Review PyTorch pull requests for code quality, test coverage, security, and backward compatibility. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".
| name | pr-review |
| description | Review PyTorch pull requests for code quality, test coverage, security, and backward compatibility. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR". |
Review PyTorch pull requests focusing on what CI cannot check: code quality, test coverage adequacy, security vulnerabilities, and backward compatibility.
If the user invokes /pr-review with no arguments, do not perform a review. Instead, ask the user what they would like to review:
What would you like me to review?
- A PR number or URL (e.g.,
/pr-review 12345)- A local branch (e.g.,
/pr-review branch)
The user provides a PR number or URL:
/pr-review 12345
/pr-review https://github.com/pytorch/pytorch/pull/12345
For a detailed review with line-by-line specific comments:
/pr-review 12345 detailed
Use gh CLI to fetch PR data:
# Get PR details
gh pr view <PR_NUMBER> --json title,body,author,baseRefName,headRefName,files,additions,deletions,commits
# Get the diff
gh pr diff <PR_NUMBER>
# Get PR comments
gh pr view <PR_NUMBER> --json comments,reviews
Review changes in the current branch that are not in main:
/pr-review branch
/pr-review branch detailed
Use git commands to get branch changes:
# Get current branch name
git branch --show-current
# Get list of changed files compared to main
git diff --name-only main...HEAD
# Get full diff compared to main
git diff main...HEAD
# Get commit log for the branch
git log main..HEAD --oneline
# Get diff stats (files changed, insertions, deletions)
git diff --stat main...HEAD
For local branch reviews:
When invoked via @claude /pr-review on a GitHub PR, the action pre-fetches PR
metadata and injects it into the prompt. Detect this mode by the presence of
<formatted_context>, <pr_or_issue_body>, and <comments> tags in the prompt.
The prompt already contains:
Use git commands to get the diff and commit history. The base branch name is in the
prompt context (look for PR Branch: <head> -> <base> or the baseBranch field).
# Get the full diff against the base branch
git diff origin/<baseBranch>...HEAD
# Get diff stats
git diff --stat origin/<baseBranch>...HEAD
# Get commit history for this PR
git log origin/<baseBranch>..HEAD --oneline
# If the base branch ref is not available, fetch it first
git fetch origin <baseBranch> --depth=1
Do NOT use gh CLI commands in this mode -- only git commands are available.
All PR metadata, comments, and reviews are already in the prompt context;
only the diff and commit log need to be fetched via git.
A single line of code can have deep cross-cutting implications: a missing device guard causes silent data corruption on multi-GPU, a missing Composite dispatch key breaks every out-of-tree backend, a manual dtype check instead of TensorIterator silently skips type promotion. Treat every line as potentially load-bearing.
The review checklist is large. You cannot hold the full context of every infrastructure system in your head. Spawn sub-agents to investigate whether checklist items apply: read surrounding code, infrastructure the PR should be using, or tests that should exist. Spawn them in parallel for independent areas. A typical medium PR should spawn 3-8 sub-agents.
Before reviewing, build understanding of what the PR touches and why:
Go through every changed line in the diff and evaluate it against the review checklist in review-checklist.md.
Evaluate BC implications per bc-guidelines.md. For non-trivial BC questions, spawn a sub-agent to search for existing callers of the modified API.
Structure your review with actionable feedback organized by category. Every finding should be traceable to a specific line in the diff.
After drafting the review, spawn a sub-agent per reported issue (in parallel) to independently verify the claim by re-reading the relevant code and surrounding context. Each sub-agent returns valid, invalid, or needs rewording. Drop invalid issues, reword the rest. If unsure, leave the issue with a comment for the author that this low confidence.
Structure your review as follows. Omit sections where you have no problems to report — most reviews should only have a few sections. Do not write "No concerns", "Looks good", or any affirmative commentary. Every sentence in the review must identify a problem or request a change.
The Summary section is the one exception: it should briefly state what the PR does (1 sentence) and then state the problems found, or explicitly say no issues were found.
## PR Review: #<number>
<!-- Or for local branch reviews: -->
## Branch Review: <branch-name> (vs main)
### Summary
What the PR does (1 sentence), then the overall verdict.
### Code Quality
[Problems only]
### Infrastructure
[Problems only — flag checklist items that are violated]
### Testing
[Problems only — missing tests, wrong patterns, inadequate coverage]
### API Design
[Problems only]
### Security
[Problems only]
### Thread Safety
[Problems only]
### Backward Compatibility
[Problems only]
### Performance
[Problems only]
### Recommendation
**Approve** / **Request Changes** / **Needs Discussion**
Missing tests (new functionality without tests, bug fixes without regression tests) always means **Request Changes**.
[Brief justification — focus on what blocks approval, if anything]
Only include this section if the user requests a "detailed" or "in depth" review.
Do not repeat observations already made in other sections. This section is for additional file-specific feedback that doesn't fit into the categorized sections above.
When requested, add file-specific feedback with line references:
### Specific Comments
- `src/module.py:42` - Consider extracting this logic into a named function for clarity
- `test/test_feature.py:100-105` - Missing test for error case when input is None
- `torch/nn/modules/linear.py:78` - This allocation could be moved outside the loop
When reviewing, consult these project files for context — read them rather than relying on memory, as they change frequently:
CLAUDE.md - Coding style philosophy and testing patternsCONTRIBUTING.md - PR requirements and review processtorch/testing/_internal/common_utils.py - Test patterns and utilitiestorch/testing/_internal/opinfo/core.py - OpInfo test frameworkaten/src/ATen/native/native_functions.yaml - Operator declarations (for checking tags, dispatch keys, structured kernels)tools/autograd/derivatives.yaml - Backward formulas (for checking if an op should register here)aten/src/ATen/native/tags.yaml - Operator semantic tagsTriages GitHub issues by routing to oncall teams, applying labels, and closing questions. Use when processing new PyTorch issues or when asked to triage an issue.
Fix bugs reported in PyTorch GitHub issues by reproducing, root-causing, and implementing a fix in the local working tree. Use when the user asks to fix a PyTorch GitHub issue.
Write Metal/MPS kernels for PyTorch operators. Use when adding MPS device support to operators, implementing Metal shaders, or porting CUDA kernels to Apple Silicon. Covers native_functions.yaml dispatch, host-side operators, and Metal kernel implementation.
Sub-triages issues in the oncall:distributed queue by assigning distributed module labels, routing to sub-oncalls, and marking triaged. Use when an issue has been routed to oncall:distributed and needs second-level triage.
Migrate a file to use stricter Pyrefly type checking with annotations required for all functions, classes, and attributes.
Debug PyTorch 2 compiler stack failures including Dynamo graph breaks, Inductor codegen errors, AOTAutograd crashes, and accuracy mismatches. Use when encountering torch.compile errors, BackendCompilerFailed exceptions, recompilation issues, Triton kernel failures, FX graph problems, or when the user mentions debugging PT2, Dynamo, Inductor, or compiled model issues.