[OMNIML-3050] Enable torch.compile on _get_log_softmax_dist (#1479)

### What does this PR do?

Type of change: new feature

Wraps `_get_log_softmax_dist`
(`modelopt/torch/quantization/algorithms.py`) — the distributed
log-softmax helper used by `AutoQuantizeKLDivSearcher` under TP > 1 —
with `@torch.compile(dynamic=True)`. Inductor fuses the `amax /
all_reduce(MAX) / logsumexp / all_reduce(SUM) / log+sub+cast` pipeline
into one kernel. `dynamic=True` avoids recompiles across the varying
`[batch, seq]` shapes seen during calibration, matching the existing
pattern in `backends/fp8_per_tensor_gemm.py`. Stale TODOs are removed;
the prior ONNX-Windows concern no longer applies because the function is
only reachable when a TP group is initialized (never on the Windows CPU
unit-test job), and `@torch.compile` is import-time safe.

### Usage

```python
# Internal — invoked automatically by AutoQuantize KL-divergence search under TP > 1:
import modelopt.torch.quantization as mtq

model, _ = mtq.auto_quantize(
    model,
    constraints={"effective_bits": 6.0},
    quantization_formats=[mtq.INT4_AWQ_CFG, mtq.INT8_DEFAULT_CFG],
    data_loader=calib_loader,
    forward_step=lambda m, b: m(b),
    method="kl_div",
)
```

### Testing

Verified locally against the affected unit-test paths:

- `pytest tests/unit/torch/quantization/test_autoquant.py -k kl_div` →
22 passed (covers the call chain into `_get_log_prob`).
- `pytest tests/unit/onnx/` → 516 passed. The single failure
(`test_autocast_quantize.py::test_autocast_quantize_int8[False-True]` —
onnxruntime `CopyTensorAsync is not implemented`) is a pre-existing
failure on `main`, unrelated to this change (confirmed by stashing the
diff and re-running).
- `pytest tests/unit/torch/quantization/test_autoquant.py
tests/unit/torch/quantization/test_quantize_cpu.py
tests/unit/torch/quantization/test_config_validation.py` → 159 passed.
- Functional sanity in a single-process gloo group: compiled output
matches `torch.log_softmax` reference for fp32 and fp16, with no
recompiles across varying `[batch, seq]` shapes.
- `pre-commit run --files modelopt/torch/quantization/algorithms.py` →
ruff / mypy / bandit all pass.

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(e.g. avoiding hardcoded \`trust_remote_code=True\`, \`torch.load(...,
weights_only=False)\`, \`pickle\`, etc.).

- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in \`CONTRIBUTING.md\`: N/A
- Did you write any new necessary tests?: N/A — existing \`kl_div\`
autoquant tests already exercise the call chain.
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: ❌ — will run \`/claude
review\` after marking ready.

### Additional Information

Single-line behavior change: \`_get_log_softmax_dist\` is now
\`@torch.compile(dynamic=True)\`-decorated. No API surface changes.

Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>

modelopt/torch/quantization/algorithms.py

@@ -1094,10 +1094,8 @@ class AutoQuantizeGradientSearcher(_AutoQuantizeBaseSearcher):
         return best_recipes, is_satisfied
 
 
-# TODO: Enable torch compile for this function
-# Currently modelopt.onnx is breaking this
+@torch.compile(dynamic=True)
 def _get_log_softmax_dist(logits: torch.Tensor, tp_group) -> torch.Tensor:
-    # TODO: test this
     dtype = logits.dtype
     max_logits = torch.amax(logits, dim=-1, keepdim=True)
     torch.distributed.all_reduce(max_logits, op=torch.distributed.ReduceOp.MAX, group=tp_group)