[PyTorch] Add torch.compile custom ops for GroupedLinear

[pggPL]

Description

Add torch.compile(fullgraph=True) support to te.pytorch.GroupedLinear on top of the eager refactor in #50. Both custom ops reuse the shared forward/backward computation from that PR.

This is the second of two stacked PRs; its base is grouped_linear_refactor.

Type of change

• New feature (non-breaking change which adds functionality)

Changes

• Add custom-op registrations and fake implementations for both paths, sharing weight/cache metadata, gradient allocation descriptions and saved-alias restoration.
• Extend _FieldPlan / _ArgPlan with tensor-list packing, per-element gradients, subclass flattening and SymInt[] inputs. Flatten mixed tensor/storage lists in one pass, and cover empty SymInt lists in autograd with a regression test.
• Deduplicate saved-tensor aliases and apply compile-specific allocation restrictions. Preallocate the grouped cuBLAS workspace for CUDA-graph capture.
• Use shared compile eligibility checks and dispatch for both paths, with path-specific restrictions and dimension checks reusing Linear's guards. Compile tests share numerical comparisons, including bias gradients across weight-cache updates.

Validation

RTX Ada workstation, current source:

• test_torch_compile.py + test_grouped_linear.py: 849 passed, 608 skipped, 2 XPASS.
• Additional fused BF16 fullgraph=True probe: forward/backward numerics and changing device splits without recompilation pass with grouped GEMM replaced by matmuls. This does not validate native fused kernels or CUDA-graph capture.
• A temporary mixed plain-tensor/FP8-wrapper/storage/None list probe preserves exact values and buffer ordering.
• Changed production files pass pylint; Black and diff checks pass. Full Python lint reports the existing import-order diagnostic in unchanged attention/fused_mla_q_uproj.py.

The installed PyTorch 2.14 development build exposes is_opaque_value_type instead of the is_opaque_constant_type name expected by TE main. Tests used a temporary compatibility alias outside the repository.

Native fused grouped GEMM tests require Hopper/Blackwell and were skipped on RTX Ada.

Limitations

Unsupported configurations fall back to eager: debug instrumentation, distributed weights, CPU offloading, delayed wgrad, fused wgrad accumulation, caller-provided output buffers, single grouped parameters and non-value-opaque quantizers. Legacy compiled execution requires host-side splits; fused execution requires CUDA splits. Fused save_original_input=True falls back to eager.

Checklist

• I have read and followed the contributing guidelines
• New and existing tests pass on the available GPU
• Changes introduce no lint diagnostics
• Native fused compilation revalidated on Hopper/Blackwell