test: expand test suite with fixtures, utils, integration, and model forward-pass tests

[atong01]

Expands the test suite with shared fixtures, utility tests, an integration smoke test, and model forward-pass tests.

Changes

• tests/conftest.py (new): shared fixtures — shapes (parametrized over [(2,3),(5,2),(10,1),(3,3,2)]), rng (torch.Generator seed 42), sample_data (x0/x1 standard normal), and one fixture per matcher class
• tests/test_utils.py (new): Arrange-Act-Assert tests for eight_normal_sample, sample_moons, sample_8gaussians, and torch_wrapper. plot_trajectories is @pytest.mark.skip (needs matplotlib display)
• tests/test_integration.py (new): @pytest.mark.slow end-to-end CFM training smoke test — 256 moons samples, MLP(dim=2, w=32, time_varying=True), ConditionalFlowMatcher(sigma=0.0), 100 Adam steps at lr=1e-2; asserts final_loss < initial_loss
• **tests/test_models.py(expanded 13 → 78 lines): added forward-pass tests forMLP(time-varying / non-time-varying / out_dim) andGradModel` (instantiation, forward, gradient correctness via autograd comparison)
• Fix typo classers → classes in test_conditional_flow_matcher.py and test_optimal_transport.py docstrings

Motivation

Previously tests/ had only 4 files, no conftest.py, no integration tests, no tests for utils.py, and test_models.py only instantiated models without testing the forward pass. This fills those gaps.

Validation

All new tests pass against a merged branch containing all 4 PRs in this batch:

pytest -v --ignore=examples --ignore=runner
172 passed, 1 skipped, 0 failed

Coverage increased from 30% → 47% total. Core modules at 100%.

Notes

• torchcfm/utils.py function signatures differed from initial expectations (eight_normal_sample(n, dim, scale=1, var=1) requires dim; sample_moons(n) returns numpy; torch_wrapper is a class). Tests were written against the actual source.
• The --doctest-modules flag in pyproject.toml is a harmless no-op (no >>> doctests exist in torchcfm/).

Summary by Sourcery

Expand the test suite with shared fixtures, utility function coverage, model forward-pass checks, and an end-to-end CFM training smoke test.

Tests:

• Add shared pytest fixtures for common shapes, RNG, sample data, and conditional flow matcher variants in tests/conftest.py.
• Introduce tests for sampling utilities and the torch_wrapper helper, including shape, type, and finiteness checks plus a skipped plotting test.
• Add forward-pass and gradient correctness tests for MLP and GradModel to ensure outputs have expected shapes and match autograd gradients.
• Add a slow integration test that trains a small CFM model on two-moons data and verifies the training loss decreases.
• Fix minor docstring typos in conditional flow matcher and optimal transport test modules.

[sourcery-ai]

Reviewer's Guide

Expands the TorchCFM test suite with shared fixtures, utility function coverage, model forward-pass tests, and an end-to-end CFM training smoke test, plus minor docstring typo fixes.

File-Level Changes

Change	Details	Files
Add shared pytest fixtures for common shapes, RNG, sample data, and conditional flow matcher instances.	Introduce SHAPES constant and parametrized shapes fixture covering several batch/feature combinations. Add seeded torch.Generator fixture and reusable (x0, x1) sample_data fixture for 2D standard normal batches. Provide one fixture per ConditionalFlowMatcher variant to simplify and standardize matcher setup across tests.	tests/conftest.py
Add tests for utility sampling and wrapper functions in torchcfm.utils.	Test eight_normal_sample across multiple batch sizes/dims and edge parameters, checking tensor type, shape, and finiteness. Test sample_moons and sample_8gaussians for correct shapes, dtypes, and finite values, including single-sample edge cases. Test torch_wrapper returns a torch.nn.Module wrapper with correct call signature, output shape, and underlying model reference. Add a skipped test for plot_trajectories to document its interactive backend requirement.	tests/test_utils.py
Add an end-to-end slow integration test to verify CFM training reduces loss on a two-moons task.	Seed NumPy and PyTorch RNGs for deterministic behavior in the integration test. Construct two-moons source/target datasets via sample_moons and a small time-varying MLP model with Adam optimizer. Use ConditionalFlowMatcher to sample training pairs and measure MSE loss on a fixed evaluation batch before and after 100 training steps, asserting final loss decreases.	tests/test_integration.py
Expand model tests to cover MLP forward behavior and GradModel gradient semantics.	Import torch and GradModel, and add a module docstring for the models test file. Add tests for time-varying vs non-time-varying MLP inputs and for explicit out_dim handling, asserting shapes and finiteness. Add tests that GradModel wraps an action network, returns gradients with expected truncated shape, and matches autograd-computed gradients (excluding last dimension).	tests/test_models.py
Fix minor docstring typos in existing tests.	Update conditional flow matcher test docstrings to say "classes" instead of "classers" in two files.	tests/test_conditional_flow_matcher.py tests/test_optimal_transport.py

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[sourcery-ai]

Hey - I've left some high level feedback:

• In tests/conftest.py, consider removing the from torchcfm.conditional_flow_matching import * star import and explicitly importing only the matcher classes that are actually used to keep the test namespace minimal and avoid accidental name clashes.
• The gradient correctness test in test_grad_model_is_gradient uses create_graph=True even though the resulting graph is not reused; you can drop this flag to reduce autograd overhead while keeping the test behavior identical.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `tests/conftest.py`, consider removing the `from torchcfm.conditional_flow_matching import *` star import and explicitly importing only the matcher classes that are actually used to keep the test namespace minimal and avoid accidental name clashes.
- The gradient correctness test in `test_grad_model_is_gradient` uses `create_graph=True` even though the resulting graph is not reused; you can drop this flag to reduce autograd overhead while keeping the test behavior identical.

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