test: cover the second zero-coverage VLM architecture (Qwen3VL) - #50
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Follow-up to #49. Measured coverage after that PR still showed the VLM half of the model layer almost entirely untested; Qwen3VL.swift (1770 lines) was the new largest zero-coverage file in the model layer. Its config shape differs from Gemma4's in a way worth testing on its own terms: text and vision decode as two independent nested structs (Qwen3VLConfiguration.TextConfiguration / .VisionConfiguration) rather than one flat shape sharing fields, so a misplaced field looks like a wrong nesting level rather than a missing top-level key. The vision tower also has two constraints a hand-written config can violate silently — numPositionEmbeddings must be a perfect square (a grid side is derived via sqrt) and hiddenSize must divide evenly by numHeads — both exercised here by construction succeeding at all. Four tests: config decoding across both nested structs, instantiation, text-only forward pass shape (the vision tower is built at init but must not be required for a plain-text request, which is what most chat traffic is), determinism. Verified with coverage: Qwen3VL.swift moves from 0% to 26.0% line coverage. Full suite: 112 tests, 19 suites, passing (108 before, +4 here). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Points at 3f6f13e (SharpAI/mlx-swift-lm#50), which adds the second set of unit tests for anything under MLXVLM — Qwen3VL.swift, 1770 lines, the largest zero-coverage file in the model layer after #49/#147 covered Gemma4. No SwiftLM-side code changes; verified the umbrella builds clean against the bumped pointer. Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
This was referenced Aug 15, 2026
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Follow-up to #49/#50. MLXVLM/Models/Qwen35.swift was, alongside LFM2VL.swift, one of the largest remaining zero-coverage files in the model layer. Its language model is a hybrid: layers alternate between full self-attention and a linear-attention block (GatedDeltaNet) on a fixed period, full_attention_interval. DecoderLayer.isLinear is (layerIdx + 1) % fullAttentionInterval != 0, so a config that never turns the linear branch on would leave GatedDeltaNet entirely dead code. Setting the interval to the layer count (2/2) makes layer 0 linear and layer 1 full attention, exercising both branches deliberately rather than by chance. Vision config reuses Qwen3VLConfiguration.VisionConfiguration directly (Qwen35 is literally typealiased to it), so it inherits the same two silent constraints already covered in #50: num_position_embeddings must be a perfect square, and hidden_size must divide evenly by num_heads. The forward-pass test uses the model's own newCache(parameters:), not nil — that is what routes the linear layer to a MambaCache and the attention layer to a standard KV cache, which is model-specific behaviour worth exercising against the same config that built the model rather than assuming it lines up. MoE fields (num_experts, decoder_sparse_step, ...) are left at their dense defaults; the routed-MoE path is a separate shape from the attention hybrid this covers. Verified with coverage: MLXVLM/Models/Qwen35.swift moves from 0% to 66.4% line coverage. Full suite: 120 tests, 21 suites, passing (112 before, +8 across this PR and its sibling for LFM2VL). Co-authored-by: Aegis AI Assistant <simba@aegis-ai.dev> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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Follow-up to #49/#50. MLXVLM/Models/LFM2VL.swift was, alongside Qwen35.swift, one of the largest remaining zero-coverage files in the model layer. Its hybrid mechanism differs from Qwen35's fixed-period one: layers are attention only at explicit indices, full_attn_idxs (or derived from layer_types), and a short causal convolution (LFM2ShortConv) everywhere else — a sparser, more arbitrary pattern than a modulus. Setting full_attn_idxs: [1] over 2 layers makes layer 0 the conv block and layer 1 the attention block, so both branches of LFM2DecoderLayer build and run. newCache reads fullAttnIdxs independently of how the layers were built to decide MambaCache versus KVCacheSimple per layer, so the forward-pass test uses it directly (model.newCache(parameters: nil)) rather than assuming construction and cache selection stay in agreement — that agreement is exactly what this exercises. The vision half carries the same silent constraint as Qwen3VL's tower (#50): num_patches must be a perfect square, since a grid side is derived via sqrt. Verified with coverage: MLXVLM/Models/LFM2VL.swift moves from 0% to 34.9% line coverage. Full suite: 120 tests, 21 suites, passing (112 before, +8 across this PR and its sibling for Qwen35). Co-authored-by: Aegis AI Assistant <simba@aegis-ai.dev> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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Follow-up to #49. After that PR,
Qwen3VL.swift(1770 lines) became the new largest zero-coverage file in the model layer — the VLM half of the model layer is still almost entirely untested by unit tests.Why Qwen3VL specifically, and what's different about it
Its config shape is a genuinely different pattern from Gemma4's: text and vision decode as two independent nested structs (
Qwen3VLConfiguration.TextConfiguration/.VisionConfiguration) rather than one flat shape sharing fields. A misplaced field here looks like a wrong nesting level, not a missing top-level key — different failure mode, worth its own test rather than reusing Gemma4's pattern blind.The vision tower also has two silent constraints a hand-written config can violate:
numPositionEmbeddingsmust be a perfect square (a grid side is derived viasqrt)hiddenSizemust divide evenly bynumHeadsBoth are exercised simply by construction succeeding.
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