feat(model): add GLM-5.3-Flash support

[HollowMan6]

Pending on:

• GLM5.3 Flash (KDA + mHC + KPool DSA) support + FP8 NVIDIA/Megatron-LM#7054

What does this PR do ?

Add HF ↔ Megatron support for GLM-5.3-Flash (model_type glm5_next, Glm5NextForConditionalGeneration): a unified bridge, provider, and VLM wrapper for its hybrid KDA/DSA + MoE + mHC language model with a replicated HF vision tower, plus the shared conversion, PEFT, and packed-sequence pieces it needs.

Changelog

GLM-5.3-Flash (src/megatron/bridge/models/glm5next/)

• Add GLM53FlashBridge, GLM53FlashModelProvider, and GLM53FlashModel, registered for Glm5NextForConditionalGeneration with model_type="glm5_next" and exported from megatron.bridge.models.
• Build hybrid_layer_pattern from the HF layer_types / mlp_layer_types: each HF decoder layer becomes two physical layers (KDA K or DSA D attention at 2N, dense - or MoE E FFN at 2N+1), with moe_layer_freq derived per physical module and num_hidden_layers recovered from the pattern on export.
• Configure MLA NoPE geometry (qk_pos_emb_head_dim=0, kv_channels=v_head_dim), the kpool DSA indexer (FP8 kpool; index_kpool / index_kpool_always_select_tail read from the HF config), KDA two-stage low-rank gates with gate_lower_bound, mHC multi-stream hyper-connections with fp32 mappings, sigmoid routing with expert bias and an always-on shared expert (moe_shared_expert_gate=False), grouped GEMM with the HybridEP flex dispatcher, and a one-layer DE MTP using the repeated-layer spec.
• Weight mappings: fused KDA in_proj / conv1d built from separate HF Q/K/V tensors with section-wise TP sharding (_ColumnParallelConcatMapping), MLA projections and indexer weights, dense and MoE FFN (grouped-GEMM and sequential expert layouts), mHC hc_{attn,ffn}_{fn,base,scale}, and the MTP eh_proj split into e_proj / h_proj (_EhProjSplitMapping, import only).
• Dequantize blockwise FP8 checkpoint weights on import; keep Megatron init for the MTP inner-layer hyper-connection params, which have no HF source.
• VLM wrapper: replicated HF Glm5NextVisionModel on pre-process ranks, image/video features scattered into the embeddings before THD packing and the sequence-parallel scatter, text-only fallback when no pixel inputs are given, and freeze_{language_model,vision_model,vision_projection} options.

Hybrid providers

• Add HybridMLAModelProvider (HybridModelProvider + MLATransformerConfig). With pipeline_model_parallel_size > 1 and DSA layers in the pattern, it inserts balanced | separators only before full-indexer DSA layers so IndexShare groups never cross a stage, validates user-provided separators, and rejects num_layers_in_first/last_pipeline_stage.
• Add scatter_embedding_sequence_parallel to HybridModelProvider (default True, matching HybridModel) and pass it through; the VLM provider sets it to False so vision features are injected before the SP scatter.

Shared conversion infrastructure

• Add HCAlphaMapping, mapping an HF [3] hc_*_scale tensor to Megatron's alpha_pre / alpha_post / alpha_res (index 0 exports all three), and migrate DeepSeekV4Bridge from its private _HCAlphaMapping / _HCAlphaSecondaryMapping to it with equivalent import/export behavior.
• Add InitOnlyMapping for Megatron params with no HF counterpart (import keeps init, export is a no-op).
• Add MegatronModelBridge._prefix_mapping_registry to re-key a text-model registry under a VLM prefix while keeping nested _tp_mapping names in sync.

PEFT

• Split fused KDA in_proj LoRA linear_out weights by Q/K/V section sizes when kda_two_stage_gates is set, instead of the GQA-style split_qkv_weights path, so KDA LoRA adapters export correctly.

Packed sequences

• Move preprocess_packed_seqs from models/qwen_vl/modelling_qwen3_vl/utils.py to training/utils/packed_seq_utils.py (shared by Qwen3-VL and GLM-5.3-Flash) and add use_fp8_padding, which aligns each sequence to lcm(16, align) and the packed total to align * 128.

Tests

• tests/unit_tests/models/glm5next/test_glm53flash_mapping.py: provider precision contract and multimodal config copy, language_model. prefixing of text mappings, HCAlphaMapping import/export and wildcard resolution, section-wise KDA TP sharding round-trips (TP 1 to 16).
• tests/unit_tests/models/qwen_vl/modelling_qwen3_vl/test_utils.py: packed-sequence alignment and gradient flow with and without FP8 padding.
• tests/unit_tests/models/test_model_bridge_lora.py: KDA Q/K/V LoRA linear_out split.

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Pre checks:

• Make sure you read and followed Contributor guidelines
• Did you write any new necessary tests?
• Did you add or update any necessary documentation? (no model page yet; see follow-ups below)
• Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  • Reviewer: Does the PR have correct import guards for all optional libraries?

Glm5NextForConditionalGeneration is imported behind a try/except that falls back to the class name, Glm5NextModel / Glm5NextVisionModel are imported lazily inside GLM53FlashModel.__init__, and the new unit tests use pytest.importorskip on the GLM-5.3-Flash config module.

Additional Information

• No linked issue.
• Dependency: transformers. The glm5_next model family ships in transformers v5.17.0; pyproject.toml currently pins transformers<=5.15.0, so on the pinned version the bridge only resolves by class name, the VLM wrapper cannot be instantiated, and the new unit tests are skipped. The pin bump should go in a separate build: PR per the contributing guide.
• preprocess_packed_seqs moved without a compatibility re-export from its old Qwen3-VL module.
• Follow-ups not in this PR: docs/models/glm/ page and README / docs model tables, recipes and examples, verification card, and a functional HF ↔ Megatron round-trip test.

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