Merge branch 'master' into coverage-ggerganov#295

goerch · Jul 18, 2023 · a9c9c03 · a9c9c03
2 parents 702c8ab + 35171f5
commit a9c9c03
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Showing 4 changed files with 45 additions and 16 deletions.
diff --git a/.gitignore b/.gitignore
@@ -4,8 +4,12 @@ build-release/
 build-sanitize-addr/
 build-sanitize-thread/
 build-cov/
+build-ci-debug/
+build-ci-release/
 out/
+tmp/
 models/
+models-mnt
 
 compile_commands.json
 CMakeSettings.json

diff --git a/ci/run.sh b/ci/run.sh
@@ -1,11 +1,23 @@
 #/bin/bash
 
+if [ -z "$2" ]; then
+    echo "usage: $0 <output-dir> <mnt-dir>"
+    exit 1
+fi
+
+mkdir -p "$1"
+mkdir -p "$2"
+
+OUT=$(realpath "$1")
+MNT=$(realpath "$2")
+
+rm -v $OUT/*.log
+rm -v $OUT/*.exit
+rm -v $OUT/*.md
+
 sd=`dirname $0`
 cd $sd/../
-
 SRC=`pwd`
-OUT="$1"
-MNT="$2"
 
 ## helpers
 
@@ -183,8 +195,9 @@ function gg_sum_mpt {
 if [ -z $GG_BUILD_LOW_PERF ]; then
     rm -rf ${SRC}/models-mnt
 
-    mkdir -p $(realpath ${MNT}/models)
-    ln -sfn ${MNT}/models ${SRC}/models-mnt
+    mnt_models=$(realpath ${MNT}/models)
+    mkdir -p ${mnt_models}
+    ln -sfn ${mnt_models} ${SRC}/models-mnt
 
     python3 -m pip install -r ${SRC}/requirements.txt
 fi

diff --git a/src/ggml-cuda.cu b/src/ggml-cuda.cu
@@ -3537,6 +3537,11 @@ void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
     (void) dst;
 }
 
+void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_cpy(src0, dst, nullptr);
+    (void) src1;
+}
+
 void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
     ggml_cuda_op(src0, src1, dst, ggml_cuda_op_diag_mask_inf, true, true);
@@ -3670,7 +3675,7 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
     // recursively assign CUDA buffers until a compute tensor is found
     if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
         const ggml_op src0_op = tensor->src[0]->op;
-        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW) {
+        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
             ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace);
         }
     }
@@ -3776,6 +3781,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
         || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
 
     switch (tensor->op) {
+        case GGML_OP_DUP:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_dup;
+            break;
         case GGML_OP_ADD:
             if (!any_on_device) {
                 return false;
@@ -3830,6 +3841,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
             }
             func = ggml_cuda_cpy;
             break;
+        case GGML_OP_CONT:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_dup;
+            break;
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:

diff --git a/src/ggml.c b/src/ggml.c
@@ -4420,8 +4420,8 @@ void ggml_free(struct ggml_context * ctx) {
         if (&g_state.contexts[i].context == ctx) {
             g_state.contexts[i].used = false;
 
-            GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n",
-                    __func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
+            GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n",
+                    __func__, i, ggml_used_mem(ctx));
 
             if (ctx->mem_buffer_owned) {
                 GGML_ALIGNED_FREE(ctx->mem_buffer);
@@ -16362,8 +16362,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 if (GGML_OP_HAS_FINALIZE[node->op]) {
                     params.nth = n_tasks_arr[node_n];
                     ggml_compute_forward(&params, node);
-                    ggml_graph_compute_perf_stats_node(node, state->shared);
                 }
+                ggml_graph_compute_perf_stats_node(node, state->shared);
             }
 
             // distribute new work or execute it direct if 1T
@@ -16393,8 +16393,9 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                     if (GGML_OP_HAS_FINALIZE[node->op]) {
                         params.type = GGML_TASK_FINALIZE;
                         ggml_compute_forward(&params, node);
-                        ggml_graph_compute_perf_stats_node(node, state->shared);
                     }
+
+                    ggml_graph_compute_perf_stats_node(node, state->shared);
                 } else {
                     break;
                 }
@@ -16936,9 +16937,6 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
 }
 
 void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
-    //assert(cgraph->work      == NULL);
-    //assert(cgraph->work_size == 0);
-
     uint64_t size_eval = 0;
 
     // compute size of intermediate results
@@ -17377,9 +17375,6 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
 
     GGML_PRINT("=== GRAPH ===\n");
 
-    // GGML_PRINT_DEBUG("n_threads       = %d\n",        cgraph->n_threads);
-    // GGML_PRINT_DEBUG("total work size = %zu bytes\n", cgraph->work_size);
-
     GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];