[FEA] Slow unbucketize permute operation in SequenceEmbeddingsAllToAll for row-wise sharding

Background

Currently, DynamicEmb has custom input_dist implementation (RwSparseFeaturesDist in input_dist.py) but still relies on TorchRec's original output_dist implementation. This causes:

1. Performance issue: The unbucketize_permute operation in TorchRec's output distribution is slow, especially for non-contiguous distribution patterns (e.g., round-robin)
2. Limited customization: Cannot optimize the output distribution without modifying TorchRec source code

Objective

Port TorchRec's output distribution classes to DynamicEmb library, enabling future performance optimizations.

Tasks

PR 1: Port output distribution classes to DynamicEmb

• Create dynamicemb/output_dist.py with:
  • RwSequenceEmbeddingDist
  • RwPooledEmbeddingDist
• Update dynamicemb/planner/rw_sharding.py to override create_output_dist() methods
• Verify with existing tests (test_sequence_embedding_fw.py, test_pooled_embedding_fw.py)

PR 2: Optimize unbucketize permute with custom kernel

• Design optimized data format for permute tensor
• Implement CUDA kernel for efficient unbucketize operation
• Integrate with output_dist.py
• Benchmark and validate performance improvement