Add transformer_engine.test() installation smoke check

[CodersAcademy006]

Problem

There is no way to verify a Transformer Engine installation without cloning the
repository and running the test suite. Users installing from PyPI or from source
have no quick sanity check that the package and its framework extensions loaded
correctly on their machine (#170).

Solution

Add a top-level transformer_engine.test() entry point, similar to
numpy.test(). It runs a small set of smoke checks:

• Installation integrity, reusing the existing PyPI sanity check.
• For the PyTorch backend, when it is available: a minimal te.Linear forward
  pass on the current device, reporting the device compute capability and which
  low-precision formats it supports. Checks that need a GPU are skipped when no
  CUDA device is present, so the call is safe to run anywhere.

It prints the result of each check and returns True if every executed check
passed.

import transformer_engine
transformer_engine.test()

Closes #170

[greptile-apps]

Greptile Summary

This PR adds a transformer_engine.test() top-level function that gives users a quick, dependency-free smoke check after installation — without needing to clone the repo and run the full test suite.

• Installation check: reuses the existing sanity_checks_for_pypi_installation() helper; always runs and records a pass/fail result.
• PyTorch backend check: conditionally imported; on a CUDA device, runs a minimal te.Linear(16, 16) forward pass, verifies the output shape, and probes for FP8/MXFP8/NVFP4 format availability; GPU-dependent steps are skipped (not failed) when no CUDA device is present.
• Previously flagged issues (assert replaced with explicit if/raise, FileNotFoundError now caught alongside ImportError) are addressed in the implementation.

Confidence Score: 5/5

Safe to merge — the change is purely additive and does not touch any existing code paths.

Purely additive change; all previously flagged blocking issues are resolved; helper symbols verified as exported from transformer_engine.pytorch.

No files require special attention.

Important Files Changed

Filename	Overview
transformer_engine/init.py	Adds test() entry point with installation integrity check and PyTorch forward-pass smoke test; previously flagged issues (assert→if/raise, FileNotFoundError catch) are addressed

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A["transformer_engine.test()"] --> B["Import sanity_checks_for_pypi_installation"]
    B -->|Success| C["Run installation check"]
    B -->|Exception| D["_record('installation', False)"]
    C -->|Success| E["_record('installation', True)"]
    C -->|Exception| D
    E --> F["from . import pytorch as te"]
    D --> F
    F -->|ImportError / FileNotFoundError| G["te = None"]
    F -->|Success| H["te = pytorch module"]
    G --> N["No PyTorch checks added"]
    H --> I{"torch.cuda.is_available?"}
    I -->|No| J["_record('pytorch', True, 'no CUDA, skipped')"]
    I -->|Yes| K["Run te.Linear forward pass\ncheck output shape\ncollect format support"]
    K -->|Success| L["_record('pytorch', True, detail)"]
    K -->|Exception| M["_record('pytorch', False, err)"]
    J --> O["all(results)"]
    L --> O
    M --> O
    N --> O
    O --> P["Return True / False"]

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flowchart TD
    A["transformer_engine.test()"] --> B["Import sanity_checks_for_pypi_installation"]
    B -->|Success| C["Run installation check"]
    B -->|Exception| D["_record('installation', False)"]
    C -->|Success| E["_record('installation', True)"]
    C -->|Exception| D
    E --> F["from . import pytorch as te"]
    D --> F
    F -->|ImportError / FileNotFoundError| G["te = None"]
    F -->|Success| H["te = pytorch module"]
    G --> N["No PyTorch checks added"]
    H --> I{"torch.cuda.is_available?"}
    I -->|No| J["_record('pytorch', True, 'no CUDA, skipped')"]
    I -->|Yes| K["Run te.Linear forward pass\ncheck output shape\ncollect format support"]
    K -->|Success| L["_record('pytorch', True, detail)"]
    K -->|Exception| M["_record('pytorch', False, err)"]
    J --> O["all(results)"]
    L --> O
    M --> O
    N --> O
    O --> P["Return True / False"]

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