[gemma4_31b_vision support] vision tower model defination with prequant update#19932
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/19932
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New FailuresAs of commit 5dea98c with merge base ac3003e ( NEW FAILURES - The following jobs have failed:
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…ision (g4-vision-quant) First branch of the g4-vision decomposition. Adds the vision tower, vision quantization, and the Python eager (CUDA) vision inference path, plus tests. The ExecuTorch export + C++ runtime stay TEXT-ONLY with the exact same exported contract as `main` (token-input `prefill` + `decode`), so `main.cpp` and `CMakeLists.txt` are byte-identical to `main` and the existing runner keeps working unchanged. CUDA/MLX/GGUF vision are added in the following branches. Scope (built on top of `main`) ------------------------------ Vision model + quant + Python eager: - vision_tower.py: full Gemma 4 vision tower (patch embedder, 2D-RoPE encoder, pooler, multimodal embedder, Gemma4_31BVisionTower wrapper, HF key maps). - model.py: multimodal-by-default model (vision_tower / embed_vision attached), embeds-based `forward(inputs_embeds, ...)`, `embed_text`, `decode_forward`, `_run_blocks`, vision RoPE buffer materialization, vision-aware HF loading. - pack_vision.py: backend-agnostic INT8 vision position-table quant + patch embedder packer; quant/pack.py buffer-registration tweak for the PE int8 buffers; quantize_and_save.py made vision-aware. - inference.py: Python eager vision path (`generate_with_image`, `_build_vision_encoder`, `--image-path`, `--max-vision-soft-tokens`); text loop uses `decode_forward` (forward now takes embeds). Text-only ET export (main-compatible contract via a fake-prefill wrapper): - export.py: exports the SAME two methods as `main` -- token-input `prefill` (T>=2, dynamic) and `decode` (T=1). The token-input `prefill` is realized with a temporary bound-method that fuses `embed_text` + the embeds-`forward` into one method (mirroring the MLX `_mlx_model_forward(tokens) = prefill(embed_text(tokens))` pattern); `decode` maps to `decode_forward`. The vision head is dropped (`_drop_vision_head`) before lowering, so the exported .pte method names + signatures are identical to `main`. Loading stays vision-aware (the prequant checkpoint carries vision weights). No `embed_text` / `vision_encoder` method, no `--max-vision-soft-tokens`. - main.cpp / CMakeLists.txt: unchanged from `main` (verified byte-identical). Refactors landed at the model/quant layer: - R1: vision_tower.py reuses the shared `Gemma4MLP` and `rotate_half` from examples/models/gemma4/text_decoder (drops the duplicate `Gemma4VisionMLP` and local `_rotate_half`; submodule names gate/up/down_proj unchanged so HF key maps stay valid). - R3: drops the `RMSNorm` / `RMSNormNoWeight` stopgap from text_decoder/__init__.py; vision_tower.py uses `nn.RMSNorm(...)` and `nn.RMSNorm(..., elementwise_affine=False)` inline. text_decoder/__init__.py is now byte-identical to `main`. - R2 (Python half): the 5 chat-template special-token IDs + `build_vision_input_ids` are extracted into a shared module examples/models/gemma4/chat_template.py; inference.py imports from it. The C++ header half lands in branch 2. Docs (review finding #3): model.py `forward` and vision_tower.py state the scaling convention explicitly -- `embed_text` scales text rows by sqrt(hidden_size); the vision-tower output is NOT pre-scaled (matches HF). Test fixes: - test_cuda_pipeline.py: updated for the new model contract (review finding #2) -- T=1 calls use `decode_forward`; multi-token prefill uses `forward(embed_text(tokens), ...)`. Fixes the previously-failing TestInt4Inference::test_inference_produces_valid_output. - test_pipeline.py: added vision-aware quant/save/load coverage + TestVisionConfigRequired. Also fixed a pre-existing fixture bug in build_hf_checkpoint -- vision encoder-layer attn/mlp projections are now written with the HF `.linear.` segment (Gemma4ClippableLinear) so they match hf_vision_per_layer_key_map(); without this the --model-dir export test silently skipped 7 vision projection weights. - New: tests/test_vision_tower.py, tests/test_vision_quant_roundtrip.py. Verification ------------ - CPU: pytest test_vision_tower.py test_vision_quant_roundtrip.py test_pipeline.py -> 20 passed. - CUDA (2x A100): pytest test_cuda_pipeline.py -> 8 passed, incl. both export tests (text-only prefill+decode .pte/.ptd produced and serialized) and the chunked-prefill / int4 inference contract tests. - flake8 + ufmt clean on all changed/new files. - MLX export+runtime e2e and the Python eager --image-path smoke are to be run by the user (no MLX hardware here; image checkpoint user-provided).
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