test-gen

name	test-gen
argument-hint	["file-path or \"local\""]
description	Generate tests for TorchRec source files with correct patterns (unit, distributed, hypothesis), proper BUCK targets, and test utilities. Use when asked to generate tests, add test coverage, or write tests for a module.
allowed-tools	Read, Write, Edit, Bash(sl:*), Bash(buck2:*), Grep, Glob, Task

TorchRec Test Generator

Generate idiomatic TorchRec tests by reading source files, detecting the appropriate test type, scaffolding test code with correct patterns, and creating/updating BUCK targets.

Usage Modes

File Path Mode

/test-gen torchrec/distributed/sharding/my_sharder.py
/test-gen torchrec/modules/new_module.py

Generate tests for the specified source file.

Auto-Detect Mode

/test-gen local
/test-gen

Detect changed files via sl status and generate tests for new/modified source files that lack test coverage.

Workflow

Phase 1: Identify Source Files

File path mode: Read the specified file.

Auto-detect mode:

1. Run sl status to find changed files
2. Filter to .py source files in torchrec/ (exclude test files, __init__.py, BUCK files)
3. For each source file, check if a corresponding test file exists at $(dirname)/test(s)/test_$(basename)
4. Present the list of untested files and ask the user which to generate tests for

Phase 2: Analyze Source Code

Read the source file and classify it:

Detection rules (in priority order):

1. Distributed test if ANY of:

   • File is under torchrec/distributed/
   • Imports from torch.distributed, torchrec.distributed, or uses ProcessGroup
   • Defines sharders, sharded modules, or uses ShardingType
   • Uses LazyAwaitable, all_to_all, all_reduce, all_gather

2. Hypothesis-parameterized test if ANY of:

   • Source defines enums, configs, or strategies with multiple variants
   • Source handles multiple ShardingType or EmbeddingComputeKernel values
   • Source has branching behavior based on config parameters

3. Unit test (default) if:

   • File is under torchrec/modules/, torchrec/sparse/, torchrec/optim/, torchrec/metrics/
   • No distributed primitives used

A file can be both distributed AND hypothesis-parameterized.

Extract from the source file:

• Public classes and their methods
• Public functions
• Constructor signatures and required arguments
• Key data types (KJT, EBC, KeyedTensor, etc.)
• Dependencies and imports needed for tests

Phase 3: Determine Test Location

Follow TorchRec convention:

• Source: torchrec/foo/bar/my_module.py
• Test: torchrec/foo/bar/tests/test_my_module.py

If a tests/ directory doesn't exist, create it. If a test file already exists, add new test methods rather than overwriting.

Phase 4: Generate Test Code

Generate tests following the patterns below. See test-patterns.md for complete templates.

For all test types:

• BSD license header + # pyre-strict
• Type hints on all methods (return -> None for test methods)
• Use self.assertEqual, self.assertTrue, torch.testing.assert_close for assertions
• Cover: happy path, edge cases (empty inputs, single element), error conditions
• Name tests descriptively: test_<what>_<condition>

For unit tests:

• Inherit from unittest.TestCase
• Test each public method/function independently
• For modules: test forward() with representative inputs, verify output shapes and types

For distributed tests:

• Inherit from MultiProcessTestBase
• Use @staticmethod or module-level _test_func(rank, world_size, **kwargs) pattern
• Wrap per-rank logic in with MultiProcessContext(rank, world_size, backend) as ctx:
• Default world_size=2, add world_size=4 for sharding tests
• Use backend="gloo" unless testing GPU-specific behavior
• Add @unittest.skipIf(torch.cuda.device_count() < N, "Not enough GPUs...") for CUDA tests

For hypothesis tests:

• Add @given(...) with st.sampled_from([...]) for enum/config parameters
• Add @settings(verbosity=Verbosity.verbose, max_examples=N, deadline=None)
• Use assume() to filter invalid parameter combinations
• Keep max_examples reasonable (4-8 for distributed tests, 10-20 for unit tests)

Phase 5: Create/Update BUCK Target

Read the existing BUCK file in the tests/ directory (or create one if it doesn't exist).

For CPU-only unit tests:

python_unittest(
    name = "test_my_module",
    srcs = ["test_my_module.py"],
    deps = [
        "//caffe2:_torch",
        # ... source deps ...
    ],
)

For GPU/distributed tests:

python_unittest(
    name = "test_my_module",
    srcs = ["test_my_module.py"],
    remote_execution = re_test_utils.remote_execution(
        platform = "gpu-remote-execution",
        resource_units = 2,
    ),
    deps = [
        "//caffe2:_torch",
        "//torchrec/distributed/test_utils:multi_process",
        # ... source deps ...
    ],
)

If hypothesis is used, add:

    supports_static_listing = False,

and add to deps:

    "fbsource//third-party/pypi/hypothesis:hypothesis",

BUCK rules:

• Use load("@fbcode_macros//build_defs:python_unittest.bzl", "python_unittest") for standard tests
• Add load("@fbcode_macros//build_defs/lib:re_test_utils.bzl", "re_test_utils") for GPU tests
• Include oncall("torchrec") if already present in the BUCK file
• Derive deps from the test file's imports — map each torchrec.* import to its BUCK target by checking the source directory's BUCK file

Phase 6: Verify

1. Ask the user to review the generated test file
2. Suggest running the test:

   buck2 test fbcode//torchrec/path/to/tests:test_my_module
   

3. If hypothesis is used, suggest running with more examples:

   buck2 test fbcode//torchrec/path/to/tests:test_my_module -- -s
   

Test Utilities Reference

Use these utilities when generating tests:

Utility	Import	When to Use
MultiProcessTestBase	torchrec.distributed.test_utils.multi_process	All distributed tests
MultiProcessContext	torchrec.distributed.test_utils.multi_process	Per-rank setup/teardown
ModelInput	torchrec.distributed.test_utils.test_model	Generating test inputs for models
TestSparseNN	torchrec.distributed.test_utils.test_model	Test model with embedding tables
sharding_single_rank_test	torchrec.distributed.test_utils.test_sharding	Testing sharders
create_test_sharder	torchrec.distributed.test_utils.test_sharding	Creating test sharder instances
skip_if_asan_class	torchrec.test_utils	Skip entire class under ASAN
seed_and_log	torchrec.test_utils	Deterministic seeding with logging
get_free_port	torchrec.test_utils	Getting available port for dist init

Constraints

• NEVER overwrite existing test methods. Add new methods to existing test classes or create new classes.
• NEVER add tests for private methods (starting with _) unless they contain complex logic that's critical to test.
• ALWAYS match the import style of the source file (modern list[str] vs List[str]).
• ALWAYS check if similar tests already exist before generating duplicates.
• ALWAYS prefer real implementations over mocks. Use MultiProcessContext + real gloo PG for distributed tests, ScopedConfigeratorFake / JK overrides for config and feature flags, and in-memory fakes where they exist. Reach for mock.patch / MagicMock only when no real fake exists for the dependency, and call out why in a one-line comment.
• Keep generated tests focused and minimal — don't test framework behavior or trivial getters/setters.

Instructions from User

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