feat(xla): support Youtu-VL windowed vision and MLA inference #872

Parent and dependency

• Parent epic: epic: OpenXLA multimodal / VLM architecture support (Qwen2-VL, Gemma3n, Phi4MM, Molmo, and more) #566
• Blocked by test(xla): validate a LLaVA reference architecture end to end #862
• Existing MLX implementation: src/loading/vlm_youtu_vl.rs, src/vision/youtu_vl.rs, src/vision/encoders/youtu_vl.rs, src/vision/processors/youtu_vl.rs, and src/models/youtu_vl_lm.rs

Problem

Youtu-VL combines a SigLIP2-style visual encoder with flattened Linear patches, variable spatial shapes, windowed attention, 2D vision RoPE, and a built-in merger that already projects to language hidden size. Its text backbone is a dense DeepSeek-V3-style MLA model with traditional/interleaved RoPE, not a Qwen M-RoPE model. The processor enforces a per-image patch cap before allocation.

Aliasing this family to either generic SigLIP/LLaVA or Qwen-VL would lose window/grid semantics or use the wrong language position model.

Goal

Support a pinned Youtu-VL checkpoint end to end on XLA, including safe flattened-patch preprocessing, IREE windowed vision/merger execution, DeepSeek-style MLA text prefill/decode, placeholder replacement, CLI, and serving.

Required implementation

• Reuse/share the processor's smart resize, SigLIP normalization, flattened [total_patches, channels * patch_size^2] layout, per-image (h_patches, w_patches) metadata, divisibility rules, checked allocation arithmetic, and max_patches_per_image enforcement.
• Define static patch/media buckets with actual counts and spatial shapes. Reject over-capacity images before allocating/uploading the flattened tensor.
• Emit Youtu patch projection, position/2D RoPE helpers, window index and cumulative boundaries, full/window block selection, normalization/MLP variants, and built-in patch merger through StableHLO/IREE.
• Preserve per-image/window attention isolation and output restoration order. The merger output is already in language hidden size; do not add another projector.
• Replace configured image_token_id positions with features. Preserve the current fallback to video_token_id only when no image token is present, and advertise video support only if its request/preprocessor semantics have an oracle fixture.
• Reuse the existing XLA DeepSeek-V3/MLA primitives only after mapping Youtu's dense/bias/traditional-interleaved-RoPE configuration and sanitized weights exactly. Do not select Qwen M-RoPE because the input is visual.
• Include patch/window/grid/merger config, placeholder IDs, MLA head decomposition, RoPE flags, and static limits in artifact compatibility identity.
• Integrate filtered loading, capabilities, CLI/server batching, cancellation, and text fallback without a duplicate full MLX decoder or silent MLX vision fallback.

Non-goals

• General Qwen-VL M-RoPE.
• Unbounded dynamic spatial shapes.
• Additional connector layers after the built-in Youtu merger.

Validation

• Pin one Youtu-VL checkpoint/revision and compare resized pixels, flattened patch ordering, spatial shapes, window indices/boundaries, vision RoPE, selected full/window block outputs, merged visual tokens, placeholder selection, MLA prefill logits/KV, and greedy tokens against MLX and an independent oracle.
• Cover multiple aspect ratios, maximum patch boundary, one-patch overflow rejected before allocation, multiple images, cross-image/window isolation, image-token and documented video-token fallback, no-media text, cancellation, and slot reuse.
• Add negative fixtures for CHW/patch flattening order, extra projection, Qwen position routing, and window restoration errors.
• Run CLI and mixed continuous-batch server tests on a production-relevant IREE target, plus test(xla): validate a LLaVA reference architecture end to end #862 and text-only DeepSeek/MLA gates.

Acceptance criteria

• Checked processor allocation and per-image patch limits are preserved before XLA upload.
• Windowed vision and built-in merger match intermediate oracles; no extra projector is applied.
• Youtu's dense MLA/traditional-interleaved-RoPE text contract is selected explicitly.
• Placeholder replacement and any advertised video fallback are independently validated.
• Deterministic output is token-exact on the pinned fixture.
• No full decoder duplication or silent MLX vision fallback remains.
• Existing text-only DeepSeek/MLA regressions remain green.