[Th-Underscore]

Purpose

Add Gemma-4 serving support on V100 (sm70). Gemma-4's full-attention layers use head_dim=512, which exceeds the 256-dim cap of existing FlashAttention paths. This PR adds native D=512 SM70 CUDA kernels, fixes the transformers ≥5.6 per-layer attribute system that blocks Gemma-4 config loading, and ports the gemma4_unified (12B dense+audio) variant.

Changes

• D=512 SM70 kernels (flash_v100/): Add BLOCK_M_512/BLOCK_N_512 tiling, LAUNCH_XQA_WIDE(GROUP_SIZE, 512), and case 512: dispatches to the existing paged prefill/decode kernels.
• Transformers heterogeneity (gemma4.py, gemma4_mtp.py, model_arch_config_convertor.py): Set allow_global_per_layer_attribute_access = True in the convertor; use config.__dict__ and per_layer_config[layer_type] access instead of the old getattr escape-hatch pattern.
• Restore raw config fields (model_arch_config_convertor.py): Preserve global_head_dim / num_global_key_value_heads from the checkpoint's config.json through the convertor.
• Dynamo compatibility (sm70_grouped_long.py, sm70_grouped_scalar.py, platforms/cuda.py): Replace @lru_cache/@cache with plain dict caches so CUDA graph capture doesn't hit C-level _lru_cache_wrapper objects.
• gemma4_unified port (gemma4_unified.py, gemma3n_mm.py, gpu_sync_debug.py, envs.py): Copy from upstream + register in model registry, config convertor, and VERIFY_UPDATE_CONFIG.

Quantization support

Method	Status
NVFP4 (compressed-tensors, config_groups)	Tested — Gemma-4-26B-A4B-StyleTune-V2-NVFP4-W4ATTN-AWQ + gemma-4-12b-it-NVFP4
WNA16 (compressed-tensors)	Tested — routes to generic fused MoE kernel (not SM70 per-expert dispatch); ~420× slower per call. Fix planned in a separate PR.
AWQ (quant_method: "awq")	Expected to work (routes to SM70 per-expert dispatch); untested
BF16	Expected to work; untested

Test Plan

• 26B-A4B NVFP4: multi-length prompt test (500–30K tokens), TP2, V100-SXM2-16GB.
• 12B unified NVFP4: multi-length prompt test (500–30K tokens), TP2, V100-SXM2-16GB.

Test Result

Hardware: 2× V100-SXM2-16GB (underclocked to 765 MHz / 150 W), TP2.

• 26B-A4B NVFP4: all 6 prompt lengths (500/2K/5K/10K/20K/30K) produce coherent, correct responses. Prefill ~5K tok/s, decode ~35 tok/s. Math reasoning test (solve 2^(x^1.5)=3) produces correct step-by-step derivation (x ≈ 1.351).
• 12B unified NVFP4: coherent, correct responses at all tested lengths. Prefill ~2500 tok/s, decode ~38 tok/s. Math reasoning test produces correct step-by-step derivation.
• 31B (dense): not tested (insufficient VRAM on 2×16 GB).
• E2B/E4B: not supported — KeyError: 'embed_tokens_per_layer.weight_packed' during NVFP4 weight load (per-layer embedding layout not handled).

Duplicate-work check

Searched open PRs for: gemma, gemma4, head_dim 512, flash_v100 sm70, heterogeneity per-layer. No other open PR addresses Gemma-4 V100 support, D=512 SM70 kernels, or the transformers heterogeneity fix.

AI assistance

This PR was developed with AI assistance (Pi-Bolt coding agent). The human submitter reviewed every changed line and ran all listed tests.

Test commands

# Pre-commit / CI checks
python tools/pre_commit/check_env_metadata.py
python -m tools.generate_env_reference --check
python -m tools.generate_architecture_reference --check
python -m tools.config_inventory --check

# Runtime tests (require GPU)
python -m pytest tests/config/test_runtime_default_ownership.py -q
python -m pytest tests/config/test_sm70_native_runtime.py tests/config/test_flash_v100_lifecycle.py -q

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[Th-Underscore]

All pre-commit and CI checks pass on the final branch state:

Check	Result
check_env_metadata.py	✅
generate_env_reference --check	✅
generate_architecture_reference --check	✅
config_inventory --check	✅
test_runtime_default_ownership.py	✅ 31 passed
test_sm70_native_runtime.py + test_flash_v100_lifecycle.py	✅ 24 passed