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Merge pull request #107 from AdamBien/main
 Devstral 2 support (Mistral 3 architecture, Tekken tokenizer, YaRN …
2 parents 5324c09 + 36043f5 commit 2e8ed31

19 files changed

Lines changed: 1583 additions & 0 deletions

‎src/main/java/org/beehive/gpullama3/inference/InferenceCore.java‎

Lines changed: 90 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -12,6 +12,7 @@
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import org.beehive.gpullama3.model.Configuration;
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import org.beehive.gpullama3.model.Model;
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import org.beehive.gpullama3.model.granite.GraniteConfiguration;
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import org.beehive.gpullama3.model.devstral.DevstralConfiguration;
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import org.beehive.gpullama3.model.phi3.Phi3Configuration;
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import org.beehive.gpullama3.model.qwen2.Qwen2Configuration;
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import org.beehive.gpullama3.model.qwen3.Qwen3Configuration;
@@ -179,6 +180,95 @@ public static FloatTensor forwardJava(Model model, State state, int token, int p
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return state.logits;
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}
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/**
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* Forward pass for Devstral 2 models where head_dim != dim/num_heads.
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* Q projection outputs qDim (num_heads * head_dim) instead of dim.
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*/
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public static FloatTensor forwardJavaDevstral(Model model, State state, int token, int position) {
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final DevstralConfiguration config = (DevstralConfiguration) model.configuration();
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final StandardWeights weights = (StandardWeights) model.weights();
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int dim = config.dim();
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int headSize = config.headSize(); // 128 (independent head_dim)
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int qDim = config.qDim(); // 4096 = 32 * 128
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int kvDim = config.kvDim(); // 1024 = 8 * 128
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int kvMul = config.kvMul();
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float sqrtHeadSize = (float) Math.sqrt(headSize);
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weights.token_embedding_table.copyTo(token * dim, state.x, 0, dim);
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for (int l = 0; l < config.numberOfLayers(); l++) {
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rmsnorm(state.xb, state.x, weights.rms_att_weight[l], 0, dim, config.rmsNormEps());
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weights.wq[l].matmul(state.xb, state.q, qDim, dim);
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weights.wk[l].matmul(state.xb, state.k, kvDim, dim);
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weights.wv[l].matmul(state.xb, state.v, kvDim, dim);
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// RoPE over qDim (not dim)
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for (int i = 0; i < qDim; i += 2) {
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int head_dim = i % headSize;
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float fcr = weights.freq_cis_real.getFloat(position * (headSize / 2) + (head_dim / 2));
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float fci = weights.freq_cis_imag.getFloat(position * (headSize / 2) + (head_dim / 2));
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int rotn = i < kvDim ? 2 : 1;
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for (int v = 0; v < rotn; v++) {
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FloatTensor vec = v == 0 ? state.q : state.k;
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float v0 = vec.getFloat(i);
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float v1 = vec.getFloat(i + 1);
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vec.setFloat(i, v0 * fcr - v1 * fci);
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vec.setFloat(i + 1, v0 * fci + v1 * fcr);
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}
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}
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state.k.copyTo(0, state.keyCache[l], position * kvDim, kvDim);
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state.v.copyTo(0, state.valueCache[l], position * kvDim, kvDim);
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int curLayer = l;
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Parallel.parallelFor(0, config.numberOfHeads(), h -> {
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int qOffset = h * headSize;
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int attOffset = h * config.contextLength();
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for (int t = 0; t <= position; t++) {
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int keyCacheOffset = t * kvDim + (h / kvMul) * headSize;
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float score = state.q.dot(qOffset, state.keyCache[curLayer], keyCacheOffset, headSize);
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score /= sqrtHeadSize;
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state.att.setFloat(attOffset + t, score);
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}
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state.att.softmaxInPlace(attOffset, position + 1);
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int xbOffset = h * headSize;
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state.xb.fillInPlace(xbOffset, headSize, 0f);
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for (int t = 0; t <= position; t++) {
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int vOffset = t * kvDim + (h / kvMul) * headSize;
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float a = state.att.getFloat(attOffset + t);
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state.xb.saxpyInPlace(xbOffset, state.valueCache[curLayer], vOffset, headSize, a);
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}
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});
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// O projection: input qDim, output dim
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weights.wo[l].matmul(state.xb, state.xb2, dim, qDim);
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state.x.addInPlace(state.xb2);
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rmsnorm(state.xb, state.x, weights.rms_ffn_weight[l], 0, dim, config.rmsNormEps());
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weights.w1[l].matmul(state.xb, state.hb, config.hiddenDim(), dim);
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weights.w3[l].matmul(state.xb, state.hb2, config.hiddenDim(), dim);
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state.hb.mapInPlace(value -> value / (float) (1.0 + Math.exp(-value)));
260+
state.hb.multiplyInPlace(state.hb2);
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weights.w2[l].matmul(state.hb, state.xb, dim, config.hiddenDim());
263+
state.x.addInPlace(state.xb);
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}
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rmsnorm(state.x, state.x, weights.rms_final_weight, 0, dim, config.rmsNormEps());
267+
weights.wcls.matmul(state.x, state.logits, config.vocabularySize(), dim);
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return state.logits;
270+
}
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public static FloatTensor forwardJavaQwen2(Model model, State state, int token, int position) {
183273
final Qwen2Configuration config = (Qwen2Configuration) model.configuration();
184274
final Qwen2StandardWeights weights = (Qwen2StandardWeights) model.weights();

‎src/main/java/org/beehive/gpullama3/inference/operation/RoPE.java‎

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@@ -35,4 +35,50 @@ public static Pair<float[], float[]> precomputeFreqsCis(int contextLength, int h
3535
assert contextLength * (headSize / 2) == n;
3636
return new Pair<>(cr, ci);
3737
}
38+
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public static Pair<float[], float[]> precomputeFreqsCisYaRN(int contextLength, int headSize, double theta,
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float factor, float betaFast, float betaSlow, float logMultiplier, int originalContextLength) {
41+
assert headSize % 2 == 0;
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float[] cr = new float[contextLength * (headSize / 2)];
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float[] ci = new float[contextLength * (headSize / 2)];
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float freqScale = 1.0f / factor;
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// Compute correlation dimensions for ramp interpolation
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float corrDim0 = yarnCorrDim(headSize, originalContextLength, betaFast, (float) theta);
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float corrDim1 = yarnCorrDim(headSize, originalContextLength, betaSlow, (float) theta);
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// Compute mscale (attention scaling for extended context)
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// Formula: mscale = 0.1 * logMultiplier * log(factor) + 1.0
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float mscale = logMultiplier > 0
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? 1.0f + 0.1f * logMultiplier * (float) Math.log(1.0f / freqScale)
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: 1.0f;
56+
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int n = 0;
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for (int pos = 0; pos < contextLength; ++pos) {
59+
for (int i = 0; i < headSize; i += 2) {
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float freqExtrap = (float) (1.0 / Math.pow(theta, i / (double) headSize));
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float freqInterp = freqScale * freqExtrap;
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float rampMix = yarnRamp(corrDim0, corrDim1, i / 2);
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float freq = freqInterp * (1.0f - rampMix) + freqExtrap * rampMix;
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float val = pos * freq;
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cr[n] = (float) Math.cos(val) * mscale;
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ci[n] = (float) Math.sin(val) * mscale;
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n++;
70+
}
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}
72+
assert contextLength * (headSize / 2) == n;
73+
return new Pair<>(cr, ci);
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}
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private static float yarnCorrDim(int nDims, int nCtxOrig, float nRot, float base) {
77+
return nDims * (float) Math.log(nCtxOrig / (nRot * 2.0f * (float) Math.PI)) / (2.0f * (float) Math.log(base));
78+
}
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80+
private static float yarnRamp(float low, float high, int i0) {
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float y = (i0 - low) / Math.max(0.001f, high - low);
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return 1.0f - Math.min(1.0f, Math.max(0.0f, y));
83+
}
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}

‎src/main/java/org/beehive/gpullama3/inference/state/DevstralState.java‎

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package org.beehive.gpullama3.inference.state;
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import org.beehive.gpullama3.tensor.standard.ArrayFloatTensor;
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import org.beehive.gpullama3.tensor.standard.FloatTensor;
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import org.beehive.gpullama3.model.Configuration;
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import org.beehive.gpullama3.model.devstral.DevstralConfiguration;
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import uk.ac.manchester.tornado.api.types.arrays.FloatArray;
8+
import uk.ac.manchester.tornado.api.types.arrays.HalfFloatArray;
9+
import uk.ac.manchester.tornado.api.types.arrays.IntArray;
10+
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import java.util.stream.Stream;
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/**
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* State for Devstral 2 models where head_dim != dim/num_heads.
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* Allocates Q with qDim (num_heads * head_dim) and K/V with kvDim (num_kv_heads * head_dim).
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*/
17+
public final class DevstralState extends State {
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public DevstralState(Configuration config, int batchsize) {
20+
super(config, batchsize);
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}
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@Override
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protected StateFields createStateFields(Configuration config) {
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DevstralConfiguration dc = (DevstralConfiguration) config;
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StateFields fields = new StateFields();
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int qDim = dc.qDim();
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int kvDim = dc.kvDim();
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fields.x = ArrayFloatTensor.allocate(dc.dim());
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fields.xb = ArrayFloatTensor.allocate(dc.dim());
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fields.xb2 = ArrayFloatTensor.allocate(dc.dim());
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fields.hb = ArrayFloatTensor.allocate(dc.hiddenDim());
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fields.hb2 = ArrayFloatTensor.allocate(dc.hiddenDim());
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fields.q = ArrayFloatTensor.allocate(qDim);
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fields.k = ArrayFloatTensor.allocate(kvDim);
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fields.v = ArrayFloatTensor.allocate(kvDim);
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fields.att = ArrayFloatTensor.allocate(dc.numberOfHeads(), dc.contextLength());
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fields.logits = ArrayFloatTensor.allocate(dc.vocabularySize());
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42+
fields.keyCache = Stream.generate(() -> ArrayFloatTensor.allocate(dc.contextLength(), kvDim)).limit(dc.numberOfLayers()).toArray(FloatTensor[]::new);
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fields.valueCache = Stream.generate(() -> ArrayFloatTensor.allocate(dc.contextLength(), kvDim)).limit(dc.numberOfLayers()).toArray(FloatTensor[]::new);
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// TornadoVM wrappers
46+
fields.wrapX = new FloatArray(dc.dim());
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fields.wrapXb = new FloatArray(dc.dim());
48+
fields.wrapXb2 = new FloatArray(dc.dim());
49+
fields.wrapHb = new FloatArray(dc.hiddenDim());
50+
fields.wrapHb2 = new FloatArray(dc.hiddenDim());
51+
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switch (dc.quantization()) {
53+
case "FP16" -> fields.createActivationFP16(dc.dim());
54+
case "Q8_0" -> fields.createActivationQ8_0(dc.dim());
55+
default -> throw new UnsupportedOperationException("Unsupported quantization format: " + dc.quantization());
56+
}
57+
fields.wrapLogits = new FloatArray(dc.vocabularySize());
58+
fields.wrapQ = new FloatArray(qDim);
59+
fields.wrapK = new FloatArray(kvDim);
60+
fields.wrapV = new FloatArray(kvDim);
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62+
fields.wrapXFP16 = new HalfFloatArray(dc.dim());
63+
fields.wrapXbFP16 = new HalfFloatArray(dc.dim());
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fields.wrapKeyCache = new FloatArray(dc.contextLength() * kvDim * dc.numberOfLayers());
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fields.wrapValueCache = new FloatArray(dc.contextLength() * kvDim * dc.numberOfLayers());
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fields.wrapValueCache.init(0.f);
67+
fields.wrapKeyCache.init(0.f);
68+
fields.wrapAtt = new FloatArray(dc.numberOfHeads() * dc.contextLength());
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fields.positionHolder = new IntArray(1);
70+
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fields.temp = new FloatArray(1 + ((dc.dim() + localSize - 1) / localSize));
72+
fields.tempFFN = new FloatArray(1 + ((dc.dim() + localSize - 1) / localSize));
73+
fields.tempLogits = new FloatArray(1 + ((dc.dim() + localSize - 1) / localSize));
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return fields;
76+
}
77+
}

‎src/main/java/org/beehive/gpullama3/model/ModelType.java‎

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@@ -1,5 +1,6 @@
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package org.beehive.gpullama3.model;
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import org.beehive.gpullama3.model.loader.DevstralModelLoader;
34
import org.beehive.gpullama3.model.loader.GraniteLoader;
45
import org.beehive.gpullama3.tensor.GGUF;
56
import org.beehive.gpullama3.model.loader.LlamaModelLoader;
@@ -37,6 +38,13 @@ public Model loadModel(FileChannel fileChannel, GGUF gguf, int contextLength, bo
3738
}
3839
},
3940

41+
DEVSTRAL_2 {
42+
@Override
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public Model loadModel(FileChannel fileChannel, GGUF gguf, int contextLength, boolean useTornadovm) {
44+
return new DevstralModelLoader(fileChannel, gguf, contextLength, useTornadovm).loadModel();
45+
}
46+
},
47+
4048
QWEN_2 {
4149
@Override
4250
public Model loadModel(FileChannel fileChannel, GGUF gguf, int contextLength, boolean useTornadovm) {

‎src/main/java/org/beehive/gpullama3/model/devstral/Devstral.java‎

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package org.beehive.gpullama3.model.devstral;
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import org.beehive.gpullama3.inference.InferenceCore;
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import org.beehive.gpullama3.inference.InferenceEngine;
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import org.beehive.gpullama3.inference.sampler.Sampler;
6+
import org.beehive.gpullama3.inference.state.DevstralState;
7+
import org.beehive.gpullama3.inference.state.State;
8+
import org.beehive.gpullama3.inference.weights.Weights;
9+
import org.beehive.gpullama3.model.AbstractModel;
10+
import org.beehive.gpullama3.model.ModelType;
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import org.beehive.gpullama3.model.format.ChatFormat;
12+
import org.beehive.gpullama3.tokenizer.DevstralTokenizer;
13+
import org.beehive.gpullama3.tokenizer.Tokenizer;
14+
import org.beehive.gpullama3.tornadovm.TornadoVMMasterPlan;
15+
16+
import java.util.List;
17+
import java.util.Set;
18+
import java.util.function.IntConsumer;
19+
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public class Devstral extends AbstractModel {
21+
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DevstralConfiguration configuration;
23+
24+
public Devstral(DevstralConfiguration configuration, Tokenizer tokenizer, Weights weights, ChatFormat chatFormat) {
25+
super(tokenizer, weights, chatFormat, null);
26+
this.configuration = configuration;
27+
}
28+
29+
@Override
30+
public DevstralConfiguration configuration() {
31+
return configuration;
32+
}
33+
34+
@Override
35+
public DevstralTokenizer tokenizer() {
36+
return (DevstralTokenizer) tokenizer;
37+
}
38+
39+
@Override
40+
public ModelType getModelType() {
41+
return ModelType.DEVSTRAL_2;
42+
}
43+
44+
public State createNewState() {
45+
State state = new DevstralState(configuration(), -1);
46+
state.latestToken = tokenizer.getSpecialTokens().get("<s>");
47+
return state;
48+
}
49+
50+
public State createNewState(int batchsize) {
51+
State state = new DevstralState(configuration(), batchsize);
52+
state.latestToken = tokenizer.getSpecialTokens().get("<s>");
53+
return state;
54+
}
55+
56+
@Override
57+
public void forward(State state, int token, int position) {
58+
InferenceCore.forwardJavaDevstral(this, state, token, position);
59+
}
60+
61+
@Override
62+
public List<Integer> generateTokens(State state, int startPosition, List<Integer> promptTokens, Set<Integer> stopTokens, int maxTokens, Sampler sampler, boolean echo,
63+
IntConsumer onTokenGenerated) {
64+
return InferenceEngine.generateTokensLlama(this, state, startPosition, promptTokens, stopTokens, maxTokens, sampler, echo, onTokenGenerated);
65+
}
66+
67+
@Override
68+
public List<Integer> generateTokensGPU(State state, int startPosition, List<Integer> promptTokens, Set<Integer> stopTokens, int maxTokens, Sampler sampler, boolean echo,
69+
IntConsumer onTokenGenerated, TornadoVMMasterPlan tornadoVMPlan) {
70+
return InferenceEngine.generateTokensGPULlama(this, state, startPosition, promptTokens, stopTokens, maxTokens, sampler, echo, onTokenGenerated, tornadoVMPlan);
71+
}
72+
73+
}

‎src/main/java/org/beehive/gpullama3/model/devstral/DevstralConfiguration.java‎

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package org.beehive.gpullama3.model.devstral;
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import org.beehive.gpullama3.model.Configuration;
4+
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/**
6+
* Configuration for Devstral 2 models (Mistral 3 architecture).
7+
* Unlike standard Mistral, Devstral 2 has an independent head dimension
8+
* (head_dim != dim / num_heads), requiring explicit key_length/value_length.
9+
*/
10+
// @formatter:off
11+
public record DevstralConfiguration(String quantization,
12+
int dim,
13+
int hiddenDim,
14+
int numberOfLayers,
15+
int numberOfHeads,
16+
int numberOfKeyValueHeads,
17+
int headDim,
18+
int vocabularySize,
19+
int contextLength,
20+
float rmsNormEps,
21+
float ropeTheta) implements Configuration {
22+
23+
@Override public String quantization() {
24+
return quantization;
25+
}
26+
27+
/**
28+
* Q projection output dimension = numberOfHeads * headDim.
29+
* This differs from dim when headDim != dim/numberOfHeads.
30+
*/
31+
public int qDim() {
32+
return numberOfHeads * headDim;
33+
}
34+
35+
public int kvDim() {
36+
return numberOfKeyValueHeads * headDim;
37+
}
38+
39+
public int kvMul() {
40+
return numberOfHeads / numberOfKeyValueHeads;
41+
}
42+
43+
@Override
44+
public int numberOfHeadsKey() {
45+
throw new UnsupportedOperationException("Not supported for Devstral.");
46+
}
47+
48+
@Override
49+
public int contextLengthModel() {
50+
throw new UnsupportedOperationException("Not supported for Devstral.");
51+
}
52+
53+
public int headSize() {
54+
return headDim;
55+
}
56+
}

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