diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 2005b110..e232041d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -174,13 +174,16 @@ jobs:
'BatchStepEquivalenceTest', 'BertGoldenTest', 'T5GoldenTest', 'Seq2SeqMemoryTest', 'Seq2SeqGenerationTest', 'EncoderDefaultPrecisionTest',
'ModalityRejectionTest', 'Idefics3VisionTowerSmokeTest',
'SmolVlmGoldenTest', 'SmolVlmGenerationTest', 'SmolVlmDefaultPrecisionTest', 'VisionLanguageMemoryTest',
- 'DecoderSeamTest', 'VisualFeatureInjectionTest'],
+ 'DecoderSeamTest', 'VisualFeatureInjectionTest',
+ 'Qwen3VlVisionTowerGoldenTest', 'Qwen3VlVisionPeakTest',
+ 'Qwen3VlVisionTowerValidationTest'],
}
# Golden references run in their own full-float32 JVM. Batch equivalence must
# execute in both precision modes. Probe (b) remains evidence only.
float32_suites = ['DecoderRefactorGoldenTest', 'MistralModelTest', 'GemmaModelTest',
'Phi3ModelTest', 'MixtralModelTest', 'Llama31ModelTest',
- 'DecoderSlidingWindowTest', 'BertGoldenTest', 'T5GoldenTest', 'SmolVlmGoldenTest']
+ 'DecoderSlidingWindowTest', 'BertGoldenTest', 'T5GoldenTest', 'SmolVlmGoldenTest',
+ 'Qwen3VlVisionTowerGoldenTest']
model_results = {name: ['float32GoldenTest'] for name in float32_suites}
model_results['BatchStepEquivalenceTest'] = ['test', 'float32GoldenTest']
model_results['DecoderSeamTest'] = ['test', 'float32GoldenTest']
diff --git a/AGENTS.md b/AGENTS.md
index 50f265de..632a3feb 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -682,3 +682,39 @@ locked environments, never Gradle-invoked) cross-checks the component. The CI
required-suite list addition landed in PR #57 (review round 2), and `Qwen3VlModel`,
its loader dispatch and the DeepStack consumption of `VisualFeatureInjection` are
WP5/WP6. No native pins, generated bindings or build files changed.
+
+Phase 7.3b WP5 (2026-10-22) landed the Qwen3-VL vision tower in `jmlx-models`:
+`Qwen3VlVisionTower` (patch embed + 3-D interpolated position embeddings, N
+`Qwen3VlVisionBlock`s — each a LayerNorm/attention/LayerNorm/MLP two-statement residual
+block, matching the pinned 4.57.6 `Qwen3VLVisionBlock.forward` sequential semantics
+where `norm2` sees the updated residual — plus DeepStack level merges) composed with
+`jmlx-core`'s new `PatchMerger`, `TwoAxisRope`, `TwoAxisRotation` and
+`TwoAxisRotaryAttention` components (all with their own core tests), returning
+`Qwen3VlVisionFeatures` (merged rows + per-level DeepStack rows) hoisted into the
+caller's per-request target scope (`forward(MLXScope, List)`, the `SmolVlmModel`
+per-request pattern — the tower's module scope receives no per-forward handles, and
+multi-image per-image parts are released after the evaluated concatenation); each
+encoder block additionally runs in a private per-block child scope of the image scope
+that is evaluated and closed before the next block starts, and the embedding stage
+(patch embed + position-embedding gathers) runs in its own child scope of the image
+scope that is evaluated and closed before block 0 starts (the per-block and
+embedding-stage evaluate-and-release loops of the 2026-10-23 review round), so one
+block's intermediates never accumulate across blocks and the per-image peak neither
+scales with depth nor carries the embedding stage while the encoder runs. The strict
+HF intermediate-feature golden
+`tools/hf-reference/goldens/qwen3vl-vision-wp5.json` plus its tiny synthetic
+checkpoint, image and generator script (`tools/hf-reference/qwen3vl_vision.py`,
+digest-pinned in `tools/hf-reference/provenance.json`) drive
+`Qwen3VlVisionTowerGoldenTest` (full-float32), and `Qwen3VlVisionPeakTest`
+(ordinary `test`) measures the real-2B-geometry per-image peak — the tower forward's
+own transient 1.25 GiB (all three depth probes bit-for-bit equal, depth 4 − depth 1
+= 0 B against the test's 64 MiB depth-independence bound), sustained 16 MiB, and the
+isolated attention-bracket transient 0.31 GiB, all under the plan §4 5 GiB bound —
+recorded in the reference findings §11.4, which documents the pre-fix cause
+(per-image scope Java handles pinning every block's intermediates, slope
+1,174,405,136 B = 70 × 16 MiB + 16 B per block) and now covers the fix with the
+depth-independence regression test. Both suites are required by the macOS CI
+assertion. `Qwen3VlModel`, its loader dispatch and the DeepStack consumption of
+`VisualFeatureInjection` remain WP6. The review round added the opt-in
+`exactBitsRecord/VerifyVision` Gradle tasks (never part of `check`) for the
+bit-identity evidence; no native pins or generated bindings changed.
diff --git a/jmlx-core/src/main/java/se/alipsa/jmlx/nn/PatchMerger.java b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/PatchMerger.java
new file mode 100644
index 00000000..546e4324
--- /dev/null
+++ b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/PatchMerger.java
@@ -0,0 +1,192 @@
+package se.alipsa.jmlx.nn;
+
+import java.util.Arrays;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXShape;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * The Qwen3-VL vision patch merger, frozen for transformers 4.57.6 {@code Qwen3VLVisionPatchMerger}
+ * (verbatim in req/plans/phase7-3b-reference-findings.md §11.1; provenance in {@code
+ * tools/hf-reference/provenance.json}). The main merger ({@code use_postshuffle_norm=False})
+ * normalizes the per-patch rows over {@code hiddenSize} BEFORE viewing them into merged rows; the
+ * DeepStack mergers ({@code use_postshuffle_norm=True}) view into the merged rows FIRST and
+ * normalize over the merged width {@code hiddenSize * merge^2}. Both then run {@code
+ * fc2(GELU(fc1(x)))} with the EXACT (erf) GELU -- the reference's {@code nn.GELU()}, NOT the tanh
+ * approximation the vision blocks' MLP uses (findings §11.2 "Merger ordering").
+ *
+ * The normalization {@code eps} is hardwired to {@code 1e-6} exactly as in the 4.57.6 source (an
+ * {@code nn.LayerNorm(..., eps=1e-6)} literal, not a config field -- the same hardwiring the block
+ * norms use), so the constructor takes no {@code eps}.
+ */
+public final class PatchMerger extends Module {
+
+ /** The reference's hardcoded LayerNorm eps (4.57.6 literal, not a config field). */
+ private static final float EPS = 1e-6f;
+
+ private final int spatialMergeSize;
+ private final boolean usePostShuffleNorm;
+ private final int hiddenSize;
+ private final int outHiddenSize;
+ private final LayerNorm norm;
+ private final Linear linearFc1;
+ private final GELU actFn;
+ private final Linear linearFc2;
+
+ /**
+ * Registers the {@code norm}, {@code linearFc1}, {@code actFn} and {@code linearFc2} children in
+ * that order. All validation is {@link IllegalArgumentException} and runs before any child is
+ * registered: {@code spatialMergeSize} is positive; {@code fc1Weight} is square {@code [m, m]}
+ * (the reference's {@code hidden_size = config.hidden_size * merge^2}, i.e. the merged width);
+ * {@code m % merge^2 == 0} with {@code hiddenSize = m / merge^2}; {@code fc1Bias} has length
+ * {@code m}; the norm weight/bias have length exactly {@code usePostShuffleNorm ? m : hiddenSize}
+ * (the two placements' defining difference); and {@code fc2Weight} is {@code [outHidden, m]} with
+ * a matching {@code fc2Bias} (the offending array is named in each message).
+ */
+ public PatchMerger(
+ MLXScope scope,
+ int spatialMergeSize,
+ boolean usePostShuffleNorm,
+ MLXArray normWeight,
+ MLXArray normBias,
+ MLXArray fc1Weight,
+ MLXArray fc1Bias,
+ MLXArray fc2Weight,
+ MLXArray fc2Bias) {
+ super(scope);
+ if (spatialMergeSize < 1) {
+ throw new IllegalArgumentException("spatialMergeSize must be positive");
+ }
+ int mergeSq = Math.multiplyExact(spatialMergeSize, spatialMergeSize);
+ if (fc1Weight == null) {
+ throw new IllegalArgumentException("fc1Weight must not be null");
+ }
+ if (fc1Weight.ndim() != 2 || fc1Weight.shape()[0] != fc1Weight.shape()[1]) {
+ throw new IllegalArgumentException(
+ "fc1Weight must be square [m, m], got shape " + Arrays.toString(fc1Weight.shape()));
+ }
+ int m = fc1Weight.shape()[0];
+ if (m % mergeSq != 0) {
+ throw new IllegalArgumentException(
+ "fc1Width m = " + m + " must be divisible by merge^2 = " + mergeSq);
+ }
+ int hidden = m / mergeSq;
+ int expectedNormDim = usePostShuffleNorm ? m : hidden;
+ if (normWeight == null || normWeight.ndim() != 1 || normWeight.shape()[0] != expectedNormDim) {
+ throw new IllegalArgumentException(
+ "normWeight must be rank 1 with length "
+ + expectedNormDim
+ + " (usePostShuffleNorm="
+ + usePostShuffleNorm
+ + "), got shape "
+ + (normWeight == null ? "null" : Arrays.toString(normWeight.shape())));
+ }
+ if (normBias == null || normBias.ndim() != 1 || normBias.shape()[0] != expectedNormDim) {
+ throw new IllegalArgumentException(
+ "normBias must be rank 1 with length "
+ + expectedNormDim
+ + " (usePostShuffleNorm="
+ + usePostShuffleNorm
+ + "), got shape "
+ + (normBias == null ? "null" : Arrays.toString(normBias.shape())));
+ }
+ if (fc1Bias == null || fc1Bias.ndim() != 1 || fc1Bias.shape()[0] != m) {
+ throw new IllegalArgumentException(
+ "fc1Bias must be rank 1 with length m = "
+ + m
+ + ", got shape "
+ + (fc1Bias == null ? "null" : Arrays.toString(fc1Bias.shape())));
+ }
+ if (fc2Weight == null) {
+ throw new IllegalArgumentException("fc2Weight must not be null");
+ }
+ if (fc2Weight.ndim() != 2 || fc2Weight.shape()[1] != m) {
+ throw new IllegalArgumentException(
+ "fc2Weight must be [outHidden, m] with m = "
+ + m
+ + ", got shape "
+ + Arrays.toString(fc2Weight.shape()));
+ }
+ int outHidden = fc2Weight.shape()[0];
+ if (fc2Bias == null || fc2Bias.ndim() != 1 || fc2Bias.shape()[0] != outHidden) {
+ throw new IllegalArgumentException(
+ "fc2Bias must be rank 1 with length outHidden = "
+ + outHidden
+ + ", got shape "
+ + (fc2Bias == null ? "null" : Arrays.toString(fc2Bias.shape())));
+ }
+ this.spatialMergeSize = spatialMergeSize;
+ this.usePostShuffleNorm = usePostShuffleNorm;
+ this.hiddenSize = hidden;
+ this.outHiddenSize = outHidden;
+ this.norm = child("norm", new LayerNorm(scope, normWeight, normBias, EPS));
+ this.linearFc1 = child("linearFc1", new Linear(scope, fc1Weight, fc1Bias));
+ this.actFn = child("actFn", new GELU(scope));
+ this.linearFc2 = child("linearFc2", new Linear(scope, fc2Weight, fc2Bias));
+ }
+
+ /** Returns the spatial merge size (the merged width is {@code hiddenSize * merge^2}). */
+ public int spatialMergeSize() {
+ return spatialMergeSize;
+ }
+
+ /**
+ * Whether the norm runs over the merged rows (DeepStack) instead of the per-patch rows (main).
+ */
+ public boolean usePostShuffleNorm() {
+ return usePostShuffleNorm;
+ }
+
+ /** Returns the per-patch hidden size {@code m / merge^2}. */
+ public int hiddenSize() {
+ return hiddenSize;
+ }
+
+ /** Returns the output width of {@code fc2}. */
+ public int outHiddenSize() {
+ return outHiddenSize;
+ }
+
+ /**
+ * Merges one image's or block's rows. All validation is {@link IllegalArgumentException} and runs
+ * before any graph node is built: {@code x} is non-null rank 2 {@code [N, hiddenSize]} with an
+ * inexact dtype (an exact dtype would truncate silently, the same reason {@code toFloatArray()}
+ * rejects exact dtypes) and {@code N % merge^2 == 0}. Then the frozen order: {@code y = x} or,
+ * for the post-shuffle norm, {@code reshape(x, [N/merge^2, m])} first; {@code norm.forward(y)}
+ * over the corresponding width; for the main placement, the post-norm rows are then reshaped to
+ * {@code [N/merge^2, m]}; finally {@code linearFc2(actFn(linearFc1(y)))} to {@code [N/merge^2,
+ * outHiddenSize]}.
+ */
+ public MLXArray forward(MLXArray x) {
+ if (x == null) {
+ throw new IllegalArgumentException("x must not be null");
+ }
+ int[] shape = x.shape();
+ if (shape.length != 2) {
+ throw new IllegalArgumentException(
+ "forward requires [N, hiddenSize], got shape " + Arrays.toString(shape));
+ }
+ if (shape[1] != hiddenSize) {
+ throw new IllegalArgumentException(
+ "forward input must have hiddenSize = " + hiddenSize + ", got " + shape[1]);
+ }
+ int n = shape[0];
+ int mergeSq = spatialMergeSize * spatialMergeSize;
+ if (n % mergeSq != 0) {
+ throw new IllegalArgumentException("N = " + n + " must be divisible by merge^2 = " + mergeSq);
+ }
+ DType dtype = x.dtype();
+ if (!dtype.isInexact()) {
+ throw new IllegalArgumentException("forward requires an inexact dtype, got " + dtype);
+ }
+ int merged = n / mergeSq;
+ int m = hiddenSize * mergeSq;
+ MLXArray y = usePostShuffleNorm ? MLXShape.reshape(x, new int[] {merged, m}) : x;
+ y = norm.forward(y);
+ if (!usePostShuffleNorm) {
+ y = MLXShape.reshape(y, new int[] {merged, m});
+ }
+ return linearFc2.forward(actFn.forward(linearFc1.forward(y)));
+ }
+}
diff --git a/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRope.java b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRope.java
new file mode 100644
index 00000000..929a1f5f
--- /dev/null
+++ b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRope.java
@@ -0,0 +1,177 @@
+package se.alipsa.jmlx.nn;
+
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXOps;
+import se.alipsa.jmlx.core.MLXShape;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * Immutable two-axis (vision) rotary-position specification for Hugging Face's Qwen3-VL vision
+ * tower, frozen for transformers 4.57.6 {@code Qwen3VLVisionRotaryEmbedding} (provenance in {@code
+ * tools/hf-reference/provenance.json}; the verbatim transcription and the design decisions are
+ * req/plans/phase7-3b-reference-findings.md §11). The vision tower rotates queries and keys with
+ * the patch's actual (row, col) grid coordinates: one axis per spatial direction, each owning
+ * {@code rotaryDims / 2} frequencies, in the reference's {@code dim = head_dim // 2} geometry (16
+ * frequencies per axis for the 2B's head_dim 64).
+ *
+ *
Deliberately not a {@link Module}: no parameters, no children, no owned scope. Every MLX array
+ * {@link #prepare} builds allocates into the caller's scope, and every array {@link
+ * TwoAxisRotation#rotate} builds allocates per the usual {@code scopeOf} rule (the innermost
+ * related scope of its operands). The rotation arrives per-image from the caller, mirroring how
+ * {@link MultiAxisRotation} is handed into the text-decoder attentions.
+ *
+ *
{@code rotaryDims} is the per-row frequency width -- the reference's {@code dim}, i.e. {@code
+ * head_dim // 2} -- and must be even (each axis owns {@code rotaryDims / 2} frequencies) and at
+ * least 2 (at least one frequency per axis). The full head dimension a rotation can be applied to
+ * is {@code 2 * rotaryDims}: {@code prepare} doubles it via {@code emb = cat((freqs, freqs), -1)}
+ * exactly as the reference's model {@code forward} does. {@link TwoAxisRotation#rotate} then pairs
+ * every element with its half-turn partner -- element {@code i} with element {@code i + rotaryDims}
+ * -- and rotates both members of the pair by the angle from {@code freqs[i]}, which the doubled
+ * table repeats identically in its second half. The frequency table's first half holds the row
+ * ({@code axis0}) frequencies and its second half the col ({@code axis1}) frequencies, so each half
+ * of a head contains both row and col components (findings §11.2 "Vision RoPE").
+ */
+public final class TwoAxisRope {
+
+ private final float theta;
+ private final int rotaryDims;
+ private final float[] invFreq;
+
+ /**
+ * Validates {@code theta} and {@code rotaryDims} and derives the per-frequency inverse
+ * frequencies. All validation is {@link IllegalArgumentException} and runs before any MLX array
+ * is written: {@code theta} must be finite and positive (the {@code RopeSpec} convention); {@code
+ * rotaryDims} must be even and at least 2.
+ */
+ public TwoAxisRope(float theta, int rotaryDims) {
+ if (!Float.isFinite(theta) || theta <= 0) {
+ throw new IllegalArgumentException("theta must be positive and finite");
+ }
+ if (rotaryDims < 2 || rotaryDims % 2 != 0) {
+ throw new IllegalArgumentException(
+ "rotaryDims must be even and at least 2, got " + rotaryDims);
+ }
+ this.theta = theta;
+ this.rotaryDims = rotaryDims;
+ this.invFreq = deriveInvFrequencies(theta, rotaryDims);
+ }
+
+ /** Returns the frequency base {@code theta}. */
+ public float theta() {
+ return theta;
+ }
+
+ /** Returns the per-row frequency width (the reference's {@code dim}, {@code head_dim // 2}). */
+ public int rotaryDims() {
+ return rotaryDims;
+ }
+
+ /**
+ * The derived float32 inverse frequencies, {@code invFreq[j] = 1 / theta^(2j/rotaryDims)} for
+ * {@code j} in {@code [0, rotaryDims/2)}. Package-private so the same-package bit-exact test
+ * ({@code TwoAxisRopeTest}) can compare every value against the pinned torch bits; not part of
+ * the public API.
+ */
+ float[] invFreq() {
+ return invFreq.clone();
+ }
+
+ /**
+ * Builds the per-image {@link TwoAxisRotation} for {@code sequence} patch rows. All validation is
+ * {@link IllegalArgumentException} and runs before any MLX array is written: {@code sequence} is
+ * at least 1, {@code axis0}/{@code axis1} are non-null with length exactly {@code sequence} and
+ * non-negative values (they are the patches' (row, col) grid coordinates in merge-block order, as
+ * the tower's {@code rotaryCoordinates} helper produces them), and the doubled table size {@code
+ * sequence * 2 * rotaryDims} fits an {@code int} array (checked in {@code long}). The reference
+ * compute order then runs: the per-row frequency matrix is the float32 outer products {@code
+ * [outer(axis0, invFreq) | outer(axis1, invFreq)]} of shape {@code [sequence, rotaryDims]} (each
+ * axis value is exactly int-to-float-cast on the CPU first, then one float32 multiply per element
+ * -- the same two operands torch's {@code torch.outer} multiplies, bit-verified against the
+ * pinned golden's {@code cos_sin_sample} products); the rows are doubled to {@code emb =
+ * cat((freqs, freqs), -1)} of shape {@code [sequence, 2 * rotaryDims]}; {@code MLXOps.cos}/{@code
+ * sin} in float32; finally both tables are reshaped to {@code [sequence, 1, 2 * rotaryDims]} so
+ * they broadcast over the heads axis of a {@code [sequence, heads, 2 * rotaryDims]} projection.
+ */
+ public TwoAxisRotation prepare(MLXScope scope, int sequence, int[] axis0, int[] axis1) {
+ if (sequence < 1) {
+ throw new IllegalArgumentException("sequence must be at least 1");
+ }
+ if (axis0 == null) {
+ throw new IllegalArgumentException("axis0 must not be null");
+ }
+ if (axis1 == null) {
+ throw new IllegalArgumentException("axis1 must not be null");
+ }
+ if (axis0.length != sequence) {
+ throw new IllegalArgumentException(
+ "axis0 must have length sequence = " + sequence + ", got " + axis0.length);
+ }
+ if (axis1.length != sequence) {
+ throw new IllegalArgumentException(
+ "axis1 must have length sequence = " + sequence + ", got " + axis1.length);
+ }
+ for (int coordinate : axis0) {
+ if (coordinate < 0) {
+ throw new IllegalArgumentException("axis0 must be non-negative");
+ }
+ }
+ for (int coordinate : axis1) {
+ if (coordinate < 0) {
+ throw new IllegalArgumentException("axis1 must be non-negative");
+ }
+ }
+ int doubled = rotaryDims * 2;
+ if ((long) sequence * doubled > Integer.MAX_VALUE) {
+ throw new IllegalArgumentException("sequence * 2 * rotaryDims must fit an int array");
+ }
+ int half = rotaryDims / 2;
+ MLXArray freqs =
+ MLX.array(scope, outerRows(axis0, axis1, sequence, half), new int[] {sequence, rotaryDims});
+ MLXArray emb = MLXShape.concatenate(new MLXArray[] {freqs, freqs}, 1);
+ MLXArray cos = MLXShape.reshape(MLXOps.cos(emb), new int[] {sequence, 1, doubled});
+ MLXArray sin = MLXShape.reshape(MLXOps.sin(emb), new int[] {sequence, 1, doubled});
+ return new TwoAxisRotation(cos, sin, rotaryDims, theta);
+ }
+
+ /**
+ * The per-row frequency matrix in row-major {@code [sequence, rotaryDims]} order: row {@code i}
+ * is {@code [i*axis0 * invFreq (half) | i*axis1 * invFreq (half)]}, each element the float32
+ * multiply of the exact int-to-float cast of the axis coordinate by the derived {@code invFreq}
+ * -- the same two float32 operands the reference's {@code torch.outer(arange, inv_freq)} lookup
+ * multiplies per element.
+ */
+ private float[] outerRows(int[] axis0, int[] axis1, int sequence, int half) {
+ float[] out = new float[sequence * (half * 2)];
+ for (int i = 0; i < sequence; i++) {
+ float row = (float) axis0[i];
+ float col = (float) axis1[i];
+ for (int j = 0; j < half; j++) {
+ out[i * (half * 2) + j] = row * invFreq[j];
+ out[i * (half * 2) + half + j] = col * invFreq[j];
+ }
+ }
+ return out;
+ }
+
+ /**
+ * Derives {@code invFreq[j] = 1 / theta^(2j/rotaryDims)} once, in the pinned reference's float32
+ * arithmetic order: the exponent is the float32 division of the exact float32 numerator {@code
+ * 2j} by the float32 {@code rotaryDims}, the power is a float32 cast of the double power, and the
+ * reciprocal is a float32 division. The pinned transformers 4.57.6 {@code
+ * Qwen3VLVisionRotaryEmbedding} builds exactly {@code 1.0 / (theta ** (arange(0, dim, 2, float32)
+ * / dim))} in float32, and this order reproduces all 16 of the pinned 2B frequencies' bits (theta
+ * 10000, dim 32; the WP2 golden's {@code probes.inv_freq} hex bits, asserted bit-for-bit in
+ * {@code TwoAxisRopeTest}).
+ */
+ private static float[] deriveInvFrequencies(float theta, int rotaryDims) {
+ int half = rotaryDims / 2;
+ float[] invFreq = new float[half];
+ for (int j = 0; j < half; j++) {
+ float exponent = (2f * j) / (float) rotaryDims;
+ float v = (float) Math.pow(theta, (double) exponent);
+ invFreq[j] = 1.0f / v;
+ }
+ return invFreq;
+ }
+}
diff --git a/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttention.java b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttention.java
new file mode 100644
index 00000000..374a7e00
--- /dev/null
+++ b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttention.java
@@ -0,0 +1,191 @@
+package se.alipsa.jmlx.nn;
+
+import java.util.Arrays;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXFast;
+import se.alipsa.jmlx.core.MLXShape;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * Rotary bidirectional (noncausal) self-attention for the Qwen3-VL vision tower, frozen for
+ * transformers 4.57.6 {@code Qwen3VLVisionAttention} (verbatim in
+ * req/plans/phase7-3b-reference-findings.md §11.1; provenance in {@code
+ * tools/hf-reference/provenance.json}). One instance is one vision block's {@code attn} submodule:
+ * a fused {@code qkv = Linear(hidden -> 3*hidden, bias)} projection, the two-axis rotation of q and
+ * k, and a {@code proj = Linear(hidden -> hidden, bias)} output projection, with the reference's
+ * own scaling {@code head_dim ** -0.5}.
+ *
+ *
The {@link TwoAxisRope} is deliberately NOT held here: it is stateless, and the rotation
+ * arrives per-image from the caller through {@link #forward}, mirroring how {@link
+ * MultiAxisRotation} is passed into the text-decoder attentions. The constructor's {@code theta} is
+ * validated, exposed through {@link #theta()} and enforced in {@link #forward} against the
+ * rotation's own {@code theta} (a rotation prepared by a rope with a different base is rejected
+ * before any projection runs), but the module itself never derives a table.
+ *
+ *
jmlx runs one image at a time, so this module issues exactly one unmasked noncausal {@link
+ * MLXFast#scaledDotProductAttention(MLXArray, MLXArray, MLXArray, float, boolean, MLXArray,
+ * MLXArray)} call per image ({@code causal=false, maskArr=null, sinks=null}) over that image's
+ * rows, instead of the reference's packed rows split per image by {@code cu_seqlens}. The two are
+ * numerically identical -- attention mixes nothing across images, and every other op is row-wise
+ * (findings §11.2 "Per-image sequential execution") -- and looping bounds the concurrent attention
+ * workspace to a single image.
+ */
+public final class TwoAxisRotaryAttention extends Module {
+
+ private final int heads;
+ private final int headDim;
+ private final float theta;
+ private final float scale;
+ private final Linear qkv;
+ private final Linear proj;
+
+ /**
+ * Registers the {@code qkv} and {@code proj} children. All validation is {@link
+ * IllegalArgumentException} and runs before any child is registered: {@code heads} and {@code
+ * headDim} are positive, {@code theta} is positive and finite (it must match the {@link
+ * TwoAxisRope} the caller prepares rotations with; {@link #forward} enforces that match against
+ * the rotation's own {@code theta}), and the weight/bias shapes are exactly {@code qkvWeight
+ * [3*hidden, hidden]}, {@code qkvBias [3*hidden]}, {@code projWeight [hidden, hidden]} and {@code
+ * projBias [hidden]} where {@code hidden = heads * headDim} (computed in {@code long}-safe exact
+ * math, so an overflowing geometry is an arithmetic failure, not a wrapped shape). The {@code
+ * scale} is the reference's {@code head_dim ** -0.5}, derived, not passed.
+ */
+ public TwoAxisRotaryAttention(
+ MLXScope scope,
+ int heads,
+ int headDim,
+ float theta,
+ MLXArray qkvWeight,
+ MLXArray qkvBias,
+ MLXArray projWeight,
+ MLXArray projBias) {
+ super(scope);
+ if (heads <= 0) {
+ throw new IllegalArgumentException("heads must be positive");
+ }
+ if (headDim <= 0) {
+ throw new IllegalArgumentException("headDim must be positive");
+ }
+ if (!Float.isFinite(theta) || theta <= 0) {
+ throw new IllegalArgumentException("theta must be positive and finite");
+ }
+ int hidden = Math.multiplyExact(heads, headDim);
+ int triple = Math.multiplyExact(3, hidden);
+ if (qkvWeight == null) {
+ throw new IllegalArgumentException("qkvWeight must not be null");
+ }
+ if (qkvWeight.ndim() != 2 || qkvWeight.shape()[0] != triple || qkvWeight.shape()[1] != hidden) {
+ throw new IllegalArgumentException(
+ "qkvWeight must be [3*hidden, hidden] = ["
+ + triple
+ + ", "
+ + hidden
+ + "], got shape "
+ + Arrays.toString(qkvWeight.shape()));
+ }
+ if (qkvBias == null || qkvBias.ndim() != 1 || qkvBias.shape()[0] != triple) {
+ throw new IllegalArgumentException(
+ "qkvBias must be rank 1 with length 3*hidden = "
+ + triple
+ + ", got shape "
+ + (qkvBias == null ? "null" : Arrays.toString(qkvBias.shape())));
+ }
+ if (projWeight == null) {
+ throw new IllegalArgumentException("projWeight must not be null");
+ }
+ if (projWeight.ndim() != 2
+ || projWeight.shape()[0] != hidden
+ || projWeight.shape()[1] != hidden) {
+ throw new IllegalArgumentException(
+ "projWeight must be [hidden, hidden] = ["
+ + hidden
+ + ", "
+ + hidden
+ + "], got shape "
+ + Arrays.toString(projWeight.shape()));
+ }
+ if (projBias == null || projBias.ndim() != 1 || projBias.shape()[0] != hidden) {
+ throw new IllegalArgumentException(
+ "projBias must be rank 1 with length hidden = "
+ + hidden
+ + ", got shape "
+ + (projBias == null ? "null" : Arrays.toString(projBias.shape())));
+ }
+ this.heads = heads;
+ this.headDim = headDim;
+ this.theta = theta;
+ this.scale = (float) Math.pow(headDim, -0.5);
+ this.qkv = child("qkv", new Linear(scope, qkvWeight, qkvBias));
+ this.proj = child("proj", new Linear(scope, projWeight, projBias));
+ }
+
+ /** Returns the attention scaling {@code head_dim ** -0.5}. */
+ public float scale() {
+ return scale;
+ }
+
+ /** Returns the frequency base the prepared rotations must match. */
+ public float theta() {
+ return theta;
+ }
+
+ /**
+ * Runs one image's full attention. All validation is {@link IllegalArgumentException} and runs
+ * before any graph node is built: {@code input} is non-null rank 2 {@code [N, hidden]} (the
+ * image's patch rows, hidden = heads * headDim), {@code rotation} is non-null, and {@link
+ * TwoAxisRotation#validate(float, int, int)} passes for the module's {@code theta}, the row count
+ * and the head dimension (a rotation prepared with a different frequency base, for a different
+ * image size or a different rotary width, is rejected before any projection runs). Then the
+ * frozen op sequence: {@code qkv.forward(input)} to {@code [N, 3*hidden]}; reshape to {@code [N,
+ * 3, heads, headDim]} and transpose {@code {1,0,2,3}} to {@code [3, N, heads, headDim]}; split
+ * into q, k, v on axis 0 (the split keeps the axis, so each part's size-1 axis 0 is squeezed back
+ * to {@code [N, heads, headDim]}); {@code rotation.rotate} on q and k (v is unrotated, as in the
+ * reference); each of the three transposed to {@code [heads, N, headDim]} and reshaped to {@code
+ * [1, heads, N, headDim]}; one unmasked noncausal {@link MLXFast#scaledDotProductAttention} with
+ * the module's {@link #scale()}; the result reshaped back to {@code [N, hidden]} and passed
+ * through {@code proj}.
+ */
+ public MLXArray forward(MLXArray input, TwoAxisRotation rotation) {
+ if (input == null) {
+ throw new IllegalArgumentException("input must not be null");
+ }
+ if (rotation == null) {
+ throw new IllegalArgumentException("rotation must not be null");
+ }
+ int[] shape = input.shape();
+ if (shape.length != 2) {
+ throw new IllegalArgumentException(
+ "forward requires [N, hidden], got shape " + Arrays.toString(shape));
+ }
+ int hidden = heads * headDim;
+ if (shape[1] != hidden) {
+ throw new IllegalArgumentException(
+ "forward input must have hidden = heads * headDim = " + hidden + ", got " + shape[1]);
+ }
+ int n = shape[0];
+ rotation.validate(theta, n, headDim);
+ MLXArray projected =
+ MLXShape.transpose(
+ MLXShape.reshape(qkv.forward(input), new int[] {n, 3, heads, headDim}),
+ new int[] {1, 0, 2, 3});
+ MLXArray[] parts = MLXShape.split(projected, 3, 0);
+ MLXArray q = headBatch(rotation.rotate(MLXShape.squeeze(parts[0], new int[] {0})), n);
+ MLXArray k = headBatch(rotation.rotate(MLXShape.squeeze(parts[1], new int[] {0})), n);
+ MLXArray v = headBatch(MLXShape.squeeze(parts[2], new int[] {0}), n);
+ MLXArray attended = MLXFast.scaledDotProductAttention(q, k, v, scale, false, null, null);
+ MLXArray merged =
+ MLXShape.reshape(
+ MLXShape.transpose(
+ MLXShape.reshape(attended, new int[] {heads, n, headDim}), new int[] {1, 0, 2}),
+ new int[] {n, hidden});
+ return proj.forward(merged);
+ }
+
+ /**
+ * The {@code [heads, N, headDim]} single-batch layout {@code scaledDotProductAttention} wants.
+ */
+ private MLXArray headBatch(MLXArray x, int n) {
+ return MLXShape.reshape(
+ MLXShape.transpose(x, new int[] {1, 0, 2}), new int[] {1, heads, n, headDim});
+ }
+}
diff --git a/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotation.java b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotation.java
new file mode 100644
index 00000000..66ca0502
--- /dev/null
+++ b/jmlx-core/src/main/java/se/alipsa/jmlx/nn/TwoAxisRotation.java
@@ -0,0 +1,143 @@
+package se.alipsa.jmlx.nn;
+
+import java.util.Arrays;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXOps;
+import se.alipsa.jmlx.core.MLXShape;
+
+/**
+ * Per-image rotation state one {@link TwoAxisRope#prepare} produces and every vision block of that
+ * image shares: the float32 {@code cos}/{@code sin} tables of shape {@code [sequence, 1, 2 *
+ * rotaryDims]} (broadcastable over the heads axis), the frequency base {@code theta} the tables
+ * were built with and the per-row frequency width. Threading exactly like {@link
+ * MultiAxisRotation}: computed once per image, handed to each rotary attention, never cached
+ * between images.
+ *
+ *
{@link #rotate} applies the frozen composition of transformers 4.57.6 {@code
+ * apply_rotary_pos_emb_vision} (verbatim in req/plans/phase7-3b-reference-findings.md §11.1) with
+ * that function's exact compute order: the INPUT is upcast to float32 ({@code q.float()}) and the
+ * whole rotation {@code out = (x * cos) + (rotate_half(x) * sin)} runs in float32 against the
+ * float32 tables, and only then is the result cast back to {@code x.dtype()} ({@code
+ * q_embed.to(orig_q_dtype)}). This is the OPPOSITE cast order of {@link MultiAxisRotation#rotate},
+ * which casts the float32 cos/sin tables DOWN to the input dtype and computes in the input dtype
+ * (findings §11.2 "Vision RoPE"): the two orders agree bit-for-bit for float32 inputs but diverge
+ * for half-precision ones, and each component is frozen to its own reference function rather than
+ * unified.
+ */
+public final class TwoAxisRotation {
+
+ private final MLXArray cos;
+ private final MLXArray sin;
+ private final int rotaryDims;
+ private final float theta;
+
+ TwoAxisRotation(MLXArray cos, MLXArray sin, int rotaryDims, float theta) {
+ this.cos = cos;
+ this.sin = sin;
+ this.rotaryDims = rotaryDims;
+ this.theta = theta;
+ }
+
+ /** Returns the float32 cosine table, {@code [sequence, 1, 2 * rotaryDims]}. */
+ public MLXArray cos() {
+ return cos;
+ }
+
+ /** Returns the float32 sine table, {@code [sequence, 1, 2 * rotaryDims]}. */
+ public MLXArray sin() {
+ return sin;
+ }
+
+ /**
+ * Returns the per-row frequency width (the prepared head dimension is {@code 2 * rotaryDims}).
+ */
+ public int rotaryDims() {
+ return rotaryDims;
+ }
+
+ /** Returns the frequency base {@code theta} the tables were built with. */
+ public float theta() {
+ return theta;
+ }
+
+ /**
+ * The frequency-base match the attention seam checks before any array is written: the attention's
+ * {@code theta} must exactly equal the base the tables were prepared from (two ropes built from
+ * different bases produce different tables, so a rotated sequence would silently use the wrong
+ * frequencies). All validation is {@link IllegalArgumentException}; nothing is allocated. Then
+ * the same {@link #validate(int, int)} shape preflight runs for {@code sequence} and {@code
+ * headDim}.
+ */
+ public void validate(float expectedTheta, int sequence, int headDim) {
+ if (Float.compare(expectedTheta, theta) != 0) {
+ throw new IllegalArgumentException(
+ "theta must match the prepared rotation (" + expectedTheta + " against " + theta + ")");
+ }
+ validate(sequence, headDim);
+ }
+
+ /**
+ * The rotation-shape preflight {@link #rotate} runs before any array is written: the sequence
+ * axis of the heads to be rotated must match the prepared rotation, and the head dimension must
+ * be exactly {@code 2 * rotaryDims} (the reference's doubled table width -- unlike {@link
+ * MultiAxisRotation#validate}, whose rotated prefix may be narrower than the head). All
+ * validation is {@link IllegalArgumentException} and nothing is allocated. {@link #rotate}
+ * delegates its shape checks here.
+ */
+ public void validate(int sequence, int headDim) {
+ int[] cosShape = cos.shape();
+ if (sequence != cosShape[0]) {
+ throw new IllegalArgumentException(
+ "rotation sequence must match the prepared rotation ("
+ + sequence
+ + " against "
+ + cosShape[0]
+ + ")");
+ }
+ if (headDim != 2 * rotaryDims) {
+ throw new IllegalArgumentException(
+ "headDim " + headDim + " must equal 2 * rotaryDims (" + (2 * rotaryDims) + ")");
+ }
+ }
+
+ /**
+ * Rotates a {@code [sequence, heads, 2 * rotaryDims]} tensor. All validation is {@link
+ * IllegalArgumentException} and runs before any array is written: {@code x} is non-null with rank
+ * 3, an inexact dtype (an exact dtype cast back would silently truncate, the same reason {@code
+ * toFloatArray()} rejects exact dtypes), and its sequence and head dimensions match the prepared
+ * rotation. Then the vision compute order (see this class's javadoc) runs: {@code xf = x} or
+ * {@code astype(x, FLOAT32)}; {@code rotate_half(xf) = concat((−xf[…, n:], xf[…, :n]), -1)} with
+ * {@code n = headDim / 2}; {@code out = (xf * cos) + (rotate_half(xf) * sin)} in float32, the
+ * {@code [sequence, 1, ...]} tables broadcasting over the heads axis; finally {@code out} cast
+ * back to {@code x.dtype()} (a no-op for float32). Every built array allocates per the usual
+ * {@code scopeOf} rule (the innermost related scope of its operands).
+ */
+ public MLXArray rotate(MLXArray x) {
+ if (x == null) {
+ throw new IllegalArgumentException("x must not be null");
+ }
+ int[] shape = x.shape();
+ if (shape.length != 3) {
+ throw new IllegalArgumentException(
+ "rotate requires [sequence, heads, headDim], got shape " + Arrays.toString(shape));
+ }
+ validate(shape[0], shape[2]);
+ DType dtype = x.dtype();
+ if (!dtype.isInexact()) {
+ throw new IllegalArgumentException("rotate requires an inexact dtype, got " + dtype);
+ }
+ MLXArray xf = dtype == DType.FLOAT32 ? x : MLX.astype(x, DType.FLOAT32);
+ int half = shape[2] / 2;
+ int[] fullStop = shape.clone();
+ int[] frontStop = shape.clone();
+ frontStop[2] = half;
+ MLXArray front = MLXShape.slice(xf, new int[] {0, 0, 0}, frontStop);
+ int[] backStart = new int[] {0, 0, half};
+ MLXArray back = MLXShape.slice(xf, backStart, fullStop);
+ MLXArray rotated = MLXShape.concatenate(new MLXArray[] {MLXOps.negative(back), front}, 2);
+ MLXArray out = MLXOps.add(MLXOps.multiply(xf, cos), MLXOps.multiply(rotated, sin));
+ return dtype == DType.FLOAT32 ? out : MLX.astype(out, dtype);
+ }
+}
diff --git a/jmlx-core/src/test/java/se/alipsa/jmlx/nn/PatchMergerTest.java b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/PatchMergerTest.java
new file mode 100644
index 00000000..e093adc1
--- /dev/null
+++ b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/PatchMergerTest.java
@@ -0,0 +1,339 @@
+package se.alipsa.jmlx.nn;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * Strict tests for the WP5 {@link PatchMerger} (req/plans/phase7-3b-reference-findings.md §11.2
+ * "Merger ordering"). Runs in the ordinary {@code :jmlx-core:test} task. The numeric cases
+ * hand-compute the expected output (LayerNorm with population variance and eps 1e-6, exact erf
+ * GELU) in double in the test and compare with a 1e-5 tolerance; the two flag placements use the
+ * same linear weights so their different normalization width and order are the only structural
+ * source of output difference.
+ */
+@EnabledIfNativeAvailable
+class PatchMergerTest {
+
+ private static final float EPS = 1e-6f;
+
+ // merge 2 (merge^2 4), per-patch hidden 2, m = hidden * merge^2 = 8, outHidden 2. The input is
+ // N = 4 rows of width hidden, so exactly one merged row of width m.
+ private static final int MERGE = 2;
+ private static final int N = 4;
+ private static final int HIDDEN = 2;
+ private static final int M = HIDDEN * MERGE * MERGE;
+ private static final int OUT = 2;
+
+ // The shared MLP weights: all 64 fc1 entries distinct (0.01 * (i + 1)) so a wrong row/column
+ // mapping is observable; the same arrays are used by both flag placements.
+ private static final float[] FC1_WEIGHT = {
+ 0.01f, 0.02f, 0.03f, 0.04f, 0.05f, 0.06f, 0.07f, 0.08f,
+ 0.09f, 0.10f, 0.11f, 0.12f, 0.13f, 0.14f, 0.15f, 0.16f,
+ 0.17f, 0.18f, 0.19f, 0.20f, 0.21f, 0.22f, 0.23f, 0.24f,
+ 0.25f, 0.26f, 0.27f, 0.28f, 0.29f, 0.30f, 0.31f, 0.32f,
+ 0.33f, 0.34f, 0.35f, 0.36f, 0.37f, 0.38f, 0.39f, 0.40f,
+ 0.41f, 0.42f, 0.43f, 0.44f, 0.45f, 0.46f, 0.47f, 0.48f,
+ 0.49f, 0.50f, 0.51f, 0.52f, 0.53f, 0.54f, 0.55f, 0.56f,
+ 0.57f, 0.58f, 0.59f, 0.60f, 0.61f, 0.62f, 0.63f, 0.64f
+ };
+ private static final float[] FC1_BIAS = {
+ 0.05f, -0.1f, 0.15f, -0.05f, 0.02f, -0.08f, 0.11f, -0.03f
+ };
+ private static final float[] FC2_WEIGHT = {
+ 0.2f, -0.1f, 0.3f, 0.1f, -0.2f, 0.3f, -0.1f, 0.2f,
+ -0.15f, 0.25f, -0.05f, 0.1f, 0.05f, -0.2f, 0.15f, -0.1f
+ };
+ private static final float[] FC2_BIAS = {0.1f, -0.2f};
+
+ @Test
+ void mainPlacementMatchesHandComputedExpectation() {
+ float[] normWeight = {2.0f, 2.0f};
+ float[] normBias = {0.25f, 0.25f};
+ float[] x = {0.5f, -0.25f, -0.5f, 0.75f, 1.0f, 0.25f, -0.75f, -1.0f};
+ try (MLXScope scope = new MLXScope()) {
+ PatchMerger merger =
+ new PatchMerger(
+ scope,
+ MERGE,
+ false,
+ MLX.array(scope, normWeight, new int[] {HIDDEN}),
+ MLX.array(scope, normBias, new int[] {HIDDEN}),
+ MLX.array(scope, FC1_WEIGHT, new int[] {M, M}),
+ MLX.array(scope, FC1_BIAS, new int[] {M}),
+ MLX.array(scope, FC2_WEIGHT, new int[] {OUT, M}),
+ MLX.array(scope, FC2_BIAS, new int[] {OUT}));
+ assertEquals(MERGE, merger.spatialMergeSize());
+ assertFalse(merger.usePostShuffleNorm());
+ assertEquals(HIDDEN, merger.hiddenSize());
+ assertEquals(OUT, merger.outHiddenSize());
+ MLXArray input = MLX.array(scope, x, new int[] {N, HIDDEN});
+ MLXArray outArray = merger.forward(input);
+ assertArrayEquals(new int[] {N / (MERGE * MERGE), OUT}, outArray.shape());
+ float[] out = outArray.toFloatArray();
+ assertArrayEqualsLoose(expectedMerger(false, x, normWeight, normBias), out, 1e-5f);
+ }
+ }
+
+ @Test
+ void postShufflePlacementMatchesHandComputedExpectationAndDiffersFromMain() {
+ float[] normWeight = {1.5f, 2.0f, 2.5f, 3.0f, 2.0f, 1.5f, 3.0f, 2.5f};
+ float[] normBias = {0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f};
+ float[] x = {0.5f, -0.25f, -0.5f, 0.75f, 1.0f, 0.25f, -0.75f, -1.0f};
+ float[] mainNormWeight = {2.0f, 2.0f};
+ float[] mainNormBias = {0.25f, 0.25f};
+ try (MLXScope scope = new MLXScope()) {
+ PatchMerger merger =
+ new PatchMerger(
+ scope,
+ MERGE,
+ true,
+ MLX.array(scope, normWeight, new int[] {M}),
+ MLX.array(scope, normBias, new int[] {M}),
+ MLX.array(scope, FC1_WEIGHT, new int[] {M, M}),
+ MLX.array(scope, FC1_BIAS, new int[] {M}),
+ MLX.array(scope, FC2_WEIGHT, new int[] {OUT, M}),
+ MLX.array(scope, FC2_BIAS, new int[] {OUT}));
+ assertTrue(merger.usePostShuffleNorm());
+ MLXArray input = MLX.array(scope, x, new int[] {N, HIDDEN});
+ MLXArray outArray = merger.forward(input);
+ assertArrayEquals(new int[] {N / (MERGE * MERGE), OUT}, outArray.shape());
+ float[] out = outArray.toFloatArray();
+ assertArrayEqualsLoose(expectedMerger(true, x, normWeight, normBias), out, 1e-5f);
+ // The same linear weights with the main placement must produce a different output: the
+ // normalization runs over the per-patch rows instead of the merged row, which is the
+ // observable fingerprint of the flag.
+ PatchMerger mainMerger =
+ new PatchMerger(
+ scope,
+ MERGE,
+ false,
+ MLX.array(scope, mainNormWeight, new int[] {HIDDEN}),
+ MLX.array(scope, mainNormBias, new int[] {HIDDEN}),
+ MLX.array(scope, FC1_WEIGHT, new int[] {M, M}),
+ MLX.array(scope, FC1_BIAS, new int[] {M}),
+ MLX.array(scope, FC2_WEIGHT, new int[] {OUT, M}),
+ MLX.array(scope, FC2_BIAS, new int[] {OUT}));
+ float[] mainOut = mainMerger.forward(input).toFloatArray();
+ assertArrayEqualsLoose(
+ expectedMerger(false, x, mainNormWeight, mainNormBias), mainOut, 1e-5f);
+ double maxDiff = 0;
+ for (int i = 0; i < out.length; i++) {
+ maxDiff = Math.max(maxDiff, Math.abs(out[i] - mainOut[i]));
+ }
+ assertTrue(
+ maxDiff > 1e-6, "norm placement must be observable in the output, diff " + maxDiff);
+ }
+ }
+
+ @Test
+ void constructorRejectsInvalidGeometry() {
+ float[] goodNorm2 = {1.0f, 1.0f};
+ float[] goodNorm8 = {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
+ float[] goodFc1 = new float[M * M];
+ float[] goodFc1Bias = new float[M];
+ float[] goodFc2 = new float[OUT * M];
+ float[] goodFc2Bias = new float[OUT];
+ try (MLXScope scope = new MLXScope()) {
+ MLXArray norm2 = MLX.array(scope, goodNorm2, new int[] {2});
+ MLXArray fc1 = MLX.array(scope, goodFc1, new int[] {M, M});
+ MLXArray fc1Bias = MLX.array(scope, goodFc1Bias, new int[] {M});
+ MLXArray fc2 = MLX.array(scope, goodFc2, new int[] {OUT, M});
+ MLXArray fc2Bias = MLX.array(scope, goodFc2Bias, new int[] {OUT});
+ // spatialMergeSize not positive
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, 0, false, norm2, norm2, fc1, fc1Bias, fc2, fc2Bias));
+ // fc1 not square
+ MLXArray fc1Rect = MLX.array(scope, new float[6], new int[] {2, 3});
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, MERGE, false, norm2, norm2, fc1Rect, fc1Bias, fc2, fc2Bias));
+ // m not divisible by merge^2 (merge 3 -> merge^2 9; m = 8)
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, 3, false, norm2, norm2, fc1, fc1Bias, fc2, fc2Bias));
+ // norm dim mismatch for the main placement (expects hidden = 2, given 8)
+ MLXArray norm8 = MLX.array(scope, goodNorm8, new int[] {8});
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, MERGE, false, norm8, norm8, fc1, fc1Bias, fc2, fc2Bias));
+ // norm dim mismatch for the post-shuffle placement (expects m = 8, given 2)
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, MERGE, true, norm2, norm2, fc1, fc1Bias, fc2, fc2Bias));
+ // fc2 input dim mismatch (expects m = 8, given 3)
+ MLXArray fc2Bad = MLX.array(scope, new float[OUT * 3], new int[] {OUT, 3});
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new PatchMerger(scope, MERGE, false, norm2, norm2, fc1, fc1Bias, fc2Bad, fc2Bias));
+ // fc1 bias length mismatch
+ MLXArray fc1BiasShort = MLX.array(scope, new float[M - 1], new int[] {M - 1});
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new PatchMerger(scope, MERGE, false, norm2, norm2, fc1, fc1BiasShort, fc2, fc2Bias));
+ }
+ }
+
+ @Test
+ void forwardRejectsInvalidRows() {
+ float[] normWeight = {1.0f, 1.0f};
+ float[] fc1Weight = new float[M * M];
+ float[] fc1Bias = new float[M];
+ float[] fc2Weight = new float[OUT * M];
+ float[] fc2Bias = new float[OUT];
+ try (MLXScope scope = new MLXScope()) {
+ PatchMerger merger =
+ new PatchMerger(
+ scope,
+ MERGE,
+ false,
+ MLX.array(scope, normWeight, new int[] {2}),
+ MLX.array(scope, normWeight, new int[] {2}),
+ MLX.array(scope, fc1Weight, new int[] {M, M}),
+ MLX.array(scope, fc1Bias, new int[] {M}),
+ MLX.array(scope, fc2Weight, new int[] {OUT, M}),
+ MLX.array(scope, fc2Bias, new int[] {OUT}));
+ // N = 6 is not divisible by merge^2 = 4
+ MLXArray six = MLX.array(scope, new float[6 * HIDDEN], new int[] {6, HIDDEN});
+ IllegalArgumentException notDivisible =
+ assertThrows(IllegalArgumentException.class, () -> merger.forward(six));
+ assertTrue(notDivisible.getMessage().contains("merge^2"));
+ // wrong hidden size
+ MLXArray wrongHidden = MLX.array(scope, new float[N * 3], new int[] {N, 3});
+ assertThrows(IllegalArgumentException.class, () -> merger.forward(wrongHidden));
+ // wrong rank
+ MLXArray rank3 = MLX.array(scope, new float[2 * N * HIDDEN], new int[] {2, N, HIDDEN});
+ assertThrows(IllegalArgumentException.class, () -> merger.forward(rank3));
+ assertThrows(IllegalArgumentException.class, () -> merger.forward(null));
+ // exact (INT32) dtype with the right shape
+ MLXArray int32 =
+ MLX.astype(MLX.array(scope, new float[N * HIDDEN], new int[] {N, HIDDEN}), DType.INT32);
+ IllegalArgumentException notInexact =
+ assertThrows(IllegalArgumentException.class, () -> merger.forward(int32));
+ assertTrue(notInexact.getMessage().contains("inexact dtype"));
+ // the same shape in float32 is accepted
+ MLXArray floatInput = MLX.array(scope, new float[N * HIDDEN], new int[] {N, HIDDEN});
+ assertArrayEquals(new int[] {N / (MERGE * MERGE), OUT}, merger.forward(floatInput).shape());
+ }
+ }
+
+ /**
+ * The hand-computed merger output in double: population-variance LayerNorm (eps 1e-6, same
+ * placement rule as the component -- per-patch rows of width {@code HIDDEN} for the main
+ * placement, the merged row of width {@code M} for the post-shuffle placement), then {@code
+ * fc2(GELU(fc1(v)))} with the exact erf GELU.
+ */
+ private static double[] expectedMerger(
+ boolean postShuffle, float[] x, float[] normWeight, float[] normBias) {
+ int n = x.length / HIDDEN;
+ int merged = n / (MERGE * MERGE);
+ double[][] rows = new double[merged][];
+ for (int r = 0; r < merged; r++) {
+ if (postShuffle) {
+ double[] v = new double[M];
+ for (int i = 0; i < M; i++) {
+ v[i] = x[r * M + i];
+ }
+ rows[r] = layerNorm(v, normWeight, normBias, M);
+ } else {
+ double[] v = new double[M];
+ for (int p = 0; p < MERGE * MERGE; p++) {
+ double[] row = new double[HIDDEN];
+ for (int i = 0; i < HIDDEN; i++) {
+ row[i] = x[(r * MERGE * MERGE + p) * HIDDEN + i];
+ }
+ double[] normalized = layerNorm(row, normWeight, normBias, HIDDEN);
+ System.arraycopy(normalized, 0, v, p * HIDDEN, HIDDEN);
+ }
+ rows[r] = v;
+ }
+ }
+ double[][] out = new double[merged][OUT];
+ for (int r = 0; r < merged; r++) {
+ double[] h1 = new double[M];
+ for (int i = 0; i < M; i++) {
+ double sum = FC1_BIAS[i];
+ for (int j = 0; j < M; j++) {
+ sum += FC1_WEIGHT[i * M + j] * rows[r][j];
+ }
+ h1[i] = gelu(sum);
+ }
+ for (int i = 0; i < OUT; i++) {
+ double sum = FC2_BIAS[i];
+ for (int j = 0; j < M; j++) {
+ sum += FC2_WEIGHT[i * M + j] * h1[j];
+ }
+ out[r][i] = sum;
+ }
+ }
+ double[] flat = new double[merged * OUT];
+ for (int r = 0; r < merged; r++) {
+ System.arraycopy(out[r], 0, flat, r * OUT, OUT);
+ }
+ return flat;
+ }
+
+ /** Population-variance LayerNorm over {@code length} elements, double. */
+ private static double[] layerNorm(double[] v, float[] weight, float[] bias, int length) {
+ double mean = 0;
+ for (int i = 0; i < length; i++) {
+ mean += v[i];
+ }
+ mean /= length;
+ double var = 0;
+ for (int i = 0; i < length; i++) {
+ double d = v[i] - mean;
+ var += d * d;
+ }
+ var /= length;
+ double invStd = 1.0 / Math.sqrt(var + EPS);
+ double[] out = new double[length];
+ for (int i = 0; i < length; i++) {
+ out[i] = (v[i] - mean) * invStd * weight[i] + bias[i];
+ }
+ return out;
+ }
+
+ /** The exact (erf) GELU, double. */
+ private static double gelu(double v) {
+ return 0.5 * v * (1.0 + erf(v / Math.sqrt(2.0)));
+ }
+
+ /**
+ * The erf in double, Abramowitz-Stegun 7.1.26 approximation (max absolute error 1.5e-7, far
+ * inside the 1e-5 test tolerance): {@code erf(x) = sign(x) * (1 - (a1 t + a2 t^2 + a3 t^3 + a4
+ * t^4 + a5 t^5) e^{-x^2})} with {@code t = 1 / (1 + 0.3275911 |x|)} and the standard five
+ * coefficients.
+ */
+ private static double erf(double x) {
+ double sign = x < 0 ? -1.0 : 1.0;
+ double a = Math.abs(x);
+ double t = 1.0 / (1.0 + 0.3275911 * a);
+ double p =
+ t
+ * (0.254829592
+ + t * (-0.284496736 + t * (1.421413741 + t * (-1.453152027 + t * 1.061405429))))
+ * Math.exp(-a * a);
+ return sign * (1.0 - p);
+ }
+
+ private static void assertArrayEqualsLoose(double[] expected, float[] actual, float delta) {
+ assertEquals(expected.length, actual.length);
+ for (int i = 0; i < expected.length; i++) {
+ assertTrue(
+ Math.abs(expected[i] - actual[i]) <= delta,
+ "element " + i + ": expected " + expected[i] + ", got " + actual[i]);
+ }
+ }
+}
diff --git a/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRopeTest.java b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRopeTest.java
new file mode 100644
index 00000000..7bb18904
--- /dev/null
+++ b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRopeTest.java
@@ -0,0 +1,321 @@
+package se.alipsa.jmlx.nn;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * Strict tests for the WP5 two-axis (vision) RoPE component
+ * (req/plans/phase7-3b-reference-findings.md §11). Runs in the ordinary {@code :jmlx-core:test}
+ * task (no {@code full-float32} tag): the bit-exactness cases compare pure-Java arithmetic against
+ * pinned torch bits, and the rotate cases use float32 tolerances small enough for the default TF32
+ * mode (the rotate arithmetic is a handful of mul/add on values of moderate magnitude, and the
+ * golden-comparison gate for the full tower is WP5's {@code Qwen3VlVisionTowerGoldenTest}).
+ */
+@EnabledIfNativeAvailable
+class TwoAxisRopeTest {
+
+ // Provenance: tools/hf-reference/goldens/qwen3vl-vision-wp5.json (schema
+ // jmlx-phase7-3b-wp5-qwen3vl-vision-v1), probes.inv_freq.values_hex -- the exact torch
+ // float32 bits of 1.0 / (10000.0 ** (arange(0, 32, 2, float32) / 32)), generated by
+ // tools/hf-reference/qwen3vl_vision.py in the pinned venv (transformers 4.57.6, torch
+ // 2.6.0+cpu, seed 20261021) in the WU2 run of 2026-10-21. These pin the real-2B geometry
+ // (theta 10000, dim = head_dim // 2 = 32, 16 frequencies).
+ private static final String[] PINNED_INV_FREQ_HEX = {
+ "3f800000", "3f0ff59a", "3ea1e89b", "3e361887", "3dcccccd", "3d6655c2", "3d0186e3",
+ "3c91ad39", "3c23d70a", "3bb8449c", "3b4f3e38", "3ae91528", "3a83126f", "3a136a16",
+ "39a5cb60", "393a7753"
+ };
+
+ // Provenance: the same golden, probes.cos_sin_sample -- 24 (position p, frequency j) entries
+ // with the exact float32 bits of the product v = p * inv_freq[j] and of torch's cos(v)/sin(v)
+ // at that v. p is a grid coordinate, j indexes the 16 pinned inv_freq values above.
+ private static final String[][] PINNED_COS_SIN = {
+ {"0", "0", "00000000", "3f800000", "00000000"},
+ {"0", "1", "00000000", "3f800000", "00000000"},
+ {"0", "2", "00000000", "3f800000", "00000000"},
+ {"0", "3", "00000000", "3f800000", "00000000"},
+ {"1", "0", "3f800000", "3f0a5141", "3f576aa4"},
+ {"1", "1", "3f0ff59a", "3f58940e", "3f087dba"},
+ {"1", "2", "3ea1e89b", "3f734e6b", "3e9f393e"},
+ {"1", "3", "3e361887", "3f7bf683", "3e35233a"},
+ {"2", "0", "40000000", "bed51133", "3f68c7b7"},
+ {"2", "1", "3f8ff59a", "3edce8b2", "3f66f20a"},
+ {"2", "2", "3f21e89b", "3f4e7bec", "3f17541b"},
+ {"2", "3", "3eb61887", "3f6ffaa6", "3eb247f6"},
+ {"3", "0", "40400000", "bf7d7026", "3e1081c3"},
+ {"3", "1", "3fd7f067", "bded7fab", "3f7e45d7"},
+ {"3", "2", "3f72dce8", "3f152f58", "3f5009c2"},
+ {"3", "3", "3f089265", "3f5c6d29", "3f022f6c"},
+ {"5", "0", "40a00000", "3e913c2c", "bf757c10"},
+ {"5", "1", "4033f300", "bf723240", "3ea5dad8"},
+ {"5", "2", "3fca62c2", "bc2972e9", "3f7ffc7f"},
+ {"5", "3", "3f639ea9", "3f214cf1", "3f46caab"},
+ {"7", "0", "40e00000", "3f40ffbd", "3f283046"},
+ {"7", "1", "407bedce", "bf334f5e", "bf36b670"},
+ {"7", "2", "400dab88", "bf1974bc", "3f4ce86e"},
+ {"7", "3", "3f9f5576", "3ea3f8ba", "3f728444"}
+ };
+
+ @Test
+ void invFrequenciesBitMatchPinnedTorch() {
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 32);
+ assertEquals(10_000f, rope.theta());
+ assertEquals(32, rope.rotaryDims());
+ float[] invFreq = rope.invFreq();
+ assertEquals(16, invFreq.length);
+ for (int j = 0; j < 16; j++) {
+ assertEquals(
+ Integer.parseUnsignedInt(PINNED_INV_FREQ_HEX[j], 16),
+ Float.floatToRawIntBits(invFreq[j]),
+ "invFreq[" + j + "] must bit-match the pinned torch bits");
+ }
+ }
+
+ @Test
+ void zeroPositionsRotateBitIdenticalToInput() {
+ try (MLXScope scope = new MLXScope()) {
+ int[] zero = new int[4];
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 32);
+ TwoAxisRotation rotation = rope.prepare(scope, 4, zero, zero.clone());
+ // cos must be exactly 1 and sin exactly 0 everywhere: the rotation is the identity in
+ // float32 (x * 1.0 + rotate_half(x) * 0.0 is bit-exact for the positive values below).
+ float[] cos = rotation.cos().toFloatArray();
+ float[] sin = rotation.sin().toFloatArray();
+ assertEquals(4 * 2 * 32, cos.length);
+ for (float c : cos) {
+ assertEquals(1.0f, c);
+ }
+ for (float s : sin) {
+ assertEquals(0.0f, s);
+ }
+ float[] x = positiveValues(4 * 2 * 64);
+ MLXArray arrayX = MLX.array(scope, x, new int[] {4, 2, 64});
+ float[] out = rotation.rotate(arrayX).toFloatArray();
+ assertEquals(x.length, out.length);
+ for (int i = 0; i < x.length; i++) {
+ assertEquals(
+ Float.floatToRawIntBits(x[i]),
+ Float.floatToRawIntBits(out[i]),
+ "element " + i + " must rotate bit-identically at zero positions");
+ }
+ }
+ }
+
+ @Test
+ void smallHandComputedCaseMatchesDoubleArithmetic() {
+ int theta = 10_000;
+ TwoAxisRope rope = new TwoAxisRope(theta, 4); // 2 frequencies per axis, headDim 8
+ float[] invFreq = rope.invFreq();
+ assertEquals(2, invFreq.length);
+ assertEquals(Float.floatToRawIntBits(1.0f), Float.floatToRawIntBits(invFreq[0]));
+ // invFreq[1] = theta^(-(2*1)/4) = 1 / theta^(1/2) == (float) pow(theta, -0.5): the float32
+ // division by the exactly representable 100.0f rounds to the same bits as the
+ // double-power-then-cast variant (verified 2026-10-21: both are 0x3c23d70a).
+ assertEquals(
+ Float.floatToRawIntBits((float) Math.pow(10_000.0, -0.5)),
+ Float.floatToRawIntBits(invFreq[1]));
+ int[] axis0 = {0, 1};
+ int[] axis1 = {1, 2};
+ float[] x = positiveValues(2 * 1 * 8);
+ try (MLXScope scope = new MLXScope()) {
+ TwoAxisRotation rotation = rope.prepare(scope, 2, axis0, axis1);
+ MLXArray arrayX = MLX.array(scope, x, new int[] {2, 1, 8});
+ float[] out = rotation.rotate(arrayX).toFloatArray();
+ double[] invFreqDouble = {1.0, Math.pow(10_000.0, -0.5)};
+ for (int i = 0; i < 2; i++) {
+ for (int d = 0; d < 8; d++) {
+ // emb layout [row f0, row f1, col f0, col f1, row f0, row f1, col f0, col f1].
+ int halfIndex = d % 4;
+ double axis = halfIndex < 2 ? axis0[i] : axis1[i];
+ double v = axis * invFreqDouble[halfIndex % 2];
+ double c = Math.cos(v);
+ double s = Math.sin(v);
+ double expected =
+ d < 4
+ ? x[i * 8 + d] * c - x[i * 8 + d + 4] * s
+ : x[i * 8 + d] * c + x[i * 8 + d - 4] * s;
+ assertTrue(
+ Math.abs(out[i * 8 + d] - expected) <= 1e-6,
+ "element (" + i + ", " + d + "): expected " + expected + ", got " + out[i * 8 + d]);
+ }
+ }
+ }
+ }
+
+ @Test
+ void cosSinMatchPinnedTorchSamplesWithinOneUlP() {
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 32);
+ float[] invFreq = rope.invFreq();
+ int bitExact = 0;
+ int mismatches = 0;
+ for (String[] sample : PINNED_COS_SIN) {
+ int p = Integer.parseInt(sample[0]);
+ int j = Integer.parseInt(sample[1]);
+ int productBits = Integer.parseUnsignedInt(sample[2], 16);
+ // The float32 product of the position and the component's invFreq must be the pinned v.
+ assertEquals(
+ productBits,
+ Float.floatToRawIntBits(p * invFreq[j]),
+ "product bits for (p=" + p + ", j=" + j + ") must bit-match the pinned golden");
+ float v = Float.intBitsToFloat(productBits);
+ int cosBits = Integer.parseUnsignedInt(sample[3], 16);
+ int sinBits = Integer.parseUnsignedInt(sample[4], 16);
+ float javaCos = (float) Math.cos((double) v);
+ float javaSin = (float) Math.sin((double) v);
+ boolean cosBit = Float.floatToRawIntBits(javaCos) == cosBits;
+ boolean sinBit = Float.floatToRawIntBits(javaSin) == sinBits;
+ if (cosBit && sinBit) {
+ bitExact++;
+ } else {
+ mismatches++;
+ }
+ // 21 of the 24 samples are fully bit-exact; the 3 non-matching entries are (p=1, j=0),
+ // (p=1, j=2) and (p=2, j=3), each a single-ulp cos difference (sin is bit-exact 24/24)
+ // -- the Java-vs-torch trig gap recorded in findings §11. Every value must still be within
+ // one float32 ulp (relative 2^-23, absolute 2^-23 where the pinned value is 0).
+ assertWithinOneUlp(javaCos, Float.intBitsToFloat(cosBits), "cos", p, j);
+ assertWithinOneUlp(javaSin, Float.intBitsToFloat(sinBits), "sin", p, j);
+ }
+ assertEquals(
+ 3,
+ mismatches,
+ "recorded 2026-10-21: 3 of 24 samples differ by one ulp (all cos), 21 are fully bit-exact");
+ assertEquals(21, bitExact);
+ }
+
+ @Test
+ void float16InputRoundTripsThroughFloat16Output() {
+ try (MLXScope scope = new MLXScope()) {
+ int[] axis0 = {0, 1};
+ int[] axis1 = {1, 2};
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 4);
+ TwoAxisRotation rotation = rope.prepare(scope, 2, axis0, axis1);
+ float[] x = positiveValues(2 * 1 * 8);
+ MLXArray arrayX = MLX.array(scope, x, new int[] {2, 1, 8});
+ MLXArray half = MLX.astype(arrayX, DType.FLOAT16);
+ MLXArray out = rotation.rotate(half);
+ assertEquals(DType.FLOAT16, out.dtype());
+ assertArrayEquals(new int[] {2, 1, 8}, out.shape());
+ float[] values = out.toFloatArray();
+ for (float value : values) {
+ assertTrue(Float.isFinite(value), "float16 output must stay finite, got " + value);
+ }
+ // Loose value check against the float32 path: float16 round-trips each input and output,
+ // so the worst case is well inside 2^-9 of the float32 result.
+ float[] out32 = rotation.rotate(arrayX).toFloatArray();
+ for (int i = 0; i < values.length; i++) {
+ assertTrue(
+ Math.abs(values[i] - out32[i]) <= 1e-2,
+ "element " + i + ": " + values[i] + " vs float32 " + out32[i]);
+ }
+ }
+ }
+
+ @Test
+ void constructorRejectsInvalidThetaAndRotaryDims() {
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(0f, 32));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(-1f, 32));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(Float.NaN, 32));
+ assertThrows(
+ IllegalArgumentException.class, () -> new TwoAxisRope(Float.POSITIVE_INFINITY, 32));
+ assertThrows(
+ IllegalArgumentException.class, () -> new TwoAxisRope(Float.NEGATIVE_INFINITY, 32));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(10_000f, 0));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(10_000f, 1));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(10_000f, 3));
+ assertThrows(IllegalArgumentException.class, () -> new TwoAxisRope(10_000f, -2));
+ }
+
+ @Test
+ void prepareRejectsInvalidSequencesAndAxes() {
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 32);
+ int[] axis = row(4);
+ try (MLXScope scope = new MLXScope()) {
+ assertThrows(
+ IllegalArgumentException.class, () -> rope.prepare(scope, 0, axis, axis.clone()));
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, null, axis));
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, axis, null));
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, row(3), axis));
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, axis, row(5)));
+ int[] negative = row(4);
+ negative[1] = -1;
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, negative, axis));
+ assertThrows(IllegalArgumentException.class, () -> rope.prepare(scope, 4, axis, negative));
+ }
+ }
+
+ @Test
+ void rotateRejectsInvalidInputs() {
+ TwoAxisRope rope = new TwoAxisRope(10_000f, 4); // headDim 8
+ try (MLXScope scope = new MLXScope()) {
+ int[] axis = row(2);
+ TwoAxisRotation rotation = rope.prepare(scope, 2, axis, axis.clone());
+ // wrong rank (2)
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> rotation.rotate(MLX.array(scope, positiveValues(16), new int[] {1, 16})));
+ // wrong rank (4)
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ rotation.rotate(
+ MLX.array(scope, positiveValues(2 * 1 * 8 * 1), new int[] {2, 1, 8, 1})));
+ // wrong sequence (3 rows against a 2-row rotation)
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> rotation.rotate(MLX.array(scope, positiveValues(3 * 1 * 8), new int[] {3, 1, 8})));
+ // headDim mismatch (rotaryDims 4 -> headDim 8) against a 12-wide input
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ rotation.rotate(MLX.array(scope, positiveValues(2 * 1 * 12), new int[] {2, 1, 12})));
+ assertThrows(IllegalArgumentException.class, () -> rotation.rotate(null));
+ }
+ }
+
+ @Test
+ void prepareCarriesTheRopeTheta() {
+ int[] axis = row(2);
+ try (MLXScope scope = new MLXScope()) {
+ TwoAxisRope rope = new TwoAxisRope(1_024f, 4);
+ TwoAxisRotation rotation = rope.prepare(scope, 2, axis, axis.clone());
+ assertEquals(1_024f, rotation.theta(), "the rotation must carry the rope's theta");
+ // A rotation from a different base carries that base, not the other rope's.
+ TwoAxisRotation other = new TwoAxisRope(10_000f, 4).prepare(scope, 2, axis, axis.clone());
+ assertEquals(10_000f, other.theta());
+ }
+ }
+
+ private static void assertWithinOneUlp(float actual, float pinned, String fn, int p, int j) {
+ double limit = Math.pow(2, -23) * Math.max(1.0, Math.abs(pinned));
+ assertTrue(
+ Math.abs(actual - pinned) <= limit,
+ fn + " for (p=" + p + ", j=" + j + "): " + actual + " vs pinned " + pinned);
+ }
+
+ /** Distinct deterministic positive values in (0, 2.34] with a per-case seed offset. */
+ private static float[] positiveValues(int n) {
+ float[] data = new float[n];
+ for (int j = 0; j < n; j++) {
+ data[j] = (((j + 1) % 7) + 1) / 3.0f;
+ }
+ return data;
+ }
+
+ private static int[] row(int length) {
+ int[] data = new int[length];
+ for (int i = 0; i < length; i++) {
+ data[i] = i;
+ }
+ return data;
+ }
+}
diff --git a/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttentionTest.java b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttentionTest.java
new file mode 100644
index 00000000..0042424e
--- /dev/null
+++ b/jmlx-core/src/test/java/se/alipsa/jmlx/nn/TwoAxisRotaryAttentionTest.java
@@ -0,0 +1,407 @@
+package se.alipsa.jmlx.nn;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.Random;
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXOps;
+import se.alipsa.jmlx.core.MLXShape;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+
+/**
+ * Strict tests for the WP5 {@link TwoAxisRotaryAttention}
+ * (req/plans/phase7-3b-reference-findings.md §11). Runs in the ordinary {@code :jmlx-core:test}
+ * task. The random case compares the module's {@code scaledDotProductAttention} path against an
+ * explicit matmul/softmax/matmul composition in the test (same projection, same rotation tables) to
+ * validate the SDPA call and the reshapes without re-deriving the math.
+ */
+@EnabledIfNativeAvailable
+class TwoAxisRotaryAttentionTest {
+
+ private static final int N = 6;
+ private static final int HEADS = 2;
+ private static final int HEAD_DIM = 4;
+ private static final int HIDDEN = HEADS * HEAD_DIM;
+
+ @Test
+ void zeroWeightsAndBiasesGiveZeroOutput() {
+ try (MLXScope scope = new MLXScope()) {
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 10_000f,
+ MLX.zeros(scope, new int[] {3 * HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {3 * HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN}, DType.FLOAT32));
+ float[] input = randomValues(new Random(0), N * HIDDEN);
+ MLXArray arrayInput = MLX.array(scope, input, new int[] {N, HIDDEN});
+ TwoAxisRotation rotation =
+ new TwoAxisRope(10_000f, HEAD_DIM / 2).prepare(scope, N, row(N), row(N).clone());
+ float[] out = attention.forward(arrayInput, rotation).toFloatArray();
+ assertEquals(N * HIDDEN, out.length);
+ for (float value : out) {
+ assertEquals(0.0f, value, "zero weights/biases must give an exactly zero output");
+ }
+ }
+ }
+
+ @Test
+ void constantBiasWithIdentityProjectionGivesUniformMeanOfValueRows() {
+ try (MLXScope scope = new MLXScope()) {
+ float c = 0.5f;
+ float[] qkvBias = new float[3 * HIDDEN];
+ java.util.Arrays.fill(qkvBias, c);
+ float[] identity = new float[HIDDEN * HIDDEN];
+ for (int i = 0; i < HIDDEN; i++) {
+ identity[i * HIDDEN + i] = 1.0f;
+ }
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 10_000f,
+ MLX.zeros(scope, new int[] {3 * HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.array(scope, qkvBias, new int[] {3 * HIDDEN}),
+ MLX.array(scope, identity, new int[] {HIDDEN, HIDDEN}),
+ MLX.zeros(scope, new int[] {HIDDEN}, DType.FLOAT32));
+ float[] input = randomValues(new Random(1), N * HIDDEN);
+ MLXArray arrayInput = MLX.array(scope, input, new int[] {N, HIDDEN});
+ // Zero positions: the rotation is the identity, so q = k = v = the constant bias.
+ int[] zero = new int[N];
+ TwoAxisRotation rotation =
+ new TwoAxisRope(10_000f, HEAD_DIM / 2).prepare(scope, N, zero, zero.clone());
+ MLXArray outArray = attention.forward(arrayInput, rotation);
+ assertArrayEquals(new int[] {N, HIDDEN}, outArray.shape());
+ // Every k row is constant, so every score row is constant and the softmax is the uniform
+ // 1/N; the attention output is then exactly the uniform mean of the value rows. The
+ // identity proj passes it through, so the expected output is that mean (c here, computed
+ // from the actual value rows for the assertion).
+ MLXArray v = valueRows(scope, qkvBias);
+ float[] values = v.toFloatArray();
+ double expected = 0;
+ for (float value : values) {
+ expected += value;
+ }
+ expected /= values.length;
+ float[] out = outArray.toFloatArray();
+ for (int i = 0; i < out.length; i++) {
+ // 1e-3, not 1e-4: this task runs in the default TF32 mode, where the SDPA softmax weights
+ // (the uniform 1/N here) pick up an ~11-bit-mantissa rounding -- a constant-0.5 input
+ // produced 0.5 * (1 - 2^-11) locally on 2026-10-21. Structural errors are O(1), so the
+ // looser bound still discriminates.
+ assertTrue(
+ Float.isFinite(out[i]) && Math.abs(out[i] - expected) <= 1e-3,
+ "element " + i + ": expected uniform mean " + expected + ", got " + out[i]);
+ }
+ }
+ }
+
+ @Test
+ void randomSmallCaseMatchesExplicitMatmulSoftmaxComposition() {
+ Random random = new Random(20_261_021);
+ float[] qkvWeight = randomValues(random, 3 * HIDDEN * HIDDEN);
+ float[] qkvBias = randomValues(random, 3 * HIDDEN);
+ float[] projWeight = randomValues(random, HIDDEN * HIDDEN);
+ float[] projBias = randomValues(random, HIDDEN);
+ float[] input = randomValues(random, N * HIDDEN);
+ int[] axis0 = row(N);
+ int[] axis1 = {0, 1, 2, 0, 1, 2};
+ try (MLXScope scope = new MLXScope()) {
+ MLXArray qkvWeightArr = MLX.array(scope, qkvWeight, new int[] {3 * HIDDEN, HIDDEN});
+ MLXArray qkvBiasArr = MLX.array(scope, qkvBias, new int[] {3 * HIDDEN});
+ MLXArray projWeightArr = MLX.array(scope, projWeight, new int[] {HIDDEN, HIDDEN});
+ MLXArray projBiasArr = MLX.array(scope, projBias, new int[] {HIDDEN});
+ MLXArray arrayInput = MLX.array(scope, input, new int[] {N, HIDDEN});
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 10_000f,
+ qkvWeightArr,
+ qkvBiasArr,
+ projWeightArr,
+ projBiasArr);
+ TwoAxisRope rope = new TwoAxisRope(10_000f, HEAD_DIM / 2);
+ TwoAxisRotation rotation = rope.prepare(scope, N, axis0, axis1);
+ float[] actual = attention.forward(arrayInput, rotation).toFloatArray();
+
+ // The explicit composition: the same qkv projection, the same rotation tables applied by
+ // hand, then scores = scale * q @ k^T, softmax, @ v -- no SDPA call.
+ MLXArray qkvOut =
+ MLXOps.add(
+ MLXOps.matmul(arrayInput, MLXShape.transpose(qkvWeightArr, new int[] {1, 0})),
+ qkvBiasArr);
+ MLXArray projected =
+ MLXShape.transpose(
+ MLXShape.reshape(qkvOut, new int[] {N, 3, HEADS, HEAD_DIM}), new int[] {1, 0, 2, 3});
+ MLXArray[] parts = MLXShape.split(projected, 3, 0);
+ MLXArray cos = rotation.cos();
+ MLXArray sin = rotation.sin();
+ // mlx_split keeps the split axis: each part is [1, N, heads, headDim], so squeeze axis 0 to
+ // the [N, heads, headDim] layout the rotation tables and batched() expect.
+ MLXArray q = manualRotate(scope, MLXShape.squeeze(parts[0], new int[] {0}), cos, sin);
+ MLXArray k = manualRotate(scope, MLXShape.squeeze(parts[1], new int[] {0}), cos, sin);
+ MLXArray v = MLXShape.squeeze(parts[2], new int[] {0});
+ MLXArray q4 = batched(q, N);
+ MLXArray k4 = batched(k, N);
+ MLXArray v4 = batched(v, N);
+ MLXArray scores =
+ MLXOps.multiply(
+ MLXOps.matmul(q4, MLXShape.transpose(k4, new int[] {0, 1, 3, 2})),
+ MLX.full(scope, new int[] {1}, attention.scale(), DType.FLOAT32));
+ MLXArray attended = MLXOps.matmul(MLXOps.softmaxAxis(scores, -1, true), v4);
+ MLXArray merged =
+ MLXShape.reshape(
+ MLXShape.transpose(
+ MLXShape.reshape(attended, new int[] {HEADS, N, HEAD_DIM}), new int[] {1, 0, 2}),
+ new int[] {N, HIDDEN});
+ float[] expected =
+ MLXOps.add(
+ MLXOps.matmul(merged, MLXShape.transpose(projWeightArr, new int[] {1, 0})),
+ projBiasArr)
+ .toFloatArray();
+ double maxAbs = 0;
+ int worst = -1;
+ for (int i = 0; i < expected.length; i++) {
+ double diff = Math.abs(actual[i] - expected[i]);
+ if (diff > maxAbs) {
+ maxAbs = diff;
+ worst = i;
+ }
+ }
+ assertTrue(
+ maxAbs <= 1e-4, "max abs err " + maxAbs + " at element " + worst + " exceeds 1e-4");
+ }
+ }
+
+ @Test
+ void forwardRejectsMismatchedRotationAndInputs() {
+ try (MLXScope scope = new MLXScope()) {
+ float[] qkvWeight = randomValues(new Random(2), 3 * HIDDEN * HIDDEN);
+ float[] qkvBias = randomValues(new Random(3), 3 * HIDDEN);
+ float[] projWeight = randomValues(new Random(4), HIDDEN * HIDDEN);
+ float[] projBias = randomValues(new Random(5), HIDDEN);
+ MLXArray qkvWeightArr = MLX.array(scope, qkvWeight, new int[] {3 * HIDDEN, HIDDEN});
+ MLXArray qkvBiasArr = MLX.array(scope, qkvBias, new int[] {3 * HIDDEN});
+ MLXArray projWeightArr = MLX.array(scope, projWeight, new int[] {HIDDEN, HIDDEN});
+ MLXArray projBiasArr = MLX.array(scope, projBias, new int[] {HIDDEN});
+ MLXArray arrayInput =
+ MLX.array(scope, randomValues(new Random(6), N * HIDDEN), new int[] {N, HIDDEN});
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 10_000f,
+ qkvWeightArr,
+ qkvBiasArr,
+ projWeightArr,
+ projBiasArr);
+ // headDim 4 against a rotation whose 2 * rotaryDims is 8
+ TwoAxisRotation wrongWidth =
+ new TwoAxisRope(10_000f, 4).prepare(scope, N, row(N), row(N).clone());
+ IllegalArgumentException width =
+ assertThrows(
+ IllegalArgumentException.class, () -> attention.forward(arrayInput, wrongWidth));
+ assertTrue(width.getMessage().contains("2 * rotaryDims"));
+ // sequence mismatch: a 4-row rotation against 6 rows
+ TwoAxisRotation shortRotation =
+ new TwoAxisRope(10_000f, HEAD_DIM / 2).prepare(scope, 4, row(4), row(4).clone());
+ assertThrows(
+ IllegalArgumentException.class, () -> attention.forward(arrayInput, shortRotation));
+ // input with the wrong last dimension
+ MLXArray wrongHidden =
+ MLX.array(
+ scope, randomValues(new Random(7), N * (HIDDEN + 1)), new int[] {N, HIDDEN + 1});
+ assertThrows(
+ IllegalArgumentException.class, () -> attention.forward(wrongHidden, shortRotation));
+ // input with the wrong rank
+ MLXArray rank3 =
+ MLX.array(scope, randomValues(new Random(8), N * HIDDEN), new int[] {1, N, HIDDEN});
+ assertThrows(IllegalArgumentException.class, () -> attention.forward(rank3, shortRotation));
+ assertThrows(IllegalArgumentException.class, () -> attention.forward(null, shortRotation));
+ assertThrows(IllegalArgumentException.class, () -> attention.forward(arrayInput, null));
+ }
+ }
+
+ @Test
+ void forwardRejectsRotationPreparedWithDifferentTheta() {
+ try (MLXScope scope = new MLXScope()) {
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 10_000f,
+ MLX.zeros(scope, new int[] {3 * HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {3 * HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN}, DType.FLOAT32));
+ MLXArray arrayInput =
+ MLX.array(scope, randomValues(new Random(15), N * HIDDEN), new int[] {N, HIDDEN});
+ // Same sequence and width, wrong base: the theta check must fire before any projection.
+ TwoAxisRotation wrongTheta =
+ new TwoAxisRope(50_000f, HEAD_DIM / 2).prepare(scope, N, row(N), row(N).clone());
+ IllegalArgumentException theta =
+ assertThrows(
+ IllegalArgumentException.class, () -> attention.forward(arrayInput, wrongTheta));
+ assertEquals(
+ "theta must match the prepared rotation (10000.0 against 50000.0)", theta.getMessage());
+ }
+ }
+
+ @Test
+ void forwardAcceptsRotationPreparedWithMatchingTheta() {
+ try (MLXScope scope = new MLXScope()) {
+ TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ HEADS,
+ HEAD_DIM,
+ 1_024f,
+ MLX.zeros(scope, new int[] {3 * HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {3 * HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN, HIDDEN}, DType.FLOAT32),
+ MLX.zeros(scope, new int[] {HIDDEN}, DType.FLOAT32));
+ MLXArray arrayInput =
+ MLX.array(scope, randomValues(new Random(16), N * HIDDEN), new int[] {N, HIDDEN});
+ // A base other than 10_000: matching theta in rope and attention is accepted for any base.
+ TwoAxisRotation rotation =
+ new TwoAxisRope(1_024f, HEAD_DIM / 2).prepare(scope, N, row(N), row(N).clone());
+ MLXArray out = attention.forward(arrayInput, rotation);
+ assertArrayEquals(new int[] {N, HIDDEN}, out.shape());
+ }
+ }
+
+ @Test
+ void constructorRejectsInvalidGeometry() {
+ try (MLXScope scope = new MLXScope()) {
+ MLXArray qkv =
+ MLX.array(
+ scope,
+ randomValues(new Random(9), 3 * HIDDEN * HIDDEN),
+ new int[] {3 * HIDDEN, HIDDEN});
+ MLXArray qkvBias =
+ MLX.array(scope, randomValues(new Random(10), 3 * HIDDEN), new int[] {3 * HIDDEN});
+ MLXArray proj =
+ MLX.array(
+ scope, randomValues(new Random(11), HIDDEN * HIDDEN), new int[] {HIDDEN, HIDDEN});
+ MLXArray projBias =
+ MLX.array(scope, randomValues(new Random(12), HIDDEN), new int[] {HIDDEN});
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, 0, HEAD_DIM, 10_000f, qkv, qkvBias, proj, projBias));
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new TwoAxisRotaryAttention(scope, HEADS, 0, 10_000f, qkv, qkvBias, proj, projBias));
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, Float.NaN, qkv, qkvBias, proj, projBias));
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, 10_000f, null, qkvBias, proj, projBias));
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, 10_000f, qkv, null, proj, projBias));
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, 10_000f, qkv, qkvBias, proj, null));
+ // qkv weight with the wrong output width
+ MLXArray qkvWrong =
+ MLX.array(
+ scope,
+ randomValues(new Random(13), 5 * HIDDEN * HIDDEN),
+ new int[] {5 * HIDDEN, HIDDEN});
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, 10_000f, qkvWrong, qkvBias, proj, projBias));
+ // proj weight with the wrong width
+ MLXArray projWrong =
+ MLX.array(
+ scope,
+ randomValues(new Random(14), HIDDEN * (HIDDEN + 1)),
+ new int[] {HIDDEN, HIDDEN + 1});
+ assertThrows(
+ IllegalArgumentException.class,
+ () ->
+ new TwoAxisRotaryAttention(
+ scope, HEADS, HEAD_DIM, 10_000f, qkv, qkvBias, projWrong, projBias));
+ float expectedScale = attentionScaleOf(HEAD_DIM);
+ TwoAxisRotaryAttention ok =
+ new TwoAxisRotaryAttention(scope, HEADS, HEAD_DIM, 10_000f, qkv, qkvBias, proj, projBias);
+ assertEquals(expectedScale, ok.scale(), 0.0f);
+ assertEquals(10_000f, ok.theta());
+ }
+ }
+
+ /**
+ * The constant-bias value rows: with a zero qkv weight, v is the broadcast v block of the qkv
+ * bias.
+ */
+ private static MLXArray valueRows(MLXScope scope, float[] qkvBias) {
+ float[] valueBlock = java.util.Arrays.copyOfRange(qkvBias, 2 * HIDDEN, 3 * HIDDEN);
+ MLXArray v = MLX.array(scope, valueBlock, new int[] {HEADS, HEAD_DIM});
+ return MLXShape.broadcastTo(v, new int[] {N, HEADS, HEAD_DIM});
+ }
+
+ /** The half-split rotate_half composition in float32, applied with hand-written ops. */
+ private static MLXArray manualRotate(MLXScope scope, MLXArray x, MLXArray cos, MLXArray sin) {
+ int half = HEAD_DIM / 2;
+ int[] fullStop = {N, HEADS, HEAD_DIM};
+ int[] frontStop = {N, HEADS, half};
+ MLXArray front = MLXShape.slice(x, new int[] {0, 0, 0}, frontStop);
+ MLXArray back = MLXShape.slice(x, new int[] {0, 0, half}, fullStop);
+ MLXArray rotated = MLXShape.concatenate(new MLXArray[] {MLXOps.negative(back), front}, 2);
+ return MLXOps.add(MLXOps.multiply(x, cos), MLXOps.multiply(rotated, sin));
+ }
+
+ /** The {@code [1, heads, N, headDim]} batched layout the explicit composition uses. */
+ private static MLXArray batched(MLXArray x, int n) {
+ return MLXShape.reshape(
+ MLXShape.transpose(x, new int[] {1, 0, 2}), new int[] {1, HEADS, n, HEAD_DIM});
+ }
+
+ private static float[] randomValues(Random random, int n) {
+ float[] data = new float[n];
+ for (int i = 0; i < n; i++) {
+ data[i] = random.nextFloat() * 0.2f - 0.1f;
+ }
+ return data;
+ }
+
+ private static int[] row(int length) {
+ int[] data = new int[length];
+ for (int i = 0; i < length; i++) {
+ data[i] = i;
+ }
+ return data;
+ }
+
+ private static float attentionScaleOf(int headDim) {
+ return (float) Math.pow(headDim, -0.5);
+ }
+}
diff --git a/jmlx-models/build.gradle b/jmlx-models/build.gradle
index 93cde58f..a746d12d 100644
--- a/jmlx-models/build.gradle
+++ b/jmlx-models/build.gradle
@@ -205,6 +205,10 @@ def configureExactBitsTest = { Test task, String tf32, String mode, String purpo
task.testClassesDirs = sourceSets.exactBits.output.classesDirs
task.classpath = sourceSets.exactBits.runtimeClasspath
task.useJUnitPlatform()
+ // The WP5 vision probe recorder (Qwen3VlVisionExactBitsRecorderTest) records under
+ // build/exact-bits/wp5-vision/ via its own dedicated tasks; keep this 7.3a decoder tool's
+ // scope (and its recorded/verified baseline contract) unchanged.
+ task.exclude '**/Qwen3VlVisionExactBitsRecorderTest.class'
task.environment 'MLX_ENABLE_TF32', tf32
task.inputs.property('mlxEnableTf32', tf32)
task.systemProperty 'jmlx.exact.bits.mode', mode
@@ -250,6 +254,68 @@ tasks.register('exactBitsVerify') {
dependsOn 'exactBitsVerifyTf32On', 'exactBitsVerifyTf32Off'
}
+// Phase 7.3b WP5 (review round 2): opt-in exact-bit recorder/comparator for the Qwen3-VL vision
+// tower's per-block evaluate-and-release change (req/plans/phase7-3b-reference-findings.md
+// section 11.4). Same forked-JVM precision split as the 7.3a decoder recorder above, scoped to
+// the vision probe recorder only, and recording under build/exact-bits/wp5-vision/tf32-{1,0}/
+// so the 7.3a decoder baseline under build/exact-bits/tf32-{1,0}/ is left untouched. Never part
+// of check: run before (record) and after (verify) the tower change it guards.
+def configureExactBitsVisionTest = { Test task, String tf32, String mode, String purpose ->
+ task.group = 'verification'
+ task.description = "Opt-in exact-bit recorder/comparator for the Qwen3-VL vision tower probe, " +
+ "${purpose}, MLX_ENABLE_TF32=${tf32} (1 = default reduced precision, 0 = full float32). " +
+ "Recordings live under build/exact-bits/wp5-vision/tf32-${tf32}/; never part of check."
+ task.doNotTrackState('opt-in exact-bit tool; recordings are PR evidence files, not build outputs')
+ task.testClassesDirs = sourceSets.exactBits.output.classesDirs
+ task.classpath = sourceSets.exactBits.runtimeClasspath
+ task.useJUnitPlatform()
+ task.include '**/Qwen3VlVisionExactBitsRecorderTest.class'
+ task.environment 'MLX_ENABLE_TF32', tf32
+ task.inputs.property('mlxEnableTf32', tf32)
+ task.systemProperty 'jmlx.exact.bits.mode', mode
+ task.systemProperty 'jmlx.exact.bits.dir', exactBitsBaseDir
+ task.mustRunAfter tasks.named('test'), tasks.named('float32GoldenTest')
+}
+
+tasks.register('exactBitsRecordVisionTf32On', Test) {
+ configureExactBitsVisionTest(delegate, '1', 'record', 'records the baseline')
+}
+
+tasks.register('exactBitsRecordVisionTf32Off', Test) {
+ configureExactBitsVisionTest(delegate, '0', 'record', 'records the baseline')
+ mustRunAfter 'exactBitsRecordVisionTf32On'
+}
+
+tasks.register('exactBitsVerifyVisionTf32On', Test) {
+ configureExactBitsVisionTest(delegate, '1', 'verify', 'verifies against the recorded baseline')
+}
+
+tasks.register('exactBitsVerifyVisionTf32Off', Test) {
+ configureExactBitsVisionTest(delegate, '0', 'verify', 'verifies against the recorded baseline')
+ mustRunAfter 'exactBitsVerifyVisionTf32On'
+}
+
+// Sequential per mode pair: MLX allows one active thread per process, and the two precision
+// modes must not contend for the GPU mid-recording.
+tasks.register('exactBitsRecordVision') {
+ group = 'verification'
+ description = 'Opt-in: records the Qwen3-VL vision tower depth-3 probe capture in both ' +
+ 'TF32 modes (build/exact-bits/wp5-vision/tf32-1 and tf32-0). Never part of check; ' +
+ 'run before the tower change it guards.'
+ doNotTrackState('lifecycle task over the two opt-in record tasks')
+ dependsOn 'exactBitsRecordVisionTf32On', 'exactBitsRecordVisionTf32Off'
+}
+
+tasks.register('exactBitsVerifyVision') {
+ group = 'verification'
+ description = 'Opt-in: recomputes the Qwen3-VL vision tower depth-3 probe capture in both ' +
+ 'TF32 modes and requires bit-exact agreement with build/exact-bits/wp5-vision ' +
+ '(metadata may differ only in git commit). Never part of check; run after the tower ' +
+ 'change it guards.'
+ doNotTrackState('lifecycle task over the two opt-in verify tasks')
+ dependsOn 'exactBitsVerifyVisionTf32On', 'exactBitsVerifyVisionTf32Off'
+}
+
tasks.register('threadProbeTest', Test) {
group = 'verification'
description = 'Gate 0 probe (a): a worker thread uses MLX after the stream was initialized ' +
diff --git a/jmlx-models/src/exactBits/java/se/alipsa/jmlx/models/Qwen3VlVisionExactBitsRecorderTest.java b/jmlx-models/src/exactBits/java/se/alipsa/jmlx/models/Qwen3VlVisionExactBitsRecorderTest.java
new file mode 100644
index 00000000..1eec1bd9
--- /dev/null
+++ b/jmlx-models/src/exactBits/java/se/alipsa/jmlx/models/Qwen3VlVisionExactBitsRecorderTest.java
@@ -0,0 +1,631 @@
+package se.alipsa.jmlx.models;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+import java.nio.ByteBuffer;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Base64;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Properties;
+import java.util.Random;
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXRandom;
+import se.alipsa.jmlx.ffi.mlx_h;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessor;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessorResult;
+import se.alipsa.jmlx.vision.Qwen3VlProcessorConfig;
+import se.alipsa.jmlx.vision.RgbImage;
+import tools.jackson.databind.JsonNode;
+
+/**
+ * Opt-in exact-bit recorder/comparator for the WP5 Qwen3-VL vision tower's per-block
+ * evaluate-and-release change (PR #58 review, findings §11.4). It mirrors the 7.3a decoder
+ * recorder's mechanism — driven by {@code jmlx.exact.bits.mode} ({@code record} | {@code verify})
+ * and the {@code MLX_ENABLE_TF32} environment (1 = default reduced precision, 0 = full float32),
+ * with the four {@code exactBits{Record,Verify}VisionTf32{On,Off}} tasks setting both and forking
+ * their own JVMs — but records one vision-specific probe: the {@code Qwen3VlVisionPeakTest} depth-3
+ * synthetic probe, i.e. the real-2B tower geometry (hidden 1024, 16 heads, head_dim 64,
+ * intermediate 4096, out_hidden 2048, 2304 position embeddings, patch/temporal 16/2, merge 2) at
+ * depth 3 with DeepStack index [1] and one 1024×1024 image (grid (1, 64, 64), 4096 patch rows)
+ * processed through {@link Qwen3VlImageProcessor}.
+ *
+ *
Weights and pixels are seeded, not constant (findings §11.4): the first probe revision filled
+ * every weight with constant 0.001, and decoding those recordings showed the probe collapsed —
+ * every conv output channel and position-embedding column is identical, so each row's columns are
+ * all equal, LayerNorm then turns every row into its bias, and the attention output is exactly
+ * {@code v} whatever the scores, softmax or rotation are (the old {@code merged} capture held only
+ * 2 distinct values across its 1024 rows, and each block added one constant to every row). The
+ * recording was therefore insensitive to attention, RoPE and norm changes. The probe now draws
+ * every tower parameter from {@code MLXRandom.normal} (loc 0, standard deviation {@link
+ * #WEIGHT_SCALE}) after {@code MLXRandom.seed({@link #SEED})}, and every image pixel from a {@code
+ * java.util.Random({@link #SEED})} uniform in {@code [0, 1)} scaled to a full-range byte: fully
+ * deterministic (fixed seeds, no file inputs) and row-sensitive, so a bit change anywhere in the
+ * per-block loop, the attention, the RoPE or the norms shows up in a capture. The seed and scale
+ * are part of the probe spec hash, so a constant-weight baseline can never compare silently against
+ * this one.
+ *
+ *
The probe runs through {@code Qwen3VlVisionTower.forwardForTest}, and six captures are
+ * recorded: the final features (the merged rows and DeepStack level 0) plus the per-image golden
+ * captures (the interpolated position embedding, the patch-embedding output, and the block 0 and
+ * block 1 outputs) — so a bit change anywhere in the per-block loop shows up in a capture.
+ * Recordings are PR evidence under {@code build/exact-bits/wp5-vision/tf32-<0|1>} — a sibling of,
+ * and never colliding with, the 7.3a decoder recorder's {@code build/exact-bits/tf32-<0|1>}:
+ *
+ *
+ * - {@code .json} — one file per capture, carrying {@code values}, the Base64 of the
+ * big-endian IEEE-754 bits of every float32 in row-major order ({@code
+ * Float.floatToIntBits}).
+ *
- {@code manifest.json} — host metadata (git commit, the two native pins, device, macOS,
+ * precision, the probe-spec hash, and the provenance-only never-compared {@code gitDirty}
+ * tree-dirtiness flag and the SHA-256 of the guarded {@code Qwen3VlVisionTower} source) plus
+ * the capture list.
+ *
+ *
+ * In the vision baselines the manifest field {@code decoderModelSha256} holds the SHA-256 of the
+ * guarded {@code Qwen3VlVisionTower} source, not of any decoder model: the field name comes from
+ * the shared {@link ExactBitsMetadata}, whose field the 7.3a decoder recorder fills with the {@code
+ * DecoderModel} source hash. It is deliberately NOT renamed, because renaming the manifest key
+ * would invalidate the existing 7.3a decoder baselines, whose manifest format is required to stay
+ * byte-identical.
+ *
+ *
Record mode refuses to overwrite an existing {@code tf32-<0|1>} recording: a baseline is
+ * evidence, and silently replacing it with a recording from a later commit would invalidate the
+ * very comparison it feeds. Verify mode checks the recorded manifest cheaply first — existence,
+ * format version, metadata in every compared field, capture set — and then requires bit-exact
+ * agreement of every float; the only field allowed to differ is the git commit (the comparison
+ * crosses the review's own commits). Deliberately NOT {@code @EnabledIfNativeAvailable}: as an
+ * opt-in baseline tool it fails loudly when the native runtime is absent instead of silently
+ * skipping.
+ */
+class Qwen3VlVisionExactBitsRecorderTest {
+
+ static final int FORMAT_VERSION = 1;
+
+ // The Qwen3VlVisionPeakTest depth-3 probe constants (real-2B geometry at reduced depth).
+ private static final int HIDDEN = 1024;
+ private static final int NUM_HEADS = 16;
+ private static final int HEAD_DIM = 64;
+ private static final int INTERMEDIATE = 4096;
+ private static final int OUT_HIDDEN = 2048;
+ private static final int POSITION_EMBEDDINGS = 2304;
+ private static final int DEPTH = 3;
+ private static final int[] DEEPSTACK_INDEXES = {1};
+ private static final int MERGE = 2;
+ private static final int MERGE_BLOCK = HIDDEN * MERGE * MERGE;
+ private static final int IMAGE = 1024;
+ private static final int GRID = IMAGE / 16;
+ private static final int PATCH_ROWS = GRID * GRID;
+ private static final int MERGED_ROWS = (GRID / MERGE) * (GRID / MERGE);
+
+ /** The fixed seed for both the MLX weight draws and the java.util.Random pixel draws. */
+ private static final long SEED = 42L;
+
+ /** The standard deviation of the seeded normal weight draws (loc 0). */
+ private static final float WEIGHT_SCALE = 0.02f;
+
+ /** The capture names in the recorded order (final features first, then the golden captures). */
+ private static final List CAPTURES =
+ List.of("merged", "deepstack0", "posEmbedInterpolated", "patchEmbed", "block0", "block1");
+
+ @Test
+ void recordOrVerify() throws Exception {
+ String mode = System.getProperty("jmlx.exact.bits.mode", "");
+ if (!mode.equals("record") && !mode.equals("verify")) {
+ throw new AssertionError(
+ "jmlx.exact.bits.mode must be 'record' or 'verify'; run one of the "
+ + "exactBits{Record,Verify}Vision Gradle tasks, not this test directly");
+ }
+ String tf32 = System.getenv("MLX_ENABLE_TF32");
+ if (!"0".equals(tf32) && !"1".equals(tf32)) {
+ throw new AssertionError("MLX_ENABLE_TF32 must be set to 0 or 1 by the Gradle task");
+ }
+ Path repoRoot = Path.of(System.getProperty("jmlx.repository.root", "."));
+ requireNativeRuntime(repoRoot);
+ Path modeDir =
+ Path.of(System.getProperty("jmlx.exact.bits.dir", "build/exact-bits"))
+ .resolve("wp5-vision")
+ .resolve("tf32-" + tf32);
+ ExactBitsMetadata global = metadata(repoRoot, tf32);
+ // Record mode never silently replaces an existing baseline: fail before the GPU capture and
+ // before any file is written, naming the existing recording's commit.
+ if (mode.equals("record")) {
+ refuseOverwrite(modeDir);
+ }
+ JsonNode manifest = mode.equals("verify") ? verifyPreCapture(global, modeDir) : null;
+ Map captures = captureProbe();
+ if (mode.equals("record")) {
+ record(global, captures, modeDir);
+ System.out.println("exact-bits record vision tf32=" + tf32 + ": " + summary(captures));
+ } else {
+ verifyPostCapture(global, manifest, captures, modeDir);
+ // The recorded commit at a glance: whether the on-disk baseline is the pre-change one or a
+ // later re-record shows here immediately.
+ System.out.println(
+ "exact-bits verify vision tf32="
+ + tf32
+ + " (recorded at "
+ + manifest.path("metadata").path("gitCommit").asString()
+ + "): "
+ + summary(captures));
+ }
+ }
+
+ /**
+ * Runs the depth-3 probe and returns its captures in the recorded order. Every shape is checked
+ * before any capture is written or compared: a divergent shape would otherwise surface as a
+ * confusing length mismatch deep in the bit comparison instead of here, naming the capture.
+ */
+ private static Map captureProbe() {
+ try (MLXScope scope = new MLXScope()) {
+ List statics = new ArrayList<>();
+ final Qwen3VlVisionTower tower = buildTower(scope, statics);
+ MLX.eval(statics.toArray(MLXArray[]::new));
+ Qwen3VlImageProcessorResult image = processor().process(syntheticImage());
+ checkShape(image.gridThw(), "grid", 1, GRID, GRID);
+ if (image.rawPatchRows() != PATCH_ROWS) {
+ throw new AssertionError(
+ "patch rows: expected " + PATCH_ROWS + ", got " + image.rawPatchRows());
+ }
+ try (MLXScope request = scope.newChild()) {
+ Qwen3VlVisionTower.ForwardCaptures forward = tower.forwardForTest(request, List.of(image));
+ Qwen3VlVisionFeatures features = forward.features();
+ checkShape(features.merged().shape(), "merged", MERGED_ROWS, OUT_HIDDEN);
+ if (features.deepstackLevels().size() != DEEPSTACK_INDEXES.length) {
+ throw new AssertionError(
+ "deepstack levels: expected "
+ + DEEPSTACK_INDEXES.length
+ + ", got "
+ + features.deepstackLevels().size());
+ }
+ Qwen3VlVisionTower.ImageCaptures imageCaptures = forward.images().get(0);
+ checkShape(
+ features.deepstackLevels().get(0).shape(), "deepstack0", MERGED_ROWS, OUT_HIDDEN);
+ checkShape(
+ imageCaptures.posEmbedInterpolated().shape(),
+ "posEmbedInterpolated",
+ PATCH_ROWS,
+ HIDDEN);
+ checkShape(imageCaptures.patchEmbed().shape(), "patchEmbed", PATCH_ROWS, HIDDEN);
+ checkShape(imageCaptures.blockOutputs()[0].shape(), "block0", PATCH_ROWS, HIDDEN);
+ checkShape(imageCaptures.blockOutputs()[1].shape(), "block1", PATCH_ROWS, HIDDEN);
+ Map out = new LinkedHashMap<>();
+ out.put("merged", features.merged().toFloatArray());
+ out.put("deepstack0", features.deepstackLevels().get(0).toFloatArray());
+ out.put("posEmbedInterpolated", imageCaptures.posEmbedInterpolated().toFloatArray());
+ out.put("patchEmbed", imageCaptures.patchEmbed().toFloatArray());
+ out.put("block0", imageCaptures.blockOutputs()[0].toFloatArray());
+ out.put("block1", imageCaptures.blockOutputs()[1].toFloatArray());
+ return out;
+ }
+ }
+ }
+
+ /** The host/runtime metadata for one recording; the guarded source is the vision tower. */
+ static ExactBitsMetadata metadata(Path repoRoot, String tf32) throws Exception {
+ String commit = gitRevParse(repoRoot);
+ boolean dirty = gitStatusDirty(repoRoot);
+ String towerSha256 =
+ ExactBitsSpec.sha256(
+ Files.readAllBytes(
+ repoRoot.resolve(
+ "jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionTower.java")));
+ Path pinFile = repoRoot.resolve("native/install/lib/native-pin.properties");
+ Properties pins = new Properties();
+ try (InputStream in = Files.newInputStream(pinFile)) {
+ pins.load(in);
+ }
+ return new ExactBitsMetadata(
+ commit,
+ requirePin(pins, "mlxMetalVersion", pinFile),
+ requirePin(pins, "mlxcCommit", pinFile),
+ deviceName(),
+ System.getProperty("os.name"),
+ System.getProperty("os.version"),
+ tf32,
+ probeSpecHash(),
+ Map.of(),
+ Map.of(),
+ dirty,
+ towerSha256);
+ }
+
+ /**
+ * SHA-256 of the canonical encoding of the probe specification, so a probe change can never
+ * silently compare against a recording made from a different one.
+ */
+ private static String probeSpecHash() {
+ StringBuilder canonical = new StringBuilder();
+ canonical.append('{');
+ canonical.append("\"schema\":\"jmlx-phase7-3b-wp5-vision-probe-v1\"");
+ canonical.append(",\"hidden\":").append(HIDDEN);
+ canonical.append(",\"numHeads\":").append(NUM_HEADS);
+ canonical.append(",\"headDim\":").append(HEAD_DIM);
+ canonical.append(",\"intermediate\":").append(INTERMEDIATE);
+ canonical.append(",\"outHidden\":").append(OUT_HIDDEN);
+ canonical.append(",\"positionEmbeddings\":").append(POSITION_EMBEDDINGS);
+ canonical.append(",\"depth\":").append(DEPTH);
+ canonical.append(",\"deepstackIndexes\":[").append(DEEPSTACK_INDEXES[0]).append(']');
+ canonical.append(",\"merge\":").append(MERGE);
+ canonical.append(",\"image\":").append(IMAGE);
+ canonical.append(",\"seed\":").append(SEED);
+ canonical.append(",\"weightScaleBits\":").append(Float.floatToIntBits(WEIGHT_SCALE));
+ canonical.append('}');
+ return ExactBitsSpec.sha256(canonical.toString());
+ }
+
+ /** The MLX default device as a stable string: the same query the 7.3a recorder asserts. */
+ private static String deviceName() {
+ try (Arena tmp = Arena.ofConfined()) {
+ MemorySegment typeOut = tmp.allocate(ValueLayout.JAVA_INT);
+ int status = mlx_h.mlx_device_get_type(typeOut, MLX.defaultDevice());
+ if (status != 0) {
+ throw new AssertionError("mlx_device_get_type failed with status " + status);
+ }
+ int type = typeOut.get(ValueLayout.JAVA_INT, 0);
+ return type == 1 ? "gpu" : type == 0 ? "cpu" : "unknown(" + type + ")";
+ }
+ }
+
+ /** Fails (rather than skips) with the actionable bootstrap step when the runtime is absent. */
+ private static void requireNativeRuntime(Path repoRoot) {
+ String libDir = System.getProperty("jmlx.library.path", "");
+ Path metallib =
+ libDir.isBlank()
+ ? repoRoot.resolve("native/install/lib/mlx.metallib")
+ : Path.of(libDir).resolve("mlx.metallib");
+ if (!Files.isRegularFile(metallib)) {
+ throw new AssertionError(
+ "exact-bits recording needs the staged native runtime, but "
+ + metallib
+ + " is missing -- run ./scripts/bootstrap-native.sh first");
+ }
+ }
+
+ /** Reads a required native pin property, naming the missing key and file. */
+ private static String requirePin(Properties pins, String key, Path pinFile) {
+ String value = pins.getProperty(key);
+ if (value == null) {
+ throw new AssertionError(
+ "native pin property '"
+ + key
+ + "' is missing from "
+ + pinFile
+ + " -- run ./scripts/bootstrap-native.sh");
+ }
+ return value;
+ }
+
+ private static String gitRevParse(Path repoRoot) throws Exception {
+ Process process =
+ new ProcessBuilder("git", "rev-parse", "HEAD")
+ .directory(repoRoot.toFile())
+ .redirectErrorStream(true)
+ .start();
+ String out = new String(process.getInputStream().readAllBytes(), StandardCharsets.UTF_8).trim();
+ if (process.waitFor() != 0) {
+ throw new AssertionError("git rev-parse HEAD failed in " + repoRoot + ": " + out);
+ }
+ return out;
+ }
+
+ /** Whether the tree is dirty: {@code git status --porcelain} prints anything at all. */
+ private static boolean gitStatusDirty(Path repoRoot) throws Exception {
+ Process process =
+ new ProcessBuilder("git", "status", "--porcelain")
+ .directory(repoRoot.toFile())
+ .redirectErrorStream(true)
+ .start();
+ String out = new String(process.getInputStream().readAllBytes(), StandardCharsets.UTF_8);
+ if (process.waitFor() != 0) {
+ throw new AssertionError("git status --porcelain failed in " + repoRoot + ": " + out);
+ }
+ return !out.isEmpty();
+ }
+
+ /** Refuses to replace an existing recording (its {@code manifest.json} already present). */
+ private static void refuseOverwrite(Path modeDir) throws IOException {
+ Path manifestPath = modeDir.resolve("manifest.json");
+ if (!Files.isRegularFile(manifestPath)) {
+ return;
+ }
+ JsonNode manifest = JsonFiles.read(manifestPath);
+ throw new AssertionError(
+ "refusing to overwrite the existing exact-bit recording under "
+ + modeDir
+ + " (recorded at git commit "
+ + manifest.path("metadata").path("gitCommit").asString()
+ + ") -- a baseline is never silently replaced; to re-record, move or delete "
+ + modeDir
+ + " first");
+ }
+
+ /**
+ * The cheap half of verification, before the GPU capture is spent: the recording must exist, use
+ * the supported format version, carry comparable metadata (every field except the git commit and
+ * the provenance-only gitDirty/tower hash) and name exactly the probe captures. Returns the
+ * parsed manifest for {@link #verifyPostCapture}.
+ */
+ static JsonNode verifyPreCapture(ExactBitsMetadata global, Path modeDir) throws IOException {
+ Path manifestPath = modeDir.resolve("manifest.json");
+ if (!Files.isRegularFile(manifestPath)) {
+ throw new AssertionError(
+ "no exact-bit recordings under "
+ + modeDir
+ + " -- record first: ./gradlew :jmlx-models:exactBitsRecordVision");
+ }
+ JsonNode manifest = JsonFiles.read(manifestPath);
+ if (manifest.path("formatVersion").asInt() != FORMAT_VERSION) {
+ throw new AssertionError(
+ "unsupported recording format version " + manifest.path("formatVersion").asInt());
+ }
+ List mismatches =
+ global.mismatches(ExactBitsMetadata.fromJson(manifest.path("metadata")), false);
+ if (!mismatches.isEmpty()) {
+ throw new AssertionError(
+ "recordings are not comparable on this host; re-record: metadata mismatch in "
+ + mismatches);
+ }
+ List recordedNames = new ArrayList<>();
+ for (JsonNode node : manifest.path("captures")) {
+ recordedNames.add(node.asString());
+ }
+ if (!recordedNames.equals(CAPTURES)) {
+ throw new AssertionError(
+ "capture set differs from the recording: recorded "
+ + recordedNames
+ + ", candidate "
+ + CAPTURES
+ + " -- re-record after recording the probe at the base commit");
+ }
+ return manifest;
+ }
+
+ /** The expensive half of verification, after the probe has been re-run: bit-exact everywhere. */
+ private static void verifyPostCapture(
+ ExactBitsMetadata global, JsonNode manifest, Map captures, Path modeDir)
+ throws IOException {
+ for (String capture : CAPTURES) {
+ Path file = modeDir.resolve(capture + ".json");
+ if (!Files.isRegularFile(file)) {
+ throw new AssertionError("missing recording " + file + " -- record first");
+ }
+ JsonNode node = JsonFiles.read(file);
+ List mismatches =
+ global.mismatches(ExactBitsMetadata.fromJson(node.path("metadata")), true);
+ if (!mismatches.isEmpty()) {
+ throw new AssertionError(
+ "recordings are not comparable on this host; re-record: metadata mismatch in "
+ + mismatches
+ + " ["
+ + capture
+ + "]");
+ }
+ verifyCapture(capture, node, captures.get(capture), modeDir);
+ }
+ System.out.println("verified the vision probe against " + modeDir.resolve("manifest.json"));
+ }
+
+ /** One capture's bit-exact check: equal length, then equal bits in row-major order. */
+ private static void verifyCapture(String capture, JsonNode node, float[] actual, Path modeDir) {
+ int[] expected = floatBits(node.path("values").asString());
+ int[] actualBits = new int[actual.length];
+ for (int i = 0; i < actual.length; i++) {
+ actualBits[i] = Float.floatToIntBits(actual[i]);
+ }
+ if (expected.length != actualBits.length) {
+ throw new AssertionError(
+ "exact-bit mismatch ["
+ + capture
+ + "]: lengths differ: recorded "
+ + expected.length
+ + " floats, candidate "
+ + actualBits.length);
+ }
+ for (int i = 0; i < expected.length; i++) {
+ if (expected[i] != actualBits[i]) {
+ throw new AssertionError(
+ "exact-bit mismatch ["
+ + capture
+ + "]: first divergent element "
+ + i
+ + "; expected bits 0x"
+ + Integer.toHexString(expected[i])
+ + " ("
+ + Float.intBitsToFloat(expected[i])
+ + "), actual bits 0x"
+ + Integer.toHexString(actualBits[i])
+ + " ("
+ + Float.intBitsToFloat(actualBits[i])
+ + ")");
+ }
+ }
+ }
+
+ private static void record(ExactBitsMetadata global, Map captures, Path modeDir)
+ throws IOException {
+ Files.createDirectories(modeDir);
+ int floats = 0;
+ for (Map.Entry capture : captures.entrySet()) {
+ floatBits(global, capture.getKey(), capture.getValue(), modeDir);
+ floats += capture.getValue().length;
+ }
+ writeManifest(global, captures, modeDir);
+ }
+
+ /** One capture file: the Base64 of the big-endian IEEE-754 bits, row-major. */
+ private static void floatBits(ExactBitsMetadata global, String capture, float[] values, Path dir)
+ throws IOException {
+ ByteBuffer buffer = ByteBuffer.allocate(4 * values.length);
+ for (float value : values) {
+ buffer.putFloat(value);
+ }
+ Files.writeString(
+ dir.resolve(capture + ".json"),
+ captureJson(
+ capture,
+ global.toJsonString(),
+ "\"values\":\"" + Base64.getEncoder().encodeToString(buffer.array()) + '"'));
+ }
+
+ /** Decodes a recorded {@code values} string into the raw bits of each float, in order. */
+ private static int[] floatBits(String encoded) {
+ byte[] bytes = Base64.getDecoder().decode(encoded);
+ if (bytes.length % 4 != 0) {
+ throw new AssertionError("recorded values are not a multiple of 4 bytes: " + bytes.length);
+ }
+ ByteBuffer buffer = ByteBuffer.wrap(bytes);
+ int[] bits = new int[bytes.length / 4];
+ for (int i = 0; i < bits.length; i++) {
+ bits[i] = buffer.getInt();
+ }
+ return bits;
+ }
+
+ private static String captureJson(String capture, String metadataJson, String payload) {
+ return "{\"formatVersion\":"
+ + FORMAT_VERSION
+ + ",\"capture\":\""
+ + ExactBitsMetadata.escapeJson(capture)
+ + "\",\"metadata\":"
+ + metadataJson
+ + ","
+ + payload
+ + "}\n";
+ }
+
+ private static void writeManifest(
+ ExactBitsMetadata global, Map captures, Path modeDir) throws IOException {
+ StringBuilder out = new StringBuilder();
+ out.append("{\"formatVersion\":").append(FORMAT_VERSION).append(',');
+ out.append("\"metadata\":").append(global.toJsonString()).append(',');
+ out.append("\"captures\":[");
+ int i = 0;
+ for (String capture : captures.keySet()) {
+ if (i++ > 0) {
+ out.append(',');
+ }
+ out.append('"').append(ExactBitsMetadata.escapeJson(capture)).append('"');
+ }
+ out.append("]}\n");
+ Files.writeString(modeDir.resolve("manifest.json"), out.toString());
+ }
+
+ /** The run summary: capture count and total float count. */
+ private static String summary(Map captures) {
+ int floats = 0;
+ for (float[] values : captures.values()) {
+ floats += values.length;
+ }
+ return captures.size() + " captures, " + floats + " floats";
+ }
+
+ private static void checkShape(int[] shape, String capture, int... expected) {
+ if (!Arrays.equals(shape, expected)) {
+ throw new AssertionError(
+ capture
+ + " shape: expected "
+ + Arrays.toString(expected)
+ + ", got "
+ + Arrays.toString(shape));
+ }
+ }
+
+ /**
+ * The real-2B geometry at reduced depth 3 / DeepStack [1], seeded non-constant weights: one
+ * {@code MLXRandom.normal} draw per parameter (loc 0, scale {@link #WEIGHT_SCALE}) after {@code
+ * MLXRandom.seed(SEED)} — the constant-weight probe collapsed and was insensitive to attention,
+ * RoPE and norm changes (findings §11.4).
+ */
+ private static Qwen3VlVisionTower buildTower(MLXScope scope, List statics) {
+ MLXRandom.seed(SEED);
+ Map weights = new LinkedHashMap<>();
+ put(weights, statics, scope, "patchEmbedding.weight", HIDDEN, 2, 16, 16, 3);
+ put(weights, statics, scope, "patchEmbedding.bias", HIDDEN);
+ put(weights, statics, scope, "positionEmbedding.weight", POSITION_EMBEDDINGS, HIDDEN);
+ for (int i = 0; i < DEPTH; i++) {
+ String layer = "layer" + i + ".";
+ put(weights, statics, scope, layer + "attention.qkv.weight", 3 * HIDDEN, HIDDEN);
+ put(weights, statics, scope, layer + "attention.qkv.bias", 3 * HIDDEN);
+ put(weights, statics, scope, layer + "attention.proj.weight", HIDDEN, HIDDEN);
+ put(weights, statics, scope, layer + "attention.proj.bias", HIDDEN);
+ for (String norm : new String[] {"norm1", "norm2"}) {
+ put(weights, statics, scope, layer + norm + ".weight", HIDDEN);
+ put(weights, statics, scope, layer + norm + ".bias", HIDDEN);
+ }
+ put(weights, statics, scope, layer + "mlp.0.weight", INTERMEDIATE, HIDDEN);
+ put(weights, statics, scope, layer + "mlp.0.bias", INTERMEDIATE);
+ put(weights, statics, scope, layer + "mlp.2.weight", HIDDEN, INTERMEDIATE);
+ put(weights, statics, scope, layer + "mlp.2.bias", HIDDEN);
+ }
+ put(weights, statics, scope, "merger.norm.weight", HIDDEN);
+ put(weights, statics, scope, "merger.norm.bias", HIDDEN);
+ put(weights, statics, scope, "merger.linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc1.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc2.bias", OUT_HIDDEN);
+ put(weights, statics, scope, "deepstackMerger0.norm.weight", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.norm.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc1.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc2.bias", OUT_HIDDEN);
+ return new Qwen3VlVisionTower(scope, NUM_HEADS, weights, DEEPSTACK_INDEXES);
+ }
+
+ private static void put(
+ Map weights,
+ List statics,
+ MLXScope scope,
+ String key,
+ int... shape) {
+ weights.put(key, draw(statics, scope, shape));
+ }
+
+ /** One seeded weight draw: a normal with loc 0 and scale {@link #WEIGHT_SCALE}. */
+ private static MLXArray draw(List statics, MLXScope scope, int... shape) {
+ MLXArray value = MLXRandom.normal(scope, shape, DType.FLOAT32, 0f, WEIGHT_SCALE);
+ statics.add(value);
+ return value;
+ }
+
+ /** The 2B artifact's slow-processor options, with maxPixels at the default 4096-row hard cap. */
+ private static Qwen3VlImageProcessor processor() {
+ return new Qwen3VlImageProcessor(
+ Qwen3VlProcessorConfig.builder()
+ .patchSize(16)
+ .temporalPatchSize(2)
+ .mergeSize(2)
+ .minPixels(3136)
+ .maxPixels(4096 * 16 * 16)
+ .imageMean(new float[] {0.5f, 0.5f, 0.5f})
+ .imageStd(new float[] {0.5f, 0.5f, 0.5f})
+ .build());
+ }
+
+ /**
+ * One deterministic 1024×1024 image: every channel byte is one {@code java.util.Random} uniform
+ * in {@code [0, 1)} (seed {@link #SEED}) scaled to the full 0-255 range, so the patch rows into
+ * the conv are non-constant across spatial positions (findings §11.4).
+ */
+ private static RgbImage syntheticImage() {
+ byte[] pixels = new byte[IMAGE * IMAGE * 3];
+ Random random = new Random(SEED);
+ for (int i = 0; i < pixels.length; i++) {
+ pixels[i] = (byte) (random.nextFloat() * 256f);
+ }
+ return new RgbImage(IMAGE, IMAGE, pixels);
+ }
+}
diff --git a/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionBlock.java b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionBlock.java
new file mode 100644
index 00000000..13f9ff59
--- /dev/null
+++ b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionBlock.java
@@ -0,0 +1,66 @@
+package se.alipsa.jmlx.models;
+
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXOps;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.nn.LayerNorm;
+import se.alipsa.jmlx.nn.Module;
+import se.alipsa.jmlx.nn.Sequential;
+import se.alipsa.jmlx.nn.TwoAxisRotaryAttention;
+import se.alipsa.jmlx.nn.TwoAxisRotation;
+
+/**
+ * One pre-norm vision transformer block of {@code Qwen3VLVisionBlock} (pinned 4.57.6 source,
+ * findings §11.1): the reference's two sequential statements {@code h = h + attn(norm1(h))} then
+ * {@code h = h + mlp(norm2(h))} — the second statement's {@code norm2} and residual both see the
+ * block's updated residual (the attention output already added), not the block input. The
+ * attention sublayer runs on the block's two-axis rotation for this image only (the tower feeds
+ * each image's rows as a standalone sequence, so the rows and mask match the reference's
+ * variable-length per-image attention chunks exactly; any difference from the reference is kernel
+ * precision, not structure).
+ *
+ * The submodules are supplied by the tower and registered here as the children {@code norm1},
+ * {@code norm2}, {@code attention} and {@code mlp}, so the tower's dotted parameter paths mirror
+ * the checkpoint's {@code blocks.{i}.norm{1,2}.*}, {@code blocks.{i}.attn.{qkv,proj}.*} and {@code
+ * blocks.{i}.mlp.linear_fc{1,2}.*} trees.
+ */
+public final class Qwen3VlVisionBlock extends Module {
+
+ private final LayerNorm norm1;
+ private final LayerNorm norm2;
+ private final TwoAxisRotaryAttention attention;
+ private final Sequential mlp;
+
+ /**
+ * Registers the tower-built submodules as this block's children.
+ *
+ * @throws NullPointerException if a submodule is {@code null}
+ */
+ public Qwen3VlVisionBlock(
+ MLXScope scope,
+ LayerNorm norm1,
+ LayerNorm norm2,
+ TwoAxisRotaryAttention attention,
+ Sequential mlp) {
+ super(scope);
+ this.norm1 = child("norm1", norm1);
+ this.norm2 = child("norm2", norm2);
+ this.attention = child("attention", attention);
+ this.mlp = child("mlp", mlp);
+ }
+
+ /**
+ * Runs one pre-norm block over the image's {@code [N, hidden]} rows.
+ *
+ * @param hidden the block input, {@code [N, hidden]}
+ * @param rotation the two-axis rotation prepared for exactly these {@code N} rows
+ * @return the block output, {@code [N, hidden]}
+ * @throws NullPointerException if an argument is {@code null}
+ * @throws IllegalArgumentException if a submodule rejects the shape or rotation
+ */
+ public MLXArray forward(MLXArray hidden, TwoAxisRotation rotation) {
+ MLXArray afterAttention =
+ MLXOps.add(hidden, attention.forward(norm1.forward(hidden), rotation));
+ return MLXOps.add(afterAttention, mlp.forward(norm2.forward(afterAttention)));
+ }
+}
diff --git a/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionFeatures.java b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionFeatures.java
new file mode 100644
index 00000000..1b38771e
--- /dev/null
+++ b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionFeatures.java
@@ -0,0 +1,29 @@
+package se.alipsa.jmlx.models;
+
+import java.util.List;
+import java.util.Objects;
+import se.alipsa.jmlx.core.MLXArray;
+
+/**
+ * The full output of {@link Qwen3VlVisionTower#forward(MLXScope, List)}: the main-merger rows of
+ * every input image concatenated on axis 0 ({@code merged}), and, for each DeepStack level in the
+ * tower's {@code deepstackIndexes} order, that level's rows of every input image concatenated the
+ * same way ({@code deepstackLevels}).
+ *
+ *
{@code merged} and every level have shape {@code [totalMergedRows, outHiddenSize]} where
+ * {@code totalMergedRows} is the sum of the input images' post-merge row counts, in input order.
+ * The list is an immutable copy: the caller's list is not retained, and the returned arrays live in
+ * the target scope the caller passed to {@code forward}, so they outlive each image's temporary
+ * scope (and are released when the caller closes the target scope).
+ */
+public record Qwen3VlVisionFeatures(MLXArray merged, List deepstackLevels) {
+
+ /**
+ * Compact constructor: requires both components and replaces {@code deepstackLevels} with a
+ * defensive copy so the caller's list is never retained.
+ */
+ public Qwen3VlVisionFeatures {
+ Objects.requireNonNull(merged, "merged");
+ deepstackLevels = List.copyOf(Objects.requireNonNull(deepstackLevels, "deepstackLevels"));
+ }
+}
diff --git a/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionTower.java b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionTower.java
new file mode 100644
index 00000000..192aabe2
--- /dev/null
+++ b/jmlx-models/src/main/java/se/alipsa/jmlx/models/Qwen3VlVisionTower.java
@@ -0,0 +1,1303 @@
+package se.alipsa.jmlx.models;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashSet;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.Set;
+import java.util.TreeSet;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXOps;
+import se.alipsa.jmlx.core.MLXShape;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.nn.Conv3d;
+import se.alipsa.jmlx.nn.Embedding;
+import se.alipsa.jmlx.nn.GELU;
+import se.alipsa.jmlx.nn.LayerNorm;
+import se.alipsa.jmlx.nn.Linear;
+import se.alipsa.jmlx.nn.Module;
+import se.alipsa.jmlx.nn.PatchMerger;
+import se.alipsa.jmlx.nn.Sequential;
+import se.alipsa.jmlx.nn.TwoAxisRope;
+import se.alipsa.jmlx.nn.TwoAxisRotaryAttention;
+import se.alipsa.jmlx.nn.TwoAxisRotation;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessorResult;
+
+/**
+ * The Qwen3-VL vision tower: the patch-embedding convolution, the bilinear-interpolated position
+ * embedding, the two-axis-RoPE pre-norm encoder stack, the main merger and the DeepStack mergers of
+ * {@code Qwen3VLVisionModel}, ported from the pinned Hugging Face Transformers 4.57.6 {@code
+ * modeling_qwen3_vl.py} (see {@code tools/hf-reference/provenance.json}) — {@code
+ * Qwen3VLVisionModel} (including {@code rot_pos_emb} and {@code fast_pos_embed_interpolate}),
+ * {@code Qwen3VLVisionBlock}, {@code Qwen3VLVisionAttention}, {@code Qwen3VLVisionMLP}, {@code
+ * Qwen3VLVisionPatchMerger}, {@code Qwen3VLVisionPatchEmbed} and {@code
+ * Qwen3VLVisionRotaryEmbedding}; the verbatim transcription is findings §11.1. The reference runs
+ * in float32 (its default dtype), so every weight is converted to float32 by this constructor; on
+ * float32 inputs, {@link #forward(MLXScope, List)} matches the reference's op order within the
+ * golden test's 1e-4 tolerance (measured max diff 3.1e-6) — MLX's GPU kernels (matmul, SDPA) are
+ * not bit-identical to torch on the CPU.
+ *
+ * The module child names mirror the HF checkpoint tree through the mapping of {@link
+ * #visionKeyToTowerKey(String, int, int[])}: {@code patchEmbedding} is {@code patch_embed.proj}
+ * (the weight transposed to jmlx's channels-last {@code [hidden, temporal, height, width,
+ * inChannels]} layout by {@link #visionWeights(Map, int, int[])}), {@code positionEmbedding} is
+ * {@code pos_embed}, {@code layer0..layerN-1} are {@code blocks.{i}} (each a {@link
+ * Qwen3VlVisionBlock} with {@code norm1}/{@code norm2} for {@code norm1/norm2}, {@code
+ * attention.qkv}/{@code attention.proj} for {@code attn.qkv/attn.proj} and {@code mlp.0}/{@code
+ * mlp.2} for {@code mlp.linear_fc1/linear_fc2}, the tanh-approximation GELU {@code mlp.1} carrying
+ * no parameters — the reference's {@code hidden_act = gelu_pytorch_tanh}), {@code merger} is {@code
+ * merger} (post-shuffle norm off, norm width {@code hidden}) and {@code
+ * deepstackMerger0..} are {@code deepstack_merger_list.{j}} (post- shuffle norm on, norm
+ * width {@code hidden * merge^2}). {@link #forward(MLXScope, List)} stops at the mergers; it
+ * returns the main-merger rows and, per DeepStack index, that level's rows.
+ *
+ *
Two layout facts worth knowing before reasoning about the math:
+ *
+ *
+ * - The reference convolves channels-first pixel batches; MLX's convolutions are channels-last,
+ * so both the conv weight (in {@link #visionWeights(Map, int, int[])}) and the per-image
+ * pixel tensor (in {@link #forward(MLXScope, List)}) are transposed {@code {0, 2, 3, 4, 1}}
+ * before the convolution.
+ *
- The reference builds the bilinear position-embedding rows in per-image row-major patch
+ * order, sums the four weighted corner rows, and only then permutes into merge-block order;
+ * {@link #forward(MLXScope, List)} gathers the corners directly in merge-block order instead.
+ * The sum is per-row, so the two orderings produce identical bits.
+ *
+ *
+ * Memory: {@link #forward(MLXScope, List)} follows the {@code SmolVlmModel} per-request pattern:
+ * every activation of image {@code i} is allocated into a private child scope of the caller's
+ * target scope, the embedding stage's activations (the pixel tensor, the patch-embedding
+ * convolution, the position-embedding gathers and their weighted sum) are confined to a private
+ * child scope of the image scope that closes before block 0 starts — only {@code x = patchRows +
+ * pe} is evaluated and hoisted out of it (plus, when captures are requested, the {@code pe} and
+ * {@code patchRows} rows) — and each encoder block's activations are further confined to a private
+ * per-block child scope of the image scope: a block's output (and its DeepStack rows, when tapped)
+ * is evaluated and hoisted out before the block scope closes. The embedding stage's intermediates
+ * are therefore released before the first block and one block's intermediates do not accumulate
+ * across blocks, so the per-image peak neither scales with depth nor carries the embedding stage
+ * while the encoder runs (findings §11.4). The image's DeepStack and main-merger rows are hoisted
+ * into the target scope, and the embedding-stage, block and image scopes are closed on return — the
+ * tower's own module scope receives no per-forward handle. The returned {@link
+ * Qwen3VlVisionFeatures} arrays therefore live in the target scope, which the caller owns and
+ * closes (a per-request scope under the model scope, as {@code SmolVlmModel}'s tower runs under its
+ * per-request prefill scope). With more than one image, each output level's per-image parts are
+ * concatenated on axis 0, the concatenation is evaluated, and the parts are closed, so only the
+ * concatenations are retained. If a forward fails partway, the parts already hoisted for earlier
+ * images — and, for a failing image, the {@code pe}/{@code patchRows} capture rows (when captures
+ * are requested) and the per-level parts hoisted from its earlier blocks — remain in the target
+ * scope (the caller's to release); the failing image's embedding-stage, block and image scopes are
+ * still closed.
+ */
+public final class Qwen3VlVisionTower extends Module {
+
+ /**
+ * The reference's {@code Qwen3VLVisionRotaryEmbedding} constructor default: {@code theta =
+ * 10000.0}, evaluated in float64 and cast to the float32 the module uses.
+ */
+ public static final float ROPE_THETA = 10000.0f;
+
+ /** The reference's hard-wired vision LayerNorm epsilon: {@code eps = 1e-6}. */
+ public static final float LAYER_NORM_EPS = 1e-6f;
+
+ /** The {@code model.visual.} checkpoint prefix. */
+ static final String VISUAL_PREFIX = "model.visual.";
+
+ private final int depth;
+ private final int numHeads;
+ private final int hidden;
+ private final int headDim;
+ private final int gridSide;
+ private final int merge;
+ private final int outHiddenSize;
+ private final int inChannels;
+ private final int temporal;
+ private final int patchHeight;
+ private final int patchWidth;
+ private final int[] deepstackIndexes;
+ private final Conv3d patchEmbedding;
+ private final Embedding positionEmbedding;
+ private final List layers;
+ private final PatchMerger merger;
+ private final List deepstackMergers;
+ private final TwoAxisRope rope;
+
+ /**
+ * Builds the tower from checkpoint weights keyed by this module's own dotted parameter paths (see
+ * the class javadoc for the mapping; {@link #visionWeights(Map, int, int[])} produces such a map
+ * from a raw checkpoint). Every weight must be present and shaped consistently with every other
+ * weight; any extra or missing key is rejected. Every weight must be an inexact (floating) dtype,
+ * and each non-float32 weight is converted to float32 (evaluated and the original closed, the
+ * {@code SmolVlmModel} pattern) before any child module is built.
+ *
+ * The geometry is derived from the weight shapes the way {@code Idefics3VisionTower} derives
+ * its own: the patch-embedding weight is rank-5 channels-last {@code [hidden, temporal,
+ * patchHeight, patchWidth, inChannels]}, {@code headDim = hidden / numHeads} must be a multiple
+ * of 4 (the two-axis RoPE rotates {@code headDim / 2} even frequencies), {@code
+ * positionEmbedding.weight} must be a square {@code gridSide x gridSide} grid, the main merger's
+ * {@code linearFc1} width {@code m} must be {@code hidden * merge^2} for a square {@code
+ * merge^2}, and every DeepStack merger must share that {@code m} and the merger's output width.
+ *
+ * @param numHeads the attention head count (not derivable from the weights)
+ * @param deepstackIndexes the strictly-increasing layer indices (in {@code [0, depth)}) at which
+ * a DeepStack feature is captured; must be non-empty and match the {@code deepstackMerger*}
+ * subtrees present in {@code weights}
+ * @throws IllegalArgumentException if a dimension is invalid, a weight is missing or extra, a
+ * weight is not floating, or the weights are mutually inconsistent
+ * @throws NullPointerException if {@code weights} or {@code deepstackIndexes} is {@code null}
+ */
+ public Qwen3VlVisionTower(
+ MLXScope scope, int numHeads, Map weights, int[] deepstackIndexes) {
+ super(scope);
+ Objects.requireNonNull(weights, "weights");
+ Objects.requireNonNull(deepstackIndexes, "deepstackIndexes");
+ if (numHeads <= 0) {
+ throw new IllegalArgumentException("the vision tower needs at least one attention head");
+ }
+ if (deepstackIndexes.length == 0) {
+ throw new IllegalArgumentException("deepstackIndexes must not be empty");
+ }
+ int depth = 0;
+ while (weights.containsKey("layer" + depth + ".norm1.weight")) {
+ depth++;
+ }
+ if (depth == 0) {
+ throw new IllegalArgumentException("the vision tower needs at least one layer");
+ }
+ for (int i = 0; i < deepstackIndexes.length; i++) {
+ int index = deepstackIndexes[i];
+ if (index < 0 || index >= depth) {
+ throw new IllegalArgumentException(
+ "deepstack index " + i + " = " + index + " is out of range [0, " + depth + ")");
+ }
+ if (i > 0 && index <= deepstackIndexes[i - 1]) {
+ throw new IllegalArgumentException(
+ "deepstack indexes must be strictly increasing; index "
+ + i
+ + " has "
+ + index
+ + " after "
+ + deepstackIndexes[i - 1]);
+ }
+ }
+
+ MLXArray convWeight = weight(weights, "patchEmbedding.weight");
+ if (convWeight.ndim() != 5) {
+ throw new IllegalArgumentException(
+ "patchEmbedding.weight must be rank-5 channels-last, got rank " + convWeight.ndim());
+ }
+ int[] conv = convWeight.shape();
+ for (int i = 0; i < conv.length; i++) {
+ if (conv[i] <= 0) {
+ throw new IllegalArgumentException(
+ "patchEmbedding.weight must have positive dimensions, got " + Arrays.toString(conv));
+ }
+ }
+ int hidden = conv[0];
+ requireShape(weight(weights, "patchEmbedding.bias"), "patchEmbedding.bias", new int[] {hidden});
+ if (hidden % numHeads != 0) {
+ throw new IllegalArgumentException(
+ "hidden size " + hidden + " is not divisible by " + numHeads + " attention heads");
+ }
+ int headDim = hidden / numHeads;
+ if (headDim < 4 || headDim % 4 != 0) {
+ throw new IllegalArgumentException(
+ "headDim "
+ + headDim
+ + " must be at least 4 and a multiple of 4 (the two-axis RoPE rotates headDim/2"
+ + " even frequencies)");
+ }
+ MLXArray positionTable = weight(weights, "positionEmbedding.weight");
+ if (positionTable.ndim() != 2) {
+ throw new IllegalArgumentException(
+ "positionEmbedding.weight must be a matrix, got rank " + positionTable.ndim());
+ }
+ int numPatches = positionTable.shape()[0];
+ int gridSide = (int) Math.sqrt(numPatches);
+ if (numPatches <= 0 || gridSide <= 0 || gridSide * gridSide != numPatches) {
+ throw new IllegalArgumentException(
+ "positionEmbedding.weight must contain a square grid, got " + numPatches + " rows");
+ }
+ requireShape(positionTable, "positionEmbedding.weight", new int[] {numPatches, hidden});
+
+ int intermediate = 0;
+ for (int i = 0; i < depth; i++) {
+ String layer = "layer" + i + ".";
+ requireShape(
+ weight(weights, layer + "attention.qkv.weight"),
+ layer + "attention.qkv.weight",
+ new int[] {Math.multiplyExact(3, hidden), hidden});
+ requireShape(
+ weight(weights, layer + "attention.qkv.bias"),
+ layer + "attention.qkv.bias",
+ new int[] {Math.multiplyExact(3, hidden)});
+ requireShape(
+ weight(weights, layer + "attention.proj.weight"),
+ layer + "attention.proj.weight",
+ new int[] {hidden, hidden});
+ requireShape(
+ weight(weights, layer + "attention.proj.bias"),
+ layer + "attention.proj.bias",
+ new int[] {hidden});
+ for (String norm : new String[] {"norm1", "norm2"}) {
+ requireShape(
+ weight(weights, layer + norm + ".weight"),
+ layer + norm + ".weight",
+ new int[] {hidden});
+ requireShape(
+ weight(weights, layer + norm + ".bias"), layer + norm + ".bias", new int[] {hidden});
+ }
+ MLXArray mlp0 = weight(weights, layer + "mlp.0.weight");
+ if (i == 0) {
+ // The feed-forward width is derived from this weight's rows, so its rank is checked here
+ // before the index; the other layers' rows are pinned by the requireShape below.
+ if (mlp0.ndim() != 2) {
+ throw new IllegalArgumentException(
+ layer + "mlp.0.weight must be a matrix, got rank " + mlp0.ndim());
+ }
+ intermediate = mlp0.shape()[0];
+ if (intermediate <= 0) {
+ throw new IllegalArgumentException("the feed-forward width must be positive");
+ }
+ }
+ requireShape(mlp0, layer + "mlp.0.weight", new int[] {intermediate, hidden});
+ requireShape(
+ weight(weights, layer + "mlp.0.bias"), layer + "mlp.0.bias", new int[] {intermediate});
+ requireShape(
+ weight(weights, layer + "mlp.2.weight"),
+ layer + "mlp.2.weight",
+ new int[] {hidden, intermediate});
+ requireShape(weight(weights, layer + "mlp.2.bias"), layer + "mlp.2.bias", new int[] {hidden});
+ }
+
+ MLXArray fc1 = weight(weights, "merger.linearFc1.weight");
+ if (fc1.ndim() != 2 || fc1.shape()[0] != fc1.shape()[1]) {
+ throw new IllegalArgumentException(
+ "merger.linearFc1.weight must be square [m, m], got shape "
+ + Arrays.toString(fc1.shape()));
+ }
+ int m = fc1.shape()[0];
+ if (m % hidden != 0) {
+ throw new IllegalArgumentException(
+ "merger width m = " + m + " must be divisible by hidden size " + hidden);
+ }
+ int mergeSquare = m / hidden;
+ int merge = (int) Math.sqrt(mergeSquare);
+ if (merge < 1 || merge * merge != mergeSquare) {
+ throw new IllegalArgumentException(
+ "merger width m = "
+ + m
+ + " must be the hidden size times a square merge^2, got merge^2 ="
+ + " "
+ + mergeSquare);
+ }
+ requireShape(weight(weights, "merger.norm.weight"), "merger.norm.weight", new int[] {hidden});
+ requireShape(weight(weights, "merger.norm.bias"), "merger.norm.bias", new int[] {hidden});
+ requireShape(weight(weights, "merger.linearFc1.bias"), "merger.linearFc1.bias", new int[] {m});
+ MLXArray fc2 = weight(weights, "merger.linearFc2.weight");
+ if (fc2.ndim() != 2) {
+ throw new IllegalArgumentException(
+ "merger.linearFc2.weight must be a matrix, got rank " + fc2.ndim());
+ }
+ int outHiddenSize = fc2.shape()[0];
+ if (outHiddenSize <= 0) {
+ throw new IllegalArgumentException("merger.linearFc2.weight must have positive output rows");
+ }
+ requireShape(fc2, "merger.linearFc2.weight", new int[] {outHiddenSize, m});
+ requireShape(
+ weight(weights, "merger.linearFc2.bias"),
+ "merger.linearFc2.bias",
+ new int[] {outHiddenSize});
+ for (int j = 0; j < deepstackIndexes.length; j++) {
+ String prefix = "deepstackMerger" + j + ".";
+ requireShape(weight(weights, prefix + "norm.weight"), prefix + "norm.weight", new int[] {m});
+ requireShape(weight(weights, prefix + "norm.bias"), prefix + "norm.bias", new int[] {m});
+ MLXArray deepFc1 = weight(weights, prefix + "linearFc1.weight");
+ if (deepFc1.ndim() != 2 || deepFc1.shape()[0] != m || deepFc1.shape()[1] != m) {
+ throw new IllegalArgumentException(
+ prefix
+ + "linearFc1.weight must be square [m, m] = "
+ + Arrays.toString(new int[] {m, m})
+ + ", got shape "
+ + Arrays.toString(deepFc1.shape()));
+ }
+ requireShape(
+ weight(weights, prefix + "linearFc1.bias"), prefix + "linearFc1.bias", new int[] {m});
+ requireShape(
+ weight(weights, prefix + "linearFc2.weight"),
+ prefix + "linearFc2.weight",
+ new int[] {outHiddenSize, m});
+ requireShape(
+ weight(weights, prefix + "linearFc2.bias"),
+ prefix + "linearFc2.bias",
+ new int[] {outHiddenSize});
+ }
+
+ for (Map.Entry entry : weights.entrySet()) {
+ if (!entry.getValue().dtype().isInexact()) {
+ throw new IllegalArgumentException(
+ "vision tower weights must be floating: " + entry.getKey());
+ }
+ }
+ rejectUnexpected(weights, expectedKeys(depth, deepstackIndexes.length));
+
+ Map f32 = new LinkedHashMap<>(weights.size());
+ for (Map.Entry entry : weights.entrySet()) {
+ MLXArray original = entry.getValue();
+ MLXArray value =
+ original.dtype() == DType.FLOAT32 ? original : MLX.astype(original, DType.FLOAT32);
+ if (value != original) {
+ MLX.eval(value);
+ original.close();
+ }
+ f32.put(entry.getKey(), value);
+ }
+
+ this.depth = depth;
+ this.numHeads = numHeads;
+ this.hidden = hidden;
+ this.headDim = headDim;
+ this.gridSide = gridSide;
+ this.merge = merge;
+ this.outHiddenSize = outHiddenSize;
+ this.inChannels = conv[4];
+ this.temporal = conv[1];
+ this.patchHeight = conv[2];
+ this.patchWidth = conv[3];
+ this.deepstackIndexes = deepstackIndexes.clone();
+ this.patchEmbedding =
+ child(
+ "patchEmbedding",
+ new Conv3d(
+ scope,
+ weight(f32, "patchEmbedding.weight"),
+ weight(f32, "patchEmbedding.bias"),
+ new int[] {this.temporal, this.patchHeight, this.patchWidth},
+ new int[] {0, 0, 0},
+ new int[] {1, 1, 1},
+ 1));
+ this.positionEmbedding =
+ child("positionEmbedding", new Embedding(scope, weight(f32, "positionEmbedding.weight")));
+ this.layers = new ArrayList<>(depth);
+ for (int i = 0; i < depth; i++) {
+ String layer = "layer" + i + ".";
+ this.layers.add(
+ child(
+ "layer" + i,
+ new Qwen3VlVisionBlock(
+ scope,
+ new LayerNorm(
+ scope,
+ weight(f32, layer + "norm1.weight"),
+ weight(f32, layer + "norm1.bias"),
+ LAYER_NORM_EPS),
+ new LayerNorm(
+ scope,
+ weight(f32, layer + "norm2.weight"),
+ weight(f32, layer + "norm2.bias"),
+ LAYER_NORM_EPS),
+ new TwoAxisRotaryAttention(
+ scope,
+ numHeads,
+ headDim,
+ ROPE_THETA,
+ weight(f32, layer + "attention.qkv.weight"),
+ weight(f32, layer + "attention.qkv.bias"),
+ weight(f32, layer + "attention.proj.weight"),
+ weight(f32, layer + "attention.proj.bias")),
+ new Sequential(
+ scope,
+ new Linear(
+ scope,
+ weight(f32, layer + "mlp.0.weight"),
+ weight(f32, layer + "mlp.0.bias")),
+ new GELU(scope, true),
+ new Linear(
+ scope,
+ weight(f32, layer + "mlp.2.weight"),
+ weight(f32, layer + "mlp.2.bias"))))));
+ }
+ this.merger =
+ child(
+ "merger",
+ new PatchMerger(
+ scope,
+ merge,
+ false,
+ weight(f32, "merger.norm.weight"),
+ weight(f32, "merger.norm.bias"),
+ weight(f32, "merger.linearFc1.weight"),
+ weight(f32, "merger.linearFc1.bias"),
+ weight(f32, "merger.linearFc2.weight"),
+ weight(f32, "merger.linearFc2.bias")));
+ this.deepstackMergers = new ArrayList<>(deepstackIndexes.length);
+ for (int j = 0; j < deepstackIndexes.length; j++) {
+ String prefix = "deepstackMerger" + j + ".";
+ this.deepstackMergers.add(
+ child(
+ "deepstackMerger" + j,
+ new PatchMerger(
+ scope,
+ merge,
+ true,
+ weight(f32, prefix + "norm.weight"),
+ weight(f32, prefix + "norm.bias"),
+ weight(f32, prefix + "linearFc1.weight"),
+ weight(f32, prefix + "linearFc1.bias"),
+ weight(f32, prefix + "linearFc2.weight"),
+ weight(f32, prefix + "linearFc2.bias"))));
+ }
+ this.rope = new TwoAxisRope(ROPE_THETA, headDim / 2);
+ train(false);
+ }
+
+ /** The encoder stack depth (the {@code blocks} count). */
+ public int depth() {
+ return depth;
+ }
+
+ /** The attention head count. */
+ public int numHeads() {
+ return numHeads;
+ }
+
+ /** The vision hidden size (the patch-embedding output width). */
+ public int hidden() {
+ return hidden;
+ }
+
+ /** The per-head dimension, {@code hidden / numHeads}. */
+ public int headDim() {
+ return headDim;
+ }
+
+ /** The position-embedding grid side ({@code sqrt(num_position_embeddings)}). */
+ public int gridSide() {
+ return gridSide;
+ }
+
+ /** The spatial merge size ({@code m / hidden} in the merger's {@code m x m} width). */
+ public int spatialMergeSize() {
+ return merge;
+ }
+
+ /** The merger output width (the reference's {@code out_hidden_size}). */
+ public int outHiddenSize() {
+ return outHiddenSize;
+ }
+
+ /** A copy of the DeepStack capture layer indices. */
+ public int[] deepstackIndexes() {
+ return deepstackIndexes.clone();
+ }
+
+ /**
+ * Runs the tower on one or more processed still images. For each image, in order: the pixel rows
+ * are transposed to the channels-last conv layout and patch-embedded, the bilinear position
+ * embedding is gathered directly in merge-block order and added, the two-axis RoPE is prepared
+ * for the image's merge-block coordinates, the pre-norm encoder stack runs while capturing the
+ * DeepStack features at {@code deepstackIndexes}, and the main merger runs. The image's DeepStack
+ * rows and main-merger row are evaluated and hoisted into {@code target}, and the image's private
+ * child scope is closed. The per-image results are concatenated on axis 0 across the input images
+ * (a single image is returned as-is).
+ *
+ * Every image must be a still image ({@code gridThw()} {@code [1, h, w]}), with {@code h} and
+ * {@code w} positive multiples of {@link #spatialMergeSize()}, {@code h * w} equal to the
+ * result's {@code rawPatchRows()}, {@code patchStride()} equal to the tower's {@code inChannels *
+ * temporal * patchHeight * patchWidth} and {@code values().length} equal to {@code rawPatchRows()
+ * * patchStride()}.
+ *
+ *
Memory contract (the {@code SmolVlmModel} per-request pattern): every image's activations
+ * are allocated into a private child scope of {@code target}. The embedding stage (pixel tensor,
+ * patch-embedding convolution, position-embedding gathers and their weighted sum) runs in a
+ * further private child scope of the image scope: only {@code x = patchRows + pe} is evaluated
+ * and hoisted to the image scope before that stage scope closes, so the stage's other
+ * intermediates are released before block 0. Each encoder block's activations run in a further
+ * per-block child scope of the image scope that closes after the block's evaluated output is
+ * hoisted out, so one block's intermediates do not accumulate across blocks and the per-image
+ * peak neither scales with depth nor carries the embedding stage while the encoder runs. This
+ * tower's own module scope receives no per-forward handle. The returned arrays live in {@code
+ * target}, which the caller owns and closes. When more than one image is given, each level's
+ * per-image parts are concatenated, the concatenation is evaluated, and the per-image parts are
+ * closed, so only the concatenations are retained in {@code target}. If a forward fails partway,
+ * the parts already hoisted for earlier images — and, for the failing image, the per-level parts
+ * hoisted from its earlier blocks — remain in {@code target} (the caller's to release), while the
+ * failing image's embedding-stage, block and image scopes are still closed.
+ *
+ * @param target the caller's per-request scope; must be this tower's module scope or a descendant
+ * of it (the per-image child scopes mix this tower's weights with the image activations, and
+ * unrelated scopes are rejected by the first such op)
+ * @param images the processed images, in the order their rows must be concatenated
+ * @return the concatenated main-merger rows and per-level DeepStack rows, in {@code target}
+ * @throws NullPointerException if {@code target}, {@code images} or an element is {@code null}
+ * @throws IllegalArgumentException if {@code target} is not this tower's module scope or a
+ * descendant of it, or if an image's geometry disagrees with the tower's
+ */
+ public Qwen3VlVisionFeatures forward(MLXScope target, List images) {
+ return run(target, images, null).features();
+ }
+
+ /**
+ * The golden-verification view of {@link #forward(MLXScope, List)} (findings §11.3): the
+ * identical computation and scope handling, plus one {@link ImageCaptures} per input image
+ * holding the bilinear-interpolated position-embedding rows, the patch-embedding (convolution)
+ * output before the position-embedding add, and {@code 1 + deepstackIndexes.length} block outputs
+ * — block 0 first, then each DeepStack source block in this tower's {@code deepstackIndexes}
+ * order (so when the first index is 0, block 0's output is captured twice) — the reference's
+ * hook-based per-image captures. Every captured array is evaluated and hoisted into {@code
+ * target} with the final features, so it outlives each image's private scopes (the
+ * embedding-stage scope, the image scope and the per-block scopes). Package-private: the golden
+ * test ({@code Qwen3VlVisionTowerGoldenTest}) is the intended consumer; {@link #forward(MLXScope,
+ * List)} stays the production surface and discards the captures.
+ *
+ * @param target the caller's scope for the features and captures; see {@link #forward(MLXScope,
+ * List)}
+ * @param images the processed images; see {@link #forward(MLXScope, List)}
+ * @return the final features and the per-image captures, in {@code target}
+ */
+ ForwardCaptures forwardForTest(MLXScope target, List images) {
+ List captures = new ArrayList<>(images.size());
+ return run(target, images, captures);
+ }
+
+ private ForwardCaptures run(
+ MLXScope target, List images, List captures) {
+ Objects.requireNonNull(target, "target");
+ if (!scope().isAncestorOf(target)) {
+ throw new IllegalArgumentException(
+ "target scope must be the tower's module scope or a descendant of it, got " + target);
+ }
+ Objects.requireNonNull(images, "images");
+ if (images.isEmpty()) {
+ throw new IllegalArgumentException("at least one image is required");
+ }
+ int levelCount = deepstackIndexes.length;
+ List mergedParts = new ArrayList<>(images.size());
+ List> deepstackParts = new ArrayList<>(levelCount);
+ for (int level = 0; level < levelCount; level++) {
+ deepstackParts.add(new ArrayList<>(images.size()));
+ }
+ for (Qwen3VlImageProcessorResult image : images) {
+ Objects.requireNonNull(image, "images element");
+ runImage(target, image, mergedParts, deepstackParts, captures);
+ }
+ MLXArray merged = mergedParts.size() == 1 ? mergedParts.get(0) : concatenate(mergedParts);
+ List levels = new ArrayList<>(levelCount);
+ for (int level = 0; level < levelCount; level++) {
+ List parts = deepstackParts.get(level);
+ levels.add(parts.size() == 1 ? parts.get(0) : concatenate(parts));
+ }
+ return new ForwardCaptures(
+ new Qwen3VlVisionFeatures(merged, levels),
+ captures == null ? List.of() : List.copyOf(captures));
+ }
+
+ /**
+ * Joins the per-image parts of one output level: concatenates them on axis 0 (the parts all live
+ * in the caller's target scope, so the concatenation lands there too), evaluates the
+ * concatenation, and closes the per-image parts, so only the concatenation's own buffer is
+ * retained in the target scope.
+ */
+ private static MLXArray concatenate(List parts) {
+ MLXArray joined = MLXShape.concatenate(parts.toArray(MLXArray[]::new), 0);
+ MLX.eval(joined);
+ for (MLXArray part : parts) {
+ part.close();
+ }
+ return joined;
+ }
+
+ /**
+ * One image of {@link #forward(MLXScope, List)}: the {@code SmolVlmModel} per-request pattern — a
+ * private child scope of the caller's target scope holds the image's own activations. The
+ * embedding stage (pixel tensor, patch-embedding convolution, position-embedding gathers and
+ * their weighted sum) runs in a further private child scope of the image scope: only {@code x =
+ * patchRows + pe} is evaluated and hoisted to the image scope before it closes (plus, for the
+ * golden test, the evaluated {@code pe} and {@code patchRows} capture rows, hoisted to the target
+ * scope), so the stage's other intermediates are released before block 0. Each encoder block runs
+ * in a further private child scope of the image scope: the block's output (and its DeepStack
+ * rows, when tapped) is evaluated and hoisted out, then the block scope closes, so one block's
+ * intermediates never accumulate across blocks and the per-image peak neither scales with depth
+ * nor carries the embedding stage while the encoder runs. The DeepStack and main-merger rows
+ * (and, for the golden test, the intermediate captures) are hoisted into the target scope, and
+ * the embedding-stage, block and image scopes are all closed on return, so this tower's own
+ * module scope receives no handle from the image. If the image's forward fails partway, the
+ * capture rows hoisted from its embedding stage (when captures are requested) and the per-level
+ * rows hoisted from its earlier blocks remain in the target scope (the caller's to release); the
+ * embedding-stage, block and image scopes are still closed.
+ */
+ private void runImage(
+ MLXScope target,
+ Qwen3VlImageProcessorResult image,
+ List mergedParts,
+ List> deepstackParts,
+ List captures) {
+ final int levelCount = deepstackIndexes.length;
+ int[] grid = image.gridThw();
+ if (grid.length != 3 || grid[0] != 1) {
+ throw new IllegalArgumentException(
+ "the tower accepts still images with gridThw [1, h, w], got " + Arrays.toString(grid));
+ }
+ int h = grid[1];
+ int w = grid[2];
+ if (h <= 0 || w <= 0) {
+ throw new IllegalArgumentException(
+ "the image grid dimensions must be positive, got " + h + "x" + w);
+ }
+ if (h % merge != 0 || w % merge != 0) {
+ throw new IllegalArgumentException(
+ "image grid " + h + "x" + w + " must be multiples of the spatial merge size " + merge);
+ }
+ int n = Math.multiplyExact(h, w);
+ if (image.rawPatchRows() != n) {
+ throw new IllegalArgumentException(
+ "image grid "
+ + h
+ + "x"
+ + w
+ + " implies "
+ + n
+ + " raw patch rows, got "
+ + image.rawPatchRows());
+ }
+ int expectedStride =
+ Math.multiplyExact(
+ Math.multiplyExact(Math.multiplyExact(inChannels, temporal), patchHeight), patchWidth);
+ if (image.patchStride() != expectedStride) {
+ throw new IllegalArgumentException(
+ "image patch stride "
+ + image.patchStride()
+ + " does not match the tower's "
+ + expectedStride
+ + " (inChannels * temporal * patchHeight * patchWidth)");
+ }
+ if (image.values().length != Math.multiplyExact(n, expectedStride)) {
+ throw new IllegalArgumentException(
+ "image values must be rawPatchRows * patchStride = "
+ + Math.multiplyExact(n, expectedStride)
+ + " entries, got "
+ + image.values().length);
+ }
+
+ try (MLXScope img = target.newChild()) {
+ // The embedding stage (pixels -> conv -> position-embedding gathers -> weighted sum) runs in
+ // its own child scope of the image scope: only x = patchRows + pe is needed after it (plus,
+ // when captures are requested, the pe and patchRows rows themselves), so everything else the
+ // stage computes is released before block 0 starts (findings §11.4).
+ // Pure-Java coordinates, needed both by the stage (corner gather order) and by the RoPE
+ // preparation after the stage's scope has closed.
+ int[][] coords = rotaryCoordinates(h, w, merge);
+ int[] rowCoords = coords[0];
+ int[] colCoords = coords[1];
+ MLXArray capturedPe = null;
+ MLXArray capturedPatchRows = null;
+ MLXArray x;
+ try (MLXScope embed = img.newChild()) {
+ MLXArray pixels =
+ MLX.array(
+ embed,
+ image.values(),
+ new int[] {n, inChannels, temporal, patchHeight, patchWidth});
+ // PyTorch [N, C, T, H, W] -> MLX channels-last [N, T, H, W, C].
+ MLXArray channelsLast = MLXShape.transpose(pixels, new int[] {0, 2, 3, 4, 1});
+ MLXArray patchRows =
+ MLXShape.reshape(patchEmbedding.forward(channelsLast), new int[] {n, hidden});
+
+ PositionLookup lookup = positionLookup(h, w, gridSide);
+ int[] flatIndices = new int[Math.multiplyExact(4, n)];
+ float[] cornerWeights = new float[Math.multiplyExact(4, n)];
+ for (int m = 0; m < n; m++) {
+ int k = rowCoords[m] * w + colCoords[m];
+ for (int c = 0; c < 4; c++) {
+ flatIndices[c * n + m] = lookup.cornerIndices()[c][k];
+ cornerWeights[c * n + m] = lookup.cornerWeights()[c][k];
+ }
+ }
+ // pos_embed(idx) * weight[:, :, None], then the reference's left-associative 4-way add,
+ // gathered directly in merge-block order (per-row, so identical bits to the reference's
+ // sum-then-permute).
+ MLXArray idxTensor = MLX.array(embed, flatIndices, new int[] {4, n});
+ MLXArray cornerRows = positionEmbedding.forward(idxTensor);
+ MLXArray weightTensor = MLX.array(embed, cornerWeights, new int[] {4, n, 1});
+ MLXArray[] parts = MLXShape.split(MLXOps.multiply(cornerRows, weightTensor), 4, 0);
+ MLXArray pe = MLXShape.squeeze(parts[0], new int[] {0});
+ for (int c = 1; c < 4; c++) {
+ pe = MLXOps.add(pe, MLXShape.squeeze(parts[c], new int[] {0}));
+ }
+ x = MLXOps.add(patchRows, pe);
+ if (captures != null) {
+ // The captures contract keeps the pe and patchRows rows: evaluate them (with x) and
+ // hoist them to the target scope before the stage's scope closes, so the stage's
+ // release drops only the stage intermediates, not the captured rows.
+ MLX.eval(new MLXArray[] {x, pe, patchRows});
+ capturedPe = MLX.hoist(pe, target);
+ capturedPatchRows = MLX.hoist(patchRows, target);
+ } else {
+ // Production: only x is needed after the stage; evaluate it and hoist it to the image
+ // scope, so closing the stage scope below releases every other stage intermediate
+ // before block 0's intermediates start to accumulate.
+ MLX.eval(x);
+ }
+ x = MLX.hoist(x, img);
+ }
+
+ TwoAxisRotation rotation = rope.prepare(img, n, rowCoords, colCoords);
+ List capturedBlocks = captures == null ? null : new ArrayList<>(levelCount + 1);
+ int level = 0;
+ for (int i = 0; i < depth; i++) {
+ try (MLXScope block = img.newChild()) {
+ // Pin this block's entry row into the block scope the way T5Model.project does: the
+ // same-shape broadcast view is a no-op that allocates a handle into `block`, so every
+ // op result of the block resolves into it (the weights and the rotation tables live in
+ // ancestor scopes, and GELU's scalar constants follow the input's scope).
+ MLXArray in = MLXShape.broadcastTo(x, block, x.shape());
+ MLXArray out = layers.get(i).forward(in, rotation);
+ boolean tap = level < levelCount && i == deepstackIndexes[level];
+ MLXArray ds = tap ? deepstackMergers.get(level).forward(out) : null;
+ // Evaluate this block before the next one starts: closing the block scope below frees
+ // every intermediate of this block (the peak does not accumulate across blocks), while
+ // `out` (and `ds`) are hoisted out first, so the computation is unchanged — uncompiled
+ // MLX does not fuse across eval boundaries (verified bit-identical in both TF32 modes
+ // against the pre-change recording, with the probe's seeded non-constant weights and
+ // pixels, findings §11.4).
+ MLX.eval(tap ? new MLXArray[] {out, ds} : new MLXArray[] {out});
+ // The block-0 capture and the DeepStack capture are independent conditions: when
+ // deepstackIndexes[0] == 0 both fire in this order, so blockOutputs keeps the
+ // documented 1 + levelCount entries (block 0 first, then the DeepStack source blocks in
+ // deepstackIndexes order).
+ if (captures != null && i == 0) {
+ capturedBlocks.add(MLX.hoist(out, target));
+ }
+ if (tap) {
+ if (captures != null) {
+ capturedBlocks.add(MLX.hoist(out, target));
+ }
+ deepstackParts.get(level).add(MLX.hoist(ds, target));
+ level++;
+ }
+ // Drop the previous residual before the next block's intermediates accumulate: only
+ // the hoisted `out` (now `x`) survives this block's scope.
+ x.close();
+ x = MLX.hoist(out, img);
+ }
+ }
+ MLXArray merged = merger.forward(x);
+
+ List toEvaluate = new ArrayList<>(2);
+ toEvaluate.add(merged);
+ if (captures != null) {
+ // The pe and patchRows captures were hoisted to the target scope by the embedding-stage
+ // scope (above); the block captures likewise, so the stage and block scopes are already
+ // closed by the time this evaluates the hoisted copies.
+ toEvaluate.add(capturedPe);
+ toEvaluate.add(capturedPatchRows);
+ toEvaluate.addAll(capturedBlocks);
+ }
+ MLX.eval(toEvaluate.toArray(MLXArray[]::new));
+ mergedParts.add(MLX.hoist(merged, target));
+ if (captures != null) {
+ captures.add(
+ new ImageCaptures(
+ capturedPe, capturedPatchRows, capturedBlocks.toArray(MLXArray[]::new)));
+ }
+ }
+ }
+
+ /**
+ * The bilinear corner indices and weights of {@code fast_pos_embed_interpolate} for one image
+ * grid, in row-major patch order: for patch {@code (ph, pw)} at position {@code k = ph * w + pw},
+ * the fractional coordinates are {@code linspaceValue(gridSide, h, ph)} and {@code
+ * linspaceValue(gridSide, w, pw)}; the floor index (torch's {@code .int()} truncation, always
+ * non-negative here) and its clipped ceiling give the four row-major corner indices {@code
+ * [floorH * gridSide + floorW, floorH * gridSide + ceilW, ceilH * gridSide + floorW, ceilH *
+ * gridSide + ceilW]}, and the weights are the single-float32-multiply products {@code (1 - dh) *
+ * (1 - dw)}, {@code (1 - dh) * dw}, {@code dh * (1 - dw)}, {@code dh * dw} with one float32
+ * subtraction each for {@code dh = hv - floorH} and {@code dw} (the reference computes {@code 1 -
+ * dh} in float32 before the broadcast multiplication).
+ *
+ * Consumers that need merge-block order gather {@code [c][row * w + col]} with the {@code
+ * (row, col)} pairs of {@link #rotaryCoordinates(int, int, int)} — the per-row sum of the four
+ * weighted corner rows is then bit-identical to the reference's sum-then-permute.
+ *
+ * @throws IllegalArgumentException if a dimension is not positive
+ */
+ static PositionLookup positionLookup(int h, int w, int gridSide) {
+ if (h <= 0 || w <= 0 || gridSide <= 0) {
+ throw new IllegalArgumentException("h, w and gridSide must be positive");
+ }
+ int n = Math.multiplyExact(h, w);
+ int[][] cornerIndices = new int[4][n];
+ float[][] cornerWeights = new float[4][n];
+ for (int ph = 0; ph < h; ph++) {
+ float hv = linspaceValue(gridSide, h, ph);
+ int floorH = (int) hv;
+ int ceilH = Math.min(floorH + 1, gridSide - 1);
+ float dh = hv - (float) floorH;
+ float oneMinusDh = 1f - dh;
+ int baseFloor = floorH * gridSide;
+ int baseCeil = ceilH * gridSide;
+ for (int pw = 0; pw < w; pw++) {
+ float wv = linspaceValue(gridSide, w, pw);
+ int floorW = (int) wv;
+ int ceilW = Math.min(floorW + 1, gridSide - 1);
+ float dw = wv - (float) floorW;
+ final float oneMinusDw = 1f - dw;
+ int k = ph * w + pw;
+ cornerIndices[0][k] = baseFloor + floorW;
+ cornerIndices[1][k] = baseFloor + ceilW;
+ cornerIndices[2][k] = baseCeil + floorW;
+ cornerIndices[3][k] = baseCeil + ceilW;
+ cornerWeights[0][k] = oneMinusDh * oneMinusDw;
+ cornerWeights[1][k] = oneMinusDh * dw;
+ cornerWeights[2][k] = dh * oneMinusDw;
+ cornerWeights[3][k] = dh * dw;
+ }
+ }
+ return new PositionLookup(cornerIndices, cornerWeights);
+ }
+
+ /**
+ * The reference's float32 {@code torch.linspace(0, gridSide - 1, n)} value at index {@code i},
+ * ported exactly from the torch 2.6.0 CPU {@code linspace_kernel} ({@code
+ * aten/src/ATen/native/cpu/RangeFactoriesKernel.cpp} at tag {@code v2.6.0}): the step is the
+ * float32 rounding of the double division {@code (double)(end - start) / (n - 1)}, and each value
+ * is the float32 rounding of a double expression — {@code start + (double) step * i} for {@code i
+ * < n / 2}, otherwise {@code end - (double) step * (n - i - 1)} (the C++
+ * usual-arithmetic-conversion promotions of the kernel's float/int64 operands). The composite
+ * {@code linspace_out} ({@code aten/src/ATen/native/RangeFactories.cpp}) fills a one-step result
+ * with {@code start}; the kernel itself would produce {@code NaN} there. A naive closed form
+ * ({@code start + (end - start) * i / (n - 1)}) diverges by one float32 ulp for {@code n = 4, i =
+ * 2} (versus the pinned golden); the pure-float32 split-halfway port diverges at further grid
+ * points — the double intermediates are load-bearing.
+ *
+ * @param side the position-grid side ({@code gridSide})
+ * @param n the requested count (the image's patch count along the axis)
+ * @param i the output index, in {@code [0, n)}
+ * @return the float32 value
+ * @throws IllegalArgumentException if {@code side} or {@code n} is not positive or {@code i} is
+ * out of range
+ */
+ static float linspaceValue(int side, int n, int i) {
+ if (side <= 0 || n <= 0) {
+ throw new IllegalArgumentException("side and n must be positive");
+ }
+ if (i < 0 || i >= n) {
+ throw new IllegalArgumentException("i = " + i + " is out of range [0, " + n + ")");
+ }
+ if (n == 1) {
+ return 0f; // the composite's fill_(start) guard; start is always 0 here
+ }
+ float end = (float) (side - 1);
+ float step = (float) ((double) (end - 0f) / (double) (n - 1));
+ if (i < n / 2) {
+ return (float) (0.0d + (double) step * (double) i);
+ }
+ return (float) ((double) end - (double) step * (double) (n - i - 1));
+ }
+
+ /**
+ * The merge-block (row, col) full-resolution patch coordinates of {@code rot_pos_emb}, the
+ * block-row, block-col, intra-block-row, intra-block-col loop order of the reference's {@code
+ * row_idx}/{@code col_idx} expansion: output position {@code m} is the patch whose merge-block
+ * position is the {@code m}-th element of {@code (blockRow, blockCol, intraRow, intraCol)}
+ * flattened row-major, at full-resolution row {@code blockRow * merge + intraRow} and column
+ * {@code blockCol * merge + intraCol}.
+ *
+ *
For the 2-by-2 merge of an 8-by-8 grid the first eight coordinates are {@code (0,0), (0,1),
+ * (1,0), (1,1), (0,2), (0,3), (1,2), (1,3)} and the last four are {@code (6,6), (6,7), (7,6),
+ * (7,7)}.
+ *
+ * @return the {@code {rows, cols}} coordinate arrays, both in merge-block order
+ * @throws IllegalArgumentException if a dimension is not a positive multiple of {@code merge}
+ */
+ public static int[][] rotaryCoordinates(int h, int w, int merge) {
+ if (merge < 1) {
+ throw new IllegalArgumentException("merge must be positive");
+ }
+ if (h <= 0 || w <= 0) {
+ throw new IllegalArgumentException("h and w must be positive");
+ }
+ if (h % merge != 0 || w % merge != 0) {
+ throw new IllegalArgumentException(
+ "h = " + h + " and w = " + w + " must be multiples of merge = " + merge);
+ }
+ int n = Math.multiplyExact(h, w);
+ int[] rows = new int[n];
+ int[] cols = new int[n];
+ int m = 0;
+ for (int blockRow = 0; blockRow < h / merge; blockRow++) {
+ for (int blockCol = 0; blockCol < w / merge; blockCol++) {
+ for (int intraRow = 0; intraRow < merge; intraRow++) {
+ for (int intraCol = 0; intraCol < merge; intraCol++) {
+ rows[m] = blockRow * merge + intraRow;
+ cols[m] = blockCol * merge + intraCol;
+ m++;
+ }
+ }
+ }
+ }
+ return new int[][] {rows, cols};
+ }
+
+ /**
+ * The checkpoint-key to tower-key rename of the class javadoc, as a pure function: {@code
+ * model.visual.patch_embed.proj.*} to {@code patchEmbedding.*}, {@code model.visual.pos_embed.
+ * weight} to {@code positionEmbedding.weight}, {@code model.visual.blocks.{i}.norm{1,2}.*} to
+ * {@code layer{i}.norm{1,2}.*}, {@code model.visual.blocks.{i}.attn.{qkv,proj}.*} to {@code
+ * layer{i}.attention.{qkv,proj}.*}, {@code model.visual.blocks.{i}.mlp.linear_fc{1,2}.*} to
+ * {@code layer{i}.mlp.{0,2}.*}, {@code model.visual.merger.{norm,linear_fc1,linear_fc2}.*} to
+ * {@code merger.{norm,linearFc1,linearFc2}.*} and {@code model.visual.
+ * deepstack_merger_list.{j}.{norm,linear_fc1,linear_fc2}.*} to {@code deepstackMerger{j}.{norm,
+ * linearFc1,linearFc2}.*}.
+ *
+ * @param key the full checkpoint key
+ * @param depth the tower depth (blocks {@code {i}} with {@code i >= depth} map to {@code null})
+ * @param deepstackIndexes the DeepStack indices; list position {@code j} maps to {@code
+ * deepstackMerger{j}} (any {@code j >= length} maps to {@code null})
+ * @return the tower key, or {@code null} for non-visual keys, out-of-scheme indices, or unknown
+ * sub-keys
+ * @throws NullPointerException if an argument is {@code null}
+ */
+ public static String visionKeyToTowerKey(String key, int depth, int[] deepstackIndexes) {
+ Objects.requireNonNull(key, "key");
+ Objects.requireNonNull(deepstackIndexes, "deepstackIndexes");
+ if (!key.startsWith(VISUAL_PREFIX)) {
+ return null;
+ }
+ String rest = key.substring(VISUAL_PREFIX.length());
+ if (rest.startsWith("patch_embed.proj.")) {
+ String tail = rest.substring("patch_embed.proj.".length());
+ if (tail.equals("weight")) {
+ return "patchEmbedding.weight";
+ }
+ if (tail.equals("bias")) {
+ return "patchEmbedding.bias";
+ }
+ return null;
+ }
+ if (rest.equals("pos_embed.weight")) {
+ return "positionEmbedding.weight";
+ }
+ if (rest.startsWith("blocks.")) {
+ String tail = rest.substring("blocks.".length());
+ Integer index = visionListIndex(tail);
+ if (index == null || index >= depth) {
+ return null;
+ }
+ String sub = tail.substring(tail.indexOf('.') + 1);
+ if (sub.equals("norm1.weight")
+ || sub.equals("norm1.bias")
+ || sub.equals("norm2.weight")
+ || sub.equals("norm2.bias")) {
+ return "layer" + index + "." + sub;
+ }
+ if (sub.startsWith("attn.")) {
+ String name = sub.substring("attn.".length());
+ if (name.equals("qkv.weight")
+ || name.equals("qkv.bias")
+ || name.equals("proj.weight")
+ || name.equals("proj.bias")) {
+ return "layer" + index + ".attention." + name;
+ }
+ }
+ if (sub.startsWith("mlp.")) {
+ String name = sub.substring("mlp.".length());
+ if (name.equals("linear_fc1.weight") || name.equals("linear_fc1.bias")) {
+ return "layer" + index + ".mlp.0." + name.substring("linear_fc1.".length());
+ }
+ if (name.equals("linear_fc2.weight") || name.equals("linear_fc2.bias")) {
+ return "layer" + index + ".mlp.2." + name.substring("linear_fc2.".length());
+ }
+ }
+ return null;
+ }
+ if (rest.startsWith("merger.")) {
+ return mergerKey(rest.substring("merger.".length()), "merger");
+ }
+ if (rest.startsWith("deepstack_merger_list.")) {
+ String tail = rest.substring("deepstack_merger_list.".length());
+ Integer index = visionListIndex(tail);
+ if (index == null || index >= deepstackIndexes.length) {
+ return null;
+ }
+ return mergerKey(tail.substring(tail.indexOf('.') + 1), "deepstackMerger" + index);
+ }
+ return null;
+ }
+
+ /**
+ * Extracts the tower weights from a raw checkpoint: every key is run through {@link
+ * #visionKeyToTowerKey(String, int, int[])}, non-visual keys are skipped, and the result is a
+ * fresh map of tower keys to the checkpoint's arrays (the conv weight is the exception: it is
+ * transposed {@code {0, 2, 3, 4, 1}} from PyTorch {@code [out, in, T, H, W]} to jmlx's
+ * channels-last {@code [out, T, H, W, in]}, evaluated, with the original closed — the {@code
+ * SmolVlmModel} pattern). Every {@code model.visual.*} key that does not map to a known tower key
+ * is rejected outright rather than skipped: a block or DeepStack merger index past the configured
+ * depth or DeepStack count is rejected by name with the limit that excludes it (dropping it would
+ * truncate the tower to the configured depth instead of surfacing the mismatch), and any other
+ * unrecognised visual key — an unknown sub-key, an unknown top-level visual key, or a non-numeric
+ * or overflowing index — is rejected by name. Only genuinely non-visual checkpoint keys are
+ * skipped. Completeness against the tower's expected-key set is the constructor's job ({@code
+ * rejectUnexpected}), not this method's.
+ *
+ *
All-or-nothing: the method makes two passes. The first pass maps and validates every key —
+ * the duplicate, out-of-range and unrecognised-key rejections above — and does nothing else; the
+ * second pass only then materializes the result, transposing the conv weight there. A thrown
+ * {@link IllegalArgumentException} therefore leaves the caller's checkpoint map byte-for-byte
+ * untouched: every entry is still its original, still-open {@code MLXArray}, and no transposed
+ * copy has been allocated. On success the returned map holds the caller's own arrays for every
+ * mapped key except {@code patchEmbedding.weight}, which holds the transposed, evaluated copy
+ * (allocated into the original's scope); the caller's original conv weight array is closed, and
+ * the caller's map itself is never written to.
+ *
+ * @throws IllegalArgumentException if two checkpoint keys map to the same tower key, if a key
+ * names a block or DeepStack merger index out of the configured range, or if a {@code
+ * model.visual.*} key is not recognised
+ * @throws NullPointerException if an argument is {@code null}
+ */
+ public static Map visionWeights(
+ Map checkpoint, int depth, int[] deepstackIndexes) {
+ Objects.requireNonNull(checkpoint, "checkpoint");
+ Objects.requireNonNull(deepstackIndexes, "deepstackIndexes");
+ // Pass 1: map and validate every key before transposing, closing or inserting anything, so a
+ // rejection leaves the caller's map untouched.
+ Set towerKeys = new HashSet<>();
+ for (String key : checkpoint.keySet()) {
+ String towerKey = visionKeyToTowerKey(key, depth, deepstackIndexes);
+ if (towerKey == null) {
+ if (!key.startsWith(VISUAL_PREFIX)) {
+ continue;
+ }
+ String outOfRange = outOfRangeVisionIndex(key, depth, deepstackIndexes);
+ if (outOfRange != null) {
+ throw new IllegalArgumentException(outOfRange);
+ }
+ throw new IllegalArgumentException("unrecognized vision checkpoint key \"" + key + "\"");
+ }
+ if (!towerKeys.add(towerKey)) {
+ throw new IllegalArgumentException(
+ "duplicate vision tower key \"" + towerKey + "\" from checkpoint key \"" + key + "\"");
+ }
+ }
+ // Pass 2: pass 1 proved every visual key maps to a distinct tower key, so this cannot fail;
+ // the transpose happens only here, after the whole map is known valid.
+ Map weights = new LinkedHashMap<>();
+ for (Map.Entry entry : checkpoint.entrySet()) {
+ String towerKey = visionKeyToTowerKey(entry.getKey(), depth, deepstackIndexes);
+ if (towerKey == null) {
+ continue; // pass 1 proved every unmapped key is non-visual
+ }
+ MLXArray value = entry.getValue();
+ if (towerKey.equals("patchEmbedding.weight")) {
+ MLXArray transposed = MLXShape.transpose(value, new int[] {0, 2, 3, 4, 1});
+ MLX.eval(transposed);
+ value.close();
+ value = transposed;
+ }
+ weights.put(towerKey, value);
+ }
+ return weights;
+ }
+
+ /**
+ * The out-of-range rejection of {@link #visionWeights(Map, int, int[])}: a {@code
+ * model.visual.blocks.{i}} or {@code model.visual.deepstack_merger_list.{j}} key whose numeric
+ * index is out of the configured depth (respectively the configured DeepStack count) is a
+ * well-formed visual key that {@link #visionKeyToTowerKey(String, int, int[])} rejected only
+ * because of its index — the signature of a checkpoint deeper, or with more DeepStack mergers,
+ * than the configuration, which must fail loudly instead of truncating the tower silently.
+ * Returns the rejection message, or {@code null} for every other key — the non-visual keys {@code
+ * visionWeights} skips, and the non-numeric, overflowing or missing indices it rejects as
+ * unrecognised.
+ */
+ private static String outOfRangeVisionIndex(String key, int depth, int[] deepstackIndexes) {
+ if (!key.startsWith(VISUAL_PREFIX)) {
+ return null;
+ }
+ String rest = key.substring(VISUAL_PREFIX.length());
+ if (rest.startsWith("blocks.")) {
+ Integer index = visionListIndex(rest.substring("blocks.".length()));
+ if (index != null && index >= depth) {
+ return "checkpoint key \""
+ + key
+ + "\" names block "
+ + index
+ + ", out of range [0, "
+ + depth
+ + ") for the configured depth";
+ }
+ return null;
+ }
+ if (rest.startsWith("deepstack_merger_list.")) {
+ Integer index = visionListIndex(rest.substring("deepstack_merger_list.".length()));
+ if (index != null && index >= deepstackIndexes.length) {
+ return "checkpoint key \""
+ + key
+ + "\" names DeepStack merger "
+ + index
+ + ", out of range [0, "
+ + deepstackIndexes.length
+ + ") for the configured DeepStack count";
+ }
+ }
+ return null;
+ }
+
+ /** The {@code {norm,linearFc1,linear_fc2}} rename shared by the main and DeepStack mergers. */
+ private static String mergerKey(String sub, String prefix) {
+ if (sub.equals("norm.weight") || sub.equals("norm.bias")) {
+ return prefix + "." + sub;
+ }
+ if (sub.equals("linear_fc1.weight") || sub.equals("linear_fc1.bias")) {
+ return prefix + ".linearFc1." + sub.substring("linear_fc1.".length());
+ }
+ if (sub.equals("linear_fc2.weight") || sub.equals("linear_fc2.bias")) {
+ return prefix + ".linearFc2." + sub.substring("linear_fc2.".length());
+ }
+ return null;
+ }
+
+ /**
+ * The numeric list index before the tail's first dot (the {@code i} of {@code blocks.{i}.} or the
+ * {@code j} of {@code deepstack_merger_list.{j}.}), or {@code null} for a dot-less tail or a
+ * non-numeric or overflowing digit run.
+ */
+ private static Integer visionListIndex(String tail) {
+ int dot = tail.indexOf('.');
+ if (dot < 1) {
+ return null;
+ }
+ return parseNonNegativeInt(tail.substring(0, dot));
+ }
+
+ /** The non-negative decimal parse, or {@code null} for a non-numeric or overflowing digit run. */
+ private static Integer parseNonNegativeInt(String digits) {
+ if (digits.isEmpty()) {
+ return null;
+ }
+ for (int i = 0; i < digits.length(); i++) {
+ if (digits.charAt(i) < '0' || digits.charAt(i) > '9') {
+ return null;
+ }
+ }
+ try {
+ return Integer.parseInt(digits);
+ } catch (NumberFormatException e) {
+ return null;
+ }
+ }
+
+ /**
+ * The bilinear corner data of {@link #positionLookup(int, int, int)}: both arrays are {@code
+ * [4][n]} with {@code n = h * w} and patch position {@code k = ph * w + pw} row-major; corner
+ * order is {@code (floorH, floorW), (floorH, ceilW), (ceilH, floorW), (ceilH, ceilW)}.
+ */
+ static final record PositionLookup(int[][] cornerIndices, float[][] cornerWeights) {}
+
+ /**
+ * One image's golden-verification captures of {@link #forwardForTest(MLXScope, List)} (findings
+ * §11.3): the interpolated position-embedding rows {@code [n, hidden]}, the patch-embedding
+ * (convolution) output before the position-embedding add {@code [n, hidden]}, and the block
+ * outputs — exactly {@code 1 + deepstackIndexes.length} entries: block 0 first, then the
+ * DeepStack source blocks in this tower's {@code deepstackIndexes} order — each {@code [n,
+ * hidden]}. When {@code deepstackIndexes[0]} is 0, block 0's output appears twice: once as its
+ * own entry 0 and once as the DeepStack level-0 source at entry 1. Every array lives in the
+ * target scope passed to {@link #forwardForTest(MLXScope, List)}.
+ */
+ record ImageCaptures(
+ MLXArray posEmbedInterpolated, MLXArray patchEmbed, MLXArray[] blockOutputs) {
+ ImageCaptures {
+ Objects.requireNonNull(posEmbedInterpolated, "posEmbedInterpolated");
+ Objects.requireNonNull(patchEmbed, "patchEmbed");
+ Objects.requireNonNull(blockOutputs, "blockOutputs");
+ blockOutputs = blockOutputs.clone();
+ }
+ }
+
+ /**
+ * The final features of {@link #forwardForTest(MLXScope, List)} plus the per-image {@link
+ * ImageCaptures}, one per input image in input order (empty for {@link #forward(MLXScope,
+ * List)}).
+ */
+ record ForwardCaptures(Qwen3VlVisionFeatures features, List images) {
+ ForwardCaptures {
+ Objects.requireNonNull(features, "features");
+ images = List.copyOf(Objects.requireNonNull(images, "images"));
+ }
+ }
+
+ private static MLXArray weight(Map weights, String name) {
+ MLXArray value = weights.get(name);
+ if (value == null) {
+ throw new IllegalArgumentException("missing vision tower weight \"" + name + "\"");
+ }
+ return value;
+ }
+
+ private static void requireShape(MLXArray array, String name, int[] expected) {
+ if (array.ndim() != expected.length) {
+ throw new IllegalArgumentException(
+ name + " must be rank " + expected.length + ", got " + array.ndim());
+ }
+ for (int i = 0; i < expected.length; i++) {
+ if (array.shape()[i] != expected[i]) {
+ throw new IllegalArgumentException(
+ name
+ + " must be "
+ + Arrays.toString(expected)
+ + ", got "
+ + Arrays.toString(array.shape()));
+ }
+ }
+ }
+
+ private static Set expectedKeys(int depth, int deepstackCount) {
+ Set expected = new TreeSet<>();
+ expected.add("patchEmbedding.weight");
+ expected.add("patchEmbedding.bias");
+ expected.add("positionEmbedding.weight");
+ for (int i = 0; i < depth; i++) {
+ String layer = "layer" + i + ".";
+ for (String name : new String[] {"qkv", "proj"}) {
+ expected.add(layer + "attention." + name + ".weight");
+ expected.add(layer + "attention." + name + ".bias");
+ }
+ for (String norm : new String[] {"norm1", "norm2"}) {
+ expected.add(layer + norm + ".weight");
+ expected.add(layer + norm + ".bias");
+ }
+ expected.add(layer + "mlp.0.weight");
+ expected.add(layer + "mlp.0.bias");
+ expected.add(layer + "mlp.2.weight");
+ expected.add(layer + "mlp.2.bias");
+ }
+ expected.add("merger.norm.weight");
+ expected.add("merger.norm.bias");
+ expected.add("merger.linearFc1.weight");
+ expected.add("merger.linearFc1.bias");
+ expected.add("merger.linearFc2.weight");
+ expected.add("merger.linearFc2.bias");
+ for (int j = 0; j < deepstackCount; j++) {
+ String prefix = "deepstackMerger" + j + ".";
+ expected.add(prefix + "norm.weight");
+ expected.add(prefix + "norm.bias");
+ expected.add(prefix + "linearFc1.weight");
+ expected.add(prefix + "linearFc1.bias");
+ expected.add(prefix + "linearFc2.weight");
+ expected.add(prefix + "linearFc2.bias");
+ }
+ return expected;
+ }
+
+ private static void rejectUnexpected(Map weights, Set expected) {
+ Set extra = new TreeSet<>(weights.keySet());
+ extra.removeAll(expected);
+ if (!extra.isEmpty()) {
+ throw new IllegalArgumentException("unexpected vision tower weights: " + extra);
+ }
+ }
+}
diff --git a/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionPeakTest.java b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionPeakTest.java
new file mode 100644
index 00000000..a6f232c8
--- /dev/null
+++ b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionPeakTest.java
@@ -0,0 +1,383 @@
+package se.alipsa.jmlx.models;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.time.LocalDate;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXMemory;
+import se.alipsa.jmlx.core.MLXMemoryTestSupport;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.nn.TwoAxisRope;
+import se.alipsa.jmlx.nn.TwoAxisRotaryAttention;
+import se.alipsa.jmlx.nn.TwoAxisRotation;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessor;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessorResult;
+import se.alipsa.jmlx.vision.Qwen3VlProcessorConfig;
+import se.alipsa.jmlx.vision.RgbImage;
+
+/**
+ * Measured per-image peak of the WP5 vision tower ({@code
+ * req/plans/phase7-3b-reference-findings.md} §11.4). Runs the real-2B tower geometry — hidden 1024,
+ * 16 heads, head_dim 64, intermediate 4096, out_hidden 2048, 2304 position embeddings (48×48),
+ * patch/temporal 16/2, merge 2 — at three reduced depths, each with one DeepStack index (depth 1:
+ * index 0; depths 3 and 4: index 1). The depth reduction is documented in §11.4: the tower's
+ * per-block evaluate-and-release loop confines every block's intermediates to a private per-block
+ * child scope that closes before the next block starts, and the embedding stage's intermediates
+ * (pixel tensor, patch-embedding convolution, position-embedding gathers and their weighted sum) to
+ * a private child scope of the image scope that closes before block 0 starts, so the per-image peak
+ * neither scales with depth nor carries the embedding stage while the encoder runs — the pre-fix
+ * tower leaked ≈1.09 GiB per block (the full depth-24 tower extrapolated ≈26.8 GiB). The
+ * depth-1-versus-depth-4 transient comparison below is the regression guard for that: the delta
+ * must stay under 64 MiB, so the depth-3 transient is a fair proxy for the per-image transient the
+ * plan §4 bound applies to at any depth. The weights are constant 0.001 via {@link MLX#full} — the
+ * peak depends on geometry and dtype, not on values. One synthetic 1024×1024 image (grid (1, 64,
+ * 64), 4096 patch rows, the default 4096-row raw budget's exact hard cap) is processed through
+ * {@link Qwen3VlImageProcessor}.
+ *
+ * Four tower measurements share one settled baseline, taken with every static array (all three
+ * towers' weights, the bracket attention's weights, inputs and rotations) already evaluated:
+ *
+ *
+ * - The tower forward's own per-image transient peak at depth 1: the depth-independent floor —
+ * one block's intermediates plus the entry row and the mergers, the conv and the
+ * position-embedding gathers having been evaluated and released in the embedding stage before
+ * block 0.
+ *
- The same transient at the depth-3 proxy: must stay under the plan §4 planning bound of 5
+ * GiB and at least the 16 MiB of evaluated rows the forward hoists into the per-request
+ * scope.
+ *
- The sustained per-image evaluated peak at depth 3: a settled active-bytes delta taken while
+ * the per-request scope is still open — the per-block and per-image scopes close inside the
+ * forward, and the hoisted merged and DeepStack rows live in the per-request scope until it
+ * closes, as in production — the rows the per-request scope retains for the request.
+ *
- The same transient at depth 4; together with depth 1 it bounds per-block accumulation — the
+ * difference must stay under 64 MiB, so a deeper tower (the real depth 24) incurs no larger
+ * per-image transient than the depth-3 proxy.
+ *
+ *
+ * A fifth bracket, the transient per-image attention workspace of one standalone {@link
+ * TwoAxisRotaryAttention} forward at 4096 rows with constant weights (run after every per-request
+ * scope closes, so it carries no retained-row offset), must also stay under the plan §4 bound (one
+ * float32 score matrix at 4096 rows / 16 heads is 1 GiB).
+ *
+ *
CI headroom: this suite is required by the {@code macos-26} hosted runner's native job in
+ * {@code .github/workflows/ci.yml} and runs in the ordinary {@code test} task, where it is the
+ * module's largest memory consumer. Its largest transient is any of the depth probes — all three
+ * are bit-for-bit equal at 1,345,847,352 B (≈1.25 GiB, M5 Max, 2026-10-23) — on top of the settled
+ * statics of the three towers and the bracket (≈0.65 GiB), so the concurrent Metal peak is ≈1.9 GiB
+ * — the per-block and embedding-stage evaluate-and-release fixes (this review round) dropped it
+ * from the pre-fix depth-3 peak of 3.81 GiB. Checked against the hosted runner's ≈7 GB RAM (a
+ * reviewer-recorded figure, not verified against the runner image): ≈1.9 GiB Metal + ≤0.5 GiB
+ * test-JVM working set (no configured max heap; the JVM's default ¼-RAM max is a ceiling, not a
+ * reservation) + ≈0.5 GiB Gradle daemon (512 MiB default) + ≈1.2 GiB OS ≈ 4.1 GiB, leaving ≈2.9 GiB
+ * of headroom, versus ≈1 GiB or less for the pre-fix peak. See {@code
+ * req/plans/phase7-3b-reference-findings.md} §11.4 for the full arithmetic.
+ */
+@EnabledIfNativeAvailable
+class Qwen3VlVisionPeakTest {
+
+ private static final int HIDDEN = 1024;
+ private static final int NUM_HEADS = 16;
+ private static final int HEAD_DIM = 64;
+ private static final int INTERMEDIATE = 4096;
+ private static final int OUT_HIDDEN = 2048;
+ private static final int POSITION_EMBEDDINGS = 2304;
+ private static final int DEPTH_MIN = 1;
+ private static final int DEPTH_PROXY = 3;
+ private static final int DEPTH_MAX = 4;
+
+ /**
+ * The depth-independence bound: the depth-4 transient minus the depth-1 transient must stay under
+ * 64 MiB, so the per-image peak does not scale with depth.
+ */
+ private static final long DEPTH_DELTA_BOUND = 64L * 1024L * 1024L;
+
+ private static final int MERGE = 2;
+ private static final int MERGE_BLOCK = HIDDEN * MERGE * MERGE;
+ private static final int IMAGE = 1024;
+ private static final int GRID = IMAGE / 16;
+ private static final int PATCH_ROWS = GRID * GRID;
+ private static final int MERGED_ROWS = (GRID / MERGE) * (GRID / MERGE);
+ private static final long GIB = 1024L * 1024L * 1024L;
+ private static final long PLANNING_BOUND = 5 * GIB;
+
+ @Test
+ void realGeometryPeakIsDepthIndependentAndUnderPlanningBound() throws Exception {
+ try (MLXScope scope = new MLXScope()) {
+ List statics = new ArrayList<>();
+ final Qwen3VlVisionTower towerMin = buildTower(scope, statics, DEPTH_MIN, new int[] {0});
+ final Qwen3VlVisionTower towerProxy = buildTower(scope, statics, DEPTH_PROXY, new int[] {1});
+ final Qwen3VlVisionTower towerMax = buildTower(scope, statics, DEPTH_MAX, new int[] {1});
+ // The transient bracket: one standalone attention of the same geometry with constant
+ // weights, whose input rows and prepared rotation live in this scope too, so the baseline
+ // below counts every array the bracket forward reads.
+ final TwoAxisRotaryAttention attention =
+ new TwoAxisRotaryAttention(
+ scope,
+ NUM_HEADS,
+ HEAD_DIM,
+ Qwen3VlVisionTower.ROPE_THETA,
+ full(statics, scope, new int[] {3 * HIDDEN, HIDDEN}),
+ full(statics, scope, new int[] {3 * HIDDEN}),
+ full(statics, scope, new int[] {HIDDEN, HIDDEN}),
+ full(statics, scope, new int[] {HIDDEN}));
+ final MLXArray attentionInput = full(statics, scope, new int[] {PATCH_ROWS, HIDDEN});
+ int[][] coordinates = Qwen3VlVisionTower.rotaryCoordinates(GRID, GRID, MERGE);
+ final TwoAxisRotation rotation =
+ new TwoAxisRope(Qwen3VlVisionTower.ROPE_THETA, HEAD_DIM / 2)
+ .prepare(scope, PATCH_ROWS, coordinates[0], coordinates[1]);
+
+ Qwen3VlImageProcessor processor = processor();
+ Qwen3VlImageProcessorResult image = processor.process(syntheticImage());
+ assertArrayEquals(new int[] {1, GRID, GRID}, image.gridThw(), "grid");
+ assertEquals(PATCH_ROWS, image.rawPatchRows(), "patch rows");
+ assertEquals(MERGED_ROWS, image.mergedRowCount(), "merged rows");
+
+ // Everything the measurements read is static: materialize it (and the rotation tables)
+ // before the baseline so only the forwards' own work moves the counter.
+ List toMaterialize = new ArrayList<>(statics);
+ toMaterialize.add(rotation.cos());
+ toMaterialize.add(rotation.sin());
+ MLX.eval(toMaterialize.toArray(MLXArray[]::new));
+ MLXMemoryTestSupport.settledActiveBytes(); // warm settle before the baseline
+ final long baseline = MLXMemoryTestSupport.settledActiveBytes();
+
+ // Each tower measurement is one forward in a fresh per-request child scope of the caller
+ // scope (the SmolVlmModel pattern): the forward's per-image and per-block scopes close
+ // inside the forward, and only the hoisted merged and DeepStack rows live in the request
+ // scope until it closes, so a settled delta read while it is still open is the per-request
+ // retained rows. Each transient is bracketed with the peak counter: reset right before the
+ // forward (so no earlier work is counted), read right after it returns.
+ MLXMemory.resetPeak();
+ long peakMin;
+ try (MLXScope request = scope.newChild()) {
+ assertOutput(towerMin.forward(request, List.of(image)));
+ peakMin = MLXMemory.peakBytes() - baseline;
+ }
+ MLXMemoryTestSupport.settledActiveBytes(); // settle the delayed frees before the next window
+ MLXMemory.resetPeak();
+ long peakProxy;
+ long sustained;
+ try (MLXScope request = scope.newChild()) {
+ assertOutput(towerProxy.forward(request, List.of(image)));
+ peakProxy = MLXMemory.peakBytes() - baseline;
+ sustained = MLXMemoryTestSupport.settledActiveBytes() - baseline;
+ }
+ MLXMemoryTestSupport.settledActiveBytes(); // settle the delayed frees before the next window
+ MLXMemory.resetPeak();
+ long peakMax;
+ try (MLXScope request = scope.newChild()) {
+ assertOutput(towerMax.forward(request, List.of(image)));
+ peakMax = MLXMemory.peakBytes() - baseline;
+ }
+ MLXMemoryTestSupport.settledActiveBytes(); // settle the delayed frees before the bracket
+
+ final long logicalRetained = 2L * MERGED_ROWS * OUT_HIDDEN * Float.BYTES;
+ for (int depth : new int[] {DEPTH_MIN, DEPTH_PROXY, DEPTH_MAX}) {
+ long peak = depth == DEPTH_MIN ? peakMin : depth == DEPTH_PROXY ? peakProxy : peakMax;
+ assertTrue(
+ peak >= logicalRetained,
+ "depth-"
+ + depth
+ + " tower peak "
+ + peak
+ + " B below the logical retained rows "
+ + logicalRetained
+ + " B");
+ }
+ assertTrue(
+ peakProxy < PLANNING_BOUND,
+ "depth-3 tower transient "
+ + peakProxy
+ + " B exceeded the "
+ + PLANNING_BOUND
+ + " B planning bound");
+ assertTrue(
+ sustained >= logicalRetained,
+ "depth-3 sustained peak "
+ + sustained
+ + " B below the logical retained rows "
+ + logicalRetained
+ + " B");
+ assertTrue(
+ sustained < PLANNING_BOUND,
+ "depth-3 sustained peak "
+ + sustained
+ + " B exceeded the "
+ + PLANNING_BOUND
+ + " B planning bound");
+ final long depthDelta = peakMax - peakMin;
+ assertTrue(
+ depthDelta < DEPTH_DELTA_BOUND,
+ "per-image transient delta "
+ + depthDelta
+ + " B between depth "
+ + DEPTH_MIN
+ + " and depth "
+ + DEPTH_MAX
+ + " exceeded the "
+ + DEPTH_DELTA_BOUND
+ + " B depth-independence bound");
+
+ MLXMemory.resetPeak();
+ MLX.eval(attention.forward(attentionInput, rotation));
+ long transientDelta = MLXMemory.peakBytes() - baseline;
+ assertTrue(
+ transientDelta < PLANNING_BOUND,
+ "attention transient "
+ + transientDelta
+ + " B exceeded the "
+ + PLANNING_BOUND
+ + " B planning bound");
+
+ System.out.println(
+ "WP5 vision peak (embedding-stage and per-block scopes): grid=(1,"
+ + GRID
+ + ","
+ + GRID
+ + "), depth-1 transient="
+ + peakMin
+ + " B ("
+ + gib(peakMin)
+ + "), depth-3 transient="
+ + peakProxy
+ + " B ("
+ + gib(peakProxy)
+ + "), depth-3 sustained="
+ + sustained
+ + " B ("
+ + sustained / (1024 * 1024)
+ + " MiB), depth-4 transient="
+ + peakMax
+ + " B ("
+ + gib(peakMax)
+ + "), depth-4 minus depth-1="
+ + depthDelta
+ + " B ("
+ + depthDelta / (1024 * 1024)
+ + " MiB), attention bracket="
+ + transientDelta
+ + " B ("
+ + gib(transientDelta)
+ + "), bound="
+ + PLANNING_BOUND / GIB
+ + " GiB (plan §4); measured on M5 Max under the ordinary test task "
+ + "(MLX_ENABLE_TF32=1 default, "
+ + LocalDate.now()
+ + ")");
+ }
+ }
+
+ /** The 2B artifact's slow-processor options, with maxPixels at the default 4096-row hard cap. */
+ private static Qwen3VlImageProcessor processor() {
+ return new Qwen3VlImageProcessor(
+ Qwen3VlProcessorConfig.builder()
+ .patchSize(16)
+ .temporalPatchSize(2)
+ .mergeSize(2)
+ .minPixels(3136)
+ .maxPixels(4096 * 16 * 16)
+ .imageMean(new float[] {0.5f, 0.5f, 0.5f})
+ .imageStd(new float[] {0.5f, 0.5f, 0.5f})
+ .build());
+ }
+
+ /** One deterministic 1024×1024 image: per-pixel linear functions of the coordinates. */
+ private static RgbImage syntheticImage() {
+ byte[] pixels = new byte[IMAGE * IMAGE * 3];
+ for (int y = 0; y < IMAGE; y++) {
+ for (int x = 0; x < IMAGE; x++) {
+ int i = (y * IMAGE + x) * 3;
+ pixels[i] = (byte) ((x * 7 + y * 11) % 256);
+ pixels[i + 1] = (byte) ((x * 13 + y * 5) % 256);
+ pixels[i + 2] = (byte) ((x * 19 + y * 3) % 256);
+ }
+ }
+ return new RgbImage(IMAGE, IMAGE, pixels);
+ }
+
+ /**
+ * Asserts one image's forward output shapes for any of the measured depths (every measured tower
+ * has exactly one DeepStack level).
+ */
+ private static void assertOutput(Qwen3VlVisionFeatures features) {
+ assertArrayEquals(new int[] {MERGED_ROWS, OUT_HIDDEN}, features.merged().shape(), "merged");
+ assertEquals(1, features.deepstackLevels().size(), "deepstack levels");
+ assertArrayEquals(
+ new int[] {MERGED_ROWS, OUT_HIDDEN},
+ features.deepstackLevels().get(0).shape(),
+ "deepstack level 0");
+ }
+
+ /**
+ * The real-2B geometry at the given reduced depth and DeepStack index, constant 0.001 weights.
+ */
+ private static Qwen3VlVisionTower buildTower(
+ MLXScope scope, List statics, int depth, int[] deepstackIndexes) {
+ Map weights = new LinkedHashMap<>();
+ put(weights, statics, scope, "patchEmbedding.weight", HIDDEN, 2, 16, 16, 3);
+ put(weights, statics, scope, "patchEmbedding.bias", HIDDEN);
+ put(weights, statics, scope, "positionEmbedding.weight", POSITION_EMBEDDINGS, HIDDEN);
+ for (int i = 0; i < depth; i++) {
+ String layer = "layer" + i + ".";
+ put(weights, statics, scope, layer + "attention.qkv.weight", 3 * HIDDEN, HIDDEN);
+ put(weights, statics, scope, layer + "attention.qkv.bias", 3 * HIDDEN);
+ put(weights, statics, scope, layer + "attention.proj.weight", HIDDEN, HIDDEN);
+ put(weights, statics, scope, layer + "attention.proj.bias", HIDDEN);
+ for (String norm : new String[] {"norm1", "norm2"}) {
+ put(weights, statics, scope, layer + norm + ".weight", HIDDEN);
+ put(weights, statics, scope, layer + norm + ".bias", HIDDEN);
+ }
+ put(weights, statics, scope, layer + "mlp.0.weight", INTERMEDIATE, HIDDEN);
+ put(weights, statics, scope, layer + "mlp.0.bias", INTERMEDIATE);
+ put(weights, statics, scope, layer + "mlp.2.weight", HIDDEN, INTERMEDIATE);
+ put(weights, statics, scope, layer + "mlp.2.bias", HIDDEN);
+ }
+ put(weights, statics, scope, "merger.norm.weight", HIDDEN);
+ put(weights, statics, scope, "merger.norm.bias", HIDDEN);
+ put(weights, statics, scope, "merger.linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc1.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, statics, scope, "merger.linearFc2.bias", OUT_HIDDEN);
+ put(weights, statics, scope, "deepstackMerger0.norm.weight", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.norm.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc1.bias", MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, statics, scope, "deepstackMerger0.linearFc2.bias", OUT_HIDDEN);
+ return new Qwen3VlVisionTower(scope, NUM_HEADS, weights, deepstackIndexes);
+ }
+
+ private static void put(
+ Map weights,
+ List statics,
+ MLXScope scope,
+ String key,
+ int... shape) {
+ weights.put(key, full(statics, scope, shape));
+ }
+
+ private static MLXArray full(List statics, MLXScope scope, int... shape) {
+ MLXArray value = MLX.full(scope, shape, 0.001f, DType.FLOAT32);
+ statics.add(value);
+ return value;
+ }
+
+ /** {@code bytes} as {@code "x.xx GiB (y MiB)"} for the evidence line. */
+ private static String gib(long bytes) {
+ return String.format(Locale.ROOT, "%.2f", bytes / (double) GIB)
+ + " GiB ("
+ + bytes / (1024 * 1024)
+ + " MiB)";
+ }
+}
diff --git a/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerGoldenTest.java b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerGoldenTest.java
new file mode 100644
index 00000000..a0a50e05
--- /dev/null
+++ b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerGoldenTest.java
@@ -0,0 +1,373 @@
+package se.alipsa.jmlx.models;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.file.Path;
+import java.security.MessageDigest;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXIO;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.vision.ImageDecodeLimits;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessor;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessorResult;
+import se.alipsa.jmlx.vision.Qwen3VlProcessorConfig;
+import tools.jackson.databind.JsonNode;
+
+/**
+ * Strict CPU-HF float32 golden for the WP5 vision tower ({@code
+ * req/plans/phase7-3b-reference-findings.md} §11): the tiny synthetic {@code
+ * checkpoints/qwen3vl-vision} checkpoint (bf16 {@code model.visual.*} weights, four deterministic
+ * PNGs, packed 144-patch processor run) against {@code qwen3vl-vision-wp5.json}. The reference
+ * (pinned transformers 4.57.6 / torch 2.6.0+cpu venv, CPU, float32, seed 20261021, generated by
+ * {@code tools/hf-reference/qwen3vl_vision.py}) captures the interpolated position embedding, the
+ * patch-embedding convolution output, blocks 0/1/3/5, the main merger and all three DeepStack
+ * levels; the golden stores them rounded to 7 significant digits, so the comparisons use the
+ * established 1e-4 golden tolerance while the bit-exact arithmetic probes (linspace, the
+ * pos-embedding bilinear sample) are checked at bit level.
+ */
+@Tag("full-float32")
+@EnabledIfNativeAvailable
+class Qwen3VlVisionTowerGoldenTest {
+
+ /**
+ * The golden's 7-significant-digit storage tolerance (see qwen3vl_vision.py's {@code rounded}).
+ */
+ private static final float TOLERANCE = 1e-4f;
+
+ static Path directory() {
+ return Path.of(
+ System.getProperty("jmlx.repository.root"),
+ "tools/hf-reference/goldens/checkpoints/qwen3vl-vision");
+ }
+
+ static JsonNode golden() throws Exception {
+ return JsonFiles.read(directory().getParent().getParent().resolve("qwen3vl-vision-wp5.json"));
+ }
+
+ static Qwen3VlImageProcessor processor() {
+ // Mirrors the golden's provenance.processor (the pinned 4.57.6 slow Qwen2VL processor
+ // options); the shared WP2 qwen3vl image-oracle fixtures already pin this exact port. The
+ // artifact's 12,845,056-pixel ceiling exceeds the port's default 4,096-row hard budget, so
+ // the documented programmatic rawRowBudget override lifts the cap (12,845,056 / 16^2); every
+ // golden image is far below both, so the cap never engages for this fixture.
+ return new Qwen3VlImageProcessor(
+ Qwen3VlProcessorConfig.builder()
+ .patchSize(16)
+ .temporalPatchSize(2)
+ .mergeSize(2)
+ .minPixels(3136)
+ .maxPixels(12845056)
+ .rawRowBudget(50176)
+ .imageMean(new float[] {0.5f, 0.5f, 0.5f})
+ .imageStd(new float[] {0.5f, 0.5f, 0.5f})
+ .build());
+ }
+
+ static Qwen3VlVisionTower buildTower(MLXScope scope, JsonNode golden) throws Exception {
+ JsonNode config = golden.required("provenance").required("config");
+ int depth = config.required("depth").intValue();
+ int[] deepstackIndexes = ints(config.required("deepstack_visual_indexes"));
+ Map checkpoint =
+ MLXIO.loadSafetensors(scope, directory().resolve("model.safetensors").toString()).tensors();
+ Map weights =
+ Qwen3VlVisionTower.visionWeights(checkpoint, depth, deepstackIndexes);
+ return new Qwen3VlVisionTower(
+ scope, config.required("num_heads").intValue(), weights, deepstackIndexes);
+ }
+
+ @Test
+ void towerMatchesHfGolden() throws Exception {
+ JsonNode golden = golden();
+ JsonNode config = golden.required("provenance").required("config");
+ int depth = config.required("depth").intValue();
+ int numHeads = config.required("num_heads").intValue();
+ int[] deepstackIndexes = ints(config.required("deepstack_visual_indexes"));
+ List cases = new ArrayList<>();
+ golden.required("cases").forEach(cases::add);
+
+ try (MLXScope scope = new MLXScope()) {
+ JsonNode checkpointConfig = JsonFiles.read(directory().resolve("config.json"));
+ assertEquals(depth, checkpointConfig.required("depth").intValue(), "checkpoint depth");
+ assertEquals(numHeads, checkpointConfig.required("num_heads").intValue(), "checkpoint heads");
+ assertArrayEquals(
+ deepstackIndexes,
+ ints(checkpointConfig.required("deepstack_visual_indexes")),
+ "checkpoint deepstack indexes");
+
+ Qwen3VlVisionTower tower = buildTower(scope, golden);
+ assertEquals(depth, tower.depth(), "tower depth");
+ assertEquals(numHeads, tower.numHeads(), "tower heads");
+ assertEquals(config.required("hidden_size").intValue(), tower.hidden(), "tower hidden");
+ assertEquals(
+ config.required("num_position_embeddings").intValue(),
+ tower.gridSide() * tower.gridSide(),
+ "tower position grid");
+ assertEquals(
+ config.required("spatial_merge_size").intValue(),
+ tower.spatialMergeSize(),
+ "tower merge");
+ assertEquals(
+ config.required("out_hidden_size").intValue(), tower.outHiddenSize(), "tower out width");
+ assertArrayEquals(deepstackIndexes, tower.deepstackIndexes(), "tower deepstack indexes");
+
+ Qwen3VlImageProcessor processor = processor();
+ List results = new ArrayList<>(cases.size());
+ for (JsonNode c : cases) {
+ String name = c.required("name").asString();
+ Qwen3VlImageProcessorResult result =
+ processor.processFile(
+ directory().resolve(c.required("file").asString()), ImageDecodeLimits.DEFAULTS);
+ assertArrayEquals(ints(c.required("grid")), result.gridThw(), name + " grid");
+ assertEquals(
+ c.required("n_patches").intValue(), result.rawPatchRows(), name + " n_patches");
+ assertEquals(
+ c.required("merged_rows").intValue(), result.mergedRowCount(), name + " merged_rows");
+ // The per-case pixel_values digest pins the whole preprocessing chain byte-for-byte
+ // against the reference's packed processor output slice (row-major float32 bytes).
+ assertEquals(
+ c.required("pixel_values_sha256").asString(),
+ sha256OfFloats(result.values()),
+ name + " pixel_values sha256");
+ results.add(result);
+ }
+
+ // The tower's module scope is the test scope itself, so it is a legal target; the
+ // hoisted features and captures live in it until the test scope closes below.
+ Qwen3VlVisionTower.ForwardCaptures captures = tower.forwardForTest(scope, results);
+
+ // The final merged and DeepStack arrays are stored once over the packed rows (image
+ // order); the tower concatenates its per-image rows on axis 0 the same way.
+ JsonNode packed = golden.required("packed");
+ for (String key : new String[] {"merger", "deepstack0", "deepstack1", "deepstack2"}) {
+ JsonNode entry = packed.required(key);
+ MLXArray actual;
+ if (key.equals("merger")) {
+ actual = captures.features().merged();
+ } else {
+ int level = Integer.parseInt(key.substring("deepstack".length()));
+ actual = captures.features().deepstackLevels().get(level);
+ }
+ assertArrayEquals(ints(entry.required("shape")), actual.shape(), key + " shape");
+ assertArrayEquals(
+ floats(entry.required("values")),
+ actual.toFloatArray(),
+ TOLERANCE,
+ key + " must match the reference's packed rows");
+ }
+
+ // The per-image captures slice the same packed tensors; the merged-row offsets must be
+ // cumulative in input order (square 16 + wide 6 + tall 6 + narrow 8 = 36).
+ int mergedRowOffset = 0;
+ for (int i = 0; i < cases.size(); i++) {
+ JsonNode c = cases.get(i);
+ String name = c.required("name").asString();
+ assertEquals(
+ mergedRowOffset,
+ c.required("merged_row_offset").intValue(),
+ name + " merged_row_offset");
+ mergedRowOffset += c.required("merged_rows").intValue();
+ Qwen3VlVisionTower.ImageCaptures capture = captures.images().get(i);
+ List blocks = List.of(capture.blockOutputs());
+ assertEquals(1 + deepstackIndexes.length, blocks.size(), name + " block capture count");
+ assertArrayEquals(
+ floats(c.required("captures").required("pos_embed_interpolated").required("values")),
+ capture.posEmbedInterpolated().toFloatArray(),
+ TOLERANCE,
+ name + " pos_embed_interpolated");
+ assertArrayEquals(
+ floats(c.required("captures").required("patch_embed").required("values")),
+ capture.patchEmbed().toFloatArray(),
+ TOLERANCE,
+ name + " patch_embed");
+ assertArrayEquals(
+ floats(c.required("captures").required("block0").required("values")),
+ blocks.get(0).toFloatArray(),
+ TOLERANCE,
+ name + " block0");
+ for (int j = 0; j < deepstackIndexes.length; j++) {
+ assertArrayEquals(
+ floats(
+ c.required("captures")
+ .required("block" + deepstackIndexes[j])
+ .required("values")),
+ blocks.get(j + 1).toFloatArray(),
+ TOLERANCE,
+ name + " block" + deepstackIndexes[j]);
+ }
+ }
+ assertEquals(36, mergedRowOffset, "total merged rows");
+ }
+ }
+
+ @Test
+ void linspaceProbesBitMatchPinnedTorch() throws Exception {
+ JsonNode golden = golden();
+ // The tiny tower's num_position_embeddings 64 gives num_grid_per_side 8, the default probe
+ // side. Probes recorded with an explicit "side" (the real 2B model's side 48, PR #58 review
+ // round 2) use that geometry instead of the tiny tower's config-derived side.
+ int side =
+ (int)
+ Math.sqrt(
+ golden
+ .required("provenance")
+ .required("config")
+ .required("num_position_embeddings")
+ .intValue());
+ for (JsonNode probe : golden.required("probes").required("linspace")) {
+ int probeSide = probe.path("side").isInt() ? probe.path("side").intValue() : side;
+ int n = probe.required("n").intValue();
+ for (int i = 0; i < n; i++) {
+ long bits = Long.parseUnsignedLong(probe.required("values_hex").get(i).asString(), 16);
+ double pinned = Double.longBitsToDouble(bits);
+ // The probe stores the float32 value widened to double; the port must reproduce the
+ // float32 rounding of the pinned torch linspace kernel bits exactly.
+ assertEquals(
+ Float.floatToRawIntBits((float) pinned),
+ Float.floatToRawIntBits(Qwen3VlVisionTower.linspaceValue(probeSide, n, i)),
+ "linspace("
+ + probeSide
+ + ", "
+ + n
+ + ", "
+ + i
+ + ") must bit-match the pinned torch bits");
+ }
+ }
+ }
+
+ @Test
+ void posEmbedSampleCornerDataMatchesTower() throws Exception {
+ JsonNode golden = golden();
+ JsonNode samples = golden.required("probes").required("pos_embed_sample");
+ int merge =
+ golden.required("provenance").required("config").required("spatial_merge_size").intValue();
+ try (MLXScope scope = new MLXScope()) {
+ Qwen3VlVisionTower tower = buildTower(scope, golden);
+ Qwen3VlImageProcessor processor = processor();
+ for (JsonNode c : golden.required("cases")) {
+ if (!samples.has(c.required("name").asString())) {
+ continue;
+ }
+ JsonNode s = samples.required(c.required("name").asString());
+ String name = c.required("name").asString();
+ int[] grid = ints(c.required("grid"));
+ int h = grid[1];
+ int w = grid[2];
+ int[] patch = ints(s.required("grid"));
+ int ph = patch[0];
+ int pw = patch[1];
+ // The reference selected the first patch in merge-block order whose row AND column
+ // linspace values are both fractional; row-major row index in that order.
+ final int rowInImage =
+ (ph / merge * (w / merge) + pw / merge) * merge * merge
+ + (ph % merge) * merge
+ + (pw % merge);
+
+ Qwen3VlImageProcessorResult result =
+ processor.processFile(
+ directory().resolve(c.required("file").asString()), ImageDecodeLimits.DEFAULTS);
+ final Qwen3VlVisionTower.ImageCaptures capture =
+ tower.forwardForTest(scope, List.of(result)).images().get(0);
+
+ // The tower's bilinear corner indices for that patch must be the reference's exactly.
+ Qwen3VlVisionTower.PositionLookup lookup =
+ Qwen3VlVisionTower.positionLookup(h, w, tower.gridSide());
+ int k = ph * w + pw;
+ List flat = new ArrayList<>();
+ for (int corner = 0; corner < 4; corner++) {
+ flat.add(lookup.cornerIndices()[corner][k]);
+ }
+ List expected = new ArrayList<>();
+ for (JsonNode corner : s.required("corner_indices")) {
+ expected.add(corner.intValue());
+ }
+ assertEquals(expected, flat, name + " corner indices");
+
+ // The tower's float32-pipeline corner weights and the golden's double-pipeline hex
+ // differ by at most one float32 ulp (the probe script recomputes in double before
+ // casting); 1e-6 absolute absorbs that and still catches a wrong formula.
+ for (int corner = 0; corner < 4; corner++) {
+ float pinned =
+ Float.intBitsToFloat(
+ Integer.parseUnsignedInt(
+ s.required("corner_weights_hex").get(corner).asString(), 16));
+ float actual = lookup.cornerWeights()[corner][k];
+ assertTrue(
+ Math.abs(actual - pinned) <= 1e-6,
+ name + " corner weight " + corner + ": " + actual + " vs pinned " + pinned);
+ }
+
+ // The interpolated row the reference sampled must match the tower's row bit-for-bit
+ // within the golden's 7-significant-digit storage.
+ float[] row = capture.posEmbedInterpolated().toFloatArray();
+ assertEquals(h * w * tower.hidden(), row.length, name + " pos_embed row count");
+ List expectedRow = new ArrayList<>();
+ for (JsonNode v : s.required("expected_row")) {
+ expectedRow.add(v.floatValue());
+ }
+ for (int j = 0; j < expectedRow.size(); j++) {
+ float expectedValue = expectedRow.get(j);
+ assertTrue(
+ Math.abs(row[rowInImage * tower.hidden() + j] - expectedValue) <= TOLERANCE,
+ name
+ + " pos_embed sample row["
+ + rowInImage
+ + "]["
+ + j
+ + "]: "
+ + row[rowInImage * tower.hidden() + j]
+ + " vs "
+ + expectedValue);
+ }
+ }
+ }
+ }
+
+ static int[] ints(JsonNode node) {
+ List values = new ArrayList<>();
+ node.forEach(v -> values.add(v.intValue()));
+ return values.stream().mapToInt(Integer::intValue).toArray();
+ }
+
+ static float[] floats(JsonNode node) {
+ List values = new ArrayList<>();
+ flatten(node, values);
+ float[] result = new float[values.size()];
+ for (int i = 0; i < result.length; i++) {
+ result[i] = values.get(i);
+ }
+ return result;
+ }
+
+ private static void flatten(JsonNode node, List values) {
+ if (node.isArray()) {
+ node.forEach(v -> flatten(v, values));
+ } else {
+ values.add(node.floatValue());
+ }
+ }
+
+ /**
+ * The SHA-256 of the row-major float32 bytes (little-endian, as numpy's {@code tobytes} emits).
+ */
+ private static String sha256OfFloats(float[] values) throws Exception {
+ ByteBuffer buffer = ByteBuffer.allocate(values.length * 4).order(ByteOrder.LITTLE_ENDIAN);
+ buffer.asFloatBuffer().put(values);
+ byte[] digest = MessageDigest.getInstance("SHA-256").digest(buffer.array());
+ StringBuilder hex = new StringBuilder(digest.length * 2);
+ for (byte b : digest) {
+ hex.append(Character.forDigit((b >> 4) & 0xF, 16)).append(Character.forDigit(b & 0xF, 16));
+ }
+ return hex.toString();
+ }
+}
diff --git a/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerValidationTest.java b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerValidationTest.java
new file mode 100644
index 00000000..07c6b711
--- /dev/null
+++ b/jmlx-models/src/test/java/se/alipsa/jmlx/models/Qwen3VlVisionTowerValidationTest.java
@@ -0,0 +1,1079 @@
+package se.alipsa.jmlx.models;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotSame;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.Arrays;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.CsvSource;
+import org.junit.jupiter.params.provider.ValueSource;
+import se.alipsa.jmlx.core.DType;
+import se.alipsa.jmlx.core.MLX;
+import se.alipsa.jmlx.core.MLXArray;
+import se.alipsa.jmlx.core.MLXMemory;
+import se.alipsa.jmlx.ffi.EnabledIfNativeAvailable;
+import se.alipsa.jmlx.memory.MLXScope;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessor;
+import se.alipsa.jmlx.vision.Qwen3VlImageProcessorResult;
+import se.alipsa.jmlx.vision.Qwen3VlProcessorConfig;
+import se.alipsa.jmlx.vision.RgbImage;
+
+/**
+ * Validation coverage for the WP5 vision tower's argument and key-mapping checks (PR #58 review
+ * finding 4): the public {@link Qwen3VlVisionTower#visionKeyToTowerKey(String, int, int[])}
+ * branches, {@link Qwen3VlVisionTower#visionWeights(Map, int, int[])}'s skip/transpose/duplicate
+ * behavior, its all-or-nothing failure atomicity (a rejection leaves the caller's map untouched)
+ * and its success-path conv-weight ownership transfer, its finding-5 out-of-range rejection and its
+ * unrecognised-visual-key rejection, the weight, deepstack-index, position-grid and merge-width
+ * rejections, {@code runImage}'s geometry rejections, the {@link
+ * Qwen3VlVisionTower#rotaryCoordinates(int, int, int)}, {@link
+ * Qwen3VlVisionTower#positionLookup(int, int, int)} and {@link
+ * Qwen3VlVisionTower#linspaceValue(int, int, int)} argument checks, and the {@link
+ * Qwen3VlVisionTower#forwardForTest(MLXScope, List)} block-capture count and entry order for zero-
+ * and non-zero-leading DeepStack indexes (PR #58 review finding 9).
+ *
+ * Three of {@code runImage}'s documented rejections are untestable and intentionally not
+ * covered: {@code gridThw()[0] != 1}, a {@code rawPatchRows()} that disagrees with the grid, and a
+ * {@code values()} length that disagrees with {@code rawPatchRows * patchStride}. The {@code
+ * Qwen3VlImageProcessorResult} sole constructor hard-codes the grid to {@code [1, h, w]} and its
+ * {@code Qwen3VlGeometryPlan} validates {@code rawPatchRows == gridH * gridW} and the values
+ * length, so no malformed result object can exist through the public API; those checks remain
+ * defensive only.
+ *
+ *
Gated like the other WP5 tower suites because the constructor and {@code runImage} paths
+ * allocate MLX arrays; the pure-Java mapping and coordinate methods are covered by the same class.
+ */
+@EnabledIfNativeAvailable
+class Qwen3VlVisionTowerValidationTest {
+
+ // Tiny consistent geometry: hidden 8 with 2 heads (headDim 4), a 4x4 position grid, depth 2
+ // with one DeepStack index, merge 2 (m = 8 * 4 = 32). The processor-matching conv weight is
+ // [hidden, temporal, patch, patch, channels] = [8, 2, 16, 16, 3] (the 16x16 / temporal-2
+ // patch stride 1536 of the pinned processor chain).
+ private static final int HIDDEN = 8;
+ private static final int NUM_HEADS = 2;
+ private static final int INTERMEDIATE = 8;
+ private static final int OUT_HIDDEN = 8;
+ private static final int GRID_SIDE = 4;
+ private static final int DEPTH = 2;
+ private static final int[] DEEPSTACK = {1};
+ private static final int MERGE_BLOCK = HIDDEN * 2 * 2;
+ private static final int IMAGE = 64;
+
+ @ParameterizedTest
+ @CsvSource({
+ "model.visual.patch_embed.proj.weight, patchEmbedding.weight",
+ "model.visual.patch_embed.proj.bias, patchEmbedding.bias",
+ "model.visual.pos_embed.weight, positionEmbedding.weight",
+ "model.visual.blocks.0.norm1.weight, layer0.norm1.weight",
+ "model.visual.blocks.2.norm2.bias, layer2.norm2.bias",
+ "model.visual.blocks.1.attn.qkv.weight, layer1.attention.qkv.weight",
+ "model.visual.blocks.1.attn.qkv.bias, layer1.attention.qkv.bias",
+ "model.visual.blocks.1.attn.proj.weight, layer1.attention.proj.weight",
+ "model.visual.blocks.1.attn.proj.bias, layer1.attention.proj.bias",
+ "model.visual.blocks.0.mlp.linear_fc1.weight, layer0.mlp.0.weight",
+ "model.visual.blocks.0.mlp.linear_fc2.bias, layer0.mlp.2.bias",
+ "model.visual.merger.norm.weight, merger.norm.weight",
+ "model.visual.merger.linear_fc1.bias, merger.linearFc1.bias",
+ "model.visual.merger.linear_fc2.weight, merger.linearFc2.weight",
+ "model.visual.deepstack_merger_list.0.norm.bias, deepstackMerger0.norm.bias",
+ "model.visual.deepstack_merger_list.1.linear_fc2.weight, deepstackMerger1.linearFc2.weight",
+ })
+ void checkpointKeysMapToTowerKeys(String checkpointKey, String expected) {
+ // Depth 3 with a length-2 DeepStack list: every in-range index maps, so an off-by-one in the
+ // block/merger bounds would surface here.
+ assertEquals(
+ expected, Qwen3VlVisionTower.visionKeyToTowerKey(checkpointKey, 3, new int[] {1, 5}));
+ }
+
+ @ParameterizedTest
+ @ValueSource(
+ strings = {
+ "model.language_model.embed_tokens.weight", // non-visual
+ "visual.patch_embed.proj.weight", // missing model.visual. prefix
+ "model.visual.patch_embed.proj.gain", // unknown patch-embed sub-key
+ "model.visual.pos_embed.bias", // pos_embed maps weight only
+ "model.visual.blocks.3.norm1.weight", // block index out of range (depth 3)
+ "model.visual.blocks.x.norm1.weight", // non-numeric block index
+ "model.visual.blocks.-1.norm1.weight", // negative block index
+ "model.visual.blocks.99999999999999999999.norm1.weight", // overflowing block index
+ "model.visual.blocks..norm1.weight", // empty block index
+ "model.visual.blocks.1.", // block key without a sub-key
+ "model.visual.blocks.0.attn.gate.weight", // unknown attention sub-key
+ "model.visual.blocks.0.attn.qkv.gain", // unknown attention tail
+ "model.visual.blocks.0.mlp.linear_fc1.scales", // known linear, unknown sub-key
+ "model.visual.blocks.0.mlp.linear_fc3.weight", // unknown MLP sub-key
+ "model.visual.blocks.0.other.weight", // unknown block sub-key
+ "model.visual.merger.linear_fc3.weight", // unknown merger sub-key
+ "model.visual.merger.gain", // unknown merger tail
+ "model.visual.deepstack_merger_list.2.norm.weight", // merger index out of range (2)
+ "model.visual.deepstack_merger_list.x.norm.weight", // non-numeric merger index
+ "model.visual.deepstack_merger_list.99999999999999999999.norm.weight", // overflowing index
+ "model.visual.deepstack_merger_list.0.other.weight", // unknown merger sub-key
+ })
+ void unknownAndOutOfRangeKeysMapToNull(String checkpointKey) {
+ assertNull(Qwen3VlVisionTower.visionKeyToTowerKey(checkpointKey, 3, new int[] {1, 5}));
+ }
+
+ @Test
+ void visionKeyToTowerKeyRejectsNullArguments() {
+ assertEquals(
+ "key",
+ assertThrows(
+ NullPointerException.class,
+ () -> Qwen3VlVisionTower.visionKeyToTowerKey(null, DEPTH, DEEPSTACK))
+ .getMessage());
+ assertEquals(
+ "deepstackIndexes",
+ assertThrows(
+ NullPointerException.class,
+ () ->
+ Qwen3VlVisionTower.visionKeyToTowerKey(
+ "model.visual.pos_embed.weight", DEPTH, null))
+ .getMessage());
+ }
+
+ @Test
+ void visionWeightsSkipsNonVisualKeysAndTransposesConvWeight() {
+ try (MLXScope scope = new MLXScope()) {
+ float[] input = new float[24];
+ for (int i = 0; i < input.length; i++) {
+ input[i] = i + 1f;
+ }
+ Map checkpoint = new LinkedHashMap<>();
+ // PyTorch layout [out, in, T, H, W] = [2, 3, 1, 2, 2], distinct values per position.
+ checkpoint.put(
+ "model.visual.patch_embed.proj.weight",
+ MLX.array(scope, input, new int[] {2, 3, 1, 2, 2}));
+ checkpoint.put("model.visual.blocks.1.norm1.bias", full(scope, HIDDEN));
+ checkpoint.put(
+ "model.language_model.embed_tokens.weight", full(scope, new int[] {4, HIDDEN}));
+ // The non-visual key is the only kind that stays skipped: every other unrecognised
+ // model.visual.* key is rejected instead; see visionWeightsRejectsUnrecognizedVisualKeys,
+ // visionWeightsRejectsOutOfRangeBlockKeys and
+ // visionWeightsRejectsOutOfRangeDeepstackMergerKeys.
+
+ Map weights =
+ Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK);
+
+ assertEquals(2, weights.size(), "only the in-range visual keys survive");
+ assertArrayEquals(
+ new String[] {"patchEmbedding.weight", "layer1.norm1.bias"}, weights.keySet().toArray());
+ // jmlx's channels-last layout [out, T, H, W, in], transposed {0, 2, 3, 4, 1}; see
+ // transposedConvValues for the index math.
+ assertArrayEquals(
+ transposedConvValues(input), weights.get("patchEmbedding.weight").toFloatArray(), 0f);
+ }
+ }
+
+ @Test
+ void visionWeightsRejectsOutOfRangeBlockKeys() {
+ // A numeric block index past the configured DEPTH (2) is rejected for every sub-key family.
+ expectVisionWeightsRejection(
+ "model.visual.blocks.2.norm1.weight",
+ "checkpoint key \"model.visual.blocks.2.norm1.weight\" names block 2, out of range [0, 2)"
+ + " for the configured depth");
+ expectVisionWeightsRejection(
+ "model.visual.blocks.9.attn.qkv.bias",
+ "checkpoint key \"model.visual.blocks.9.attn.qkv.bias\" names block 9, out of range [0, 2)"
+ + " for the configured depth");
+ expectVisionWeightsRejection(
+ "model.visual.blocks.77.mlp.linear_fc2.weight",
+ "checkpoint key \"model.visual.blocks.77.mlp.linear_fc2.weight\" names block 77,"
+ + " out of range [0, 2) for the configured depth");
+ // An unknown sub-key does not make the key non-visual: a numeric block index past the
+ // configured depth is a depth mismatch either way.
+ expectVisionWeightsRejection(
+ "model.visual.blocks.4.other.weight",
+ "checkpoint key \"model.visual.blocks.4.other.weight\" names block 4, out of range [0, 2)"
+ + " for the configured depth");
+ }
+
+ @Test
+ void visionWeightsRejectsOutOfRangeDeepstackMergerKeys() {
+ // DEEPSTACK has one entry, so every numeric merger index >= 1 is out of range.
+ expectVisionWeightsRejection(
+ "model.visual.deepstack_merger_list.1.norm.weight",
+ "checkpoint key \"model.visual.deepstack_merger_list.1.norm.weight\" names DeepStack"
+ + " merger 1, out of range [0, 1) for the configured DeepStack count");
+ expectVisionWeightsRejection(
+ "model.visual.deepstack_merger_list.5.linear_fc2.bias",
+ "checkpoint key \"model.visual.deepstack_merger_list.5.linear_fc2.bias\" names DeepStack"
+ + " merger 5, out of range [0, 1) for the configured DeepStack count");
+ }
+
+ @ParameterizedTest
+ @ValueSource(
+ strings = {
+ "model.visual.blocks.0.mlp.linear_fc1.scales", // unknown MLP sub-key
+ "model.visual.blocks.0.mlp.linear_fc2.scales", // unknown MLP sub-key (second linear)
+ "model.visual.blocks.0.attn.qkv.scales", // unknown attention tail
+ "model.visual.blocks.0.attn.gate.weight", // unknown attention sub-key
+ "model.visual.blocks.0.norm3.weight", // unknown norm sub-key
+ "model.visual.blocks.1.", // block key without a sub-key
+ "model.visual.patch_embed.proj.gain", // unknown patch-embed sub-key
+ "model.visual.pos_embed.bias", // pos_embed maps weight only
+ "model.visual.foo.weight", // unknown top-level visual key
+ "model.visual.blocks.x.norm1.weight", // non-numeric block index
+ "model.visual.blocks.99999999999999999999.norm1.weight", // overflowing block index
+ "model.visual.merger.gain", // unknown merger tail
+ "model.visual.deepstack_merger_list.0.other.weight", // unknown DeepStack merger sub-key
+ "model.visual.deepstack_merger_list.x.norm.weight", // non-numeric merger index
+ "model.visual.deepstack_merger_list.99999999999999999999.norm.weight", // overflowing index
+ })
+ void visionWeightsRejectsUnrecognizedVisualKeys(String checkpointKey) {
+ expectVisionWeightsRejection(
+ checkpointKey, "unrecognized vision checkpoint key \"" + checkpointKey + "\"");
+ }
+
+ @Test
+ void visionWeightsAcceptsEveryInRangeKeyFamily() {
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ // One in-range key from every mapped family (DEPTH 2, one DeepStack) plus a non-visual key
+ // that stays skipped.
+ float[] conv = new float[24];
+ for (int i = 0; i < conv.length; i++) {
+ conv[i] = i + 1f;
+ }
+ checkpoint.put(
+ "model.visual.patch_embed.proj.weight",
+ MLX.array(scope, conv, new int[] {2, 3, 1, 2, 2}));
+ checkpoint.put("model.visual.pos_embed.weight", full(scope, GRID_SIDE * GRID_SIDE, HIDDEN));
+ checkpoint.put("model.visual.blocks.0.norm1.weight", full(scope, HIDDEN));
+ checkpoint.put("model.visual.blocks.1.norm2.bias", full(scope, HIDDEN));
+ checkpoint.put("model.visual.blocks.1.attn.qkv.weight", full(scope, 3 * HIDDEN, HIDDEN));
+ checkpoint.put("model.visual.blocks.1.attn.proj.bias", full(scope, HIDDEN));
+ checkpoint.put(
+ "model.visual.blocks.0.mlp.linear_fc1.weight", full(scope, INTERMEDIATE, HIDDEN));
+ checkpoint.put("model.visual.blocks.0.mlp.linear_fc2.bias", full(scope, HIDDEN));
+ checkpoint.put("model.visual.merger.norm.weight", full(scope, HIDDEN));
+ checkpoint.put("model.visual.merger.linear_fc1.bias", full(scope, MERGE_BLOCK));
+ checkpoint.put("model.visual.merger.linear_fc2.weight", full(scope, OUT_HIDDEN, MERGE_BLOCK));
+ checkpoint.put("model.visual.deepstack_merger_list.0.norm.bias", full(scope, MERGE_BLOCK));
+ checkpoint.put(
+ "model.visual.deepstack_merger_list.0.linear_fc1.weight",
+ full(scope, MERGE_BLOCK, MERGE_BLOCK));
+ checkpoint.put(
+ "model.visual.deepstack_merger_list.0.linear_fc2.bias", full(scope, OUT_HIDDEN));
+ checkpoint.put(
+ "model.language_model.embed_tokens.weight", full(scope, new int[] {4, HIDDEN}));
+
+ Map weights =
+ Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK);
+
+ // Every in-range key of every family maps, in checkpoint order; the non-visual key is
+ // skipped.
+ assertArrayEquals(
+ new String[] {
+ "patchEmbedding.weight",
+ "positionEmbedding.weight",
+ "layer0.norm1.weight",
+ "layer1.norm2.bias",
+ "layer1.attention.qkv.weight",
+ "layer1.attention.proj.bias",
+ "layer0.mlp.0.weight",
+ "layer0.mlp.2.bias",
+ "merger.norm.weight",
+ "merger.linearFc1.bias",
+ "merger.linearFc2.weight",
+ "deepstackMerger0.norm.bias",
+ "deepstackMerger0.linearFc1.weight",
+ "deepstackMerger0.linearFc2.bias",
+ },
+ weights.keySet().toArray());
+ }
+ }
+
+ @Test
+ void visionWeightsRejectsDuplicateTowerKeys() {
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ checkpoint.put("model.visual.blocks.0.norm1.weight", full(scope, HIDDEN));
+ // "00" parses to the same index as "0", so both keys collide on the same tower key.
+ checkpoint.put("model.visual.blocks.00.norm1.weight", full(scope, HIDDEN));
+ IllegalArgumentException e =
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK));
+ assertEquals(
+ "duplicate vision tower key \"layer0.norm1.weight\" from checkpoint key"
+ + " \"model.visual.blocks.00.norm1.weight\"",
+ e.getMessage());
+ }
+ }
+
+ @Test
+ void visionWeightsOutOfRangeRejectionLeavesMapUntouched() {
+ // The conv weight is reached before the rejecting key: the pre-fix code had already
+ // transposed and closed it when the out-of-range key threw.
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ float[] conv = putConvWeightFirst(scope, checkpoint);
+ checkpoint.put("model.visual.blocks.2.norm1.weight", full(scope, HIDDEN));
+ expectVisionWeightsFailureLeavesMapUntouched(
+ checkpoint,
+ checkpoint.get("model.visual.patch_embed.proj.weight"),
+ conv,
+ "checkpoint key \"model.visual.blocks.2.norm1.weight\" names block 2, out of range [0, 2)"
+ + " for the configured depth");
+ }
+ }
+
+ @Test
+ void visionWeightsUnrecognizedRejectionLeavesMapUntouched() {
+ // Same ordering: the conv weight is transposed and closed in the pre-fix code before the
+ // unrecognised key throws.
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ float[] conv = putConvWeightFirst(scope, checkpoint);
+ checkpoint.put("model.visual.foo.weight", full(scope, HIDDEN));
+ expectVisionWeightsFailureLeavesMapUntouched(
+ checkpoint,
+ checkpoint.get("model.visual.patch_embed.proj.weight"),
+ conv,
+ "unrecognized vision checkpoint key \"model.visual.foo.weight\"");
+ }
+ }
+
+ @Test
+ void visionWeightsDuplicateRejectionLeavesMapUntouched() {
+ // Same ordering: the conv weight is transposed and closed in the pre-fix code before the
+ // colliding key throws.
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ float[] conv = putConvWeightFirst(scope, checkpoint);
+ checkpoint.put("model.visual.blocks.0.norm1.weight", full(scope, HIDDEN));
+ // "00" parses to the same index as "0", so both keys collide on the same tower key.
+ checkpoint.put("model.visual.blocks.00.norm1.weight", full(scope, HIDDEN));
+ expectVisionWeightsFailureLeavesMapUntouched(
+ checkpoint,
+ checkpoint.get("model.visual.patch_embed.proj.weight"),
+ conv,
+ "duplicate vision tower key \"layer0.norm1.weight\" from checkpoint key"
+ + " \"model.visual.blocks.00.norm1.weight\"");
+ }
+ }
+
+ @Test
+ void visionWeightsSuccessTransfersConvWeightOwnership() {
+ try (MLXScope scope = new MLXScope()) {
+ float[] conv = new float[24];
+ for (int i = 0; i < conv.length; i++) {
+ conv[i] = i + 1f;
+ }
+ MLXArray original = MLX.array(scope, conv, new int[] {2, 3, 1, 2, 2});
+ Map checkpoint = new LinkedHashMap<>();
+ checkpoint.put("model.visual.patch_embed.proj.weight", original);
+
+ Map weights =
+ Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK);
+
+ MLXArray transposed = weights.get("patchEmbedding.weight");
+ assertNotSame(original, transposed, "the conv weight is replaced by its transposed copy");
+ // The transposed copy is allocated into the original's (the caller's) scope...
+ assertSame(scope, transposed.scope(), "the transposed copy lives in the caller's scope");
+ assertArrayEquals(new int[] {2, 1, 2, 2, 3}, transposed.shape(), "channels-last layout");
+ // ...and holds the {0, 2, 3, 4, 1} transpose of the original.
+ assertArrayEquals(transposedConvValues(conv), transposed.toFloatArray(), 0f);
+ // ...while the caller's map still references the original array, which is now closed
+ // (the SmolVlmModel ownership transfer the Javadoc documents).
+ assertSame(original, checkpoint.get("model.visual.patch_embed.proj.weight"));
+ assertThrows(
+ IllegalStateException.class, original::shape, "the original conv weight is closed");
+ }
+ }
+
+ @Test
+ void constructorRejectsMissingWeight() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.remove("merger.norm.weight");
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "missing vision tower weight \"merger.norm.weight\"");
+ }
+ }
+
+ @Test
+ void constructorRejectsExtraWeight() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.put("patchEmbedding.extra", full(scope, HIDDEN));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "unexpected vision tower weights: [patchEmbedding.extra]");
+ }
+ }
+
+ @Test
+ void constructorRejectsNonFloatingWeight() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.put(
+ "positionEmbedding.weight",
+ MLX.full(scope, new int[] {GRID_SIDE * GRID_SIDE, HIDDEN}, 1f, DType.INT32));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "vision tower weights must be floating: positionEmbedding.weight");
+ }
+ }
+
+ @Test
+ void constructorRejectsInvalidDeepstackIndexes() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ expectConstructorMessage(
+ scope, weights, NUM_HEADS, new int[] {}, "deepstackIndexes must not be empty");
+ expectConstructorMessage(
+ scope, weights, NUM_HEADS, new int[] {5}, "deepstack index 0 = 5 is out of range [0, 2)");
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ new int[] {-1},
+ "deepstack index 0 = -1 is out of range [0, 2)");
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ new int[] {1, 1},
+ "deepstack indexes must be strictly increasing; index 1 has 1 after 1");
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ new int[] {1, 0},
+ "deepstack indexes must be strictly increasing; index 1 has 0 after 1");
+ }
+ }
+
+ @Test
+ void constructorRejectsLayerlessTower() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.remove("layer0.norm1.weight");
+ expectConstructorMessage(
+ scope, weights, NUM_HEADS, DEEPSTACK, "the vision tower needs at least one layer");
+ }
+ }
+
+ @Test
+ void constructorRejectsRankZeroFeedForwardWeight() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ // A rank-0 (scalar) layer-0 fc1 weight used to reach shape()[0] before any rank check,
+ // throwing ArrayIndexOutOfBoundsException instead of the documented IllegalArgumentException.
+ weights.put("layer0.mlp.0.weight", MLX.array(scope, new float[] {0.001f}, new int[] {}));
+ expectConstructorMessage(
+ scope, weights, NUM_HEADS, DEEPSTACK, "layer0.mlp.0.weight must be a matrix, got rank 0");
+ }
+ }
+
+ @Test
+ void constructorRejectsNonSquarePositionGrid() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.put("positionEmbedding.weight", full(scope, 12, HIDDEN));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "positionEmbedding.weight must contain a square grid, got 12 rows");
+ }
+ }
+
+ @Test
+ void constructorRejectsNonSquareMergeWidth() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ weights.put("merger.linearFc1.weight", full(scope, 24, 24));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "merger width m = 24 must be the hidden size times a square merge^2, got merge^2 = 3");
+ weights.put("merger.linearFc1.weight", full(scope, 20, 20));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "merger width m = 20 must be divisible by hidden size 8");
+ weights.put("merger.linearFc1.weight", full(scope, 24, 16));
+ expectConstructorMessage(
+ scope,
+ weights,
+ NUM_HEADS,
+ DEEPSTACK,
+ "merger.linearFc1.weight must be square [m, m], got shape [24, 16]");
+ }
+ }
+
+ @Test
+ void constructorRejectsInvalidHeadCounts() {
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ expectConstructorMessage(
+ scope, weights, 0, DEEPSTACK, "the vision tower needs at least one attention head");
+ expectConstructorMessage(
+ scope, weights, 3, DEEPSTACK, "hidden size 8 is not divisible by 3 attention heads");
+ }
+ }
+
+ @Test
+ void constructorRejectsNullArguments() {
+ try (MLXScope scope = new MLXScope()) {
+ assertEquals(
+ "weights",
+ assertThrows(
+ NullPointerException.class,
+ () -> new Qwen3VlVisionTower(scope, NUM_HEADS, null, DEEPSTACK))
+ .getMessage());
+ Map weights = baseWeights(scope);
+ assertEquals(
+ "deepstackIndexes",
+ assertThrows(
+ NullPointerException.class,
+ () -> new Qwen3VlVisionTower(scope, NUM_HEADS, weights, null))
+ .getMessage());
+ }
+ }
+
+ @Test
+ void runImageRejectsPatchStrideMismatch() {
+ Qwen3VlImageProcessorResult image = processedImage();
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ // The tower's 4x4 temporal-1 patch embed gives an expected stride of 3 * 1 * 4 * 4 = 48,
+ // disagreeing with the processor's 1536.
+ weights.put("patchEmbedding.weight", full(scope, HIDDEN, 1, 4, 4, 3));
+ Qwen3VlVisionTower tower = buildTower(scope, weights);
+ IllegalArgumentException e =
+ assertThrows(IllegalArgumentException.class, () -> tower.forward(scope, List.of(image)));
+ assertEquals(
+ "image patch stride 1536 does not match the tower's 48 (inChannels * temporal *"
+ + " patchHeight * patchWidth)",
+ e.getMessage());
+ }
+ }
+
+ @Test
+ void runImageRejectsGridNotMultipleOfMerge() {
+ Qwen3VlImageProcessorResult image = processedImage();
+ try (MLXScope scope = new MLXScope()) {
+ Map weights = baseWeights(scope);
+ // Merge size 3 (m = 8 * 9 = 72): the processor's 4x4 grid is not a multiple of 3.
+ putMergeWidth(weights, scope, 72);
+ Qwen3VlVisionTower tower = buildTower(scope, weights);
+ IllegalArgumentException e =
+ assertThrows(IllegalArgumentException.class, () -> tower.forward(scope, List.of(image)));
+ assertEquals("image grid 4x4 must be multiples of the spatial merge size 3", e.getMessage());
+ }
+ }
+
+ @Test
+ void forwardRejectsUnrelatedTargetScopeAndEmptyImages() {
+ Qwen3VlImageProcessorResult image = processedImage();
+ try (MLXScope towerScope = new MLXScope();
+ MLXScope stranger = new MLXScope()) {
+ Qwen3VlVisionTower tower = buildTower(towerScope, baseWeights(towerScope));
+ IllegalArgumentException e =
+ assertThrows(
+ IllegalArgumentException.class, () -> tower.forward(stranger, List.of(image)));
+ assertTrue(
+ e.getMessage()
+ .startsWith(
+ "target scope must be the tower's module scope or a descendant of it, got "),
+ e.getMessage());
+ assertEquals(
+ "at least one image is required",
+ assertThrows(IllegalArgumentException.class, () -> tower.forward(towerScope, List.of()))
+ .getMessage());
+ }
+ }
+
+ @Test
+ void forwardRejectsNullArguments() {
+ Qwen3VlImageProcessorResult image = processedImage();
+ try (MLXScope scope = new MLXScope()) {
+ Qwen3VlVisionTower tower = buildTower(scope, baseWeights(scope));
+ assertEquals(
+ "target",
+ assertThrows(NullPointerException.class, () -> tower.forward(null, List.of(image)))
+ .getMessage());
+ assertEquals(
+ "images",
+ assertThrows(NullPointerException.class, () -> tower.forward(scope, null)).getMessage());
+ assertEquals(
+ "images element",
+ assertThrows(
+ NullPointerException.class,
+ // Arrays.asList, not List.of: List.of rejects the null at creation with an
+ // unattributed message; the tower's own element check must fire instead.
+ () -> tower.forward(scope, Arrays.asList((Qwen3VlImageProcessorResult) null)))
+ .getMessage());
+ }
+ }
+
+ @Test
+ void forwardForTestCapturesBlockZeroWhenFirstDeepstackIndexIsZero() {
+ Qwen3VlImageProcessorResult image = processedImage();
+ try (MLXScope scope = new MLXScope()) {
+ Map oneLevel = baseWeights(scope);
+ // A second DeepStack level over the same tiny depth: the extra merger's keys carry the
+ // same constant values, so every weight shared with oneLevel is the very same array.
+ Map twoLevels = new LinkedHashMap<>(oneLevel);
+ putDeepstackMerger(twoLevels, scope, 1);
+
+ // The block loop is independent of the DeepStack indexes, so these towers produce the same
+ // block outputs for the same image; the cross-tower equalities below pin which block each
+ // capture entry came from.
+ Qwen3VlVisionTower indexZero =
+ new Qwen3VlVisionTower(scope, NUM_HEADS, oneLevel, new int[] {0});
+ Qwen3VlVisionTower indexOne =
+ new Qwen3VlVisionTower(scope, NUM_HEADS, oneLevel, new int[] {1});
+ Qwen3VlVisionTower indexZeroOne =
+ new Qwen3VlVisionTower(scope, NUM_HEADS, twoLevels, new int[] {0, 1});
+
+ Qwen3VlVisionTower.ForwardCaptures zeroFeatures =
+ indexZero.forwardForTest(scope, List.of(image));
+ Qwen3VlVisionTower.ForwardCaptures oneFeatures =
+ indexOne.forwardForTest(scope, List.of(image));
+ Qwen3VlVisionTower.ForwardCaptures zeroOneFeatures =
+ indexZeroOne.forwardForTest(scope, List.of(image));
+ Qwen3VlVisionTower.ImageCaptures zeroCaptures = zeroFeatures.images().get(0);
+ Qwen3VlVisionTower.ImageCaptures oneCaptures = oneFeatures.images().get(0);
+ Qwen3VlVisionTower.ImageCaptures zeroOneCaptures = zeroOneFeatures.images().get(0);
+
+ // The documented count is 1 + levelCount even when the first DeepStack index is 0 (the
+ // pre-fix code captured block 0 only once, losing one entry and misaligning the rest).
+ assertEquals(2, zeroCaptures.blockOutputs().length, "1 + 1 level, deepstackIndexes [0]");
+ assertEquals(2, oneCaptures.blockOutputs().length, "1 + 1 level, deepstackIndexes [1]");
+ assertEquals(
+ 3, zeroOneCaptures.blockOutputs().length, "1 + 2 levels, deepstackIndexes [0, 1]");
+ int n = image.rawPatchRows();
+ for (MLXArray block : zeroOneCaptures.blockOutputs()) {
+ assertArrayEquals(new int[] {n, HIDDEN}, block.shape(), "capture shape");
+ }
+
+ // Entry 0 is block 0's own output, independent of the DeepStack indexes.
+ assertArrayEquals(
+ zeroCaptures.blockOutputs()[0].toFloatArray(),
+ oneCaptures.blockOutputs()[0].toFloatArray(),
+ 0f,
+ "entry 0 is block 0 for both [0] and [1]");
+ // [0]: the DeepStack level-0 source block is block 0 itself, so entry 1 repeats entry 0.
+ assertArrayEquals(
+ zeroCaptures.blockOutputs()[0].toFloatArray(),
+ zeroCaptures.blockOutputs()[1].toFloatArray(),
+ 0f,
+ "[0] entry 1 is block 0's output too");
+ // [0, 1]: entry 1 repeats block 0, and entry 2 is block 1's output, equal to the [1]
+ // tower's entry 1.
+ assertArrayEquals(
+ zeroOneCaptures.blockOutputs()[0].toFloatArray(),
+ zeroOneCaptures.blockOutputs()[1].toFloatArray(),
+ 0f,
+ "[0, 1] entry 1 is block 0's output");
+ assertArrayEquals(
+ oneCaptures.blockOutputs()[1].toFloatArray(),
+ zeroOneCaptures.blockOutputs()[2].toFloatArray(),
+ 0f,
+ "[0, 1] entry 2 is block 1's output");
+ // The feature rows line up with the blocks that fed them: DeepStack level 0 consumed block
+ // 0 in both the [0] and the [0, 1] towers (same merger, same input), and the main merger
+ // consumed block 1 in every tower, so the merged rows are equal across all three.
+ assertArrayEquals(
+ zeroOneFeatures.features().deepstackLevels().get(0).toFloatArray(),
+ zeroFeatures.features().deepstackLevels().get(0).toFloatArray(),
+ 0f,
+ "level 0 DeepStack rows come from block 0 in both towers");
+ assertArrayEquals(
+ oneFeatures.features().merged().toFloatArray(),
+ zeroOneFeatures.features().merged().toFloatArray(),
+ 0f,
+ "main merger rows come from block 1 in every tower");
+ }
+ }
+
+ @Test
+ void rotaryCoordinatesRejectsInvalidArguments() {
+ assertEquals(
+ "merge must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.rotaryCoordinates(8, 8, 0))
+ .getMessage());
+ assertEquals(
+ "merge must be positive",
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> Qwen3VlVisionTower.rotaryCoordinates(8, 8, -1))
+ .getMessage());
+ assertEquals(
+ "h and w must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.rotaryCoordinates(0, 4, 2))
+ .getMessage());
+ assertEquals(
+ "h and w must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.rotaryCoordinates(4, 0, 2))
+ .getMessage());
+ assertEquals(
+ "h = 6 and w = 8 must be multiples of merge = 4",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.rotaryCoordinates(6, 8, 4))
+ .getMessage());
+ assertEquals(
+ "h = 8 and w = 7 must be multiples of merge = 2",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.rotaryCoordinates(8, 7, 2))
+ .getMessage());
+ }
+
+ @Test
+ void rotaryCoordinatesEmitMergeBlockOrder() {
+ int[][] coordinates = Qwen3VlVisionTower.rotaryCoordinates(8, 8, 2);
+ assertEquals(2, coordinates.length, "rows and cols");
+ assertEquals(64, coordinates[0].length, "row count");
+ assertEquals(64, coordinates[1].length, "col count");
+ // The reference loop order documented in the method's Javadoc: the first eight coordinates
+ // are (0,0),(0,1),(1,0),(1,1),(0,2),(0,3),(1,2),(1,3), the last four (6,6),(6,7),(7,6),(7,7).
+ assertArrayEquals(new int[] {0, 0, 1, 1, 0, 0, 1, 1}, Arrays.copyOf(coordinates[0], 8));
+ assertArrayEquals(new int[] {0, 1, 0, 1, 2, 3, 2, 3}, Arrays.copyOf(coordinates[1], 8));
+ assertArrayEquals(new int[] {6, 6, 7, 7}, Arrays.copyOfRange(coordinates[0], 60, 64));
+ assertArrayEquals(new int[] {6, 7, 6, 7}, Arrays.copyOfRange(coordinates[1], 60, 64));
+ for (int blockRow = 0; blockRow < 4; blockRow++) {
+ for (int blockCol = 0; blockCol < 4; blockCol++) {
+ for (int intraRow = 0; intraRow < 2; intraRow++) {
+ for (int intraCol = 0; intraCol < 2; intraCol++) {
+ int m = (blockRow * 4 + blockCol) * 4 + intraRow * 2 + intraCol;
+ assertEquals(blockRow * 2 + intraRow, coordinates[0][m], "row " + m);
+ assertEquals(blockCol * 2 + intraCol, coordinates[1][m], "col " + m);
+ }
+ }
+ }
+ }
+ }
+
+ @Test
+ void positionLookupRejectsInvalidArguments() {
+ assertEquals(
+ "h, w and gridSide must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.positionLookup(0, 4, 4))
+ .getMessage());
+ assertEquals(
+ "h, w and gridSide must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.positionLookup(4, 0, 4))
+ .getMessage());
+ assertEquals(
+ "h, w and gridSide must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.positionLookup(4, 4, 0))
+ .getMessage());
+ }
+
+ @Test
+ void positionLookupComputesBilinearCorners() {
+ // Exact 4x4 grid over itself: every patch sits on a grid point, so the weights are {1, 0,
+ // 0, 0}; the corner indices are still the floor/ceil grid points, with the ceiling clipping
+ // at gridSide - 1 on the bottom and right edges (where it collapses back onto the floor).
+ Qwen3VlVisionTower.PositionLookup exact = Qwen3VlVisionTower.positionLookup(4, 4, 4);
+ for (int ph = 0; ph < 4; ph++) {
+ for (int pw = 0; pw < 4; pw++) {
+ int k = ph * 4 + pw;
+ int ceilH = Math.min(ph + 1, 3);
+ int ceilW = Math.min(pw + 1, 3);
+ int[] expectedCorners =
+ new int[] {ph * 4 + pw, ph * 4 + ceilW, ceilH * 4 + pw, ceilH * 4 + ceilW};
+ assertArrayEquals(expectedCorners, corners(exact, k), "exact corners " + ph + "x" + pw);
+ assertArrayEquals(
+ new float[] {1f, 0f, 0f, 0f},
+ cornerWeights(exact, k),
+ "exact weights " + ph + "x" + pw);
+ }
+ }
+ // Fractional 3x3 grid over the 4x4 table: linspace(0, 3, 3) = {0, 1.5, 3}, so the middle
+ // patch sits at (1.5, 1.5) and takes all four corners equally, while the top-left and
+ // bottom-right patches sit exactly on grid points (the bottom-right at the clipped (3, 3)).
+ Qwen3VlVisionTower.PositionLookup fractional = Qwen3VlVisionTower.positionLookup(3, 3, 4);
+ assertArrayEquals(new int[] {5, 6, 9, 10}, corners(fractional, 4));
+ assertArrayEquals(new float[] {0.25f, 0.25f, 0.25f, 0.25f}, cornerWeights(fractional, 4));
+ assertArrayEquals(new int[] {0, 1, 4, 5}, corners(fractional, 0));
+ assertArrayEquals(new float[] {1f, 0f, 0f, 0f}, cornerWeights(fractional, 0));
+ assertArrayEquals(new int[] {15, 15, 15, 15}, corners(fractional, 8));
+ assertArrayEquals(new float[] {1f, 0f, 0f, 0f}, cornerWeights(fractional, 8));
+ }
+
+ @Test
+ void linspaceValueRejectsInvalidArguments() {
+ assertEquals(
+ "side and n must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.linspaceValue(0, 4, 0))
+ .getMessage());
+ assertEquals(
+ "side and n must be positive",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.linspaceValue(4, 0, 0))
+ .getMessage());
+ assertEquals(
+ "i = -1 is out of range [0, 4)",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.linspaceValue(4, 4, -1))
+ .getMessage());
+ assertEquals(
+ "i = 4 is out of range [0, 4)",
+ assertThrows(
+ IllegalArgumentException.class, () -> Qwen3VlVisionTower.linspaceValue(4, 4, 4))
+ .getMessage());
+ assertEquals(0f, Qwen3VlVisionTower.linspaceValue(4, 1, 0), 0f, "the n = 1 fill guard");
+ float[] uniform = new float[4];
+ for (int i = 0; i < 4; i++) {
+ uniform[i] = Qwen3VlVisionTower.linspaceValue(4, 4, i);
+ }
+ assertArrayEquals(new float[] {0f, 1f, 2f, 3f}, uniform, 0f);
+ }
+
+ /** The all-expected-key weight map of the tiny geometry, constant 0.001 float32. */
+ private static Map baseWeights(MLXScope scope) {
+ Map weights = new LinkedHashMap<>();
+ put(weights, scope, "patchEmbedding.weight", HIDDEN, 2, 16, 16, 3);
+ put(weights, scope, "patchEmbedding.bias", HIDDEN);
+ put(weights, scope, "positionEmbedding.weight", GRID_SIDE * GRID_SIDE, HIDDEN);
+ for (int i = 0; i < DEPTH; i++) {
+ String layer = "layer" + i + ".";
+ put(weights, scope, layer + "attention.qkv.weight", 3 * HIDDEN, HIDDEN);
+ put(weights, scope, layer + "attention.qkv.bias", 3 * HIDDEN);
+ put(weights, scope, layer + "attention.proj.weight", HIDDEN, HIDDEN);
+ put(weights, scope, layer + "attention.proj.bias", HIDDEN);
+ for (String norm : new String[] {"norm1", "norm2"}) {
+ put(weights, scope, layer + norm + ".weight", HIDDEN);
+ put(weights, scope, layer + norm + ".bias", HIDDEN);
+ }
+ put(weights, scope, layer + "mlp.0.weight", INTERMEDIATE, HIDDEN);
+ put(weights, scope, layer + "mlp.0.bias", INTERMEDIATE);
+ put(weights, scope, layer + "mlp.2.weight", HIDDEN, INTERMEDIATE);
+ put(weights, scope, layer + "mlp.2.bias", HIDDEN);
+ }
+ put(weights, scope, "merger.norm.weight", HIDDEN);
+ put(weights, scope, "merger.norm.bias", HIDDEN);
+ put(weights, scope, "merger.linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, scope, "merger.linearFc1.bias", MERGE_BLOCK);
+ put(weights, scope, "merger.linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, scope, "merger.linearFc2.bias", OUT_HIDDEN);
+ for (int j = 0; j < DEEPSTACK.length; j++) {
+ putDeepstackMerger(weights, scope, j);
+ }
+ return weights;
+ }
+
+ /** Adds the tiny geometry's {@code deepstackMerger.*} keys (constant 0.001) to the map. */
+ private static void putDeepstackMerger(Map weights, MLXScope scope, int index) {
+ String prefix = "deepstackMerger" + index + ".";
+ put(weights, scope, prefix + "norm.weight", MERGE_BLOCK);
+ put(weights, scope, prefix + "norm.bias", MERGE_BLOCK);
+ put(weights, scope, prefix + "linearFc1.weight", MERGE_BLOCK, MERGE_BLOCK);
+ put(weights, scope, prefix + "linearFc1.bias", MERGE_BLOCK);
+ put(weights, scope, prefix + "linearFc2.weight", OUT_HIDDEN, MERGE_BLOCK);
+ put(weights, scope, prefix + "linearFc2.bias", OUT_HIDDEN);
+ }
+
+ /**
+ * Rewires the main merger and the first DeepStack merger to the non-default merge-block width
+ * {@code m} (kept consistent across the two so the constructor accepts the tower).
+ */
+ private static void putMergeWidth(Map weights, MLXScope scope, int m) {
+ weights.put("merger.linearFc1.weight", full(scope, m, m));
+ weights.put("merger.linearFc1.bias", full(scope, m));
+ weights.put("merger.linearFc2.weight", full(scope, OUT_HIDDEN, m));
+ weights.put("deepstackMerger0.norm.weight", full(scope, m));
+ weights.put("deepstackMerger0.norm.bias", full(scope, m));
+ weights.put("deepstackMerger0.linearFc1.weight", full(scope, m, m));
+ weights.put("deepstackMerger0.linearFc1.bias", full(scope, m));
+ weights.put("deepstackMerger0.linearFc2.weight", full(scope, OUT_HIDDEN, m));
+ }
+
+ private static void put(Map weights, MLXScope scope, String key, int... shape) {
+ weights.put(key, full(scope, shape));
+ }
+
+ private static MLXArray full(MLXScope scope, int... shape) {
+ return MLX.full(scope, shape, 0.001f, DType.FLOAT32);
+ }
+
+ private static Qwen3VlVisionTower buildTower(MLXScope scope, Map weights) {
+ return new Qwen3VlVisionTower(scope, NUM_HEADS, weights, DEEPSTACK);
+ }
+
+ private static void expectConstructorMessage(
+ MLXScope scope,
+ Map weights,
+ int numHeads,
+ int[] deepstackIndexes,
+ String message) {
+ IllegalArgumentException e =
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> new Qwen3VlVisionTower(scope, numHeads, weights, deepstackIndexes));
+ assertEquals(message, e.getMessage());
+ }
+
+ /**
+ * Runs {@code visionWeights} with the class's DEPTH and DEEPSTACK against one in-range weight
+ * followed by the {@code checkpointKey} and asserts the rejection message.
+ */
+ private static void expectVisionWeightsRejection(String checkpointKey, String expectedMessage) {
+ try (MLXScope scope = new MLXScope()) {
+ Map checkpoint = new LinkedHashMap<>();
+ // The in-range key is accepted and processed first, then the out-of-range key rejects the
+ // whole map.
+ checkpoint.put("model.visual.blocks.0.norm1.weight", full(scope, HIDDEN));
+ checkpoint.put(checkpointKey, full(scope, HIDDEN));
+ IllegalArgumentException e =
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK));
+ assertEquals(expectedMessage, e.getMessage());
+ }
+ }
+
+ /**
+ * Runs {@code visionWeights} with the class's DEPTH and DEEPSTACK against the already-built
+ * {@code checkpoint} (a well-formed conv weight first, then the rejecting key(s)) and asserts the
+ * all-or-nothing contract: the rejection throws {@code expectedMessage} and the caller's map is
+ * left byte-for-byte untouched — the conv weight entry is still the very same, still-open array
+ * with its original values, the map's size is intact, and no MLX memory was stranded by the
+ * failed call (no transposed copy allocated, nothing closed).
+ */
+ private static void expectVisionWeightsFailureLeavesMapUntouched(
+ Map checkpoint,
+ MLXArray convWeight,
+ float[] convValues,
+ String expectedMessage) {
+ int sizeBefore = checkpoint.size();
+ long baseline = MLXMemory.activeBytes();
+ IllegalArgumentException e =
+ assertThrows(
+ IllegalArgumentException.class,
+ () -> Qwen3VlVisionTower.visionWeights(checkpoint, DEPTH, DEEPSTACK));
+ assertEquals(expectedMessage, e.getMessage());
+ // Sampled before any further MLX work in this test: the failed call allocates and frees
+ // nothing (the validation pass does no MLX work at all), so the counter must sit exactly at
+ // the pre-call baseline.
+ assertTrue(
+ MLXMemory.activeBytes() <= baseline,
+ "a rejected visionWeights call must not strand MLX memory; baseline "
+ + baseline
+ + ", after "
+ + MLXMemory.activeBytes());
+ assertEquals(sizeBefore, checkpoint.size(), "the caller's map is intact");
+ assertSame(convWeight, checkpoint.get("model.visual.patch_embed.proj.weight"));
+ // toFloatArray throws IllegalStateException on a closed array, so this pins both that the
+ // original is still open and that its bytes are the original values.
+ assertArrayEquals(
+ convValues, convWeight.toFloatArray(), 0f, "the original conv weight is intact");
+ }
+
+ /**
+ * Puts the well-formed conv weight (distinct values 1..24 in the {@code [2, 3, 1, 2, 2]} PyTorch
+ * layout) first into {@code checkpoint} and returns its values, so callers append the rejecting
+ * key(s) after it — the ordering the pre-fix code mangled the map.
+ */
+ private static float[] putConvWeightFirst(MLXScope scope, Map checkpoint) {
+ float[] conv = new float[24];
+ for (int i = 0; i < conv.length; i++) {
+ conv[i] = i + 1f;
+ }
+ checkpoint.put(
+ "model.visual.patch_embed.proj.weight", MLX.array(scope, conv, new int[] {2, 3, 1, 2, 2}));
+ return conv;
+ }
+
+ /**
+ * The {@code {0, 2, 3, 4, 1}} transpose of the tests' {@code [2, 3, 1, 2, 2]} conv weight: jmlx's
+ * channels-last output {@code [o, t, h, w, c]} reads the PyTorch input {@code [o, c, t, h, w]};
+ * with T = 1 the t term is always zero.
+ */
+ private static float[] transposedConvValues(float[] input) {
+ float[] expected = new float[24];
+ for (int o = 0; o < 2; o++) {
+ for (int h = 0; h < 2; h++) {
+ for (int w = 0; w < 2; w++) {
+ for (int c = 0; c < 3; c++) {
+ expected[o * 12 + h * 6 + w * 3 + c] = input[o * 12 + c * 4 + h * 2 + w];
+ }
+ }
+ }
+ }
+ return expected;
+ }
+
+ /** One 64x64 processor output: grid (1, 4, 4), 16 raw patch rows of stride 1536. */
+ private static Qwen3VlImageProcessorResult processedImage() {
+ byte[] pixels = new byte[IMAGE * IMAGE * 3];
+ for (int i = 0; i < pixels.length; i++) {
+ pixels[i] = (byte) (i * 7 + 13);
+ }
+ Qwen3VlImageProcessorResult result = processor().process(new RgbImage(IMAGE, IMAGE, pixels));
+ assertArrayEquals(new int[] {1, 4, 4}, result.gridThw(), "grid");
+ assertEquals(16, result.rawPatchRows(), "raw patch rows");
+ assertEquals(1536, result.patchStride(), "patch stride");
+ return result;
+ }
+
+ /** The 2B artifact's slow-processor options (the golden/peak tests' pinned chain). */
+ private static Qwen3VlImageProcessor processor() {
+ return new Qwen3VlImageProcessor(
+ Qwen3VlProcessorConfig.builder()
+ .patchSize(16)
+ .temporalPatchSize(2)
+ .mergeSize(2)
+ .minPixels(3136)
+ .maxPixels(4096 * 16 * 16)
+ .imageMean(new float[] {0.5f, 0.5f, 0.5f})
+ .imageStd(new float[] {0.5f, 0.5f, 0.5f})
+ .build());
+ }
+
+ private static int[] corners(Qwen3VlVisionTower.PositionLookup lookup, int k) {
+ return new int[] {
+ lookup.cornerIndices()[0][k],
+ lookup.cornerIndices()[1][k],
+ lookup.cornerIndices()[2][k],
+ lookup.cornerIndices()[3][k]
+ };
+ }
+
+ private static float[] cornerWeights(Qwen3VlVisionTower.PositionLookup lookup, int k) {
+ return new float[] {
+ lookup.cornerWeights()[0][k],
+ lookup.cornerWeights()[1][k],
+ lookup.cornerWeights()[2][k],
+ lookup.cornerWeights()[3][k]
+ };
+ }
+}
diff --git a/req/plans/phase7-3b-reference-findings.md b/req/plans/phase7-3b-reference-findings.md
index 09f4f4b8..4cc4d844 100644
--- a/req/plans/phase7-3b-reference-findings.md
+++ b/req/plans/phase7-3b-reference-findings.md
@@ -1400,3 +1400,838 @@ anything, exiting non-zero on any drift. The probes' check paths use explicit
`sys.exit`/raise checks (no bare `assert`s), so the gates survive `python -O`, and their
recorded result fields are computed values rather than hard-coded literals.
+## 11. Vision tower design freeze and measured peak (WP5, 2026-10-21)
+
+**Scope.** WP5 = vision tower + main/DeepStack mergers (milestone table in
+`req/plans/phase7-3b-plan.md`: "Vision tower, main/DeepStack mergers", exit evidence "HF
+intermediate-feature references"). Loader dispatch, the composed `Qwen3VlModel` and the
+affine (MLX-provenance) tensor plan are WP6. The WP5 exit evidence is the HF
+intermediate-feature reference: a tiny synthetic vision checkpoint plus a golden capturing
+the patch embeddings, selected block outputs, the final merged features, all DeepStack
+levels, and the pure-math bit probes (§11.4).
+
+### 11.1 Verbatim 4.57.6 source transcription
+
+Transcribed byte-for-byte from the pinned `tools/hf-reference/.venv`
+(`inspect.getsource` over `transformers.models.qwen3_vl.modeling_qwen3_vl` and
+`transformers.models.qwen3_vl.configuration_qwen3_vl`); these blocks are the authoritative
+reference for the WP5 math. `Qwen3VLVisionModel` has no `get_position_embeds` method in
+4.57.6 — its model-specific methods are `__init__`, `rot_pos_emb`,
+`fast_pos_embed_interpolate` and `forward`, all included below.
+
+**`Qwen3VLVisionModel`** (8,180 source chars):
+
+```python
+class Qwen3VLVisionModel(Qwen3VLPreTrainedModel):
+ config: Qwen3VLVisionConfig
+ _no_split_modules = ["Qwen3VLVisionBlock"]
+
+ def __init__(self, config, *inputs, **kwargs) -> None:
+ super().__init__(config, *inputs, **kwargs)
+ self.spatial_merge_size = config.spatial_merge_size
+ self.patch_size = config.patch_size
+ self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+ self.patch_embed = Qwen3VLVisionPatchEmbed(
+ config=config,
+ )
+
+ self.pos_embed = nn.Embedding(config.num_position_embeddings, config.hidden_size)
+ self.num_grid_per_side = int(config.num_position_embeddings**0.5)
+
+ head_dim = config.hidden_size // config.num_heads
+ self.rotary_pos_emb = Qwen3VLVisionRotaryEmbedding(head_dim // 2)
+
+ self.blocks = nn.ModuleList([Qwen3VLVisionBlock(config) for _ in range(config.depth)])
+ self.merger = Qwen3VLVisionPatchMerger(
+ config=config,
+ use_postshuffle_norm=False,
+ )
+
+ self.deepstack_visual_indexes = config.deepstack_visual_indexes
+ self.deepstack_merger_list = nn.ModuleList(
+ [
+ Qwen3VLVisionPatchMerger(
+ config=config,
+ use_postshuffle_norm=True,
+ )
+ for _ in range(len(config.deepstack_visual_indexes))
+ ]
+ )
+
+ self.gradient_checkpointing = False
+
+ def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+ merge_size = self.spatial_merge_size
+
+ max_hw = int(grid_thw[:, 1:].max().item())
+ freq_table = self.rotary_pos_emb(max_hw) # (max_hw, dim // 2)
+ device = freq_table.device
+
+ total_tokens = int(torch.prod(grid_thw, dim=1).sum().item())
+ pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
+
+ offset = 0
+ for num_frames, height, width in grid_thw:
+ merged_h, merged_w = height // merge_size, width // merge_size
+
+ block_rows = torch.arange(merged_h, device=device) # block row indices
+ block_cols = torch.arange(merged_w, device=device) # block col indices
+ intra_row = torch.arange(merge_size, device=device) # intra-block row offsets
+ intra_col = torch.arange(merge_size, device=device) # intra-block col offsets
+
+ # Compute full-resolution positions
+ row_idx = block_rows[:, None, None, None] * merge_size + intra_row[None, None, :, None]
+ col_idx = block_cols[None, :, None, None] * merge_size + intra_col[None, None, None, :]
+
+ row_idx = row_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+ col_idx = col_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+
+ coords = torch.stack((row_idx, col_idx), dim=-1)
+
+ if num_frames > 1:
+ coords = coords.repeat(num_frames, 1)
+
+ num_tokens = coords.shape[0]
+ pos_ids[offset : offset + num_tokens] = coords
+ offset += num_tokens
+
+ embeddings = freq_table[pos_ids] # lookup rotary embeddings
+ embeddings = embeddings.flatten(1)
+ return embeddings
+
+ def fast_pos_embed_interpolate(self, grid_thw):
+ grid_ts, grid_hs, grid_ws = grid_thw[:, 0], grid_thw[:, 1], grid_thw[:, 2]
+
+ idx_list = [[] for _ in range(4)]
+ weight_list = [[] for _ in range(4)]
+
+ for t, h, w in zip(grid_ts, grid_hs, grid_ws):
+ h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h)
+ w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w)
+
+ h_idxs_floor = h_idxs.int()
+ w_idxs_floor = w_idxs.int()
+ h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+ w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+
+ dh = h_idxs - h_idxs_floor
+ dw = w_idxs - w_idxs_floor
+
+ base_h = h_idxs_floor * self.num_grid_per_side
+ base_h_ceil = h_idxs_ceil * self.num_grid_per_side
+
+ indices = [
+ (base_h[None].T + w_idxs_floor[None]).flatten(),
+ (base_h[None].T + w_idxs_ceil[None]).flatten(),
+ (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
+ (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
+ ]
+
+ weights = [
+ ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
+ ((1 - dh)[None].T * dw[None]).flatten(),
+ (dh[None].T * (1 - dw)[None]).flatten(),
+ (dh[None].T * dw[None]).flatten(),
+ ]
+
+ for i in range(4):
+ idx_list[i].extend(indices[i].tolist())
+ weight_list[i].extend(weights[i].tolist())
+
+ idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=self.pos_embed.weight.device)
+ weight_tensor = torch.tensor(
+ weight_list, dtype=self.pos_embed.weight.dtype, device=self.pos_embed.weight.device
+ )
+ pos_embeds = self.pos_embed(idx_tensor) * weight_tensor[:, :, None]
+ patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
+
+ patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
+
+ patch_pos_embeds_permute = []
+ merge_size = self.config.spatial_merge_size
+ for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
+ pos_embed = pos_embed.repeat(t, 1)
+ pos_embed = (
+ pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
+ .permute(0, 1, 3, 2, 4, 5)
+ .flatten(0, 4)
+ )
+ patch_pos_embeds_permute.append(pos_embed)
+ patch_pos_embeds = torch.cat(patch_pos_embeds_permute)
+ return patch_pos_embeds
+
+ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+ The final hidden states of the model.
+ grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+ The temporal, height and width of feature shape of each image in LLM.
+
+ Returns:
+ `torch.Tensor`: hidden_states.
+ """
+ hidden_states = self.patch_embed(hidden_states)
+
+ pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
+ hidden_states = hidden_states + pos_embeds
+
+ rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+ seq_len, _ = hidden_states.size()
+ hidden_states = hidden_states.reshape(seq_len, -1)
+ rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+ emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+ position_embeddings = (emb.cos(), emb.sin())
+
+ cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+ dim=0,
+ # Select dtype based on the following factors:
+ # - FA2 requires that cu_seqlens_q must have dtype int32
+ # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+ # See https://github.com/huggingface/transformers/pull/34852 for more information
+ dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+ )
+ cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+ deepstack_feature_lists = []
+ for layer_num, blk in enumerate(self.blocks):
+ hidden_states = blk(
+ hidden_states,
+ cu_seqlens=cu_seqlens,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ if layer_num in self.deepstack_visual_indexes:
+ deepstack_feature = self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](
+ hidden_states
+ )
+ deepstack_feature_lists.append(deepstack_feature)
+
+ hidden_states = self.merger(hidden_states)
+
+ return hidden_states, deepstack_feature_lists
+```
+
+**`Qwen3VLVisionBlock`** (1,031 source chars):
+
+```python
+class Qwen3VLVisionBlock(GradientCheckpointingLayer):
+ def __init__(self, config, attn_implementation: str = "sdpa") -> None:
+ super().__init__()
+ self.norm1 = nn.LayerNorm(config.hidden_size, eps=1e-6)
+ self.norm2 = nn.LayerNorm(config.hidden_size, eps=1e-6)
+ self.attn = Qwen3VLVisionAttention(config=config)
+ self.mlp = Qwen3VLVisionMLP(config=config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ hidden_states = hidden_states + self.attn(
+ self.norm1(hidden_states),
+ cu_seqlens=cu_seqlens,
+ rotary_pos_emb=rotary_pos_emb,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+ return hidden_states
+```
+
+**`Qwen3VLVisionAttention`** (3,347 source chars):
+
+```python
+class Qwen3VLVisionAttention(nn.Module):
+ def __init__(self, config: Qwen3VLVisionConfig) -> None:
+ super().__init__()
+ self.dim = config.hidden_size
+ self.num_heads = config.num_heads
+ self.head_dim = self.dim // self.num_heads
+ self.num_key_value_groups = 1 # needed for eager attention
+ self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True)
+ self.proj = nn.Linear(self.dim, self.dim)
+ self.scaling = self.head_dim**-0.5
+ self.config = config
+ self.attention_dropout = 0.0
+ self.is_causal = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ seq_length = hidden_states.shape[0]
+ query_states, key_states, value_states = (
+ self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+ )
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+ query_states = query_states.transpose(0, 1).unsqueeze(0)
+ key_states = key_states.transpose(0, 1).unsqueeze(0)
+ value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ if self.config._attn_implementation == "flash_attention_2":
+ # Flash Attention 2: Use cu_seqlens for variable length attention
+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+ attn_output, _ = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ cu_seq_lens_q=cu_seqlens,
+ cu_seq_lens_k=cu_seqlens,
+ max_length_q=max_seqlen,
+ max_length_k=max_seqlen,
+ is_causal=False,
+ **kwargs,
+ )
+ else:
+ # Other implementations: Process each chunk separately
+ lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+ splits = [
+ torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+ ]
+
+ attn_outputs = [
+ attention_interface(
+ self,
+ q,
+ k,
+ v,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ is_causal=False,
+ **kwargs,
+ )[0]
+ for q, k, v in zip(*splits)
+ ]
+ attn_output = torch.cat(attn_outputs, dim=1)
+
+ attn_output = attn_output.reshape(seq_length, -1).contiguous()
+ attn_output = self.proj(attn_output)
+ return attn_output
+```
+
+**`Qwen3VLVisionMLP`** (537 source chars):
+
+```python
+class Qwen3VLVisionMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.linear_fc1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
+ self.linear_fc2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=True)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_state):
+ return self.linear_fc2(self.act_fn(self.linear_fc1(hidden_state)))
+```
+
+**`Qwen3VLVisionPatchMerger`** (836 source chars):
+
+```python
+class Qwen3VLVisionPatchMerger(nn.Module):
+ def __init__(self, config: Qwen3VLVisionConfig, use_postshuffle_norm=False) -> None:
+ super().__init__()
+ self.hidden_size = config.hidden_size * (config.spatial_merge_size**2)
+ self.use_postshuffle_norm = use_postshuffle_norm
+ self.norm = nn.LayerNorm(self.hidden_size if use_postshuffle_norm else config.hidden_size, eps=1e-6)
+ self.linear_fc1 = nn.Linear(self.hidden_size, self.hidden_size)
+ self.act_fn = nn.GELU()
+ self.linear_fc2 = nn.Linear(self.hidden_size, config.out_hidden_size)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = self.norm(x.view(-1, self.hidden_size) if self.use_postshuffle_norm else x).view(-1, self.hidden_size)
+ x = self.linear_fc2(self.act_fn(self.linear_fc1(x)))
+ return x
+```
+
+**`Qwen3VLVisionPatchEmbed`** (897 source chars):
+
+```python
+class Qwen3VLVisionPatchEmbed(nn.Module):
+ def __init__(self, config) -> None:
+ super().__init__()
+ self.patch_size = config.patch_size
+ self.temporal_patch_size = config.temporal_patch_size
+ self.in_channels = config.in_channels
+ self.embed_dim = config.hidden_size
+
+ kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
+ self.proj = nn.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=True)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ target_dtype = self.proj.weight.dtype
+ hidden_states = hidden_states.view(
+ -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
+ )
+ hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
+ return hidden_states
+```
+
+**`Qwen3VLVisionRotaryEmbedding`** (574 source chars):
+
+```python
+class Qwen3VLVisionRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, dim: int, theta: float = 10000.0) -> None:
+ super().__init__()
+ inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ def forward(self, seqlen: int) -> torch.Tensor:
+ seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+ freqs = torch.outer(seq, self.inv_freq)
+ return freqs
+```
+
+**`apply_rotary_pos_emb_vision`** (module-level, 506 source chars):
+
+```python
+def apply_rotary_pos_emb_vision(
+ q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+ orig_q_dtype = q.dtype
+ orig_k_dtype = k.dtype
+ q, k = q.float(), k.float()
+ cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ q_embed = q_embed.to(orig_q_dtype)
+ k_embed = k_embed.to(orig_k_dtype)
+ return q_embed, k_embed
+```
+
+**`rotate_half`** (module-level, 185 source chars):
+
+```python
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+```
+
+**`Qwen3VLVisionConfig`** (whole class, 1,195 source chars; the `__init__` signature and defaults
+are the frozen reference — the 2B values are §3.3):
+
+```python
+class Qwen3VLVisionConfig(PretrainedConfig):
+ model_type = "qwen3_vl"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ depth=27,
+ hidden_size=1152,
+ hidden_act="gelu_pytorch_tanh",
+ intermediate_size=4304,
+ num_heads=16,
+ in_channels=3,
+ patch_size=16,
+ spatial_merge_size=2,
+ temporal_patch_size=2,
+ out_hidden_size=3584,
+ num_position_embeddings=2304,
+ deepstack_visual_indexes=[8, 16, 24],
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.depth = depth
+ self.hidden_size = hidden_size
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.num_heads = num_heads
+ self.in_channels = in_channels
+ self.patch_size = patch_size
+ self.spatial_merge_size = spatial_merge_size
+ self.temporal_patch_size = temporal_patch_size
+ self.out_hidden_size = out_hidden_size
+ self.num_position_embeddings = num_position_embeddings
+ self.initializer_range = initializer_range
+ self.deepstack_visual_indexes = deepstack_visual_indexes
+```
+
+transformers 4.57.6, torch 2.6.0+cpu (this darwin host's venv reports `2.6.0` — the lock pins
+plain `torch==2.6.0` on darwin and `2.6.0+cpu` elsewhere; `qwen3vl_probe.py` gates on both),
+safetensors 0.6.2, extracted from `tools/hf-reference/.venv` (see `tools/hf-reference/provenance.json`).
+
+### 11.2 Design decisions
+
+**Per-image sequential execution.** jmlx processes one image at a time with one unmasked
+noncausal `MLXFast.scaledDotProductAttention` per image, instead of the reference's packed
+rows split per image by `cu_seqlens`. Numerically identical: attention mixes nothing across
+images, and every other op in the tower (RoPE, norms, MLP, merger) is row-wise, so packing
+versus looping changes only the scheduling of independent row computations, not their
+values. Looping also bounds the concurrent attention workspace to a single image (plan §4
+budget).
+
+**Patch projection: core `Conv3d` adopted.** The tower uses the core `Conv3d` (native mlx
+conv3d), with the weight transposed from PyTorch OIHW to jmlx channels-last
+`[out, kt, kh, kw, in]` via axes [0,4,1,2,3] in the HF-provenance weight map. The
+flattened-linear alternative (reshape the 5-D weight into a 2-D GEMM over the 1,536-wide
+patch rows) is recorded as rejected in favor of the native conv path; the strict evidence
+for the choice is the golden's `patch_embed` comparison
+(`Qwen3VlVisionTowerGoldenTest`, WU5), which validates the chosen conv output against the
+reference. The MLX/mlx-vlm channels-last checkpoint provenance plan is deferred to WP6's
+affine scope.
+
+**Learned-position interpolation.** Frozen candidate: double linspace
+`v_i = ((side - 1.0) / (n - 1)) * i` for i in 0..n-1 with `side` = `num_grid_per_side`; the
+WU2 golden probes pin the exact torch double bits for n ∈ {4, 6, 8}, side 8, and WU3's
+pure-Java test asserts bit-equality — if the candidate does not bit-match, this section
+gets an addendum naming the expression that does. Floor by int truncation; ceil = floor+1
+clipped to `side - 1`; the double fractions are rounded to float32 when the weight vector
+is built, matching the reference's `torch.tensor(weight_list, dtype=…)` which casts the
+doubles to the table dtype at tensor creation; the four corner rows are combined as a
+float32 bilinear sum. The merge-block ordering is applied directly: jmlx patches and the
+reference's output are both in merge-block order, so no separate permute is needed.
+
+**Vision RoPE.** `inv_freq` is computed in FLOAT32 —
+`1.0 / (theta ** (arange(0, dim, 2, float32) / dim))`, theta 10000, `dim = head_dim // 2`
+(16 frequencies for the 2B, head_dim 64); the WU2 golden pins the exact 16 float32 bits and
+the WU3 test asserts bit-equality. Positions are the patch's actual (row, col) grid
+coordinates in merge-block order (block·merge + intra). `freqs = [row_freqs | col_freqs]` →
+`[N, dim]`; `emb = cat(freqs, freqs)` → `[N, head_dim]`; cos/sin in float32. The rotation
+upcasts q/k to float32, computes `q·cos + rotate_half(q)·sin` in float32, and casts back to
+the input dtype — the OPPOSITE compute/cast order of the WP4 `MultiAxisRotation` (which
+casts cos/sin down to the input dtype and computes there); the new core `TwoAxisRotation`
+documents both orders.
+
+**Merger ordering.** The main merger (`use_postshuffle_norm False`) norms per-patch over
+`hidden` BEFORE the view into merged rows; the DeepStack mergers
+(`use_postshuffle_norm True`) view into merged rows FIRST and norm over `hidden·merge²`
+(4096 for the 2B). Both use exact (erf) GELU — `nn.GELU()`, NOT tanh; the vision BLOCKS
+use `gelu_pytorch_tanh` (`hidden_act`) for their MLP. eps 1e-6 is hardcoded in the 4.57.6
+source (block norms and both merger norm placements), not a config field, so jmlx
+hardwires 1e-6.
+
+**Weights: stored bf16, run float32.** Weights are stored bf16 in the synthetic checkpoint
+(as in the real 2B artifact); the tower upconverts everything to FLOAT32 at construction
+(plan §7: float correctness initially computed in FLOAT32, including conversion of stored
+weights). For the 2B the WP1 probe golden reports 315 bf16 `model.visual.*` tensors
+totalling 813,914,112 bytes (776.21 MiB) as stored; the float32 conversion doubles that to
+1,627,828,224 bytes (1,552.42 MiB, ≈1.52 GiB) for the tower's lifetime.
+
+**Checkpoint key scheme.** The synthetic checkpoint uses the real artifact's
+`model.visual.*` key scheme (WP1 probe inventory; §5 of this findings doc) — if the bare
+`Qwen3VLVisionModel.save_pretrained` omits the `model.visual.` prefix, the generator adds
+it back explicitly and documents the choice in the golden provenance. The tower's
+public `visionWeights` map renames the checkpoint keys to its own child-tree paths; the
+only structural rename is the conv weight transpose above. Every `model.visual.*` key
+that does not map to a known tower key is rejected rather than skipped (PR #58 review):
+a numeric block or DeepStack merger index past the configured depth or DeepStack count
+is named with the limit that excludes it, and any other unrecognised visual key — an
+unknown sub-key (the MLP branch maps only the `linear_fc1`/`linear_fc2` affine
+`weight`/`bias`), an unknown top-level visual key, a non-numeric or overflowing index —
+is named as unrecognised; only genuinely non-visual checkpoint keys are skipped. The
+method is all-or-nothing (PR #58 review round 2): it maps and validates every key
+before transposing or closing anything, so a thrown rejection leaves the caller's
+checkpoint map byte-for-byte untouched (the conv weight is transposed and its original
+closed only on success, the `SmolVlmModel` pattern).
+
+### 11.3 Golden/checkpoint inventory (planned values)
+
+Intended inventory from plan §5. **Generated by WU2; digests pinned in
+`tools/hf-reference/provenance.json`** — no sha256 values are listed here.
+
+- Generator: `tools/hf-reference/qwen3vl_vision.py` (mirrors `qwen3vl_probe.py` — gates
+ transformers 4.57.6 / torch 2.6.0 / safetensors 0.6.2, deterministic, prints digests,
+ never invoked by Gradle).
+- Checkpoint dir: `tools/hf-reference/goldens/checkpoints/qwen3vl-vision/` — a bare
+ `Qwen3VLVisionModel` (`config.json` + `model.safetensors`, weights **bf16**, exercising
+ the stored-bf16 → float32 conversion) plus `images/` with the four PNGs below. The
+ reference run loads the weights into a float32 model (`dtype=torch.float32`,
+ `attn_implementation="eager"`, `eval()`). Before saving, every LayerNorm affine (all
+ 16: `blocks.*.norm1/norm2`, `merger.norm`, `deepstack_merger_list.*.norm`) is
+ perturbed from its PyTorch default (weight 1, bias 0) — weight ~ N(1.0, 0.1), bias
+ ~ N(0, 0.05), drawn from a dedicated seed 20261021 generator — so the strict golden
+ discriminates the tower's norm wiring (a PR #58 review finding; without it the 16
+ identical norms could not catch a norm1/norm2 swap, a weight/bias swap, a wrong
+ merger/DeepStack norm, or a norm-key typo).
+- Vision config (tiny): `depth 6`, `hidden_size 256`, `hidden_act "gelu_pytorch_tanh"`,
+ `intermediate_size 512`, `num_heads 4` (head_dim 64 — same rotary width as the real 2B),
+ `in_channels 3`, `patch_size 16`, `spatial_merge_size 2`, `temporal_patch_size 2`,
+ `out_hidden_size 256`, `num_position_embeddings 64` (8×8 table, `num_grid_per_side` 8),
+ `deepstack_visual_indexes [1, 3, 5]`, `initializer_range 0.02`; seed `20261021`.
+- Images (h×w; distinct per-pixel functions of coordinates so no two rows collide; all dims
+ multiples of 32 so `smart_resize` is a no-op; all areas ≥ min_pixels 3136):
+
+| file | h×w | grid (t, h, w) | N (patches) |
+|---|---|---|---:|
+| `square.png` | 128×128 | (1, 8, 8) | 64 |
+| `wide.png` | 64×96 | (1, 4, 6) | 24 |
+| `tall.png` | 96×64 | (1, 6, 4) | 24 |
+| `narrow.png` | 128×64 | (1, 8, 4) | 32 |
+
+ Total N = 144, merged rows 36 (16 + 6 + 6 + 8). `square.png` is the exact-lookup path
+ (`linspace(0, 7, 8)` integers); `wide.png` is fractional on both axes; `tall.png` is the
+ transposed fractional case; `narrow.png` is h exact, w fractional.
+- Per-case captures (hook-based, over the packed rows): `pos_embed_interpolated [N,256]`,
+ `patch_embed [N,256]` (conv output before the pos-embed add), `block0`, `block1`,
+ `block3`, `block5` (block5 = last block = deepstack level 2 source) each `[N,256]`,
+ `merger [36,256]`, `deepstack0/1/2 [36,256]` (the returned `deepstack_feature_lists`).
+- Bit probes (golden `probes`, for the pure-Java tests): `linspace` — exact double bits of
+ `torch.linspace(0, 7, n)` for n ∈ {4, 6, 8} (16 values) plus the real-2B geometry
+ `torch.linspace(0, 47, n)` for n ∈ {16, 24, 32, 50, 64} (side 48, 186 values; those
+ entries carry an explicit `"side"` field while the side-8 entries keep their original
+ shape — §11.5); `inv_freq` — exact float32
+ bits, 16 values, theta 10000, dim 32 (the real-2B geometry); `cos_sin_sample` — float32
+ bits of `cos`/`sin` at a few representative frequencies from the linspace values;
+ `pos_embed_sample` — the interpolated float32 row for the first patch of `wide.png` and
+ `tall.png` (128 floats each).
+- Golden: `tools/hf-reference/goldens/qwen3vl-vision-wp5.json`, schema
+ `jmlx-phase7-3b-wp5-qwen3vl-vision-v1` — provenance, probes, and the 4 cases with nested
+ float lists exactly like `idefics3.json`.
+
+### 11.4 Measured per-image peak
+
+Method: `Qwen3VlVisionPeakTest` (native, ordinary `test` task) runs the real-2B tower
+geometry — hidden 1024, 16 heads, head_dim 64, intermediate 4096, out_hidden 2048,
+`num_position_embeddings` 2304, patch/temporal 16/2, merge 2 — at three reduced depths,
+each with one DeepStack index (depth 1: index 0; depths 3 and 4: index 1), constant
+0.001 weights via `MLX.full`, and one synthetic 1024×1024 image (grid (1, 64, 64), 4096
+patch rows) processed through `Qwen3VlImageProcessor`. The test settles a baseline
+`MLXMemory.activeBytes()` with every static array already evaluated (all three towers'
+weights, bracket-attention weights, inputs, rotations), then records four tower
+measurements, each one forward in a fresh per-request scope:
+
+1. The tower forward's own per-image transient peak at depth 1 — the depth-independent
+ floor: `MLXMemory.resetPeak()` right before `tower.forward(request, images)`,
+ `MLXMemory.peakBytes()` right after. The forward evaluates and releases the embedding
+ stage's intermediates before block 0 (a private child scope of the image scope) and
+ each block's intermediates internally (private per-block child scopes), so
+ `peakBytes() - baseline` is one block's live intermediates plus the entry row, the
+ mergers, and the 16 MiB of hoisted rows.
+2. The same transient at the depth-3 proxy, which must stay under the plan §4 bound.
+3. The sustained per-image evaluated peak at depth 3: a settled active-bytes delta
+ (`settledAfter - baseline`) taken while the per-request scope is still open — the
+ per-block and per-image scopes close inside the forward, and the hoisted merged and
+ DeepStack rows live in the per-request scope until it closes, as in production.
+4. The same transient at depth 4; with the depth-1 transient it bounds per-block
+ accumulation (the difference must stay under 64 MiB).
+
+plus a fifth bracket, the transient per-image attention workspace: a direct single
+`TwoAxisRotaryAttention` forward at 4096 rows (q/k/v [4096, 16, 64], constant weights),
+bracketed by `resetPeak()` before and `peakBytes()` after the evaluated forward, run
+after every per-request scope closes so the bracket peak carries no retained-row offset.
+The depth-3 transient, the depth-3 sustained peak and the attention bracket are asserted
+against the plan §4 5 GiB planning bound; the depth-1/depth-4 pair is asserted against
+the 64 MiB depth-independence bound.
+
+Measured (M5 Max, ordinary `test` task, MLX_ENABLE_TF32=1 default, 2026-10-23,
+embedding-stage and per-block evaluate-and-release in place):
+
+- Tower forward per-image transient peak (depth-3 proxy): **1,345,847,352 B ≈ 1.25 GiB
+ (1,283.45 MiB)** over the settled baseline — 2,743,205,944 B (≈2.56 GiB) below the
+ pre-fix depth-3 peak of 4,089,053,296 B, which carried every block's intermediates
+ live at once (and the embedding stage's throughout).
+- Sustained per-image evaluated peak: **16,777,216 B — exactly 16 MiB**. This is precisely the
+ logical retained output of the tower on the 4096-row image after the embedding-stage,
+ per-block and per-image scopes close: the merged 1024-row × 2048-float `out_hidden`
+ representation plus one DeepStack level (1024 × 2048 floats), i.e. 2 × 1024 × 2048 × 4 B
+ = 16 MiB — the merged rows and the `[1]` deepstack capture, nothing else retained.
+ Unchanged by the per-request-scope review fix: the retained rows now live in the caller's
+ per-request scope instead of the tower's module scope, but the single-image forward
+ concatenates nothing, so the same 16 MiB is retained until the per-request scope closes;
+ the embedding-stage fix also leaves it unchanged, since the stage's arrays were never
+ part of the retained output.
+- Transient attention workspace (bracketed by the direct `TwoAxisRotaryAttention` forward at
+ 4096 rows): **335,544,320 B = exactly 320 MiB (0.3125 GiB)** — unchanged by the
+ per-block and embedding-stage fixes, which never touch a single-attention call. Exactly five single-head 4096×4096
+ float32 matrices (67,108,864 B each); the full 16-head score matrix is
+ 1,073,741,824 B = 1 GiB. In the isolated call MLX's fused scaled-dot-product kernel
+ tiles the score computation and never materializes the full matrix, which is why the
+ measured transient is well under even one full matrix. The bracket runs after every
+ per-request scope closes, so the 16 MiB of retained rows no longer offsets the bracket
+ peak.
+- Depth sweep with the embedding-stage fix in place (same probe, 2026-10-23): depth 1 =
+ depth 3 = depth 4 = **1,345,847,352 B (1.25 GiB)** — all three transients are
+ bit-for-bit equal, and depth 4 − depth 1 = **0 B**. The per-image peak no longer
+ scales with depth, and the embedding stage no longer contributes to it at any depth.
+ The post per-block-fix sweep had depth 1 slightly *higher* than depth 3/4 (1,757,020,216
+ B vs 1,606,025,248 B, Δ = **−150,994,968 B (−144.00 MiB)**): the allocator's pool floor
+ on a shorter allocation sequence, while the embedding stage's arrays were still live
+ across the blocks at every depth. Confining the embedding stage to its own scope
+ released the only depth-independent arrays that survived the per-block loop, so all
+ three probes now agree to the byte. The test's bound stays one-sided: it fails only if
+ depth 4 exceeds depth 1 by more than 64 MiB, the signature of per-block accumulation
+ returning.
+
+Depth scaling — cause, decomposition, and the fix. The pre-fix peak **was** depth-dependent
+(pre-fix sweep, 2026-10-23: depths 1–4 gave 1,908,015,184 B, 2,914,648,160 B,
+4,089,053,296 B and 5,263,458,432 B; per-block slope exactly **1,174,405,136 B ≈ 1.09 GiB**
+in both measured intervals; extrapolated to the real depth of 24: 28,751,561,152 B ≈
+26.8 GiB ≈ 5.4× the bound — the concern that motivated the fix). The slope was not an
+attention score-matrix story; the following is an inference from arithmetic and code
+reading, not a profiler trace:
+
+- Cause: every block's ops resolved their result arrays into the one per-image scope, so
+ the Java `MLXArray` handles for every intermediate of every block stayed live until the
+ per-image scope closed at the end of the image — each block added its intermediates to
+ the peak instead of releasing them.
+- Decomposition: **1,174,405,136 B = 70 × 16 MiB + 16 B**, where 16 MiB is one
+ [4096, 1024] float32 row buffer (4096 patch rows × 1024 hidden × 4 B) — 70 pinned
+ [4096, 1024]-sized intermediate row-units per block plus 16 B of overhead. Reading the
+ block's op sequence gives ≈73 such row-units (the norm outputs, the [4096, 3072] qkv
+ row-unit and its q/k/v splits, the rotary-rotated q/k, the attention output and
+ projection, the two residual adds, and the 4096-wide MLP intermediates at 4 row-units
+ each), so the measured 70 and the read ≈73 agree within the attention-tiled workspaces
+ that never materialize full [4096, 1024] rows.
+- The 320 MiB isolated-attention bracket (= exactly 20 row-units, matching the ≈20–23
+ attention row-units in the decomposition) refutes the alternative kept-score-matrix
+ theory: one 16-head float32 score matrix would be 1,073,741,824 B = 1 GiB, and the
+ fused SDPA kernel tiles it — the bracket peaks at 20–23 row-units' worth, never a full
+ matrix.
+- Fix (per-block unit, 2026-10-23 review round): `runImage` now wraps each block in a
+ private per-block child scope of the image scope — the block's entry row is pinned
+ into it (same-shape `broadcastTo`, the `T5Model.project` idiom), the block's output
+ and (when tapped) DeepStack rows are evaluated and hoisted out, and the block scope
+ closes before the next block starts, so one block's intermediates are released before
+ the next accumulate. The computation is bit-identical to the pre-change graph —
+ uncompiled MLX does not fuse across eval boundaries — verified exact-bits in both
+ TF32 modes against the pre-change recording (`exactBitsVerifyVision`, 6 captures,
+ 20,971,520 floats total, `build/exact-bits/wp5-vision/tf32-{1,0}/`, re-recorded at
+ 0dc7595 with the probe revision below).
+- Fix (embedding-stage unit, 2026-10-23 review round): the per-block loop leaves the
+ embedding stage's arrays — the pixel tensor, the conv output, the `[4, n, hidden]`
+ corner rows and their weighted product, and the pe add chain — in the image scope for
+ the whole image, so they still live through every block (roughly 0.2 GiB at the
+ 4096-row geometry). The embedding stage now runs in its own private child scope of the
+ image scope: `x = patchRows + pe` is evaluated and hoisted to the image scope before
+ the stage scope closes (and, for the golden test's captures, the `pe` and `patchRows`
+ rows are evaluated and hoisted to the target scope there), so the rest of the stage is
+ released before block 0. Same eval-boundary property as the per-block unit — uncompiled
+ MLX does not fuse across eval boundaries — verified exact-bits in both TF32 modes
+ against the *existing* baseline (`exactBitsVerifyVision`, 6 captures, 20,971,520
+ floats total, `build/exact-bits/wp5-vision/tf32-{1,0}/` — no re-recording). Measured
+ drops: 260,177,896 B (≈248 MiB) at depth 3 and 4, 411,172,864 B (≈392 MiB) at depth 1
+ (which also shed the allocator pool-floor excess), and all three depth probes became
+ bit-for-bit equal.
+
+Probe revision (PR #58 review round 3): the first revision of the probe filled every
+tower weight with constant 0.001. Decoding those recordings showed the probe collapsed:
+every conv output channel and every position-embedding column is identical, so each row's
+columns are all equal (max distinct values per row = 1); LayerNorm then turns every row
+into its bias, so all `v` rows are identical; and the attention output is therefore `v`
+itself, whatever the scores, softmax or rotation are. The old `merged` capture held only
+2 distinct values across its 1024 rows, and `block0 − (patchEmbed + posEmbed)` was one
+constant 0.0082247 on every row — each block adds the same constant to every row, so
+dropping the rotation, rotating `v`, or swapping `q` and `k` would all have left the
+recordings unchanged. The bit-identity evidence was therefore weaker than it read (the
+change itself is carried by the strict HF golden). The probe now draws every tower weight
+from `MLXRandom.normal` (loc 0, standard deviation 0.02) after `MLXRandom.seed(42)`, and
+every image pixel from a `java.util.Random(42)` uniform in [0, 1) scaled to a full-range
+byte — fully deterministic (fixed seeds, no file inputs) and row-sensitive: with
+non-constant weights and pixels every activation row differs from the others, the
+softmax weights are position-dependent, and any change to the per-block loop, the
+attention, the RoPE or the norms changes the recorded bits. The seed and the weight scale
+are part of the probe spec hash, so the old constant-weight baseline can never compare
+silently against this one. Procedure: record at 0dc7595 (the pre per-block change; the
+only forward-numerics difference between 0dc7595 and the verify commit is the per-block
+`runImage` loop) using the byte-identical updated harness (recorder + the `a802357`
+`jmlx-models/build.gradle`, verified unchanged through HEAD) in a temporary worktree,
+then verify at HEAD in both TF32 modes — bit-identical. The sensitivity is visible in the
+recordings themselves: every capture now carries many distinct values per row (the old
+ones had 1). The peak test keeps constant weights, because peak memory depends only on
+shapes.
+
+The flagged concern is now a regression test: `Qwen3VlVisionPeakTest` asserts
+peak(4) − peak(1) < 67,108,864 B (64 MiB) in addition to the plan §4 bound on the
+depth-3 proxy, so a returning per-block accumulation fails the ordinary `test` task
+rather than waiting for a full-depth run. `Idefics3VisionTower` (the
+`SmolVlmModel`'s tower) shares the same per-request-scope pattern without per-block
+scopes and may scale the same way; it is out of scope for this fix.
+
+Planning bound (plan §4): below the 5 GiB bound — at 4096 rows and 16 heads one FLOAT32
+score matrix is 1 GiB, and the budget conservatively allows four such matrices (4 GiB)
+plus Q/K/V and block/merger activations. The depth-3 tower-forward proxy (1.25 GiB), the
+16 MiB sustained and the 0.31 GiB attention bracket all pass under it. Since the
+per-image peak is now depth-independent (all three depth probes bit-for-bit equal,
+asserted), the bound applies to the full depth-24 tower's per-image transient as well —
+the pre-fix ≈26.8 GiB extrapolation no longer holds. Not a claim that the model fits a
+7 GB host.
+
+CI headroom on the hosted runner (updated 2026-10-23, PR #58 review round 3; first
+recorded in review round 2): the round-2 reviewer flagged the (then-current) 3.81 GiB
+depth-3 transient as possibly tight in ordinary `test` on the `macos-26` hosted runner —
+≈7 GB RAM shared with the Gradle daemon and the test JVM, a reviewer-recorded figure, not
+verified against the runner image (the repo's own plan also records 7 GB RAM / 14 GB disk
+for this runner). The per-block and embedding-stage evaluate-and-release fixes resolve the
+concern: the largest transient is now any of the depth probes — all three bit-for-bit
+equal at 1,345,847,352 B ≈ 1.25 GiB — on top of the settled statics of the three towers
+and the bracket (685,053,640 B of computed weights/inputs/rotations, ≈0.65 GiB with
+allocator slack), so the concurrent Metal peak is ≈1.9 GiB. Against ≈7 GiB: ≈1.9 GiB
+Metal + ≤0.5 GiB test-JVM working set (no configured max heap; the JVM's default ¼-RAM max
+is a ceiling, not a reservation) + ≈0.5 GiB Gradle daemon (512 MiB default) + ≈1.2 GiB OS
+≈ 4.1 GiB — ≈2.9 GiB of headroom, versus ≈1 GiB or less for the pre-fix 3.81 GiB peak.
+The 4096-row geometry and the depth probes are therefore unchanged; no CI-specific probe
+reduction is needed.
+
+### 11.5 Linspace probes at the real 2B geometry (2026-10-23, PR #58 review round 2)
+
+The original bit probes pinned only `torch.linspace(0, 7, n)` for the tiny tower's
+side 8, n ∈ {4, 6, 8}. The real 2B model uses `num_position_embeddings` 2304 (side
+48), and the 4096-row grid-64×64 peak case reaches n up to 64 per axis (§11.4), so
+the kernel's larger-n branch at side 48 was unpinned. Review round 2 suspected
+torch's CPU `linspace_kernel` might switch formulas at the SIMD vector width for
+large n (a `Vectorized::arange` path), which would make the scalar port 1 ulp off
+at the real geometry and shift the bilinear weights and floor indices. The
+generator now also pins `torch.linspace(0, 47, n)` for n ∈ {16, 24, 32, 50, 64}
+(186 values): n = 64 is the grid-64×64 cap, n = 50 is odd so the kernel's
+`i < n / 2` split is pinned at a non-power-of-two. Storage: appended to
+`probes.linspace` in the existing per-entry shape with an explicit `"side"` field;
+the side-8 entries keep their original shape (no `"side"`), so the golden diff is
+purely additive. Regeneration: the checkpoint and all four images are
+byte-identical (probes are computed after the reference forward and draw no RNG),
+so only the golden digest was re-pinned in `provenance.json`.
+
+Outcome: **no Java change needed.** `Qwen3VlVisionTower.linspaceValue` (the exact
+torch 2.6.0 CPU `linspace_kernel` port: float32 step = the float32 rounding of the
+double `(end - start) / (n - 1)`; each value the float32 rounding of `start +
+(double) step * i` for `i < n / 2` and `end - (double) step * (n - i - 1)`
+otherwise) bit-matches all five new probes as-is. The golden test
+(`linspaceProbesBitMatchPinnedTorch`, which reads each probe's `"side"` with the
+config-derived side 8 as default) now asserts all eight entries — 202 values at
+float32 bit level — and is green (float32GoldenTest 50/0, the suite 3/3).
+
+Why the vectorized-path hypothesis is wrong for this pin: torch v2.6.0's
+`linspace_kernel` (`aten/src/ATen/native/cpu/RangeFactoriesKernel.cpp` @ v2.6.0)
+calls `cpu_serial_kernel` — the vectorized lambda was removed upstream ("Remove
+vectorization implementation, due to the precision issue between integer and
+double"), and the `Vectorized::arange` vector path exists only in `arange_kernel`,
+not `linspace_kernel`. The scalar half-way split is therefore the formula at every
+n. Confirmed empirically in the pinned venv (torch 2.6.0+cpu): a sweep of
+n ∈ [2, 512] at side {8, 47, 48, 4096} produces **zero** bit differences between
+`torch.linspace(0, side - 1, n)` and the Java formula, all five pinned geometries
+included. No formula switch at any vector width, so the port stays bit-exact at
+the real 2B's n = 64 and the floor index/weight consumers are pinned. Because no
+Java output changed, the `build/exact-bits/wp5-vision/` baselines are untouched
+and `exactBitsVerifyVision` stays bit-identical in both TF32 modes. Host note:
+the original captures and these five entries were pinned on the same host
+(`platform` Darwin-arm64 in the golden's provenance block); in any case the
+scalar kernel above computes the values from bit-exact float arithmetic, so
+they are host-independent.
+
diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/config.json b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/config.json
new file mode 100644
index 00000000..7f6190dd
--- /dev/null
+++ b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/config.json
@@ -0,0 +1,21 @@
+{
+ "deepstack_visual_indexes": [
+ 1,
+ 3,
+ 5
+ ],
+ "depth": 6,
+ "hidden_act": "gelu_pytorch_tanh",
+ "hidden_size": 256,
+ "in_channels": 3,
+ "initializer_range": 0.02,
+ "intermediate_size": 512,
+ "model_type": "qwen3_vl",
+ "num_heads": 4,
+ "num_position_embeddings": 64,
+ "out_hidden_size": 256,
+ "patch_size": 16,
+ "spatial_merge_size": 2,
+ "temporal_patch_size": 2,
+ "transformers_version": "4.57.6"
+}
diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/narrow.png b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/narrow.png
new file mode 100644
index 00000000..d9f47c02
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diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/square.png b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/square.png
new file mode 100644
index 00000000..9f4d711b
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diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/tall.png b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/tall.png
new file mode 100644
index 00000000..7da8e8e9
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diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/wide.png b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/images/wide.png
new file mode 100644
index 00000000..6643982b
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diff --git a/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/model.safetensors b/tools/hf-reference/goldens/checkpoints/qwen3vl-vision/model.safetensors
new file mode 100644
index 00000000..e12f9e48
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diff --git a/tools/hf-reference/goldens/qwen3vl-vision-wp5.json b/tools/hf-reference/goldens/qwen3vl-vision-wp5.json
new file mode 100644
index 00000000..34538f3b
--- /dev/null
+++ b/tools/hf-reference/goldens/qwen3vl-vision-wp5.json
@@ -0,0 +1 @@
+{"schema": "jmlx-phase7-3b-wp5-qwen3vl-vision-v1", "generated_by": "tools/hf-reference/qwen3vl_vision.py", "provenance": {"transformers": "4.57.6", "torch": "2.6.0", "safetensors": "0.6.2", "python": "3.12.15", "platform": "Darwin-arm64", "pillow": "12.3.0", "numpy": "2.5.3", "seed": 20261021, "norm_perturbation": {"seed": 20261021, "generator": "dedicated torch.Generator (does not consume the global model-init RNG)", "weight_distribution": "normal(mean=1.0, std=0.1)", "bias_distribution": "normal(mean=0.0, std=0.05)", "count": 16, "note": "every LayerNorm affine (blocks.*.norm1/norm2, merger.norm, deepstack_merger_list.*.norm) is perturbed before the checkpoint is saved so the 16 norms are mutually distinct and the strict golden discriminates the tower's norm wiring (a norm1/norm2 swap, a weight/bias swap, the wrong norm on the merger versus a DeepStack merger, or a checkpoint-key typo would all change the captures)"}, "config": {"return_dict": true, "output_hidden_states": false, "torchscript": false, "dtype": "None", "pruned_heads": {}, "tie_word_embeddings": true, "chunk_size_feed_forward": 0, "is_encoder_decoder": false, "is_decoder": false, "cross_attention_hidden_size": "None", "add_cross_attention": false, "tie_encoder_decoder": false, "architectures": "None", "finetuning_task": "None", "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "task_specific_params": "None", "problem_type": "None", "tokenizer_class": "None", "prefix": "None", "bos_token_id": "None", "pad_token_id": "None", "eos_token_id": "None", "sep_token_id": "None", "decoder_start_token_id": "None", "max_length": 20, "min_length": 0, "do_sample": false, "early_stopping": false, "num_beams": 1, "temperature": 1.0, "top_k": 50, "top_p": 1.0, "typical_p": 1.0, "repetition_penalty": 1.0, "length_penalty": 1.0, "no_repeat_ngram_size": 0, "encoder_no_repeat_ngram_size": 0, "bad_words_ids": "None", "num_return_sequences": 1, "output_scores": false, "return_dict_in_generate": false, "forced_bos_token_id": "None", "forced_eos_token_id": "None", "remove_invalid_values": false, "exponential_decay_length_penalty": "None", "suppress_tokens": "None", "begin_suppress_tokens": "None", "num_beam_groups": 1, "diversity_penalty": 0.0, "_name_or_path": "", "transformers_version": "4.57.6", "tf_legacy_loss": false, "use_bfloat16": false, "depth": 6, "hidden_size": 256, "hidden_act": "gelu_pytorch_tanh", "intermediate_size": 512, "num_heads": 4, "in_channels": 3, "patch_size": 16, "spatial_merge_size": 2, "temporal_patch_size": 2, "out_hidden_size": 256, "num_position_embeddings": 64, "initializer_range": 0.02, "deepstack_visual_indexes": [1, 3, 5], "model_type": "qwen3_vl", "output_attentions": false}, "checkpoint_files": {"config.json": "eef3eca8974dda4fb1ac79bd29c949e7e088df6b23ea829e8ca0fad0b064d0f9", "model.safetensors": "4893c7c6e4d22a7c3f2918614b421b1f5c32316a7cddf66ac4f59bf5e6be8bc9"}, "images": {"square": {"width": 128, "height": 128, "bytes": 1022, "sha256": "c2613b301b2c613e7d2b20e6c0dba007290ca52546b5dd80b2a81a1bee40f265"}, "wide": {"width": 96, "height": 64, "bytes": 512, "sha256": "002ff61c5d24038c5a9255b827e79679287f5221381da99e3dc2460a12a1f910"}, "tall": {"width": 64, "height": 96, "bytes": 650, "sha256": "6962e08d87703791d4c7e8cc59e2636a50e852718f65e53720e02d7e873e083f"}, "narrow": {"width": 64, "height": 128, "bytes": 717, "sha256": "8d5f484b21535d2a54c70955d7c067b937aab02914f95f41cd3c668ff9fd026e"}}, "key_prefix_note": "the bare Qwen3VLVisionModel state dict carries plain keys (patch_embed.proj.*, pos_embed.weight, blocks.*, merger.*, deepstack_merger_list.*); the generator prepends 'model.visual.' to every key so the tiny checkpoint matches the real Qwen3-VL artifact key scheme (model.visual.*); all 99 tensors are stored as bfloat16", "processor": {"class": "Qwen2VLImageProcessor", "module": "transformers.models.qwen2_vl.image_processing_qwen2_vl", "note": "transformers 4.57.6 has no Qwen3VLImageProcessor class; the slow PIL Qwen2VLImageProcessor is the pinned preprocessing path (WP1 probe precedent and jmlx-vision Qwen3Vl* port target). min_pixels/max_pixels are passed explicitly because the 4.57.6 __init__ overwrites any size dict with the 3136/1003520 defaults (findings \u00a74.2). Images are passed as file path strings so the load_image (exif_transpose) path matches the Java processFile path; all areas lie inside [3136, 12845056] so smart_resize is a no-op.", "min_pixels": 3136, "max_pixels": 12845056, "size": {"shortest_edge": 3136, "longest_edge": 12845056}, "do_resize": true, "resample": "3", "do_rescale": true, "rescale_factor": 0.00392156862745098, "do_normalize": true, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5], "do_convert_rgb": true, "patch_size": 16, "temporal_patch_size": 2, "merge_size": 2}}, "probes": {"linspace": [{"n": 4, "values_hex": ["0000000000000000", "4002aaaaa0000000", "4012aaaac0000000", "401c000000000000"]}, {"n": 6, "values_hex": ["0000000000000000", "3ff6666660000000", "4006666660000000", "4010ccccc0000000", "4016666660000000", "401c000000000000"]}, {"n": 8, "values_hex": ["0000000000000000", "3ff0000000000000", "4000000000000000", "4008000000000000", "4010000000000000", "4014000000000000", "4018000000000000", "401c000000000000"]}, {"side": 48, "n": 16, "values_hex": ["0000000000000000", "4009111120000000", "4019111120000000", "4022cccce0000000", "4029111120000000", "402f555560000000", "4032cccce0000000", "4035eeef00000000", "4039111100000000", "403c333320000000", "403f555540000000", "40413bbbc0000000", "4042ccccc0000000", "40445ddde0000000", "4045eeeee0000000", "4047800000000000"]}, {"side": 48, "n": 24, "values_hex": ["0000000000000000", "4000590b20000000", "4010590b20000000", "40188590c0000000", "4020590b20000000", "40246f4de0000000", "40288590c0000000", "402c9bd380000000", "4030590b20000000", "4032642c80000000", "40346f4de0000000", "40367a6f40000000", "40388590c0000000", "403a90b220000000", "403c9bd380000000", "403ea6f4e0000000", "4040590b20000000", "40415e9be0000000", "4042642c80000000", "404369bd40000000", "40446f4de0000000", "404574dea0000000", "40467a6f40000000", "4047800000000000"]}, {"side": 48, "n": 32, "values_hex": ["0000000000000000", "3ff8421080000000", "4008421080000000", 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0.5231671, 0.5395627, 0.9460134, -0.5086575, -1.634559, -0.07416248, 1.205817, -1.360611, -0.6726443, 0.2921669, 0.04918906, 0.3666162, 0.5966498, -0.03666723, -1.454524, 0.8608531, 0.8120719, -0.02777739, 0.4024836, 0.3214599, 0.2629564, -1.798126, -0.6602013, 0.8528523, 1.757879, 0.3484699, -1.013397, 0.556926, 0.2389353, -1.956503, 2.032538, -0.07811978, 0.8031756, -1.680471, -0.6911529, 1.86607, -0.1168509, -0.5020426, 0.4953171, 0.3858356, 0.2518808, 0.07790807, 0.83452, -0.7444323, -0.7623564, -0.2138046, 2.090095, 0.4653962, -0.1035662, 0.1202367, -0.1917195, -0.3310052, -1.167111, 0.3870384, 1.925372, -1.183423, 0.7442828, -1.976158, -1.104967, 0.5723149, -0.03364976, -0.1614957, 0.2574554, 0.4076163, 0.3767565, -0.8845486, 1.693693, 0.6986652, 0.6033945, -1.22131, 1.746306, 0.03126499, 0.3541563, -0.752429, -0.4752621, -1.088097, 0.1911484, -1.51085, -0.9229864, 0.112155, -0.7706705, 0.03677889, -0.00318234, -0.5480183, 0.6217517, -0.6295545, -0.1048304, 0.1460984, -0.9609135, -0.04158394, 0.7226351, 0.4605482, -1.462213, 2.521041, -0.9164686, 0.212078, 1.358996, 0.4098763, 0.1835416, 0.1390595, 1.576767, -0.9131904, 1.642332, -1.10472, 0.5946566, 0.1021198, 1.090658, 0.4404188, -0.07677031, -0.3915415, -0.08765373, -1.57129, -0.5543633, -0.6228632, -0.5649375, 0.652916, 1.197753, -0.4775212, 0.04614791, 0.5604895, -0.7542368, 1.028276, -0.7479293, -0.7041501], [-0.395691, 0.03347091, 0.9946259, 1.86935, 0.09533106, -0.3651196, 1.352798, 1.500501, 1.580465, 1.684665, 0.1061121, 1.495849, -0.270824, -0.843535, -2.322184, 0.063151, 3.050762, -1.632775, -1.317333, -1.020344, -0.2630101, 1.479639, -3.290648, 1.379171, -0.3608513, -1.061386, 0.4781051, -1.423211, 0.4134496, -0.5006562, -1.166214, 1.315161, -0.9320296, 1.439414, 0.8494893, 2.892462, 1.317677, 0.985285, 2.180461, 1.204372, -1.525618, 1.148365, 0.270447, 0.7096241, -2.382403, -1.177006, 0.5830944, 0.7000835, 0.6029807, -1.914529, 0.9684122, 0.3790045, -0.852589, -1.066046, -1.084579, 2.882524, 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diff --git a/tools/hf-reference/provenance.json b/tools/hf-reference/provenance.json
index 06fa1c18..5d2ada0b 100644
--- a/tools/hf-reference/provenance.json
+++ b/tools/hf-reference/provenance.json
@@ -83,7 +83,14 @@
"smolvlm-tier-b-logits.json": "ab8f1fe12b3ef1a318eb329c97a04abc2021e0e0a2fe45547aa1a19cddee8d22",
"qwen3-vl-2b-wp1-probe.json": "10cd4a4bd06a07f7e67d9a1739a66053f35300cda75c91c58e2448b1f8bc206b",
"qwen3-vl-2b-wp1-slow-fast-logits.json": "f19ab7458ac2754e78689eafc9470545cefd223a423d2509fcd8645afe9ae749",
- "qwen3-vl-wp3-prompt.json": "34be92ef482f537652016c1d107e88a1af51e48bd0cbee2799ba930f8fc4e0b9"
+ "qwen3-vl-wp3-prompt.json": "34be92ef482f537652016c1d107e88a1af51e48bd0cbee2799ba930f8fc4e0b9",
+ "qwen3vl-vision-wp5.json": "17cb3e35fe6c7684bfc2e605691d272699de9127ce7b976112e7ef04d079a859",
+ "checkpoints/qwen3vl-vision/config.json": "eef3eca8974dda4fb1ac79bd29c949e7e088df6b23ea829e8ca0fad0b064d0f9",
+ "checkpoints/qwen3vl-vision/model.safetensors": "4893c7c6e4d22a7c3f2918614b421b1f5c32316a7cddf66ac4f59bf5e6be8bc9",
+ "checkpoints/qwen3vl-vision/images/square.png": "c2613b301b2c613e7d2b20e6c0dba007290ca52546b5dd80b2a81a1bee40f265",
+ "checkpoints/qwen3vl-vision/images/wide.png": "002ff61c5d24038c5a9255b827e79679287f5221381da99e3dc2460a12a1f910",
+ "checkpoints/qwen3vl-vision/images/tall.png": "6962e08d87703791d4c7e8cc59e2636a50e852718f65e53720e02d7e873e083f",
+ "checkpoints/qwen3vl-vision/images/narrow.png": "8d5f484b21535d2a54c70955d7c067b937aab02914f95f41cd3c668ff9fd026e"
},
"chat_sources": {
"mistral/tokenizer.json": "f86c748546561371bca6bb8577e8e504d4022d611612a6af91d08e39d424c63f",
diff --git a/tools/hf-reference/qwen3vl_vision.py b/tools/hf-reference/qwen3vl_vision.py
new file mode 100644
index 00000000..559fb478
--- /dev/null
+++ b/tools/hf-reference/qwen3vl_vision.py
@@ -0,0 +1,664 @@
+#!/usr/bin/env python3
+"""Phase 7.3b WP5 tiny Qwen3-VL vision checkpoint + golden generator.
+
+Runs under tools/hf-reference/.venv (transformers 4.57.6, torch 2.6.0+cpu,
+safetensors 0.6.2, Pillow 12.3.0, NumPy 2.5.3, Python 3.12), CPU float32,
+single-threaded, deterministic (seed 20261021). Every LayerNorm affine
+(blocks.*.norm1/norm2, merger.norm, deepstack_merger_list.*.norm) is
+perturbed away from its PyTorch default (weight 1, bias 0) before the
+checkpoint is saved -- weight ~ N(1.0, 0.1), bias ~ N(0, 0.05), drawn from a
+dedicated seed-20261021 generator -- so the 16 norms are mutually distinct
+and the strict golden discriminates the tower's norm-to-key wiring (a
+norm1/norm2 swap, a weight/bias swap, the wrong norm on the merger versus a
+DeepStack merger, or a checkpoint-key typo would all change the captures).
+It is never invoked by Gradle. It writes:
+
+ * a tiny synthetic vision checkpoint at
+ tools/hf-reference/goldens/checkpoints/qwen3vl-vision/
+ (config.json, model.safetensors with bf16 tensors keyed with the real
+ artifact's `model.visual.` prefix, and four deterministic PNGs under
+ images/);
+ * the golden tools/hf-reference/goldens/qwen3vl-vision-wp5.json capturing
+ pure-math bit probes (linspace double bits, vision inv_freq/cos/sin
+ float32 bits, the first-fully-fractional-patch bilinear
+ position-embedding sample) plus one packed processor call and one packed
+ Qwen3VLVisionModel forward over all four images. The full-packed
+ merger/DeepStack arrays ([36, 256] each, image order) are stored exactly
+ once in the top-level "packed" section; each case stores only its
+ per-image captures plus merged_row_offset/merged_rows slice pointers.
+
+Every written file's SHA-256 is printed at the end and pinned in
+tools/hf-reference/provenance.json `files` (keys relative to goldens/).
+
+PNG determinism: the pixel arrays are fixed affine functions of (x, y) (no
+RNG) and the PNGs are written through Pillow's fixed encoder path
+(Image.frombytes("RGB", ...).save(path, format="PNG")); Pillow emits no
+timestamp or per-run metadata for PNG, so identical pixels yield identical
+bytes on a pinned Pillow.
+
+Deviation record (transformers 4.57.6): there is no Qwen3VLImageProcessor
+class or module in 4.57.6; the reference preprocessing uses the slow PIL
+Qwen2VLImageProcessor from transformers.models.qwen2_vl (the same class the
+WP1 probe and the jmlx-vision Qwen3Vl* port target), constructed with
+explicit min_pixels/max_pixels because the 4.57.6 __init__ else branch
+overwrites any (or no) size dict with the 3136/1003520 defaults (findings
+§4.2).
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import platform
+import shutil
+import struct
+import sys
+from pathlib import Path
+
+SEED = 20261021
+
+# (name, width, height, per-channel affine coefficients (a, b, k) with
+# channel = (a*x + b*y + k) % 256 for x = column, y = row). Coefficients are
+# distinct per image and per channel, every y coefficient keeps all rows
+# distinct, and every channel provably spans the full 0-255 range for these
+# dimensions (the script re-verifies both properties and exits loudly if a
+# future dimension change breaks them).
+IMAGE_SPECS = [
+ ("square", 128, 128, ((1, 2, 0), (2, 1, 0), (3, 5, 17))),
+ ("wide", 96, 64, ((2, 3, 5), (1, 7, 13), (3, 11, 29))),
+ ("tall", 64, 96, ((5, 2, 3), (2, 3, 7), (7, 5, 11))),
+ ("narrow", 64, 128, ((2, 3, 21), (1, 7, 31), (3, 5, 41))),
+]
+
+# Expected processor grids per image, [t, h, w]: all dimensions are multiples
+# of 32 and every area is within [min_pixels, max_pixels], so smart_resize is
+# a no-op and the grid is exactly image dims / patch_size 16.
+EXPECTED_GRIDS = {
+ "square": [1, 8, 8],
+ "wide": [1, 4, 6],
+ "tall": [1, 6, 4],
+ "narrow": [1, 8, 4],
+}
+
+# Patch-row offsets of the per-image slices inside the packed [144, 256]
+# intermediate tensors, in packed order square, wide, tall, narrow.
+PATCH_OFFSETS = {"square": 0, "wide": 64, "tall": 88, "narrow": 112}
+
+KEY_PREFIX = "model.visual."
+
+
+def gate_versions() -> dict:
+ import PIL
+ import numpy as np
+ import torch
+ import transformers
+ from safetensors import __version__ as safetensors_version
+
+ if (
+ transformers.__version__ != "4.57.6"
+ or PIL.__version__ != "12.3.0"
+ or np.__version__ != "2.5.3"
+ or torch.__version__ not in ("2.6.0", "2.6.0+cpu")
+ or platform.python_version_tuple()[:2] != ("3", "12")
+ ):
+ sys.exit(
+ "qwen3vl_vision requires transformers 4.57.6, Pillow 12.3.0, NumPy 2.5.3, "
+ f"torch 2.6.0 and CPython 3.12, got transformers {transformers.__version__}, "
+ f"Pillow {PIL.__version__}, NumPy {np.__version__}, torch {torch.__version__}, "
+ f"Python {platform.python_version()}"
+ )
+ return {
+ "python": platform.python_version(),
+ "platform": f"{platform.system()}-{platform.machine()}",
+ "torch": torch.__version__,
+ "transformers": transformers.__version__,
+ "pillow": PIL.__version__,
+ "numpy": np.__version__,
+ "safetensors": safetensors_version,
+ }
+
+
+def sha256_bytes(data: bytes) -> str:
+ return hashlib.sha256(data).hexdigest()
+
+
+def sha256_file(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as stream:
+ for chunk in iter(lambda: stream.read(1 << 20), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def f32_hex(values) -> list[str]:
+ """Exact float32 hex bits (8 lowercase hex chars) per value."""
+ return [
+ f"{struct.unpack(' list[str]:
+ """Exact double hex bits (16 lowercase hex chars) per value."""
+ return [
+ f"{struct.unpack(' None:
+ """Write one deterministic RGB PNG; verify full-range and row distinctness."""
+ from PIL import Image
+
+ a_r, b_r, k_r = coeffs[0]
+ a_g, b_g, k_g = coeffs[1]
+ a_b, b_b, k_b = coeffs[2]
+ px = bytearray()
+ for y in range(height):
+ for x in range(width):
+ px += bytes(
+ ((a_r * x + b_r * y + k_r) % 256,
+ (a_g * x + b_g * y + k_g) % 256,
+ (a_b * x + b_b * y + k_b) % 256)
+ )
+ # Full 0-255 range per channel and no two rows collide (row bytes differ).
+ for channel in range(3):
+ values = {px[i * 3 + channel] for i in range(width * height)}
+ if values != set(range(256)):
+ sys.exit(f"{path.name}: channel {channel} covers {len(values)} of 256 values")
+ row_bytes = [bytes(px[y * width * 3:(y + 1) * width * 3]) for y in range(height)]
+ if len(set(row_bytes)) != height:
+ sys.exit(f"{path.name}: two rows collide")
+ Image.frombytes("RGB", (width, height), bytes(px)).save(path, format="PNG")
+
+
+def vision_config():
+ from transformers.models.qwen3_vl.configuration_qwen3_vl import Qwen3VLVisionConfig
+
+ return Qwen3VLVisionConfig(
+ depth=6,
+ hidden_size=256,
+ hidden_act="gelu_pytorch_tanh",
+ intermediate_size=512,
+ num_heads=4,
+ in_channels=3,
+ patch_size=16,
+ spatial_merge_size=2,
+ temporal_patch_size=2,
+ out_hidden_size=256,
+ num_position_embeddings=64,
+ deepstack_visual_indexes=[1, 3, 5],
+ initializer_range=0.02,
+ )
+
+
+def perturb_layer_norms(model, seed: int) -> int:
+ """Perturb every LayerNorm affine with a dedicated seeded generator.
+
+ The reference's model init leaves every LayerNorm at its PyTorch default
+ (weight 1, bias 0): with all 16 norms identical, the strict golden would
+ still pass if the tower swapped norm1 and norm2, swapped a norm's weight
+ and bias, used the wrong norm for the merger versus a DeepStack merger,
+ or mistyped a norm's checkpoint key. Drawing each affine from a dedicated
+ torch.Generator (seeded once, draws in model.modules() order, weight then
+ bias per norm) keeps the perturbation deterministic and isolated from the
+ global RNG the model init already consumed. Returns the norm count.
+ """
+ import torch
+
+ gen = torch.Generator().manual_seed(seed)
+ count = 0
+ for module in model.modules():
+ if isinstance(module, torch.nn.LayerNorm):
+ torch.nn.init.normal_(module.weight, mean=1.0, std=0.1, generator=gen)
+ torch.nn.init.normal_(module.bias, mean=0.0, std=0.05, generator=gen)
+ count += 1
+ return count
+
+
+def slow_processor():
+ # The pinned 4.57.6 slow PIL processor has no Qwen3VLImageProcessor
+ # counterpart; the explicit min_pixels/max_pixels compensate for the
+ # __init__ size-dict overwrite (findings §4.2). Options mirror the
+ # jmlx-vision Qwen3VlProcessorConfig effective defaults.
+ from transformers.models.qwen2_vl.image_processing_qwen2_vl import (
+ Qwen2VLImageProcessor,
+ )
+
+ return Qwen2VLImageProcessor(
+ min_pixels=3136,
+ max_pixels=12845056,
+ image_mean=[0.5, 0.5, 0.5],
+ image_std=[0.5, 0.5, 0.5],
+ patch_size=16,
+ temporal_patch_size=2,
+ merge_size=2,
+ resample=3,
+ rescale_factor=1 / 255,
+ do_convert_rgb=True,
+ do_rescale=True,
+ do_resize=True,
+ do_normalize=True,
+ )
+
+
+def main() -> None:
+ script_dir = Path(__file__).resolve().parent
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--checkpoint-dir", type=Path,
+ default=script_dir / "goldens" / "checkpoints" / "qwen3vl-vision")
+ parser.add_argument("--out", type=Path,
+ default=script_dir / "goldens" / "qwen3vl-vision-wp5.json")
+ args = parser.parse_args()
+
+ import torch
+ from safetensors import torch as st
+ from transformers.models.qwen3_vl.modeling_qwen3_vl import (
+ Qwen3VLVisionModel,
+ Qwen3VLVisionRotaryEmbedding,
+ )
+
+ torch.set_num_threads(1)
+ torch.use_deterministic_algorithms(True)
+
+ environment = gate_versions()
+ checkpoint_dir: Path = args.checkpoint_dir
+ if checkpoint_dir.exists():
+ shutil.rmtree(checkpoint_dir)
+ (checkpoint_dir / "images").mkdir(parents=True)
+
+ # ------------------------------------------------------------- checkpoint
+ torch.manual_seed(SEED)
+ cfg = vision_config()
+ model = Qwen3VLVisionModel(cfg)
+ # The model init leaves every LayerNorm at its PyTorch default (weight 1,
+ # bias 0): with all norms identical the strict golden could not catch a
+ # norm1/norm2 swap, a weight/bias swap, the wrong norm on the merger
+ # versus a DeepStack merger, or a norm-key typo. Perturb before saving.
+ norm_count = perturb_layer_norms(model, SEED)
+ expected_norms = 2 * cfg.depth + 1 + len(cfg.deepstack_visual_indexes)
+ if norm_count != expected_norms:
+ sys.exit(f"expected {expected_norms} LayerNorm affines to perturb, found {norm_count}")
+ cfg.save_pretrained(checkpoint_dir)
+
+ bare_state = model.state_dict()
+ if any(k.startswith(KEY_PREFIX) for k in bare_state):
+ sys.exit("bare Qwen3VLVisionModel state dict already carries the model.visual. prefix")
+ bf16_state = {KEY_PREFIX + k: v.to(torch.bfloat16) for k, v in bare_state.items()}
+ st.save_file(bf16_state, checkpoint_dir / "model.safetensors")
+
+ image_paths = {}
+ for name, width, height, coeffs in IMAGE_SPECS:
+ path = checkpoint_dir / "images" / f"{name}.png"
+ build_image(width, height, coeffs, path)
+ image_paths[name] = path
+
+ # ------------------------------------------------------- reference model
+ # The reference run loads the SAVED checkpoint back (not the in-memory
+ # model) and upconverts the stored bf16 weights to float32, so the golden
+ # exercises the exact stored-bf16 -> float32 path the Java tower uses.
+ saved = st.load_file(checkpoint_dir / "model.safetensors")
+ ref_cfg = vision_config()
+ ref_cfg._attn_implementation = "eager"
+ ref = Qwen3VLVisionModel(ref_cfg)
+ ref.load_state_dict({k[len(KEY_PREFIX):]: v.float() for k, v in saved.items()})
+ ref.eval()
+
+ # ------------------------------------------------------------ processor
+ proc = slow_processor()
+ file_paths = [str(image_paths[name]) for name, _, _, _ in IMAGE_SPECS]
+ batch = proc.preprocess(file_paths, return_tensors="pt")
+ pixel_values = batch["pixel_values"].numpy()
+ grid_thw = batch["image_grid_thw"].numpy()
+ if pixel_values.shape != (144, 1536) or pixel_values.dtype != "float32":
+ sys.exit(f"packed pixel_values {pixel_values.shape} {pixel_values.dtype} != (144, 1536) float32")
+ observed = [list(map(int, row)) for row in grid_thw]
+ expected = [EXPECTED_GRIDS[name] for name, _, _, _ in IMAGE_SPECS]
+ if observed != expected:
+ sys.exit(f"packed grid_thw {observed} != expected {expected}")
+
+ # ------------------------------------------------------------- forward
+ captures: dict[str, torch.Tensor] = {}
+
+ def hook(name: str):
+ def _hook(_module, _inputs, output):
+ captures[name] = output
+
+ return _hook
+
+ ref.patch_embed.register_forward_hook(hook("patch_embed"))
+ for block_index in (0, 1, 3, 5):
+ ref.blocks[block_index].register_forward_hook(hook(f"block{block_index}"))
+
+ with torch.no_grad():
+ pos_embed_interpolated = ref.fast_pos_embed_interpolate(
+ torch.from_numpy(grid_thw).int()
+ )
+ merged, deepstack_lists = ref(
+ torch.from_numpy(pixel_values).float(),
+ grid_thw=torch.from_numpy(grid_thw).int(),
+ )
+ if list(pos_embed_interpolated.shape) != [144, 256]:
+ sys.exit(f"pos_embed_interpolated {tuple(pos_embed_interpolated.shape)} != [144, 256]")
+ if list(merged.shape) != [36, 256]:
+ sys.exit(f"merged {tuple(merged.shape)} != [36, 256]")
+ if len(deepstack_lists) != 3 or any(
+ list(d.shape) != [36, 256] for d in deepstack_lists
+ ):
+ sys.exit("deepstack_feature_lists != 3 x [36, 256]")
+ for name in ("patch_embed", "block0", "block1", "block3", "block5"):
+ if list(captures[name].shape) != [144, 256]:
+ sys.exit(f"{name} {tuple(captures[name].shape)} != [144, 256]")
+
+ packed: dict[str, torch.Tensor] = {
+ "pos_embed_interpolated": pos_embed_interpolated,
+ "patch_embed": captures["patch_embed"],
+ "block0": captures["block0"],
+ "block1": captures["block1"],
+ "block3": captures["block3"],
+ "block5": captures["block5"],
+ "merger": merged,
+ "deepstack0": deepstack_lists[0],
+ "deepstack1": deepstack_lists[1],
+ "deepstack2": deepstack_lists[2],
+ }
+
+ # ---------------------------------------------------------------- probes
+ linspace_probe = []
+ for n in (4, 6, 8):
+ values = torch.linspace(0, 7, n)
+ linspace_probe.append({"n": n, "values_hex": f64_hex(values.tolist())})
+ # The real 2B model uses num_position_embeddings 2304 (side 48) and the
+ # 4096-row grid-64x64 peak case reaches n up to 64 per axis (PR #58 review
+ # round 2: the side-8 probes never exercised the larger-n branch of the
+ # kernel). These entries carry an explicit "side" because they are not the
+ # tiny tower's config-derived side; the side-8 entries above keep their
+ # original shape.
+ for n in (16, 24, 32, 50, 64):
+ values = torch.linspace(0, 47, n)
+ linspace_probe.append({"side": 48, "n": n, "values_hex": f64_hex(values.tolist())})
+
+ rot = Qwen3VLVisionRotaryEmbedding(32, 10000.0)
+ if list(rot.inv_freq.shape) != [16]:
+ sys.exit(f"inv_freq {tuple(rot.inv_freq.shape)} != [16]")
+ inv_freq_hex = f32_hex(rot.inv_freq.tolist())
+
+ cos_sin_sample = []
+ for position in (0, 1, 2, 3, 5, 7):
+ for j in (0, 1, 2, 3):
+ v = torch.tensor(float(position), dtype=torch.float32) * rot.inv_freq[j]
+ cos_sin_sample.append({
+ "p": position,
+ "j": j,
+ "v_hex": f32_hex([float(v)])[0],
+ "cos_hex": f32_hex([float(v.cos())])[0],
+ "sin_hex": f32_hex([float(v.sin())])[0],
+ })
+
+ side = ref.num_grid_per_side
+ if side != 8:
+ sys.exit(f"num_grid_per_side {side} != 8")
+ merge = ref.config.spatial_merge_size
+ pos_embed_sample = {}
+ for name in ("wide", "tall"):
+ grid_h, grid_w = EXPECTED_GRIDS[name][1], EXPECTED_GRIDS[name][2]
+ # Mirror fast_pos_embed_interpolate's per-axis double linspace exactly,
+ # then select the FIRST patch in merge-block order whose row AND column
+ # linspace values are both fractional (the grid-(0,0) patch is a
+ # degenerate exact-corner case that would pin no bilinear math).
+ h_idxs = torch.linspace(0, side - 1, grid_h)
+ w_idxs = torch.linspace(0, side - 1, grid_w)
+ selected = None
+ for bh in range(grid_h // merge):
+ for bw in range(grid_w // merge):
+ for dh in range(merge):
+ for dc in range(merge):
+ ph, pw = bh * merge + dh, bw * merge + dc
+ h_idx = float(h_idxs[ph])
+ w_idx = float(w_idxs[pw])
+ if h_idx - int(h_idx) != 0.0 and w_idx - int(w_idx) != 0.0:
+ row_in_image = (
+ (bh * (grid_w // merge) + bw) * merge * merge
+ + dh * merge + dc
+ )
+ selected = (ph, pw, h_idx, w_idx, row_in_image)
+ break
+ if selected:
+ break
+ if selected:
+ break
+ if selected:
+ break
+ if selected is None:
+ sys.exit(f"{name}: no patch with both axis values fractional")
+ ph, pw, h_idx, w_idx, row_in_image = selected
+ h_floor, w_floor = int(h_idx), int(w_idx)
+ h_ceil = min(h_floor + 1, side - 1)
+ w_ceil = min(w_floor + 1, side - 1)
+ dh, dw = h_idx - h_floor, w_idx - w_floor
+ weights_double = [
+ (1 - dh) * (1 - dw),
+ (1 - dh) * dw,
+ dh * (1 - dw),
+ dh * dw,
+ ]
+ weights_f32 = torch.tensor(weights_double, dtype=torch.float32)
+ if not all(0.0 < w < 1.0 for w in weights_f32.tolist()):
+ sys.exit(f"{name}: sample weights {weights_f32.tolist()} not strictly fractional")
+ corner_indices = [
+ h_floor * side + w_floor,
+ h_floor * side + w_ceil,
+ h_ceil * side + w_floor,
+ h_ceil * side + w_ceil,
+ ]
+ corner_rows = ref.pos_embed.weight[torch.tensor(corner_indices)]
+ # Sequential float32 corner add, exactly as the reference does
+ # (pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]).
+ weighted = corner_rows * weights_f32[:, None]
+ expected_row = weighted[0] + weighted[1] + weighted[2] + weighted[3]
+ # Ground truth: this patch's row in the image's packed, permuted output.
+ actual_row = pos_embed_interpolated[PATCH_OFFSETS[name] + row_in_image]
+ max_err = float((expected_row - actual_row).abs().max())
+ if max_err > 1e-6:
+ sys.exit(f"{name}: corner recombination max err {max_err} > 1e-6")
+ pos_embed_sample[name] = {
+ "grid": [ph, pw],
+ "corner_indices": corner_indices,
+ "corner_weights_hex": f32_hex(weights_f32.tolist()),
+ "corner_rows": rounded(corner_rows.tolist()),
+ "expected_row": rounded(actual_row.tolist()),
+ }
+
+ # ----------------------------------------------------------------- cases
+ # The reference computes the merger and DeepStack arrays once over the
+ # packed rows (image order), so they are stored exactly once in the
+ # top-level "packed" section; each case stores only its per-image
+ # captures plus the merged-row slice pointer into the packed arrays.
+ per_image_keys = (
+ "pos_embed_interpolated", "patch_embed", "block0", "block1", "block3", "block5"
+ )
+ packed_section = {}
+ for key in ("merger", "deepstack0", "deepstack1", "deepstack2"):
+ rows = packed[key].detach().cpu().float().contiguous()
+ packed_section[key] = {"shape": list(rows.shape), "values": rounded(rows.tolist())}
+
+ cases = []
+ merged_row_offset = 0
+ for name, width, height, _coeffs in IMAGE_SPECS:
+ grid = EXPECTED_GRIDS[name]
+ n_patches = grid[1] * grid[2]
+ merged_rows = n_patches // 4
+ offset = PATCH_OFFSETS[name]
+ case_pixel_values = pixel_values[offset:offset + n_patches]
+ case: dict = {
+ "name": name,
+ "file": f"images/{name}.png",
+ "width": width,
+ "height": height,
+ "grid": grid,
+ "n_patches": n_patches,
+ "merged_rows": merged_rows,
+ "merged_row_offset": merged_row_offset,
+ "pixel_values_sha256": sha256_bytes(case_pixel_values.tobytes()),
+ "captures": {},
+ }
+ for key in per_image_keys:
+ rows = packed[key][offset:offset + n_patches].detach().cpu().float().contiguous()
+ case["captures"][key] = {
+ "shape": list(rows.shape),
+ "values": rounded(rows.tolist()),
+ }
+ merged_row_offset += merged_rows
+ cases.append(case)
+ if merged_row_offset != 36:
+ sys.exit(f"packed merged rows {merged_row_offset} != 36")
+
+ # ------------------------------------------------------------- golden
+ result = {
+ "schema": "jmlx-phase7-3b-wp5-qwen3vl-vision-v1",
+ "generated_by": "tools/hf-reference/qwen3vl_vision.py",
+ "provenance": {
+ "transformers": environment["transformers"],
+ "torch": environment["torch"],
+ "safetensors": environment["safetensors"],
+ "python": environment["python"],
+ "platform": environment["platform"],
+ "pillow": environment["pillow"],
+ "numpy": environment["numpy"],
+ "seed": SEED,
+ "norm_perturbation": {
+ "seed": SEED,
+ "generator": "dedicated torch.Generator (does not consume the global model-init RNG)",
+ "weight_distribution": "normal(mean=1.0, std=0.1)",
+ "bias_distribution": "normal(mean=0.0, std=0.05)",
+ "count": norm_count,
+ "note": (
+ "every LayerNorm affine (blocks.*.norm1/norm2, merger.norm, "
+ "deepstack_merger_list.*.norm) is perturbed before the "
+ "checkpoint is saved so the 16 norms are mutually distinct "
+ "and the strict golden discriminates the tower's norm "
+ "wiring (a norm1/norm2 swap, a weight/bias swap, the wrong "
+ "norm on the merger versus a DeepStack merger, or a "
+ "checkpoint-key typo would all change the captures)"
+ ),
+ },
+ "config": py(vision_config().to_dict()),
+ "checkpoint_files": {
+ "config.json": sha256_file(checkpoint_dir / "config.json"),
+ "model.safetensors": sha256_file(checkpoint_dir / "model.safetensors"),
+ },
+ "images": {
+ name: {
+ "width": width,
+ "height": height,
+ "bytes": image_paths[name].stat().st_size,
+ "sha256": sha256_file(image_paths[name]),
+ }
+ for name, width, height, _coeffs in IMAGE_SPECS
+ },
+ "key_prefix_note": (
+ "the bare Qwen3VLVisionModel state dict carries plain keys "
+ "(patch_embed.proj.*, pos_embed.weight, blocks.*, merger.*, "
+ "deepstack_merger_list.*); the generator prepends 'model.visual.' "
+ "to every key so the tiny checkpoint matches the real Qwen3-VL "
+ "artifact key scheme (model.visual.*); all 99 tensors are stored "
+ "as bfloat16"
+ ),
+ "processor": {
+ "class": "Qwen2VLImageProcessor",
+ "module": "transformers.models.qwen2_vl.image_processing_qwen2_vl",
+ "note": (
+ "transformers 4.57.6 has no Qwen3VLImageProcessor class; the "
+ "slow PIL Qwen2VLImageProcessor is the pinned preprocessing "
+ "path (WP1 probe precedent and jmlx-vision Qwen3Vl* port "
+ "target). min_pixels/max_pixels are passed explicitly because "
+ "the 4.57.6 __init__ overwrites any size dict with the "
+ "3136/1003520 defaults (findings §4.2). Images are passed as "
+ "file path strings so the load_image (exif_transpose) path "
+ "matches the Java processFile path; all areas lie inside "
+ "[3136, 12845056] so smart_resize is a no-op."
+ ),
+ "min_pixels": proc.min_pixels,
+ "max_pixels": proc.max_pixels,
+ "size": proc.size,
+ "do_resize": proc.do_resize,
+ "resample": repr(proc.resample),
+ "do_rescale": proc.do_rescale,
+ "rescale_factor": proc.rescale_factor,
+ "do_normalize": proc.do_normalize,
+ "image_mean": proc.image_mean,
+ "image_std": proc.image_std,
+ "do_convert_rgb": proc.do_convert_rgb,
+ "patch_size": proc.patch_size,
+ "temporal_patch_size": proc.temporal_patch_size,
+ "merge_size": proc.merge_size,
+ },
+ },
+ "probes": {
+ "linspace": linspace_probe,
+ "inv_freq": {
+ "dim": 32,
+ "theta": 10000.0,
+ "values_hex": inv_freq_hex,
+ },
+ "cos_sin_sample": cos_sin_sample,
+ "pos_embed_sample": pos_embed_sample,
+ },
+ "packed": packed_section,
+ "cases": cases,
+ }
+ args.out.parent.mkdir(parents=True, exist_ok=True)
+ # Compact single-line JSON, matching the capture-heavy golden convention
+ # (idefics3.json): indent=2 would add a line per float and roughly double
+ # the file size for no readability gain at this density.
+ args.out.write_text(json.dumps(py(result)) + "\n")
+
+ # -------------------------------------------------------------- summary
+ print(f"wrote {args.out} ({args.out.stat().st_size} bytes)")
+ print(f"config.json: {sha256_file(checkpoint_dir / 'config.json')}")
+ print(f"model.safetensors: {sha256_file(checkpoint_dir / 'model.safetensors')} "
+ f"({len(bf16_state)} tensors, bf16)")
+ for name, _width, _height, _coeffs in IMAGE_SPECS:
+ path = image_paths[name]
+ print(f"{path.name}: {sha256_file(path)} ({path.stat().st_size} bytes)")
+ print(f"grids: {observed}")
+ print(f"pixel_values_sha256: {sha256_bytes(pixel_values.tobytes())}")
+
+
+if __name__ == "__main__":
+ main()