the source of gradient computation problems.

Does Atlas require a live debugger for PyTorch bug tracing?

No, Atlas is designed to trace PyTorch runtime bugs from a production stack trace without requiring a live debugger attached. It uses static analysis, code indexing, and AI reasoning to identify the responsible line and propose fixes.

Trace a runtime bug from a stack trace in PyTorch with Atlas in 2026

Atlas helps PyTorch developers in 2026 trace runtime bugs from production stack traces directly to the responsible line of code and generate a fix, all without attaching a debugger. By integrating with your existing PyTorch toolchain, including `pytest` for testing, `uv` for package management, and `ruff format` for code formatting, Atlas provides a terminal-native AI agent experience that accelerates debugging workflows.

How Atlas Traces PyTorch Runtime Bugs from Stack Traces

Atlas traces PyTorch runtime bugs by consuming a production stack trace, reading each frame's file at its reported offset, and then intelligently reconstructing the call path. This process ensures that even a 2026 PyTorch project with complex `nn.Module` definitions can be debugged efficiently without a live debugger.

Atlas's `read` tool consumes the `file:line` pairs from a PyTorch stack trace. It validates offsets against the current file, preventing misdirection from outdated traces. For instance, if a `torch.Tensor` operation fails due to a device mismatch in `my_model/layers.py:123`, Atlas reads that exact line. If the `read` tool reports "Offset <n> is out of range for this file", it indicates the trace came from an older build. Atlas prompts to re-read the file from the top, ensuring accuracy before proceeding with any line number. Atlas then uses `grep` to search for the exact error message string. This often reveals where the error message is constructed, which can be more informative than just the top frame of a PyTorch `RuntimeError` or `ValueError`. For example, finding the source of a "Tensor on CPU, expected CUDA" error. Finally, the `lsp` tool's `findReferences` operation is used on the failing function. This helps identify all callers that could potentially reach the problematic code with the bad input, such as an incorrectly placed `tensor.to(device)` call or a `DataLoader` with an unexpected worker count. This comprehensive approach helps pinpoint the root cause of PyTorch-specific issues like device placement, autograd, and DataLoader worker counts.

Concrete PyTorch Commands and Files for Atlas Debugging

Debugging PyTorch with Atlas involves interacting with real project files and using familiar commands, ensuring a smooth workflow for developers in 2026. Atlas integrates directly with your `pyproject.toml` to understand dependencies and can analyze `nn.Module` definitions and `torch.to(device)` calls across your codebase.

Atlas operates within your PyTorch project, starting by reading your `pyproject.toml` to understand pinned `torch` versions and other dependencies managed by `uv`. This context is crucial for understanding the environment where the bug occurred. When tracing a bug, Atlas will specifically read your `nn.Module` definitions, the training loop, and every `tensor.to(device)` call. This allows it to identify common PyTorch issues like a tensor remaining on the CPU when it should be on a CUDA device, silently forcing a device sync inside an inner loop and causing performance bottlenecks or runtime errors. After identifying a potential fix, Atlas can be instructed to add a regression test using `pytest`. For example, it might generate a new test case in `tests/test_model_device.py` that specifically triggers the bug. Once the fix is applied and tested, Atlas can run `ruff format` to ensure the new code adheres to your project's formatting standards, maintaining code quality across the 2026 PyTorch ecosystem.

Review and Safety in Atlas's PyTorch Debugging Workflow

Atlas prioritizes safety and developer control throughout the PyTorch debugging process, ensuring every proposed change is transparent and approved. Every Atlas tool call, from `read` to `edit`, is permission-gated, and all file edits are presented as unified diffs for explicit approval before being written to disk, protecting your 2026 codebase.

Atlas is designed with a robust safety framework. Every tool call, whether it's `read` to inspect a `torch.nn.Module` definition or `edit` to propose a fix for a `DataLoader` bug, is permission-gated. This means you explicitly allow, ask for approval, or deny Atlas's actions, maintaining full control over your PyTorch project. The debugging workflow begins with Atlas drafting a plan in a read-only plan agent. This plan, which might involve steps like "grep for 'device mismatch' in `training_loop.py`", is presented for your review. Only after your approval does Atlas switch to a build agent to execute the plan. When Atlas proposes a fix, such as modifying a `tensor.to(device)` call or adjusting a `torch.no_grad` block, it computes a unified diff for every file edit. This diff is surfaced for your approval, allowing you to review the exact changes before they are written. Furthermore, Atlas integrates with Git, reading branches, status, and diffs. It can snapshot file changes as Git patches, enabling easy diffing and rollback of edits. This ensures that even complex PyTorch bug fixes can be applied and managed with confidence, providing a secure and auditable debugging experience.

Step by step

01Paste the PyTorch stack trace into Atlas and use the `read` tool to inspect each `file:line` frame, such as `my_model/training.py:45` where a `torch.Tensor` operation failed.
02If Atlas reports "Offset <n> is out of range for this file" for a PyTorch source file, re-read the file from the top to ensure the trace is not from an older build before trusting any line number.
03Use the `grep` tool to search for the exact PyTorch error message string, like "Tensor on CPU, expected CUDA", to find where it is constructed in your `nn.Module` or training loop.
04Employ the `lsp` tool's `findReferences` operation on the failing PyTorch function or method, such as `my_tensor.to(device)`, to identify all callers that might supply the bad input or incorrect device placement.
05Instruct Atlas to `edit` the identified line, for example, adding a missing `tensor.to(device)` call or correcting a `DataLoader` worker count, and then review the unified diff for approval.
06Ask Atlas to add a regression test using `pytest` in `tests/test_device_placement.py` to prevent the PyTorch bug from recurring silently.
07Have Atlas run `pytest` behind a permission prompt to validate the fix and the new regression test.
08Finally, instruct Atlas to run `ruff format` across the modified PyTorch files to maintain code style consistency.

Frequently asked questions

How does Atlas handle PyTorch device placement bugs from stack traces?: Atlas reads the stack trace frames, then uses `grep` to find error messages like "Tensor on CPU, expected CUDA". It then uses `lsp` to find references to `tensor.to(device)` calls or `nn.Module` definitions, helping identify where a tensor is incorrectly placed, causing device syncs or errors.
Can Atlas fix PyTorch DataLoader worker count issues?: Yes, Atlas can trace `DataLoader` related errors from stack traces. It can identify problematic worker counts or data loading logic by analyzing relevant code sections and propose `edit` changes to your `DataLoader` instantiation or dataset processing.
What PyTorch tools does Atlas integrate with for debugging?: Atlas integrates deeply with the PyTorch ecosystem. It understands `pyproject.toml` for `uv` managed dependencies, uses `pytest` for running regression tests, and applies `ruff format` for code style, all while analyzing your `nn.Module` definitions and `torch.to(device)` calls.
How does Atlas ensure safety when modifying PyTorch code?: Atlas ensures safety through permission-gated tool calls, a read-only plan agent, and presenting all proposed `edit` changes as unified diffs for explicit developer approval. It also uses Git integration to snapshot changes for easy rollback.
Can Atlas debug PyTorch autograd issues from a stack trace?: Yes, Atlas can help debug PyTorch autograd issues. By analyzing the stack trace and using `grep` for autograd-related errors, then `lsp` to trace back through `torch.no_grad` blocks or custom `autograd.Function` implementations, Atlas can pinpoint the source of gradient computation problems.
Does Atlas require a live debugger for PyTorch bug tracing?: No, Atlas is designed to trace PyTorch runtime bugs from a production stack trace without requiring a live debugger attached. It uses static analysis, code indexing, and AI reasoning to identify the responsible line and propose fixes.

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