How does Atlas find PyTorch code when I only know what it does?

Atlas employs `codebase_search` which uses AST declarations and local Ollama embeddings to understand the semantic meaning of your PyTorch code. This allows it to identify relevant `nn.Module` definitions or `DataLoader` configurations based on your behavioral description, even if your exact words aren't in the source.

Can Atlas help me debug device placement issues in PyTorch?

Yes, Atlas is specifically designed for PyTorch debugging. You can ask Atlas to "find the tensor still on CPU that is silently forcing a device sync inside your inner loop." It will use `codebase_search` to locate `.to(device)` calls and related logic, then `grep` and `lsp` to pinpoint the exact issue.

What PyTorch-specific files and commands does Atlas recognize?

Atlas recognizes `pyproject.toml` for project setup, understands `nn.Module` definitions, training loops, and `.to(device)` calls. It integrates with `pytest` for testing, `uv` for package management, and `ruff format` for code formatting, using their actual commands.

How does Atlas ensure I'm looking at the correct PyTorch file?

When you use `atlas read`, if the path is incorrect, Atlas will loudly report "File not found" and provide a "Did you mean" list. This prevents you from wasting time on non-existent or incorrect PyTorch source files, ensuring accuracy.

Can Atlas help me optimize my PyTorch training loop?

Absolutely. You can have Atlas add `torch.no_grad` around evaluation and wrap your model with `torch.compile`, then show you the throughput delta. Atlas will present these changes as a unified diff for your approval before applying them, helping you safely optimize your PyTorch code.

How does Atlas handle code changes and review in a PyTorch project?

Atlas drafts a plan in a read-only agent and asks for your approval before making any changes. It computes a unified diff for every file edit, like adding `torch.no_grad`, and surfaces it for your review. Atlas also integrates with Git, allowing it to stage and commit changes or snapshot patches for rollback, ensuring a controlled workflow for your PyTorch codebase.

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

To find the exact file and symbol responsible for a behavior in a PyTorch project, Atlas combines semantic search, keyword grep, and LSP symbol graph analysis. This powerful combination helps PyTorch developers in 2026 quickly identify the source of issues like unexpected device placement or `DataLoader` worker count problems, integrating direct with your existing `pytest`, `uv`, and `ruff format` toolchain. Atlas's approach ensures you can locate code even when you only know what it does, not what it is called, providing concrete file and line references for immediate action.

How Atlas Finds PyTorch Code by Behavior, Not Name

Atlas locates PyTorch code by its behavior, not just its name, using a three-pronged approach that includes semantic search. This is crucial for debugging issues like a tensor silently remaining on the CPU, forcing a device sync inside an inner loop, a common problem in 2026 PyTorch development. Atlas's `codebase_search` tool leverages AST declarations and local Ollama embeddings to understand code meaning.

When you describe a behavior in natural language, such as "find where device placement happens for this `nn.Module`" or "locate the `DataLoader` configuration that sets worker counts," Atlas's `codebase_search` tool goes beyond simple keyword matching. It indexes your PyTorch codebase by AST declarations using tree-sitter, allowing it to understand the structure and meaning of your `nn.Module` definitions, training loops, and every `.to(device)` call. This semantic understanding, powered by local Ollama embeddings, means Atlas can return candidate declarations even if your exact words do not appear in the source code. This is particularly effective for complex PyTorch behaviors where the implementation details might be spread across multiple files or use abstract patterns.

Confirming PyTorch Code Locations with `grep`

After Atlas's semantic search provides initial candidates, confirming the exact PyTorch code location is done with the `grep` tool, which uses `ripgrep` for speed. This step is vital for verifying that a suspected `torch.no_grad` block or a specific `optimizer.step()` call is indeed present in the identified files. In 2026, `grep` remains an indispensable tool for precise text matching within large PyTorch repositories.

Once `codebase_search` has identified potential areas, Atlas uses its `grep` tool to confirm the exact text and context. This tool takes a real regex, along with include and path filters, and runs through `ripgrep` for highly efficient searching across your PyTorch project. For instance, if you're looking for a specific pattern like `model.to\(device='cpu'\)` or `torch.compile\(`, `grep` can quickly narrow down the results. You can specify filters like `--include '*.py'` or `--path 'src/models/'` to focus the search on relevant PyTorch source files, ensuring you pinpoint the precise lines of code responsible for the behavior you're investigating.

Inspecting PyTorch Code and Call Paths with `read` and `lsp`

To deeply inspect PyTorch code and understand its call paths, Atlas provides the `read` and `lsp` tools. The `read` tool allows you to open any candidate file, and if you make a wrong guess, it fails loudly with a "File not found" message and a "Did you mean" list, preventing wasted time. This ensures you're always looking at the correct PyTorch source, especially when dealing with 100s of files.

With a confirmed file path, the `read` tool opens the best candidate PyTorch source file for your review. This is where you can examine the `nn.Module` definition, the training loop, or the specific `.to(device)` call in detail. To understand how a particular PyTorch symbol is used across the codebase, Atlas's `lsp` tool is invaluable. You can use `lsp findReferences` on a function like `forward` or a variable like `self.device` to see every callsite, providing a comprehensive view of its usage. Conversely, `lsp workspaceSymbol` allows you to jump directly to a declaration by its name, such as `MyCustomModel` or `AdamW`, making navigation through complex PyTorch projects efficient. Atlas then summarizes the call path back to you with concrete file and line references, like `src/models/my_model.py:123`.

Ensuring Safe PyTorch Code Modifications with Atlas

Atlas prioritizes safety and review in 2026 PyTorch development by implementing permission-gated tool calls and a transparent plan-and-build agent workflow. Every Atlas tool call, whether it's running `pytest` or applying `ruff format`, is permission-gated against allow, ask, and deny rules, ensuring you maintain full control over your codebase. This prevents unintended changes and provides a secure environment for experimentation.

Atlas is designed with multiple layers of safety and review, crucial for maintaining the integrity of your PyTorch project. Before any action is taken, Atlas drafts a plan in a read-only plan agent and asks for your approval before switching to a build agent. This allows you to review the proposed steps, such as adding `torch.no_grad` around evaluation or wrapping a model with `torch.compile`, before they are executed. For every file edit, Atlas computes a unified diff and surfaces it for your approval before writing, giving you a clear view of all changes. Furthermore, Atlas reads git branches, status, and diffs, and can stage and create commits on your behalf, or snapshot file changes as git patches for easy diffing and rolling back, providing robust version control integration for your PyTorch development.

Step by step

01Initialize Atlas in your PyTorch project, ensuring your `pyproject.toml` pins `torch` and Atlas has read access to your `nn.Module` definitions and training loops.
02Describe the behavior you want to locate using `atlas codebase_search`. For example: `atlas codebase_search "find where device placement happens for this nn.Module"` to identify `.to(device)` calls.
03Review the semantic search results and use `atlas grep` with a specific regex to confirm the exact text. For instance: `atlas grep "model\.to\(device='cpu'\)" --include '*.py'`.
04Open the most promising candidate file with `atlas read <file_path>` to inspect the PyTorch code directly, such as a `src/models/my_model.py` file.
05Use `atlas lsp findReferences <symbol_name>` on a PyTorch symbol like `forward` or `optimizer.step` to see all its callsites, or `atlas lsp workspaceSymbol <declaration_name>` to jump to a declaration like `MyCustomLoss`.
06Summarize the identified call path and relevant PyTorch code sections, including file and line references, back to yourself or your team.
07If modifications are needed, let Atlas draft a plan, review the proposed changes (e.g., adding `torch.no_grad`), and approve the unified diff before Atlas writes the changes and runs `ruff format`.

Frequently asked questions

How does Atlas find PyTorch code when I only know what it does?: Atlas employs `codebase_search` which uses AST declarations and local Ollama embeddings to understand the semantic meaning of your PyTorch code. This allows it to identify relevant `nn.Module` definitions or `DataLoader` configurations based on your behavioral description, even if your exact words aren't in the source.
Can Atlas help me debug device placement issues in PyTorch?: Yes, Atlas is specifically designed for PyTorch debugging. You can ask Atlas to "find the tensor still on CPU that is silently forcing a device sync inside your inner loop." It will use `codebase_search` to locate `.to(device)` calls and related logic, then `grep` and `lsp` to pinpoint the exact issue.
What PyTorch-specific files and commands does Atlas recognize?: Atlas recognizes `pyproject.toml` for project setup, understands `nn.Module` definitions, training loops, and `.to(device)` calls. It integrates with `pytest` for testing, `uv` for package management, and `ruff format` for code formatting, using their actual commands.
How does Atlas ensure I'm looking at the correct PyTorch file?: When you use `atlas read`, if the path is incorrect, Atlas will loudly report "File not found" and provide a "Did you mean" list. This prevents you from wasting time on non-existent or incorrect PyTorch source files, ensuring accuracy.
Can Atlas help me optimize my PyTorch training loop?: Absolutely. You can have Atlas add `torch.no_grad` around evaluation and wrap your model with `torch.compile`, then show you the throughput delta. Atlas will present these changes as a unified diff for your approval before applying them, helping you safely optimize your PyTorch code.
How does Atlas handle code changes and review in a PyTorch project?: Atlas drafts a plan in a read-only agent and asks for your approval before making any changes. It computes a unified diff for every file edit, like adding `torch.no_grad`, and surfaces it for your review. Atlas also integrates with Git, allowing it to stage and commit changes or snapshot patches for rollback, ensuring a controlled workflow for your PyTorch codebase.

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