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Nim testing conventions, unittest framework, and C++ compatibility patterns

mratsim By mratsim schedule Updated 5/10/2026
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name: testing description: Nim testing conventions, unittest framework, and C++ compatibility patterns license: MIT compatibility: opencode metadata: audience: developers workflow: testing

What I do

I provide guidance for writing tests in Nim that:

  • Work correctly with the std/unittest framework
  • Avoid C++ compilation errors with complex FFI types like TorchTensor
  • Follow project conventions for test organization

When to use me

Use this skill when:

  • Writing new tests for any workspace module
  • Debugging C++ compilation errors in test code
  • Organizing test fixtures and test data

The unittest framework

Nim's standard library provides a simple testing framework:

import std/unittest

suite "my module tests":
  test "addition works":
    check 1 + 1 == 2

  test "string handling":
    let result = "hello".toUpperAscii()
    check result == "HELLO"

Key procs:

  • suite(name, body) - Group related tests
  • test(name, body) - Define a single test
  • check(expr) - Assert expression is true, prints failed value on failure
  • doAssert(expr) - Like check but raises on failure (use for invariants)
  • submitTest(result) - Submit test result from a procedure

Critical pattern: Wrap tests in a proc

The problem

When you declare variables at module scope (top-level) in Nim tests, the generated C++ code uses = {} initialization:

TorchTensor expectedTensor = {};  // This fails!
expectedTensor = myFunction(a, b);

The C++ torch::Tensor type (and other FFI types with cppNonPod) does not accept brace initialization. This causes:

error: ambiguous overload for 'operator=' (operand types are 'at::Tensor' and '<brace-enclosed initializer list>')

The solution

Always wrap test code in a proc main():

import std/unittest, workspace/libtorch

proc generateTensor(): TorchTensor =
  # This works - Nim generates:
  # auto result = myFunction(a, b);
  arange(10, kFloat32)

proc runTests*() =
  suite "tensor tests":
    test "generate tensor":
      let tensor = generateTensor()
      check tensor.numel() == 10

when isMainModule:
  runTests()

This generates proper C++:

auto tensor = generateTensor();  // No {} initialization

Test organization

Fixture file pattern

Tests that load files should follow this pattern:

import std/unittest, std/os, workspace/safetensors, workspace/libtorch

const FIXTURES_DIR = currentSourcePath().parentDir() / "fixtures"

proc main() =
  suite "safetensors loading":
    test "load fixture":
      let fixturePath = FIXTURES_DIR / "model.safetensors"
      check fileExists(fixturePath)

      var mf = memfiles.open(fixturePath, mode = fmRead)
      defer: mf.close()

      let (st, offset) = safetensors.load(mf)
      check st.tensors.len > 0

when isMainModule:
  main()

Key points:

  • Use currentSourcePath().parentDir() / "fixtures" for fixture paths
  • Use memfiles.open with defer: mf.close()
  • Return early or use continue for missing fixtures

Test constants

Define test parameters as const at module level:

const Patterns = ["gradient", "alternating", "repeating"]
const Shapes: array[4, seq[int64]] = [
  @[int64 8],
  @[int64 4, 4],
  @[int64 2, 3, 4],
  @[int64 3, 2, 2, 2]
]
const TestedDtypes = [F64, F32, F16, I64, I32, I16, I8, U64, U32, U16, U8]

Helper procedures

Extract reusable logic into proc with * export:

proc generateExpectedTensor*(pattern: string, shape: seq[int64], dtype: ScalarKind): TorchTensor =
  let shapeRef = shape.asTorchView()
  let numel = shape.product()

  case pattern
  of "gradient":
    arange(numel, dtype).reshape(shapeRef).to(dtype)
  of "alternating":
    let flat = arange(numel, kInt64)
    let modVal = (flat % 2).to(kFloat64)
    modVal.reshape(shapeRef).to(dtype)
  else:
    raise newException(ValueError, "Unknown pattern: " & pattern)

Note: Each branch of a case must assign to result.

Running tests

Each module has a task defined in config.nims for running its tests:

# Test toktoktok
nim test_toktoktok

# Test libtorch
nim test_libtorch

# Test safetensors
nim test_safetensors

The command nim test_toktoktok compiles and runs all test files in workspace/toktoktok/tests/ that start with test_ or t_.

Compilation settings

The project uses:

  • --path:. - Makes workspace/module imports work
  • Tests compiled with: nim cpp -r plus flags for output and cache directories

Fixture files

For this project, fixtures are in:

workspace/toktoktok/tests/tokenizers/

Reference fixtures using:

const FIXTURES_DIR = currentSourcePath().parentDir() / "tokenizers"

Common errors and fixes

"undeclared field" with parameter shadowing

If you have a parameter named shape and access a field info.shape:

proc generateExpectedTensor*(pattern: string, shape: seq[int64], ...): TorchTensor =
  for info in tensors:  # error: 'shape' shadows info.shape
    check info.shape == shape

Fix: Rename parameter to avoid shadowing:

proc generateExpectedTensor*(pattern: string, shapeSeq: seq[int64], ...): TorchTensor =
  for info in tensors:
    check info.shape == shapeSeq  # Now works

Case statement not returning

Each branch of a case must explicitly assign to result:

proc foo(x: int): int =
  case x
  of 1: result = 10  # Must use 'result ='
  of 2: 20             # ERROR: doesn't assign!

How to add a new test

Step 1: Create the test file

Follow the naming convention: test_*.nim or t_*.nim in the module's tests/ directory.

# workspace/my_module/tests/test_myfeature.nim

import std/unittest, std/os
import workspace/my_module

proc runMyFeatureTests*() =
  suite "my feature tests":
    test "basic functionality":
      let result = myModule.function()
      check result == expectedValue

when isMainModule:
  runMyFeatureTests()

Step 2: Run the test

The test will be discovered automatically by the test command:

# If it's in my_module:
nim c -r --task:test_my_module

Or run all tests for the module:

nim test_my_module

Step 3: Add test fixtures (if needed)

Create a fixtures/ directory and add test data:

workspace/my_module/tests/fixtures/

Reference in test code:

const FIXTURES_DIR = currentSourcePath().parentDir() / "fixtures"
let fixturePath = FIXTURES_DIR / "test_data.bin"

Checklist for new tests

  • File name starts with test_ or t_
  • Located in workspace/module/tests/
  • Test code wrapped in proc runTests*()
  • Has when isMainModule: runTests() at the end
  • Uses defer for resource cleanup (files, etc.)
  • Helper procedures exported with *
  • Constants defined at module level with const

Python test vector generation

For AI/ML modules, test vectors are generated via Python scripts using torch and safetensors.

Directory structure

workspace/module/
├── tests/
│   ├── test_module.nim          # Nim tests
│   ├── fixtures/                # Generated fixture files
│   │   ├── model.safetensors
│   │   └── tokenizer.json
│   └── testgen/                 # Python test vector generators
│       └── generate_vectors.py

Convention

  • Python 3.12 standard for all test vector generation (matches vLLM/SGLang)
  • Single root pyproject.toml with [dependency-groups] for shared dependencies:
    [dependency-groups]
test-vectors = [
    "torch>=2.0.0",
    "safetensors>=0.7.0",
    "transformers>=4.40.0",
    "numpy>=2.4.2",
]
  • Run generators with: uv run --group test-vectors python workspace/module/tests/testgen/generate_vectors.py

Example testgen script

import torch
import numpy as np
from safetensors.numpy import save_file
import os

FIXTURES_DIR = os.path.join(
    os.path.dirname(os.path.dirname(__file__)),
    "fixtures",
)

def generate_vandermonde():
    x = torch.arange(1, 6, dtype=torch.float32)
    vandermonde = torch.vander(x, increasing=True).T
    return vandermonde.to(torch.bfloat16).view(torch.uint16).numpy()

def main():
    fixtures = {
        "BF16_vandermonde_5x5": generate_vandermonde(),
    }
    save_file(fixtures, os.path.join(FIXTURES_DIR, "vandermonde.safetensors"))
    print("Fixtures generated")

if __name__ == "__main__":
    main()

Fixture regeneration

When adding new test vectors, regenerate the fixture files:

uv run --group test-vectors python workspace/module/tests/testgen/generate_vectors.py

Test utilities for libtorch tests

Tests involving TorchTensor and other libtorch FFI types should use the shared test utilities:

import workspace/libtorch_testutils

C++ exception handling

Wrap test code that may throw C++ exceptions:

proc testTensorOps(): bool =
  let a = ones(@[2, 3], kFloat32)
  let b = zeros(@[2, 3], kFloat32)
  let c = a + b
  result = c.isDefined()

when isMainModule:
  runTest("tensor operations", testTensorOps)  # Handles exceptions automatically

Or use the template directly:

check catchCppExceptions(testTensorOps())

Tensor assertions

assertDefined - Check tensor is initialized:

let tensor = ones(@[2, 3], kFloat32)
assertDefined(tensor)  # Raises if not defined
assertDefined(tensor, "weight")  # Custom name in error

assertShape - Verify tensor dimensions:

let tensor = randn(@[2, 3, 4])
assertShape(tensor, 2, 3, 4)

assertDtype - Verify tensor dtype:

let tensor = ones(@[2, 3], kFloat32)
assertDtype(tensor, kFloat32)

assertAllClose / assertClose - Compare tensor values:

let actual = computeSomething()
let expected = ones(@[2, 3], kFloat32) * 2.0
assertAllClose(actual, expected)  # Default rtol=2e-2, abstol=2e-2
assertClose(actual, expected, rtol=1e-5, abstol=1e-5)  # Custom tolerance

Debug helpers

printTensor - Print tensor with label:

printTensor(myTensor, "Weight matrix")

printTensorShape - Print shape and dtype:

printTensorShape(myTensor, "Input")
# Output: Input:
#         Shape: [2, 3, 4], Dtype: kFloat32

ptrHex - Convert pointer to hex string for aliasing detection:

let tensor = ones(@[2, 3], kFloat32)
echo "data_ptr = 0x", tensor.data_ptr().ptrHex()
echo "shape.data() = 0x", tensor.shape.data().ptrHex()
# Useful for detecting memory aliasing issues

dataPtrHex / shapePtrHex - Convenience wrappers:

let tensor = ones(@[2, 3], kFloat32)
echo "data_ptr = 0x", tensor.dataPtrHex()
echo "shape_ptr = 0x", tensor.shapePtrHex()
# Equivalent to above but more convenient

printTensorShape(myTensor, "Input")
# Output: Input:
#         Shape: [2, 3, 4], Dtype: kFloat32

traceExec - Debug macro to trace execution:

traceExec:
  let a = ones(@[2, 3])
  let b = zeros(@[2, 3])
  let c = a + b
# Prints each statement before executing

Test file structure

Complete example:

# workspace/my_module/tests/test_feature.nim
import
  std/unittest,
  workspace/libtorch,
  workspace/libtorch_testutils,
  workspace/my_module

proc testBasicFunctionality(): bool =
  let input = ones(@[2, 3], kFloat32)
  let result = myModule.process(input)
  
  assertDefined(result)
  assertShape(result, 2, 3)
  result = true

proc testEdgeCase(): bool =
  let input = zeros(@[1], kFloat32)
  let output = myModule.process(input)
  assertAllClose(output, input)
  result = true

when isMainModule:
  runTest("basic functionality", testBasicFunctionality)
  runTest("edge case", testEdgeCase)

Key points

  • Always import workspace/libtorch_testutils for tests with TorchTensor
  • Use runTest for formatted output with automatic exception handling
  • Use catchCppExceptions when integrating with std/unittest check
  • Use assertion helpers (assertDefined, assertShape, etc.) for clear error messages
  • Use printTensor and printTensorShape for debugging failures
  • Test files should be in workspace/module/tests/ directory
  • File names should start with test_ or t_
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