Fix prompt usage when sharing the same session id (by stripping out t…

…he preamble)

jlama-cli/src/main/java/com/github/tjake/jlama/cli/commands/ChatCommand.java

@@ -66,6 +66,7 @@ public void run() {
 
         UUID session = UUID.randomUUID();
         PromptSupport promptSupport = m.promptSupport().get();
+        PromptSupport.Builder builder = promptSupport.builder();
         PrintWriter out = System.console().writer();
 
         out.println("\nChatting with " + modelName + "...\n");
@@ -82,7 +83,6 @@ public void run() {
                 break;
             }
 
-            PromptSupport.Builder builder = promptSupport.builder();
             if (first && systemPrompt != null) {
                 builder.addSystemMessage(systemPrompt);
             }
@@ -97,6 +97,9 @@ public void run() {
                 makeOutHandler()
             );
 
+            // New prompt builder and strip out the preamble since we're continuing the conversation
+            builder = promptSupport.builder().stripPreamble();
+
             out.println(
                 "\n\n"
                     + statsColor.format(

jlama-core/src/main/java/com/github/tjake/jlama/model/AbstractModel.java

@@ -283,8 +283,16 @@ public AbstractTensor batchForward(
         KvBufferCache.KvBuffer kvbuf,
         Optional<Consumer<List<AbstractTensor>>> tensorReducer
     ) {
-        AbstractTensor embedding = embedInput.batchInputsToEmbeddings(token_ids, startPos);
-        return forward(embedding, startPos, kvbuf, tensorReducer);
+        AbstractTensor embedding = null;
+
+        //Batch prompt into groups of 1024
+        for (int i = 0; i < token_ids.length; i += 1024) {
+            int[] batch = Arrays.copyOfRange(token_ids, i, Math.min(token_ids.length, i + 1024));
+            embedding = embedInput.batchInputsToEmbeddings(batch, startPos + i);
+            embedding = forward(embedding, startPos + i, kvbuf, tensorReducer);
+        }
+
+        return embedding;
     }
 
     public AbstractTensor forward(

jlama-core/src/main/java/com/github/tjake/jlama/safetensors/prompt/PromptSupport.java

@@ -190,6 +190,7 @@ public static class Builder {
         private boolean addGenerationPrompt = true;
 
         private List<Message> messages = new ArrayList<>(2);
+        private boolean stripPreamble = false;
 
         private Builder(TokenizerModel m) {
             this.m = m;
@@ -230,6 +231,11 @@ public Builder addAssistantMessage(String content) {
             return this;
         }
 
+        public Builder stripPreamble() {
+            stripPreamble = true;
+            return this;
+        }
+
         public PromptContext build() {
             return build(Optional.empty());
         }
@@ -259,8 +265,29 @@ private PromptContext build(Optional<List<Tool>> optionalTools) {
                 "This model does not support tools, but tools are specified"
             );
 
-            Map<String, Object> args = new HashMap<>();
 
+            String preamble = "";
+            if (stripPreamble) {
+                Map<String, Object> args = new HashMap<>();
+                args.putAll(
+                    Map.of(
+                        "messages",
+                        Map.of(),
+                        "add_generation_prompt",
+                        false,
+                        "eos_token",
+                        m.eosToken(),
+                        "bos_token",
+                        ""
+                    )
+                ); // We add the BOS ourselves
+                optionalTools.ifPresent(tools -> args.put("tools", tools));
+
+                RenderResult r = jinjava.renderForResult(template, args);
+                preamble = r.getOutput();
+            }
+
+            Map<String, Object> args = new HashMap<>();
             args.putAll(
                 Map.of(
                     "messages",
@@ -280,7 +307,8 @@ private PromptContext build(Optional<List<Tool>> optionalTools) {
 
             if (r.hasErrors()) logger.debug("Prompt template errors: " + r.getErrors());
 
-            return new PromptContext(r.getOutput(), optionalTools);
+            String output = r.getOutput();
+            return new PromptContext(output.substring(preamble.length()), optionalTools);
         }
     }
 }

jlama-native/src/main/java/com/github/tjake/jlama/tensor/operations/NativeTensorOperations.java → jlama-native/src/main/java/com/github/tjake/jlama/tensor/operations/NativeSimdTensorOperations.java

@@ -28,11 +28,11 @@
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-public class NativeTensorOperations implements TensorOperations {
-    private static final Logger logger = LoggerFactory.getLogger(NativeTensorOperations.class);
+public class NativeSimdTensorOperations implements TensorOperations {
+    private static final Logger logger = LoggerFactory.getLogger(NativeSimdTensorOperations.class);
 
     static {
-        if (!JarSupport.maybeLoadLibrary()) System.loadLibrary("jlama");
+        if (!JarSupport.maybeLoadLibrary("jlama")) System.loadLibrary("jlama");
     }
 
     public static final int HAS_F16C = NativeSimd.HAS_F16C();
@@ -52,7 +52,7 @@ public class NativeTensorOperations implements TensorOperations {
 
     final int flags;
 
-    public NativeTensorOperations() {
+    public NativeSimdTensorOperations() {
         int f = 0;
 
         if (RuntimeSupport.isLinux()) f |= HAS_F16C;
@@ -63,7 +63,7 @@ public NativeTensorOperations() {
         checkLib();
     }
 
-    NativeTensorOperations(int flags) {
+    NativeSimdTensorOperations(int flags) {
         this.flags = flags;
     }
 

jlama-native/src/main/java/com/github/tjake/jlama/tensor/operations/util/JarSupport.java

@@ -31,16 +31,16 @@
 public class JarSupport {
     private static final Logger logger = LoggerFactory.getLogger(JarSupport.class);
 
-    public static boolean maybeLoadLibrary() {
+    public static boolean maybeLoadLibrary(String libname) {
         String ext = RuntimeSupport.isMac() ? ".dylib" : RuntimeSupport.isWin() ? ".dll" : ".so";
-        URL lib = JarSupport.class.getClassLoader().getResource("META-INF/native/lib/libjlama" + ext);
+        URL lib = JarSupport.class.getClassLoader().getResource("META-INF/native/lib/lib" + libname + ext);
 
         if (lib != null) {
             try {
                 final File libpath = Files.createTempDirectory("jlama").toFile();
                 libpath.deleteOnExit(); // just in case
 
-                File libfile = Paths.get(libpath.getAbsolutePath(), "libjlama" + ext).toFile();
+                File libfile = Paths.get(libpath.getAbsolutePath(), "lib" + libname + ext).toFile();
                 libfile.deleteOnExit(); // just in case
 
                 final InputStream in = lib.openStream();
@@ -53,10 +53,10 @@ public static boolean maybeLoadLibrary() {
                 out.close();
                 in.close();
                 System.load(libfile.getAbsolutePath());
-                logger.debug("Loaded jlama-native library: {}", libfile.getAbsolutePath());
+                logger.debug("Loaded {}-native library: {}", libname, libfile.getAbsolutePath());
                 return true;
             } catch (IOException e) {
-                logger.warn("Error loading jlama-native library");
+                logger.warn("Error loading {}-native library", libname);
             }
         }
 

jlama-net/src/main/java/com/github/tjake/jlama/net/openai/OpenAIChatService.java

@@ -109,7 +109,7 @@ Object createChatCompletion(@RequestHeader Map<String, String> headers, @Valid @
         if (request.getStream() != null && request.getStream()) {
             SseEmitter emitter = new SseEmitter(-1L);
             CompletableFuture.supplyAsync(
-                () -> model.generate(sessionId, builder.build(), temperature, maxTokens, (t, f) -> CompletableFuture.supplyAsync(() -> {
+                () -> model.    generate(sessionId, builder.build(), temperature, maxTokens, (t, f) -> CompletableFuture.supplyAsync(() -> {
                     try {
                         emitter.send(
                             new CreateChatCompletionStreamResponse().id(sessionId.toString())

jlama-tests/src/test/java/com/github/tjake/jlama/model/TestCorrectness.java

@@ -345,7 +345,7 @@ public void testPromptSupportWithTools() {
     @Test
     public void testMistralTools() {
 
-        String modelPrefix = "../models/Mistral-7B-Instruct-v0.3";
+        String modelPrefix = "../models/tjake_Mistral-7B-Instruct-v0.3-JQ4";
         Assume.assumeTrue(Files.exists(Paths.get(modelPrefix)));
 
         Tokenizer tokenizer = new LlamaTokenizer(Paths.get(modelPrefix));
@@ -420,4 +420,51 @@ public void testToolParse() throws JsonProcessingException {
 
         Assert.assertEquals(2, toolCalls.size());
     }
+
+    @Test
+    public void testPromptBuilderSession() {
+        String modelPrefix = "../models/Qwen_Qwen2.5-0.5B-Instruct-JQ4";
+        Assume.assumeTrue(Files.exists(Paths.get(modelPrefix)));
+
+        Tokenizer tokenizer = new LlamaTokenizer(Paths.get(modelPrefix));
+        PromptSupport.Builder builder = tokenizer.promptSupport().get().builder();
+        builder.addSystemMessage("You always respond as a pirate");
+        builder.addUserMessage("What is the weather in paris right now?");
+        builder.addGenerationPrompt(true);
+
+        Tool t = Tool.from(
+            Function.builder()
+                .name("get_current_temperature")
+                .description("Simulates getting the current temperature at a location.")
+                .addParameter("location", "string", "The location to get the temperature for, in the format \"City, Country\".", true)
+                .addParameter("unit", "string", "The unit to return the temperature in (e.g., \"celsius\", \"fahrenheit\").", true)
+                .build()
+        );
+
+        PromptContext prompt = builder.build(t);
+        Assert.assertEquals(
+                "<|im_start|>system\n" + "You always respond as a pirate\n"
+                        + "\n"
+                        + "# Tools\n"
+                        + "\n"
+                        + "You may call one or more functions to assist with the user query.\n"
+                        + "\n"
+                        + "You are provided with function signatures within <tools></tools> XML tags:\n"
+                        + "<tools>\n"
+                        + "{\"type\": \"function\", \"function\": {\"name\": \"get_current_temperature\", \"description\": \"Simulates getting the current temperature at a location.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The location to get the temperature for, in the format \\\"City, Country\\\".\"}, \"unit\": {\"type\": \"string\", \"description\": \"The unit to return the temperature in (e.g., \\\"celsius\\\", \\\"fahrenheit\\\").\"}}, \"required\": [\"location\", \"unit\"]}}}\n"
+                        + "</tools>\n"
+                        + "\n"
+                        + "For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n"
+                        + "<tool_call>\n"
+                        + "{\"name\": <function-name>, \"arguments\": <args-json-object>}\n"
+                        + "</tool_call><|im_end|>\n"
+                        + "<|im_start|>user\n"
+                        + "What is the weather in paris right now?<|im_end|>\n"
+                        + "<|im_start|>assistant\n",
+                prompt.getPrompt());
+
+        prompt = tokenizer.promptSupport().get().builder().addUserMessage("This is a test").stripPreamble().build();
+        Assert.assertEquals(
+                "<|im_start|>user\n" + "This is a test<|im_end|>\n" + "<|im_start|>assistant\n", prompt.getPrompt());
+    }
 }

jlama-tests/src/test/java/com/github/tjake/jlama/tensor/operations/TestOperations.java

@@ -15,7 +15,7 @@
  */
 package com.github.tjake.jlama.tensor.operations;
 
-import static com.github.tjake.jlama.tensor.operations.NativeTensorOperations.*;
+import static com.github.tjake.jlama.tensor.operations.NativeSimdTensorOperations.*;
 
 import com.github.tjake.jlama.math.VectorMath;
 import com.github.tjake.jlama.safetensors.DType;
@@ -59,19 +59,19 @@ public static void init() {
         opTypes.add(new PanamaTensorOperations(MachineSpec.Type.AVX_256));
         opTypes.add(new PanamaTensorOperations(MachineSpec.Type.ARM_128));
 
-        if (globalOps instanceof NativeTensorOperations) {
-            opTypes.add(new NativeTensorOperations());
-            opTypes.add(new NativeTensorOperations(0));
+        if (globalOps instanceof NativeSimdTensorOperations) {
+            opTypes.add(new NativeSimdTensorOperations());
+            opTypes.add(new NativeSimdTensorOperations(0));
 
-            if (MachineSpec.VECTOR_TYPE == MachineSpec.Type.AVX_512) opTypes.add(new NativeTensorOperations(HAS_AVX2));
+            if (MachineSpec.VECTOR_TYPE == MachineSpec.Type.AVX_512) opTypes.add(new NativeSimdTensorOperations(HAS_AVX2));
 
             if (RuntimeSupport.isLinux() || RuntimeSupport.isWin()) {
-                opTypes.add(new NativeTensorOperations(HAS_F16C));
-                if (MachineSpec.VECTOR_TYPE == MachineSpec.Type.AVX_512) opTypes.add(new NativeTensorOperations(HAS_F16C | HAS_AVX2));
+                opTypes.add(new NativeSimdTensorOperations(HAS_F16C));
+                if (MachineSpec.VECTOR_TYPE == MachineSpec.Type.AVX_512) opTypes.add(new NativeSimdTensorOperations(HAS_F16C | HAS_AVX2));
             }
 
             if (RuntimeSupport.isArm()) {
-                opTypes.add(new NativeTensorOperations(MachineSpec.Type.ARM_128.ctag));
+                opTypes.add(new NativeSimdTensorOperations(MachineSpec.Type.ARM_128.ctag));
             }
         }
 
@@ -198,7 +198,7 @@ public void testSplitDotProduct() {
 
     @Test
     public void testNativeDotProduct() {
-        Assume.assumeTrue(globalOps instanceof NativeTensorOperations);
+        Assume.assumeTrue(globalOps instanceof NativeSimdTensorOperations);
         AbstractTensor a = makeTensor(SIZE);
         AbstractTensor b = makeTensor(SIZE);
 
@@ -476,7 +476,7 @@ public void testBatchDotProductWithResultOffset() {
     @Test
     public void testNativeBatchDotProduct() {
         // M == BATCH, N == ROWS, K == SIZE
-        Assume.assumeTrue(globalOps instanceof NativeTensorOperations);
+        Assume.assumeTrue(globalOps instanceof NativeSimdTensorOperations);
 
         FloatBufferTensor c = new FloatBufferTensor(BATCH, ROWS);
         FloatBufferTensor c1 = new FloatBufferTensor(BATCH, ROWS);
@@ -512,7 +512,7 @@ public void testNativeBatchDotProduct() {
     @Test
     public void testNativeBatchDotProductWithOffsets() {
         // M == BATCH, N == ROWS, K == SIZE
-        Assume.assumeTrue(globalOps instanceof NativeTensorOperations);
+        Assume.assumeTrue(globalOps instanceof NativeSimdTensorOperations);
 
         FloatBufferTensor c = new FloatBufferTensor(BATCH, ROWS);
         FloatBufferTensor c1 = new FloatBufferTensor(BATCH, ROWS);
@@ -548,7 +548,7 @@ public void testNativeBatchDotProductWithOffsets() {
     @Test
     public void testNativeDotProductFast() {
         // M == BATCH, N == ROWS, K == SIZE
-        Assume.assumeTrue(globalOps instanceof NativeTensorOperations);
+        Assume.assumeTrue(globalOps instanceof NativeSimdTensorOperations);
 
         FloatBufferTensor c = new FloatBufferTensor(1, SIZE);
         FloatBufferTensor c1 = new FloatBufferTensor(1, SIZE);