How does Atlas handle renaming a PyTorch class used in `nn.Module` definitions?

Atlas uses the `lsp` tool's `findReferences` to accurately identify all instances of a PyTorch class, including its use in `nn.Module` definitions, `forward` methods, and `__init__` constructors, ensuring a complete and correct rename.

Can Atlas rename a PyTorch constant that's only used in a configuration file?

Yes, Atlas's `grep` tool is specifically designed to catch occurrences of symbols in non-code contexts like configuration files, comments, and docstrings, ensuring even constants not seen by the language server are renamed.

What if Atlas finds multiple matches for a symbol I want to rename in a single PyTorch file?

Atlas's `edit` tool enforces uniqueness for single replacements. If it finds multiple matches, it will prompt you to add more context or explicitly opt into `replaceAll` to prevent unintended changes in your PyTorch code.

How does Atlas ensure my PyTorch tests still pass after a rename?

After applying renames, Atlas prompts you to run your PyTorch test suite using `atlas bash "uv run pytest"`. This step is integrated into the workflow to immediately verify the correctness of the refactoring.

Does Atlas integrate with PyTorch's `torch.compile` or `torch.no_grad` contexts?

While Atlas directly renames symbols, its understanding of your PyTorch project, including `nn.Module` definitions and `.to(device)` calls, helps ensure that refactors are compatible with performance optimizations like `torch.compile` and `torch.no_grad` by maintaining code correctness.

How does Atlas prevent accidental renames in unrelated PyTorch files?

Atlas's `lsp` tool provides an authoritative list of references, limiting the scope of code changes. For text-based `grep` matches, the `edit` tool's enforcement of uniqueness for single replacements and the requirement for explicit `replaceAll` prevents accidental modifications.

What PyTorch package manager does Atlas recommend for dependency management during refactoring?

Atlas integrates direct with `uv`, the recommended package manager for PyTorch projects in 2026. You can use `atlas bash "uv install"` or `atlas bash "uv run pytest"` within Atlas's environment.

Rename a symbol across the repo in PyTorch with Atlas in 2026

In 2026, renaming a PyTorch symbol across your entire repository with Atlas ensures accuracy by combining language server insights with robust text search and controlled edits. Atlas leverages the `lsp` tool to find authoritative references, `grep` for non-code occurrences, and `edit` for precise, permission-gated modifications, all verified by `pytest` and formatted by `ruff format`.

How Atlas renames PyTorch symbols accurately

Renaming a PyTorch symbol accurately across a large codebase in 2026 requires more than simple text replacement. Atlas employs a multi-stage approach, starting with the `lsp` tool to identify all 100% correct code references, ensuring no critical `nn.Module` or `torch.Tensor` usage is missed.

Atlas begins a PyTorch symbol rename by invoking the `lsp` tool's `findReferences` operation. This leverages the language server's deep understanding of your `pyproject.toml`-pinned `torch` environment to pinpoint every true usage of a function, class, or constant. For instance, if you're renaming `my_custom_loss` within `models/my_model.py`, `lsp` will find all calls to it, even if imported under an alias like `m.custom_loss`. This is crucial for PyTorch, where device placement logic or `DataLoader` worker counts can be sensitive to symbol changes. Atlas then uses `grep` to sweep for occurrences outside the type system, such as comments, docstrings in `__init__.py`, or configuration files that might reference the old name. This hybrid approach ensures comprehensive coverage, preventing subtle bugs that might arise from partial renames.

Concrete commands for PyTorch symbol renaming

Executing a PyTorch symbol rename with Atlas involves a sequence of specific commands, starting with `atlas lsp findReferences` to gather the initial set of 2026 code references. This ensures that every `.to(device)` call or `nn.Module` definition referencing the symbol is correctly identified.

To rename a PyTorch symbol, you'll interact with Atlas's core tools. First, you'd initiate the process by telling Atlas to find references: `atlas lsp findReferences --symbol "old_symbol_name" --file "path/to/definition.py"`. This command targets the exact definition, providing the language server with the necessary context. Next, to catch non-code references, you'd run `atlas grep "old_symbol_name"`. Once Atlas has a comprehensive list, it uses the `edit` tool. For mechanical replacements, `atlas edit replaceAll "old_symbol_name" "new_symbol_name"` will apply changes across the identified files. If a specific file requires a unique, context-aware change, Atlas's `edit` tool will enforce uniqueness, preventing accidental `replaceAll` matches in unrelated code. For example, renaming `_compute_metrics` in `train.py` to `_calculate_metrics` will be precise.

Review and safety in PyTorch refactoring with Atlas

Atlas prioritizes safety and review during PyTorch refactoring, presenting a unified diff for every proposed file edit before writing, ensuring you approve all 100% of changes. This prevents unintended modifications to critical `torch.Tensor` operations or `DataLoader` configurations.

Before any changes are committed, Atlas provides a comprehensive review process. After drafting a plan in its read-only plan agent, Atlas asks for permission to proceed to the build agent. Every file edit, whether it's a change in `models/resnet.py` or `utils/data_loader.py`, is presented as a unified diff. This allows you to inspect the exact modifications, ensuring that renaming `_init_weights` to `_initialize_weights` doesn't inadvertently affect other parts of your `nn.Module` definitions. Atlas also snapshots file changes as git patches, enabling easy rollback if a change introduces an issue. Finally, after the mechanical renames, Atlas prompts you to run `atlas bash "uv run pytest"` to execute your PyTorch test suite and `atlas bash "ruff format ."` to ensure code style consistency, followed by a final `atlas grep "old_symbol_name"` to confirm zero remaining hits of the old symbol.

Step by step

01Identify the PyTorch symbol: Pinpoint the exact function, class, or constant (e.g., `MyCustomLayer` in `my_module.py`) you intend to rename within your PyTorch project.
02Find authoritative code references: Use `atlas lsp findReferences --symbol "old_symbol_name" --file "path/to/definition.py"` to get a precise list of all code usages, including `nn.Module` definitions and `.to(device)` calls.
03Locate non-code occurrences: Run `atlas grep "old_symbol_name"` to find instances in comments, docstrings, or configuration files like `pyproject.toml` that the language server might miss.
04Apply mechanical renames: Execute `atlas edit replaceAll "old_symbol_name" "new_symbol_name"` to perform the bulk of the changes across the identified files, ensuring Atlas's uniqueness enforcement.
05Review and approve changes: Inspect the unified diffs presented by Atlas for each file edit, confirming that all modifications to your PyTorch code are correct.
06Run PyTorch tests: Validate the refactoring by running your test suite with `atlas bash "uv run pytest"` to catch any regressions in device placement or autograd.
07Format the codebase: Ensure consistent style across your PyTorch project by running `atlas bash "ruff format ."`.
08Verify complete rename: Perform a final check with `atlas grep "old_symbol_name"` to confirm no instances of the old symbol remain in your repository.

Frequently asked questions

How does Atlas handle renaming a PyTorch class used in `nn.Module` definitions?: Atlas uses the `lsp` tool's `findReferences` to accurately identify all instances of a PyTorch class, including its use in `nn.Module` definitions, `forward` methods, and `__init__` constructors, ensuring a complete and correct rename.
Can Atlas rename a PyTorch constant that's only used in a configuration file?: Yes, Atlas's `grep` tool is specifically designed to catch occurrences of symbols in non-code contexts like configuration files, comments, and docstrings, ensuring even constants not seen by the language server are renamed.
What if Atlas finds multiple matches for a symbol I want to rename in a single PyTorch file?: Atlas's `edit` tool enforces uniqueness for single replacements. If it finds multiple matches, it will prompt you to add more context or explicitly opt into `replaceAll` to prevent unintended changes in your PyTorch code.
How does Atlas ensure my PyTorch tests still pass after a rename?: After applying renames, Atlas prompts you to run your PyTorch test suite using `atlas bash "uv run pytest"`. This step is integrated into the workflow to immediately verify the correctness of the refactoring.
Does Atlas integrate with PyTorch's `torch.compile` or `torch.no_grad` contexts?: While Atlas directly renames symbols, its understanding of your PyTorch project, including `nn.Module` definitions and `.to(device)` calls, helps ensure that refactors are compatible with performance optimizations like `torch.compile` and `torch.no_grad` by maintaining code correctness.
How does Atlas prevent accidental renames in unrelated PyTorch files?: Atlas's `lsp` tool provides an authoritative list of references, limiting the scope of code changes. For text-based `grep` matches, the `edit` tool's enforcement of uniqueness for single replacements and the requirement for explicit `replaceAll` prevents accidental modifications.
What PyTorch package manager does Atlas recommend for dependency management during refactoring?: Atlas integrates direct with `uv`, the recommended package manager for PyTorch projects in 2026. You can use `atlas bash "uv install"` or `atlas bash "uv run pytest"` within Atlas's environment.

Try Atlas in your terminal

The terminal-native AI coding agent. Free core, single binary.

Install Atlas

Related guides

Rename a Symbol Across the Repo with Atlas in 2026

How to rename a symbol across a repo with Atlas in 2026: findReferences gets the true reference set, grep catches strings and docs, and edit refuses ambiguous matches.

Atlas for PyTorch: Terminal-Native AI Coding for nn.Module, Devices, and Autograd in 2026

Atlas is a terminal-native AI coding agent for PyTorch in 2026, where device placement, autograd, and DataLoader worker counts cause most bugs and most slowness.

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

Trace PyTorch runtime bugs from production stack traces with Atlas. Identify the responsible line and generate fixes without a debugger, streamlining your PyTorch development.

Locate Where a Behavior is Implemented in PyTorch with Atlas in 2026

Pinpoint the exact file and symbol responsible for a behavior in your PyTorch codebase using Atlas. Leverage semantic search, grep, and LSP tools for precise results.

Refactor a legacy PyTorch module with Atlas in 2026

Restructure old PyTorch modules without breaking callers or changing behavior. Atlas uses `lsp` for callsite mapping, `pytest` for baselining, and `apply_patch` for safe code changes.

Extract a Shared Helper from Duplicated Code in PyTorch with Atlas in 2026

Streamline your PyTorch codebase in 2026 by extracting duplicated logic into shared, tested helpers using Atlas. Improve maintainability and reduce bugs related to device placement and autograd.

Plan a Multi-File Change Before Editing in PyTorch with Atlas in 2026

Design and review complex, multi-file PyTorch changes with Atlas's plan agent before modifying a single line of code. Leverage real PyTorch tools like pytest and uv.

Onboard to an Unfamiliar PyTorch Codebase with Atlas in 2026

In 2026, Atlas helps PyTorch developers quickly build a mental model of new codebases. Pinpoint `nn.Module` definitions, identify device placement issues, and integrate with `pytest`, `uv`, and `ruff format` for

Browse this resource hub