Merge branch 'cuda-cublas-opts' into gg/phi-2

convert-lora-to-ggml.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 
 import json
 import os
-import re
 import struct
 import sys
 from typing import Any, BinaryIO, Sequence
@@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence
 import numpy as np
 import torch
 
+from pathlib import Path
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
 
 
-HF_SUBLAYER_TO_GGML = {
-    "self_attn.q_proj": "attn_q",
-    "self_attn.k_proj": "attn_k",
-    "self_attn.v_proj": "attn_v",
-    "self_attn.o_proj": "attn_output",
-    "mlp.gate_proj": "ffn_gate",
-    "mlp.down_proj": "ffn_down",
-    "mlp.up_proj": "ffn_up",
-    "input_layernorm": "attn_norm",
-    "post_attention_layernorm": "ffn_norm",
-}
-
-
-def translate_tensor_name(t: str) -> str:
-    match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
-    if match:
-        nn = match.group(1)
-        sub_layer = match.group(2)
-        lora_type = match.group(3)
-
-        sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
-        if sub_layer_renamed is None:
-            print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
-            sys.exit(1)
-
-        output_string = (
-            f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
-        )
-        return output_string
-    else:
-        print(f"Error: unrecognized tensor {t}")
-        sys.exit(1)
-
-
 def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
     fout.write(b"ggla"[::-1])  # magic (ggml lora)
     fout.write(struct.pack("i", 1))  # file version
@@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
     fout.write(struct.pack("i", int(params["lora_alpha"])))
 
 
-def write_tensor_header(
+def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
-    self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
-) -> None:
     sname = name.encode("utf-8")
     fout.write(
         struct.pack(
@@ -78,11 +47,12 @@ def write_tensor_header(
     fout.seek((fout.tell() + 31) & -32)
 
 
-if len(sys.argv) != 2:
+if len(sys.argv) < 2:
-    print(f"Usage: python {sys.argv[0]} <path>")
+    print(f"Usage: python {sys.argv[0]} <path> [arch]")
     print(
         "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
     )
+    print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
     sys.exit(1)
 
 input_json = os.path.join(sys.argv[1], "adapter_config.json")
@@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin")
 output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
 
 model = torch.load(input_model, map_location="cpu")
+arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
+
+if arch_name not in gguf.MODEL_ARCH_NAMES.values():
+    print(f"Error: unsupported architecture {arch_name}")
+    sys.exit(1)
+
+arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
+name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
 
 with open(input_json, "r") as f:
     params = json.load(f)
@@ -117,6 +95,7 @@ with open(output_path, "wb") as fout:
 
     write_file_header(fout, params)
     for k, v in model.items():
+        orig_k = k
         if k.endswith(".default.weight"):
             k = k.replace(".default.weight", ".weight")
         if k in ["llama_proj.weight", "llama_proj.bias"]:
@@ -129,7 +108,32 @@ with open(output_path, "wb") as fout:
             v = v.float()
 
         t = v.detach().numpy()
-        tname = translate_tensor_name(k)
+
+        prefix = "base_model.model."
+        if k.startswith(prefix):
+            k = k[len(prefix) :]
+
+        lora_suffixes = (".lora_A.weight", ".lora_B.weight")
+        if k.endswith(lora_suffixes):
+            suffix = k[-len(lora_suffixes[0]):]
+            k = k[: -len(lora_suffixes[0])]
+        else:
+            print(f"Error: unrecognized tensor name {orig_k}")
+            sys.exit(1)
+
+        tname = name_map.get_name(k)
+        if tname is None:
+            print(f"Error: could not map tensor name {orig_k}")
+            print(" Note: the arch parameter must be specified if the model is not llama")
+            sys.exit(1)
+
+        if suffix == ".lora_A.weight":
+            tname += ".weight.loraA"
+        elif suffix == ".lora_B.weight":
+            tname += ".weight.loraB"
+        else:
+            assert False
+
         print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
         write_tensor_header(fout, tname, t.shape, t.dtype)
         t.tofile(fout)

examples/server/public/completion.js

@@ -34,7 +34,8 @@ export async function* llama(prompt, params = {}, config = {}) {
     headers: {
       'Connection': 'keep-alive',
       'Content-Type': 'application/json',
-      'Accept': 'text/event-stream'
+      'Accept': 'text/event-stream',
+      ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
     },
     signal: controller.signal,
   });

examples/server/public/index.html

@@ -235,7 +235,8 @@
       grammar: '',
       n_probs: 0, // no completion_probabilities,
       image_data: [],
-      cache_prompt: true
+      cache_prompt: true,
+      api_key: ''
     })
 
     /* START: Support for storing prompt templates and parameters in browsers LocalStorage */
@@ -790,6 +791,10 @@
             <fieldset>
               ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
             </fieldset>
+            <fieldset>
+              <label for="api_key">API Key</label>
+              <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
+            </fieldset>
           </details>
         </form>
       `

examples/server/server.cpp

@@ -36,6 +36,7 @@ using json = nlohmann::json;
 struct server_params
 {
     std::string hostname = "127.0.0.1";
+    std::string api_key;
     std::string public_path = "examples/server/public";
     int32_t port = 8080;
     int32_t read_timeout = 600;
@@ -1953,6 +1954,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
     printf("  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
     printf("  --path PUBLIC_PATH    path from which to serve static files (default %s)\n", sparams.public_path.c_str());
+    printf("  --api-key API_KEY     optional api key to enhance server security. If set, requests must include this key for access.\n");
     printf("  -to N, --timeout N    server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
     printf("  --embedding           enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
     printf("  -np N, --parallel N   number of slots for process requests (default: %d)\n", params.n_parallel);
@@ -2002,6 +2004,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             sparams.public_path = argv[i];
         }
+        else if (arg == "--api-key")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            sparams.api_key = argv[i];
+        }
         else if (arg == "--timeout" || arg == "-to")
         {
             if (++i >= argc)
@@ -2669,6 +2680,32 @@ int main(int argc, char **argv)
 
     httplib::Server svr;
 
+    // Middleware for API key validation
+    auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
+        // If API key is not set, skip validation
+        if (sparams.api_key.empty()) {
+            return true;
+        }
+
+        // Check for API key in the header
+        auto auth_header = req.get_header_value("Authorization");
+        std::string prefix = "Bearer ";
+        if (auth_header.substr(0, prefix.size()) == prefix) {
+            std::string received_api_key = auth_header.substr(prefix.size());
+            if (received_api_key == sparams.api_key) {
+                return true; // API key is valid
+            }
+        }
+
+        // API key is invalid or not provided
+        res.set_content("Unauthorized: Invalid API Key", "text/plain");
+        res.status = 401; // Unauthorized
+
+        LOG_WARNING("Unauthorized: Invalid API Key", {});
+
+        return false;
+    };
+
     svr.set_default_headers({{"Server", "llama.cpp"},
                              {"Access-Control-Allow-Origin", "*"},
                              {"Access-Control-Allow-Headers", "content-type"}});
@@ -2711,8 +2748,11 @@ int main(int argc, char **argv)
                 res.set_content(data.dump(), "application/json");
             });
 
-    svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = json::parse(req.body);
                 const int task_id = llama.request_completion(data, false, false, -1);
                 if (!json_value(data, "stream", false)) {
@@ -2799,8 +2839,11 @@ int main(int argc, char **argv)
             });
 
     // TODO: add mount point without "/v1" prefix -- how?
-    svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = oaicompat_completion_params_parse(json::parse(req.body));
 
                 const int task_id = llama.request_completion(data, false, false, -1);
@@ -2869,8 +2912,11 @@ int main(int argc, char **argv)
                 }
             });
 
-    svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = json::parse(req.body);
                 const int task_id = llama.request_completion(data, true, false, -1);
                 if (!json_value(data, "stream", false)) {
@@ -3005,11 +3051,15 @@ int main(int argc, char **argv)
 
     svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
             {
+                if (res.status == 401)
+                {
+                    res.set_content("Unauthorized", "text/plain");
+                }
                 if (res.status == 400)
                 {
                     res.set_content("Invalid request", "text/plain");
                 }
-                else if (res.status != 500)
+                else if (res.status == 404)
                 {
                     res.set_content("File Not Found", "text/plain");
                     res.status = 404;
@@ -3032,11 +3082,15 @@ int main(int argc, char **argv)
     // to make it ctrl+clickable:
     LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
 
-    LOG_INFO("HTTP server listening", {
+    std::unordered_map<std::string, std::string> log_data;
-                                          {"hostname", sparams.hostname},
+    log_data["hostname"] = sparams.hostname;
-                                          {"port", sparams.port},
+    log_data["port"] = std::to_string(sparams.port);
-                                      });
 
+    if (!sparams.api_key.empty()) {
+        log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
+    }
+
+    LOG_INFO("HTTP server listening", log_data);
     // run the HTTP server in a thread - see comment below
     std::thread t([&]()
             {

ggml-cuda.cu

@@ -7406,27 +7406,20 @@ inline void ggml_cuda_op_mul_mat_cublas(
             to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
-        size_t dst_as = 0;
-        half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
 
-        const half alpha_f16 = 1.0f;
+        const float alpha = 1.0f;
-        const half beta_f16 = 0.0f;
+        const float beta = 0.0f;
 
         CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
         CUBLAS_CHECK(
             cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
-                    &alpha_f16, src0_ptr, CUDA_R_16F, ne00,
+                    &alpha, src0_ptr, CUDA_R_16F, ne00,
-                                src1_ptr, CUDA_R_16F, ne10,
+                            src1_ptr, CUDA_R_16F, ne10,
-                    &beta_f16,   dst_f16, CUDA_R_16F, ldc,
+                    &beta,  dst_dd_i, CUDA_R_32F, ldc,
-                    CUBLAS_COMPUTE_16F,
+                    CUBLAS_COMPUTE_32F,
                     CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
-        const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
-        to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
-
-        ggml_cuda_pool_free(dst_f16, dst_as);
-
         if (src0_as != 0) {
             ggml_cuda_pool_free(src0_as_f16, src0_as);
         }
@@ -8306,8 +8299,8 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
 }
 
-static __global__ void k_compute_batched_ptrs(
+__global__ static void k_compute_batched_ptrs(
-        const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
+        const half * src0_as_f16, const half * src1_as_f16, float * dst_f32,
         const void ** ptrs_src, void ** ptrs_dst,
         int ne12, int ne13,
         int ne23,
@@ -8327,7 +8320,7 @@ static __global__ void k_compute_batched_ptrs(
 
     ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02   + i03*nb03;
     ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)     dst_f16 + i12* nb2/2 + i13* nb3/2;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)     dst_f32 + i12* nb2   + i13* nb3  ;
 }
 
 static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -8382,9 +8375,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
     to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
 
-    size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
-
     GGML_ASSERT(ne12 % ne02 == 0);
     GGML_ASSERT(ne13 % ne03 == 0);
 
@@ -8392,8 +8382,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     const int64_t r2 = ne12/ne02;
     const int64_t r3 = ne13/ne03;
 
-    const half alpha_f16 = 1.0f;
+    const float alpha = 1.0f;
-    const half beta_f16  = 0.0f;
+    const float beta  = 0.0f;
 
 #if 0
     // use cublasGemmEx
@@ -8406,10 +8396,10 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 CUBLAS_CHECK(
                         cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                             ne01, ne11, ne10,
-                            &alpha_f16, (const char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3]  , CUDA_R_16F, nb01/sizeof(half),
+                            &alpha, (const char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3]  , CUDA_R_16F, nb01/sizeof(half),
-                                        (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
+                                    (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
-                            &beta_f16,  (      char *)     dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2, CUDA_R_16F, ne01,
+                            &beta,  (      char *)     dst_ddf + i12* dst->nb[2]   + i13* dst->nb[3]  , CUDA_R_32F, ne01,
-                            CUBLAS_COMPUTE_16F,
+                            CUBLAS_COMPUTE_32F,
                             CUBLAS_GEMM_DEFAULT_TENSOR_OP));
             }
         }
@@ -8421,11 +8411,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         CUBLAS_CHECK(
         cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
-                &alpha_f16, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half),  src0->nb[2]/sizeof(half),  // strideA
+                &alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half),  src0->nb[2]/sizeof(half),  // strideA
-                            (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
+                        (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
-                &beta_f16,  (      char *)     dst_f16, CUDA_R_16F, ne01,                dst->nb[2]/sizeof(float), // strideC
+                &beta,  (      char *)     dst_ddf, CUDA_R_32F, ne01,                dst->nb[2]/sizeof(float), // strideC
                 ne12*ne13,
-                CUBLAS_COMPUTE_16F,
+                CUBLAS_COMPUTE_32F,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
     } else {
         // use cublasGemmBatchedEx
@@ -8442,7 +8432,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
-                src0_as_f16, src1_as_f16, dst_f16,
+                src0_as_f16, src1_as_f16, dst_ddf,
                 ptrs_src, ptrs_dst,
                 ne12, ne13,
                 ne23,
@@ -8455,11 +8445,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         CUBLAS_CHECK(
         cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
-                &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
+                &alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
-                            (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
+                        (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
-                &beta_f16,  (      void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
+                &beta,  (      void **) (ptrs_dst + 0*ne23), CUDA_R_32F, ne01,
                 ne23,
-                CUBLAS_COMPUTE_16F,
+                CUBLAS_COMPUTE_32F,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
         if (ptrs_src_s != 0) {
@@ -8471,11 +8461,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     }
 #endif
 
-    const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
-    to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
-
     ggml_cuda_pool_free(src1_as_f16, src1_as);
-    ggml_cuda_pool_free(dst_f16, dst_as);
 }
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

ggml.c

@@ -9584,16 +9584,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(
 }
 #endif
 
-// off1 = offset in i11 and i1
-// cne1 = ne11 and ne1
-// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
-// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
 static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
-              struct ggml_tensor * dst,
+              struct ggml_tensor * dst) {
-              int64_t off1, int64_t cne1) {
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -9661,9 +9656,9 @@ static void ggml_compute_forward_mul_mat(
                 const int64_t i03 = i13/r3;
                 const int64_t i02 = i12/r2;
 
-                const void  * x = (char *)            src0->data +             i02*nb02 + i03*nb03;
+                const void  * x = (char *)            src0->data + i02*nb02 + i03*nb03;
-                const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
+                const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
-                      float * d = (float *) ((char *)  dst->data + off1*nb1  + i12*nb2  + i13*nb3);
+                      float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
 
                 if (type != GGML_TYPE_F32) {
                             float * const wdata    = params->wdata;
@@ -9680,7 +9675,7 @@ static void ggml_compute_forward_mul_mat(
                 }
 
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                         cne1, ne01, ne10,
+                          ne1, ne01, ne10,
                          1.0f,    y, ne10,
                                   x, ne00,
                          0.0f,    d, ne01);
@@ -9721,8 +9716,8 @@ static void ggml_compute_forward_mul_mat(
     const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
     const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
-    const int64_t nr0 = ne01;           // src0 rows
+    const int64_t nr0 = ne01;          // src0 rows
-    const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+    const int64_t nr1 = ne1*ne12*ne13; // src1 rows
 
     //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
 
@@ -9764,9 +9759,9 @@ static void ggml_compute_forward_mul_mat(
     for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
         for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
             for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
-                const int64_t i13 = (ir1/(ne12*cne1));
+                const int64_t i13 = (ir1/(ne12*ne1));
-                const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
+                const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
-                const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
+                const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
 
                 // broadcast src0 into src1
                 const int64_t i03 = i13/r3;
@@ -9806,28 +9801,191 @@ static void ggml_compute_forward_mul_mat(
 
 static void ggml_compute_forward_mul_mat_id(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
+        const struct ggml_tensor * ids,
         const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
-        // during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
-        ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
-        return;
-    }
 
-    const struct ggml_tensor * ids = src0;
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const enum ggml_type type = src0->type;
+
+    const bool src1_cont = ggml_is_contiguous(src1);
+
+    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    // row groups
     const int id   = ggml_get_op_params_i32(dst, 0);
     const int n_as = ggml_get_op_params_i32(dst, 1);
 
-    for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+    char * wdata_src1_end = (src1->type == vec_dot_type) ?
-        const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+            (char *) params->wdata :
+            (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
 
-        GGML_ASSERT(row_id >= 0 && row_id < n_as);
+    int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
+    int64_t * matrix_rows       = matrix_row_counts + n_as;     // [n_as][ne11]
 
-        const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+    #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
-        ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
+
+   if (params->type == GGML_TASK_INIT) {
+        char * wdata = params->wdata;
+        if (src1->type != vec_dot_type) {
+            const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+            assert(params->wsize >= ne11*ne12*ne13*row_size);
+            assert(src1->type == GGML_TYPE_F32);
+
+            for (int64_t i13 = 0; i13 < ne13; ++i13) {
+                for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                        wdata += row_size;
+                    }
+                }
+            }
+        }
+
+        // initialize matrix_row_counts
+        GGML_ASSERT(wdata == wdata_src1_end);
+        memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
+
+        // group rows by src0 matrix
+        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+            const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+
+            GGML_ASSERT(row_id >= 0 && row_id < n_as);
+            MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
+            matrix_row_counts[row_id] += 1;
+        }
+
+        return;
     }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // compute each matrix multiplication in sequence
+    for (int cur_a = 0; cur_a < n_as; ++cur_a) {
+        const int64_t cne1 = matrix_row_counts[cur_a];
+
+        if (cne1 == 0) {
+            continue;
+        }
+
+        const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
+
+        const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+        const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+        const int64_t nr0 = ne01;           // src0 rows
+        const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+
+        //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+
+        // distribute the thread work across the inner or outer loop based on which one is larger
+
+        const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
+        const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
+
+        const int64_t ith0 = ith % nth0;
+        const int64_t ith1 = ith / nth0;
+
+        const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
+        const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
+
+        const int64_t ir010 = dr0*ith0;
+        const int64_t ir011 = MIN(ir010 + dr0, nr0);
+
+        const int64_t ir110 = dr1*ith1;
+        const int64_t ir111 = MIN(ir110 + dr1, nr1);
+
+        //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
+
+        // threads with no work simply yield (not sure if it helps)
+        if (ir010 >= ir011 || ir110 >= ir111) {
+            sched_yield();
+            continue;
+        }
+
+        assert(ne12 % ne02 == 0);
+        assert(ne13 % ne03 == 0);
+
+        // block-tiling attempt
+        const int64_t blck_0 = 16;
+        const int64_t blck_1 = 16;
+
+        // attempt to reduce false-sharing (does not seem to make a difference)
+        float tmp[16];
+
+        for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
+            for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
+                for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+                    const int64_t  i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
+                    const int64_t  i12 = (ir1 - i13*ne12*cne1)/cne1;
+                    const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
+                    const int64_t  i11 = MMID_MATRIX_ROW(cur_a, _i11);
+
+                    // broadcast src0 into src1
+                    const int64_t i03 = i13/r3;
+                    const int64_t i02 = i12/r2;
+
+                    const int64_t i1 = i11;
+                    const int64_t i2 = i12;
+                    const int64_t i3 = i13;
+
+                    const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
+
+                    // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
+                    //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
+                    //       the original src1 data pointer, so we should index using the indices directly
+                    // TODO: this is a bit of a hack, we should probably have a better way to handle this
+                    const char * src1_col = (const char *) wdata +
+                        (src1_cont || src1->type != vec_dot_type
+                        ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
+                        : (i11*nb11 + i12*nb12 + i13*nb13));
+
+                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+                    //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                    //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
+                    //}
+
+                    for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                        vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                    }
+                    memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
+                }
+            }
+        }
+    }
+
+    #undef MMID_MATRIX_ROW
 }
 
 // ggml_compute_forward_out_prod
@@ -14217,7 +14375,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
+                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
@@ -16017,7 +16175,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
-                // FIXME: blas
                 n_tasks = n_threads;
             } break;
         case GGML_OP_OUT_PROD:
@@ -16351,20 +16508,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                 } break;
             case GGML_OP_MUL_MAT_ID:
                 {
-                    const struct ggml_tensor * a = node->src[2];
+                    const struct ggml_tensor * src0 = node->src[2];
-                    const struct ggml_tensor * b = node->src[1];
+                    const struct ggml_tensor * src1 = node->src[1];
-                    const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
+                    const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+                    if (src1->type != vec_dot_type) {
-                    if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
+                        cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
-                        if (a->type != GGML_TYPE_F32) {
-                            // here we need memory just for single 2D matrix from src0
-                            cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
-                        }
-                    } else
-#endif
-                    if (b->type != vec_dot_type) {
-                        cur = ggml_row_size(vec_dot_type, ggml_nelements(b));
                     }
+                    const int n_as = ggml_get_op_params_i32(node, 1);
+                    cur = GGML_PAD(cur, sizeof(int64_t));        // align
+                    cur += n_as * sizeof(int64_t);               // matrix_row_counts
+                    cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
                 } break;
             case GGML_OP_OUT_PROD:
                 {

llama.cpp

@@ -1521,6 +1521,10 @@ struct llama_context {
 
     // decode output (2-dimensional array: [n_tokens][n_vocab])
     std::vector<float> logits;
+#ifndef NDEBUG
+    // guard against access to unset logits
+    std::vector<bool>  logits_valid;
+#endif
     bool logits_all = false;
 
     // input embedding (1-dimensional array: [n_embd])
@@ -6386,6 +6390,14 @@ static int llama_decode_internal(
     {
         auto & logits_out = lctx.logits;
 
+#ifndef NDEBUG
+        auto & logits_valid = lctx.logits_valid;
+        logits_valid.clear();
+        logits_valid.resize(n_tokens);
+
+        logits_out.clear();
+#endif
+
         if (batch.logits) {
             logits_out.resize(n_vocab * n_tokens);
             for (uint32_t i = 0; i < n_tokens; i++) {
@@ -6393,13 +6405,22 @@ static int llama_decode_internal(
                     continue;
                 }
                 memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
+#ifndef NDEBUG
+                logits_valid[i] = true;
+#endif
             }
         } else if (lctx.logits_all) {
             logits_out.resize(n_vocab * n_tokens);
             memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
+#ifndef NDEBUG
+            std::fill(logits_valid.begin(), logits_valid.end(), true);
+#endif
         } else {
             logits_out.resize(n_vocab);
             memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
+#ifndef NDEBUG
+            logits_valid[n_tokens - 1] = true;
+#endif
         }
     }
 
@@ -8862,53 +8883,60 @@ static int llama_apply_lora_from_file_internal(
 
     const int64_t t_start_lora_us = ggml_time_us();
 
-    auto fin = std::ifstream(path_lora, std::ios::binary);
+    llama_file fin(path_lora, "rb");
-    if (!fin) {
-        LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
-        return 1;
-    }
 
     // verify magic and version
     {
-        uint32_t magic;
+        uint32_t magic = fin.read_u32();
-        fin.read((char *) &magic, sizeof(magic));
+        if (magic != LLAMA_FILE_MAGIC_GGLA) {
-        uint32_t format_version;
+            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
-        fin.read((char *) &format_version, sizeof(format_version));
+            return 1;
+        }
 
+        uint32_t format_version = fin.read_u32();
         if (format_version != 1) {
             LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
             return 1;
         }
     }
 
-    int32_t lora_r;
+    int32_t lora_r = fin.read_u32();
-    int32_t lora_alpha;
+    int32_t lora_alpha = fin.read_u32();
-    fin.read((char *) &lora_r, sizeof(lora_r));
-    fin.read((char *) &lora_alpha, sizeof(lora_alpha));
     float scaling = scale * (float)lora_alpha / (float)lora_r;
 
     LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
 
+    // create a name -> tensor map of the model to accelerate lookups
+    // find the max tensor size to estimate the required temporary buffer size
+    size_t max_tensor_size = 0;
+    std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
+    for (const auto & kv : model.tensors_by_name) {
+        model_tensors.insert(kv);
+        size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
+        max_tensor_size = std::max(max_tensor_size, f32_size);
+    }
+
     // create a temporary ggml context to store the lora tensors
-    // todo: calculate size from biggest possible tensor
+    // TODO: use ggml-alloc
-    std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
+    size_t lora_ctx_size = max_tensor_size * 3;
+    LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
+    std::vector<uint8_t> lora_buf(lora_ctx_size);
+
     struct ggml_init_params params;
     params.mem_size   = lora_buf.size();
     params.mem_buffer = lora_buf.data();
     params.no_alloc   = false;
 
-    ggml_context * lora_ctx = ggml_init(params);
+    using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
-    std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
 
-    // create a name -> tensor map of the model to accelerate lookups
+    unique_context lora_ctx(nullptr, ggml_free);
-    std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
+    lora_ctx.reset(ggml_init(params));
-    for (const auto & kv : model.tensors_by_name) {
+    std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
-        model_tensors.insert(kv);
-    }
 
     // load base model
     std::unique_ptr<llama_model_loader> ml;
-    ggml_context * base_ctx = NULL;
+
+    unique_context base_ctx(nullptr, ggml_free);
     std::vector<uint8_t> base_buf;
     if (path_base_model) {
         LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
@@ -8917,6 +8945,7 @@ static int llama_apply_lora_from_file_internal(
         size_t ctx_size;
         size_t mmapped_size;
         ml->calc_sizes(ctx_size, mmapped_size);
+
         base_buf.resize(ctx_size);
 
         ggml_init_params base_params;
@@ -8924,9 +8953,9 @@ static int llama_apply_lora_from_file_internal(
         base_params.mem_buffer = base_buf.data();
         base_params.no_alloc   = ml->use_mmap;
 
-        base_ctx = ggml_init(base_params);
+        base_ctx.reset(ggml_init(base_params));
 
-        // maybe this should in llama_model_loader
+        // maybe this should be in llama_model_loader
         if (ml->use_mmap) {
             ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
         }
@@ -8939,27 +8968,35 @@ static int llama_apply_lora_from_file_internal(
     std::vector<uint8_t> work_buffer;
 
     while (true) {
+        if (fin.tell() == fin.size) {
+            // eof
+            break;
+        }
+
         int32_t n_dims;
-        int32_t length;
+        int32_t name_len;
         int32_t ftype;
 
-        fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+        fin.read_raw(&n_dims, sizeof(n_dims));
-        fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+        fin.read_raw(&name_len, sizeof(name_len));
-        fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+        fin.read_raw(&ftype,  sizeof(ftype));
-        if (fin.eof()) {
+
-            break;
+        if (n_dims != 1 && n_dims != 2) {
+            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
+            return 1;
         }
 
         int32_t ne[2] = { 1, 1 };
         for (int i = 0; i < n_dims; ++i) {
-            fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+            fin.read_raw(&ne[i], sizeof(ne[i]));
         }
 
         std::string name;
         {
+            GGML_ASSERT(name_len <= 1024);
             char buf[1024];
-            fin.read(buf, length);
+            fin.read_raw(buf, name_len);
-            name = std::string(buf, length);
+            name = std::string(buf, name_len);
         }
 
         // check for lora suffix and get the type of tensor
@@ -8973,7 +9010,7 @@ static int llama_apply_lora_from_file_internal(
         std::string lora_type = name.substr(pos + lora_suffix.length());
         std::string base_name = name;
         base_name.erase(pos);
-        // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
+        // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
 
         if (model_tensors.find(base_name) == model_tensors.end()) {
             LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
@@ -8992,22 +9029,15 @@ static int llama_apply_lora_from_file_internal(
                         return false;
                     }
         }
-        ggml_tensor * lora_tensor;
+        ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
-        if (n_dims == 2) {
+        ggml_set_name(lora_tensor, name.c_str());
-            lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
-        }
-        else {
-            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
-            return 1;
-        }
-        ggml_set_name(lora_tensor, "lora_tensor");
 
         // load tensor data
-        size_t offset = fin.tellg();
+        size_t offset = fin.tell();
         size_t tensor_data_size = ggml_nbytes(lora_tensor);
         offset = (offset + 31) & -32;
-        fin.seekg(offset);
+        fin.seek(offset, SEEK_SET);
-        fin.read((char*)lora_tensor->data, tensor_data_size);
+        fin.read_raw(lora_tensor->data, tensor_data_size);
 
         lora_tensors[name] = lora_tensor;
 
@@ -9037,13 +9067,11 @@ static int llama_apply_lora_from_file_internal(
 
                 // load from base model
                 if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
-                    // TODO: throw
                     LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
                     return 1;
                 }
 
-                // TODO: not tested!! maybe not working!
+                base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU);
-                base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
                 ml->load_data_for(base_t);
             } else {
                 base_t = dest_t;
@@ -9072,43 +9100,45 @@ static int llama_apply_lora_from_file_internal(
             }
 
             // w = w + BA*s
-            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
             offload_func(BA);
             ggml_set_name(BA, "BA");
 
             if (scaling != 1.0f) {
-                ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+                ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling);
                 ggml_set_name(scale_tensor, "scale_tensor");
 
-                BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
+                BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor);
                 offload_func(BA);
                 ggml_set_name(BA, "BA_scaled");
             }
 
             ggml_tensor * r;
             if (base_t == dest_t) {
-                r = ggml_add_inplace(lora_ctx, dest_t, BA);
+                r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
                 offload_func_force_inplace(r);
                 ggml_set_name(r, "r_add_inplace");
             }
             else {
-                r = ggml_add(lora_ctx, base_t, BA);
+                r = ggml_add(lora_ctx.get(), base_t, BA);
                 offload_func(r);
                 ggml_set_name(r, "r_add");
 
-                r = ggml_cpy(lora_ctx, r, dest_t);
+                r = ggml_cpy(lora_ctx.get(), r, dest_t);
                 offload_func(r);
                 ggml_set_name(r, "r_cpy");
             }
 
-            struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
+            struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
             ggml_build_forward_expand(gf, r);
 
             ggml_graph_compute_helper(work_buffer, gf, n_threads);
 
+            // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
+            GGML_ASSERT(lora_tensors.size() == 2);
+
             // we won't need these tensors again, reset the context to save memory
-            ggml_free(lora_ctx);
+            lora_ctx.reset(ggml_init(params));
-            lora_ctx = ggml_init(params);
             lora_tensors.clear();
 
             n_tensors++;
@@ -9118,12 +9148,6 @@ static int llama_apply_lora_from_file_internal(
         }
     }
 
-    // TODO: this should be in a destructor, it will leak on failure
-    ggml_free(lora_ctx);
-    if (base_ctx) {
-        ggml_free(base_ctx);
-    }
-
     const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
     LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
 
@@ -10288,6 +10312,7 @@ float * llama_get_logits(struct llama_context * ctx) {
 }
 
 float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
+    assert(ctx->logits_valid.at(i));
     return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
 }
 

llama.h

@@ -39,6 +39,7 @@
 
 #define LLAMA_MAX_RNG_STATE (64*1024)
 
+#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN