tripy-testing
Write tests for nvtripy following project conventions. Use when: adding tests for ops, modules, trace operations, or compilation, using pytest parametrize, testing error cases with helper.raises, testing dtype combinations, understanding test directory structure.
ソース情報
- リポジトリ
- NVIDIA/TensorRT-Incubator
- ソースの最終更新活動
- 2026年3月24日 22:48
- 検出された SKILL.md の言語
- 英語
- スター
- 128
- フォーク
- 30
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- tripy-testing
- description
- Write tests for nvtripy following project conventions. Use when: adding tests for ops, modules, trace operations, or compilation, using pytest parametrize, testing error cases with helper.raises, testing dtype combinations, understanding test directory structure.
# Testing Patterns for nvtripy
## When to Use
- Adding tests for new operations, modules, or features
- Understanding the test directory structure and conventions
- Testing error cases and dtype validation
- Writing parametrized tests for multiple configurations
## Test Directory Structure
The test directory mirrors the source tree:
```
tests/
├── frontend/
│ ├── ops/
│ │ ├── test_binary.py
│ │ ├── test_softmax.py
│ │ └── ...
│ ├── module/
│ │ ├── test_linear.py
│ │ ├── test_layernorm.py
│ │ └── ...
│ ├── test_tensor.py
│ └── ...
├── trace/
│ ├── ops/
│ │ ├── test_binary.py
│ │ └── ...
│ └── ...
├── backend/
│ └── ...
├── integration/
│ └── ...
├── helper.py # Test utilities
└── conftest.py # Shared fixtures
```
## Test Utilities (`tests/helper.py`)
### `helper.raises` — Error Testing
```python
from tests import helper
import nvtripy as tp
# Basic error test
with helper.raises(tp.TripyException):
result = bad_operation()
# With message matching (regex)
with helper.raises(tp.TripyException, match="Expected.*one of"):
result = bad_operation()
# With stack info verification
a = tp.Tensor([1.0])
with helper.raises(tp.TripyException, has_stack_info_for=[a]):
result = bad_operation(a)
```
### `helper.config` — Temporary Config Changes
```python
with helper.config("enable_input_validation", False):
# Validation disabled in this block
result = operation()
# Automatically restored
```
### `NUMPY_TO_TRIPY` — Dtype Mapping
```python
from tests.helper import NUMPY_TO_TRIPY
# Maps numpy dtypes to tripy dtypes:
# bool → tp.bool, np.int8 → tp.int8, np.int32 → tp.int32,
# np.int64 → tp.int64, np.float16 → tp.float16, np.float32 → tp.float32
```
## Common Test Patterns
### Basic Op Test
```python
import cupy as cp
import numpy as np
import nvtripy as tp
class TestMyOp:
def test_basic(self):
input = tp.Tensor([1.0, 2.0, 3.0])
output = tp.my_op(input)
expected = np.array([...]) # Compute expected result
assert np.array_equal(cp.from_dlpack(output).get(), expected)
def test_with_specific_dim(self):
input = tp.iota([2, 3], dtype=tp.float32)
output = tp.my_op(input, dim=1)
assert output.shape == [2, 3]
```
### Parametrized Dtype Tests
```python
import pytest
class TestMyOp:
@pytest.mark.parametrize("dtype", [tp.float32, tp.float16, tp.bfloat16])
def test_supported_dtypes(self, dtype):
input = tp.ones([2, 3], dtype=dtype)
output = tp.my_op(input)
assert output.dtype == dtype
@pytest.mark.parametrize(
"dtype",
[tp.int8, tp.int32],
ids=["int8", "int32"],
)
def test_unsupported_dtypes_fail(self, dtype):
input = tp.ones([2, 3], dtype=dtype)
with helper.raises(tp.TripyException, match="Expected.*one of"):
tp.my_op(input)
```
### Testing from NumPy Data
```python
from tests.helper import NUMPY_TO_TRIPY
class TestTensor:
@pytest.mark.parametrize("dtype", list(NUMPY_TO_TRIPY.keys()))
def test_dtype_from_numpy(self, dtype):
np_array = np.array([1, 2, 3], dtype=dtype)
tensor = tp.Tensor(np_array)
assert tensor.dtype == NUMPY_TO_TRIPY[dtype]
```
### Mismatched Dtype Error Tests
```python
class TestBinaryOps:
def test_mismatched_dtypes_fails(self):
a = tp.Tensor([1.0, 2.0])
b = tp.ones((2,), dtype=tp.float16)
with helper.raises(tp.TripyException):
c = a + b
```
### Module Tests
```python
class TestLinear:
def test_basic(self):
linear = tp.Linear(3, 4)
linear.weight = tp.iota(linear.weight.shape)
linear.bias = tp.iota(linear.bias.shape)
input = tp.iota((2, 3))
output = linear(input)
assert cp.from_dlpack(output).get().shape == (2, 4)
def test_no_bias(self):
linear = tp.Linear(3, 4, bias=False)
linear.weight = tp.iota(linear.weight.shape)
input = tp.iota((2, 3))
output = linear(input)
assert output.shape == [2, 4]
def test_state_dict(self):
linear = tp.Linear(3, 4)
sd = linear.state_dict()
assert "weight" in sd
assert "bias" in sd
```
### Trace Op Tests
```python
from nvtripy.trace.ops.my_op import MyOp
from nvtripy.trace.ops.base import TraceOp
class TestMyTraceOp:
def test_creates_correct_op(self):
input = tp.Tensor([1.0, 2.0])
output = tp.my_op(input)
assert isinstance(output.trace_tensor.producer, MyOp)
def test_infer_rank(self):
input = tp.Tensor([1.0, 2.0])
output = tp.my_op(input)
assert output.trace_tensor.rank == 1
```
### Allclose Comparisons
```python
class TestSoftmax:
def test_matches_torch(self):
input = tp.iota([2, 3], dtype=tp.float32)
output = tp.softmax(input, dim=1)
torch_input = torch.from_dlpack(input)
torch_output = torch.softmax(torch_input, dim=1)
assert tp.allclose(output, tp.Tensor(torch_output))
```
### Parametrize with IDs
```python
@pytest.mark.parametrize(
"tensor_a, tensor_b, rtol, atol, expected",
[
(tp.Tensor([1.0]), tp.Tensor([1.0]), 1e-5, 1e-8, True),
(tp.Tensor([1.0]), tp.Tensor([2.0]), 1e-5, 1e-8, False),
],
ids=["equal", "not_equal"],
)
def test_allclose(self, tensor_a, tensor_b, rtol, atol, expected):
result = tp.allclose(tensor_a, tensor_b, rtol=rtol, atol=atol)
assert result == expected
```
## Verifying Outputs
| Method | Use When |
|--------|----------|
| `cp.from_dlpack(output).get()` | Convert tripy tensor → numpy array (via cupy) |
| `np.array_equal(a, b)` | Exact equality for integer/bool results |
| `np.allclose(a, b)` | Approximate equality for float results |
| `tp.allclose(a, b)` | Compare two tripy tensors directly |
| `torch.from_dlpack(tensor)` | Convert tripy tensor → torch tensor |
| `output.shape` | Check output shape |
| `output.dtype` | Check output dtype |
## Running Tests
```bash
# Run all tests
pytest tests/
# Run specific test file
pytest tests/frontend/ops/test_softmax.py
# Run specific test
pytest tests/frontend/ops/test_softmax.py::TestSoftmax::test_basic
# Run with verbose output
pytest -v tests/frontend/ops/test_softmax.py
# Run tests matching a pattern
pytest -k "softmax"
```
## Checklist
- [ ] Test file created at `tests/<mirror_of_source_path>/test_<name>.py`
- [ ] Tests organized in a class (e.g., `TestMyOp`)
- [ ] Basic functionality test with assertion
- [ ] Parametrized dtype tests for all supported dtypes
- [ ] Error case tests using `helper.raises(tp.TripyException)`
- [ ] Shape validation tests
- [ ] Comparison against reference implementation (torch/numpy) where applicable
- [ ] Test IDs provided for parametrized tests to make failures readable
GitHubで見る