Merge branch 'master' into concedo_experimental

# Conflicts:
#	CMakeLists.txt
#	Makefile

.gitignore

@@ -29,6 +29,7 @@ build-sanitize-thread/
 /result
 /perplexity
 /embedding
+/train-text-from-scratch
 /benchmark-matmult
 /vdot
 /Pipfile

Package.swift

@@ -11,6 +11,7 @@ let package = Package(
         .target(
             name: "llama",
             path: ".",
+            exclude: ["ggml-metal.metal"],
             sources: ["ggml.c", "llama.cpp"],
             publicHeadersPath: "spm-headers",
             cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")],

examples/chat-vicuna.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+
+set -e
+
+cd "$(dirname "$0")/.." || exit
+
+MODEL="${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}"
+PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}
+USER_NAME="### Human"
+AI_NAME="### Assistant"
+
+# Adjust to the number of CPU cores you want to use.
+N_THREAD="${N_THREAD:-8}"
+# Number of tokens to predict (made it larger than default because we want a long interaction)
+N_PREDICTS="${N_PREDICTS:-2048}"
+
+# Note: you can also override the generation options by specifying them on the command line:
+# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
+GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
+
+DATE_TIME=$(date +%H:%M)
+DATE_YEAR=$(date +%Y)
+
+PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)
+
+sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
+    -e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \
+    -e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \
+    -e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \
+     $PROMPT_TEMPLATE > $PROMPT_FILE
+
+# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
+./bin/main $GEN_OPTIONS \
+  --model "$MODEL" \
+  --threads "$N_THREAD" \
+  --n_predict "$N_PREDICTS" \
+  --color --interactive \
+  --file ${PROMPT_FILE} \
+  --reverse-prompt "### Human:" \
+  --in-prefix ' ' \
+  "$@"

examples/common.cpp

@@ -412,6 +412,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         gpt_print_usage(argc, argv, default_params);
         exit(1);
     }
+
+#ifdef GGML_USE_CUBLAS
+    if (!params.lora_adapter.empty() && params.n_gpu_layers > 0) {
+        fprintf(stderr, "%s: error: the simultaneous use of LoRAs and GPU acceleration is not supported", __func__);
+        exit(1);
+    }
+#endif // GGML_USE_CUBLAS
+
     if (escape_prompt) {
         process_escapes(params.prompt);
     }

examples/server/README.md

@@ -16,6 +16,10 @@ This example allow you to have a llama.cpp http server to interact from a web pa
 To get started right away, run the following command, making sure to use the correct path for the model you have:
 
 #### Unix-based systems (Linux, macOS, etc.):
+Make sure to build with the server option on
+```bash
+LLAMA_BUILD_SERVER=1 make
+```
 
 ```bash
 ./server -m models/7B/ggml-model.bin --ctx_size 2048

ggml-cuda.cu

@@ -25,7 +25,7 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
         }                                                                               \
     } while (0)
 
-#if CUDART_VERSION >= 12
+#if CUDART_VERSION >= 12000
 #define CUBLAS_CHECK(err)                                                               \
     do {                                                                                \
         cublasStatus_t err_ = (err);                                                    \
@@ -2366,7 +2366,7 @@ void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
 }
 
 void ggml_cuda_set_main_device(int main_device) {
-    if (main_device > g_device_count) {
+    if (main_device >= g_device_count) {
         fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
                 main_device, g_device_count, g_main_device);
         return;

ggml-metal.h

@@ -55,6 +55,7 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
 void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
 
 // same as ggml_graph_compute but uses Metal
+// creates gf->n_threads command buffers in parallel
 void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
 
 #ifdef __cplusplus

ggml-metal.m

@@ -287,15 +287,40 @@ void ggml_metal_graph_compute(
                struct ggml_cgraph * gf) {
     metal_printf("%s: evaluating graph\n", __func__);
 
+    // create multiple command buffers and enqueue them
+    // then, we encode the graph into the command buffers in parallel
+
+    const int n_cb = gf->n_threads;
+
+    NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
+
+    for (int i = 0; i < n_cb; ++i) {
+        command_buffers[i] = [ctx->queue commandBuffer];
+
+        // enqueue the command buffers in order to specify their execution order
+        [command_buffers[i] enqueue];
+    }
+
+    // TODO: is this the best way to start threads?
+    dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
+
+    for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
+        const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
+
+        dispatch_async(queue, ^{
             size_t offs_src0 = 0;
             size_t offs_src1 = 0;
             size_t offs_dst  = 0;
 
-    id<MTLCommandBuffer> command_buffer  = [ctx->queue commandBuffer];
+            id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
+
             id<MTLComputeCommandEncoder> encoder = nil;
 
-    for (int i = 0; i < gf->n_nodes; ++i) {
-        //metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
+            const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
+            const int node_end   = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
+
+            for (int i = node_start; i < node_end; ++i) {
+                metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
 
                 struct ggml_tensor * src0 = gf->nodes[i]->src0;
                 struct ggml_tensor * src1 = gf->nodes[i]->src1;
@@ -626,7 +651,6 @@ void ggml_metal_graph_compute(
                                         }
                                 };
 
-
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
@@ -800,12 +824,11 @@ void ggml_metal_graph_compute(
             }
 
             [command_buffer commit];
-    [command_buffer waitUntilCompleted];
-
-    {
-        const double time_elapsed = [command_buffer GPUEndTime] - [command_buffer GPUStartTime];
-        UNUSED(time_elapsed);
-
-        metal_printf("%s: time elapsed = %f ms\n", __func__, time_elapsed * 1000.0);
+        });
     }
+
+    // wait for all threads to finish
+    dispatch_barrier_sync(queue, ^{});
+
+    [command_buffers[n_cb - 1] waitUntilCompleted];
 }

llama.h

@@ -244,9 +244,9 @@ extern "C" {
     LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
 
     // Special tokens
-    LLAMA_API llama_token llama_token_bos();
-    LLAMA_API llama_token llama_token_eos();
-    LLAMA_API llama_token llama_token_nl();
+    LLAMA_API llama_token llama_token_bos();  // beginning-of-sentence
+    LLAMA_API llama_token llama_token_eos();  // end-of-sentence
+    LLAMA_API llama_token llama_token_nl();   // next-line
 
     // Sampling functions
 

spm-headers/ggml.h

@@ -0,0 +1 @@
+../ggml.h