cache more and re-factor

1 files changed, 64 insertions, 55 deletions

diff --git a/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java b/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
index 1b9f9dd2fe3..20d8b6362d3 100644
--- a/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
+++ b/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
@@ -106,54 +106,21 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
 
     @SuppressWarnings("unchecked")
     @Override
-    public Tensor embed(String s, Context context, TensorType tensorType) {
-        var start = System.nanoTime();
-        var encoding = tokenizer.encode(s, context.getLanguage());
-        runtime.sampleSequenceLength(encoding.ids().size(), context);
-        Tensor inputSequence = createTensorRepresentation(encoding.ids(), "d1");
-        Tensor attentionMask = createTensorRepresentation(encoding.attentionMask(), "d1");
-        Tensor tokenTypeIds = tokenTypeIdsName.isEmpty() ? null : createTensorRepresentation(encoding.typeIds(), "d1");
-
-        Map<String, Tensor> inputs;
-        if (tokenTypeIdsName.isEmpty() || tokenTypeIds.isEmpty()) {
-            inputs = Map.of(inputIdsName, inputSequence.expand("d0"),
-                            attentionMaskName, attentionMask.expand("d0"));
-        } else {
-            inputs = Map.of(inputIdsName, inputSequence.expand("d0"),
-                            attentionMaskName, attentionMask.expand("d0"),
-                            tokenTypeIdsName, tokenTypeIds.expand("d0"));
+    public Tensor embed(String text, Context context, TensorType tensorType) {
+        if (tensorType.dimensions().size() != 1) {
+            throw new IllegalArgumentException("Error in embedding to type '" + tensorType + "': should only have one dimension.");
         }
-        IndexedTensor tokenEmbeddings = (IndexedTensor) evaluateIfNotPresent(inputs,context,s).get(outputName);
-        long[] resultShape = tokenEmbeddings.shape();
-        //shape batch, sequence, embedding dimensionality
-        if (resultShape.length != 3) {
-            throw new IllegalArgumentException("" +
-                    "Expected 3 output dimensions for output name '" +
-                    outputName + "': [batch, sequence, embedding], got " + resultShape.length);
+        if (!tensorType.dimensions().get(0).isIndexed()) {
+            throw new IllegalArgumentException("Error in embedding to type '" + tensorType + "': dimension should be indexed.");
         }
-        Tensor result;
-        if (tensorType.valueType() == TensorType.Value.INT8) { // binary quantization
-            long outputDimensions = resultShape[2];
-            long targetDim = tensorType.dimensions().get(0).size().get();
-            //🪆 flexibility - packing only the first 8*targetDim float values from the model output
-            long floatDimensions = 8 * targetDim;
-            if(floatDimensions > outputDimensions) {
-                throw new IllegalArgumentException("Cannot pack " + outputDimensions + " into " + targetDim + " int8s");
-            }
-            //perform pooling and normalizing using float version before binary quantization
-            TensorType poolingType = new TensorType.Builder(TensorType.Value.FLOAT).
-                    indexed(tensorType.indexedSubtype().dimensions().get(0).name(),
-                            floatDimensions).build();
-            result = poolingStrategy.toSentenceEmbedding(poolingType, tokenEmbeddings, attentionMask);
-            result = normalize? normalize(result, poolingType) : result;
-            result = binarize((IndexedTensor) result, tensorType);
-
-        } else { // regular float embeddings up to the target dimensionality
-            result = poolingStrategy.toSentenceEmbedding(tensorType, tokenEmbeddings, attentionMask);
-            result = normalize ? normalize(result, tensorType) : result;
+        var embeddingResult = lookupOrEvaluate(context, text);
+        IndexedTensor tokenEmbeddings = embeddingResult.output;
+        if (tensorType.valueType() == TensorType.Value.INT8) {
+            return binaryQuantization(embeddingResult, tensorType);
+        } else {
+            Tensor result = poolingStrategy.toSentenceEmbedding(tensorType, tokenEmbeddings, embeddingResult.attentionMask);
+            return  normalize ? normalize(result, tensorType) : result;
         }
-        runtime.sampleEmbeddingLatency((System.nanoTime() - start)/1_000_000d, context);
-        return result;
     }
 
     Tensor normalize(Tensor embedding, TensorType tensorType) {
@@ -175,15 +142,56 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
         return builder.build();
     }
 
-    /**
-     * Evaluate the model if the result is not present in the context cache.
-     * @param inputs the tensor inputs
-     * @param context the context accompanying the request, a singleton per embedder instance and request
-     * @param hashKey the key to the cached value
-     * @return the model output
-     */
-    protected Map<String, Tensor> evaluateIfNotPresent(Map<String, Tensor> inputs, Context context, String hashKey) {
-        return context.computeCachedValueIfAbsent(hashKey, () -> evaluator.evaluate(inputs));
+    private HuggingFaceEmbedder.HFEmbeddingResult lookupOrEvaluate(Context context, String text) {
+        var key = new HFEmbedderCacheKey(context.getEmbedderId(), text);
+        return context.computeCachedValueIfAbsent(key, () -> evaluate(context, text));
+    }
+
+    private HuggingFaceEmbedder.HFEmbeddingResult evaluate(Context context, String text) {
+        var start = System.nanoTime();
+        var encoding = tokenizer.encode(text, context.getLanguage());
+        runtime.sampleSequenceLength(encoding.ids().size(), context);
+        Tensor inputSequence = createTensorRepresentation(encoding.ids(), "d1");
+        Tensor attentionMask = createTensorRepresentation(encoding.attentionMask(), "d1");
+        Tensor tokenTypeIds = tokenTypeIdsName.isEmpty() ? null : createTensorRepresentation(encoding.typeIds(), "d1");
+
+        Map<String, Tensor> inputs;
+        if (tokenTypeIdsName.isEmpty() || tokenTypeIds.isEmpty()) {
+            inputs = Map.of(inputIdsName, inputSequence.expand("d0"),
+                    attentionMaskName, attentionMask.expand("d0"));
+        } else {
+            inputs = Map.of(inputIdsName, inputSequence.expand("d0"),
+                    attentionMaskName, attentionMask.expand("d0"),
+                    tokenTypeIdsName, tokenTypeIds.expand("d0"));
+        }
+        IndexedTensor tokenEmbeddings = (IndexedTensor) evaluator.evaluate(inputs).get(outputName);
+        long[] resultShape = tokenEmbeddings.shape();
+        //shape batch, sequence, embedding dimensionality
+        if (resultShape.length != 3) {
+            throw new IllegalArgumentException("" +
+                    "Expected 3 output dimensions for output name '" +
+                    outputName + "': [batch, sequence, embedding], got " + resultShape.length);
+        }
+        runtime.sampleEmbeddingLatency((System.nanoTime() - start)/1_000_000d, context);
+        return new HFEmbeddingResult(tokenEmbeddings, attentionMask, context.getEmbedderId());
+    }
+
+    private Tensor binaryQuantization(HuggingFaceEmbedder.HFEmbeddingResult embeddingResult, TensorType tensorType) {
+        long outputDimensions = embeddingResult.output().shape()[2];
+        long targetDim = tensorType.dimensions().get(0).size().get();
+        //🪆 flexibility - packing only the first 8*targetDim float values from the model output
+        long floatDimensions = 8 * targetDim;
+        if(floatDimensions > outputDimensions) {
+            throw new IllegalArgumentException("Cannot pack " + outputDimensions + " into " + targetDim + " int8s");
+        }
+        //perform pooling and normalizing using float version before binary quantization
+        TensorType poolingType = new TensorType.Builder(TensorType.Value.FLOAT).
+                indexed(tensorType.indexedSubtype().dimensions().get(0).name(),
+                        floatDimensions).build();
+        Tensor result = poolingStrategy.toSentenceEmbedding(poolingType, embeddingResult.output(), embeddingResult.attentionMask());
+        result = normalize? normalize(result, poolingType) : result;
+        result = binarize((IndexedTensor) result, tensorType);
+        return result;
     }
 
     /**
@@ -222,6 +230,7 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
         return builder.build();
     }
 
-
+    protected record HFEmbeddingResult(IndexedTensor output, Tensor attentionMask, String embedderId) {}
+    protected record HFEmbedderCacheKey(String embedderId, Object embeddedValue) { }
 }