Revert "Make loading weights 10-100x faster"

This reverts commit 78ca9838ee.

.gitignore

@@ -22,7 +22,6 @@ models/*
 /result
 /perplexity
 /embedding
-/Pipfile
 
 arm_neon.h
 compile_commands.json

convert-ggml-to-pth.py

@@ -84,11 +84,6 @@ def read_variables(fin):
         shape = shape[::-1]
         name = fin.read(name_length).decode()
 
-        # ensure tensor data is aligned
-        tensor_data_offset = fin.tell()
-        tensor_data_offset = (tensor_data_offset + 31) & -32
-        fin.seek(tensor_data_offset)
-
         if ftype_cur == 2:
             # 4-bit quantized weights
             dtype = np.uint8

convert-gptq-to-ggml.py

@@ -72,11 +72,6 @@ def write_header(shape, dst_name, ftype_cur):
     fout.write(struct.pack("i" * len(shape), *shape[::-1]))
     fout.write(sname)
 
-    # ensure tensor data is aligned
-    tensor_data_offset = fout.tell()
-    tensor_data_offset = (tensor_data_offset + 31) & -32
-    fout.seek(tensor_data_offset)
-
 def convert_non_q4(src_name, dst_name):
     v = model[src_name]
     shape = v.shape

convert-pth-to-ggml.py

@@ -24,57 +24,8 @@ import torch
 
 from sentencepiece import SentencePieceProcessor
 
-QK = 32
-
-GGML_TYPE_Q4_0  = 0
-GGML_TYPE_Q4_1  = 1
-GGML_TYPE_I8    = 2
-GGML_TYPE_I16   = 3
-GGML_TYPE_I32   = 4
-GGML_TYPE_F16   = 5
-GGML_TYPE_F32   = 6
-
-WTYPES = {
-    0: GGML_TYPE_F32,
-    1: GGML_TYPE_F16,
-    2: GGML_TYPE_Q4_0,
-    3: GGML_TYPE_Q4_1,
-}
-
-GGML_BLCK_SIZE = {
-    GGML_TYPE_Q4_0:  QK,
-    GGML_TYPE_Q4_1:  QK,
-    GGML_TYPE_I8:    1,
-    GGML_TYPE_I16:   1,
-    GGML_TYPE_I32:   1,
-    GGML_TYPE_F16:   1,
-    GGML_TYPE_F32:   1,
-}
-
-GGML_TYPE_SIZE = {
-    GGML_TYPE_Q4_0: 4   + QK/2,
-    GGML_TYPE_Q4_1: 4*2 + QK/2,
-    GGML_TYPE_I8:   1,
-    GGML_TYPE_I16:  2,
-    GGML_TYPE_I32:  4,
-    GGML_TYPE_F16:  2,
-    GGML_TYPE_F32:  4,
-}
-
-def ggml_nelements(shape):
-    r = 1
-    for i in shape:
-        r *= i
-    return r
-
-def ggml_nbytes(shape, ftype):
-    x = ggml_nelements(shape)
-    t = WTYPES[ftype]
-    x *= GGML_TYPE_SIZE[t]
-    x //= GGML_BLCK_SIZE[t]
-    return x
-
 def parse_args():
+
     parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
     parser.add_argument('dir_model',  help='directory containing the model checkpoint')
     parser.add_argument('ftype',      help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1)
@@ -82,6 +33,7 @@ def parse_args():
     return parser.parse_args()
 
 def get_n_parts(dim):
+
     mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
     n_parts = mappings.get(dim)
     if n_parts is None:
@@ -92,24 +44,30 @@ def get_n_parts(dim):
     return n_parts
 
 def load_hparams_and_tokenizer(dir_model):
+
     # `dir_model` is something like `models/7B` or `models/7B/`.
     # "tokenizer.model" is expected under model's parent dir.
     # When `dir_model` is a symlink, f"{dir_model}/../tokenizer.model" would not be found.
     # Let's use the model's parent dir directly.
     model_parent_dir = os.path.dirname(os.path.normpath(dir_model))
+
     fname_hparams = f"{dir_model}/params.json"
     fname_tokenizer = f"{model_parent_dir}/tokenizer.model"
+
     with open(fname_hparams, "r") as f:
         hparams = json.load(f)
         print(hparams)
+
     tokenizer = SentencePieceProcessor(fname_tokenizer)
     hparams.update({"vocab_size": tokenizer.vocab_size()})
+
     return hparams, tokenizer
 
 def write_header(fout, hparams, ftype):
+
     keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
     values = [
-        0x67676a74,  # magic: ggjt in hex
+        0x67676d66,  # magic: ggmf in hex
         1, # file version
         *[hparams[key] for key in keys],
         hparams["dim"] // hparams["n_heads"],  # rot (obsolete)
@@ -118,6 +76,7 @@ def write_header(fout, hparams, ftype):
     fout.write(struct.pack("i" * len(values), *values))
 
 def write_tokens(fout, tokenizer):
+
     for i in range(tokenizer.vocab_size()):
         if tokenizer.is_unknown(i):
             text = " \u2047 ".encode()
@@ -136,141 +95,85 @@ def write_tokens(fout, tokenizer):
         fout.write(text)
         fout.write(struct.pack("f", tokenizer.get_score(i)))
 
-def process_and_write_variables(fout, model, ftype, part_id, n_parts):
+def process_and_write_variables(fout, model, ftype):
+
     for name, datao in model.items():
+
         if name.endswith("freqs"):
             continue
 
-        # remove dimensions with a single element
+        shape = datao.shape
+
+        print(f"Processing variable: {name} with shape: {shape} and type: {datao.dtype}")
+
         data = datao.numpy().squeeze()
-        partshape = data.shape
+        n_dims = len(shape)
-        n_dims = len(data.shape)
-        assert n_dims in (1, 2)
 
-        print(f"Processing variable: {name} with shape: {partshape} and type: {datao.dtype}")
+        # default type is fp16
-
-        # coerce single-dimensional tensors from float16 to float32
         ftype_cur = 1
         if ftype == 0 or n_dims == 1:
             print("  Converting to float32")
             data = data.astype(np.float32)
             ftype_cur = 0
-        blck_size = GGML_BLCK_SIZE[WTYPES[ftype_cur]]
-        type_size = GGML_TYPE_SIZE[WTYPES[ftype_cur]]
 
-        # determine dimension along which multipart tensor is sharded
+        # header
-        #
+        sname = name.encode('utf-8')
-        # split_dim 0 regex:
+        fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
-        #   - output.*
+        for dim in reversed(data.shape):
-        #   - layers.*.attention.wq.weight
-        #   - layers.*.attention.wk.weight
-        #   - layers.*.attention.wv.weight
-        #   - layers.*.feed_forward.w1.weight
-        #   - layers.*.feed_forward.w3.weight
-        #
-        # split_dim 1 regex:
-        #   - tok_embeddings.*
-        #   - layers.*.attention.wo.weight
-        #   - layers.*.feed_forward.w2.weight
-        #
-        if n_dims > 1:
-            split_dim = 1
-            if "tok_embeddings" in name:
-                split_dim = 1
-            elif "layers" in name:
-                if "attention.wo.weight" in name:
-                    split_dim = 1
-                elif "feed_forward.w2.weight" in name:
-                    split_dim = 1
-                else:
-                    split_dim = 0
-            elif "output" in name:
-                split_dim = 0
-
-        # output tensor header
-        fullshape = list(partshape)
-        if n_dims > 1:
-            fullshape[split_dim] *= n_parts
-        sname = name.encode()
-        fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
-        for dim in reversed(fullshape):
             fout.write(struct.pack("i", dim))
         fout.write(sname)
 
-        # ensure tensor data is aligned
+        # data output to file
-        tensor_data_offset = fout.tell()
-        while tensor_data_offset % QK != 0:
-            fout.write(struct.pack("B", 0))
-            tensor_data_offset += 1
-
-        # output unified mappable tensor data
-        if n_dims == 1 or n_parts == 1:
-            # copy tensor which we thankfully received in one piece
-            if part_id == 0:
         data.tofile(fout)
-        elif split_dim == 0:
-            # reassemble multifile tensor containing some of the rows
-            rows_per_chunk = partshape[0]
-            current_row = part_id * rows_per_chunk
-            bytes_per_row = fullshape[1] // blck_size * type_size
-            offset = current_row * bytes_per_row
-            fout.seek(tensor_data_offset + offset)
-            data.tofile(fout)
-        elif split_dim == 1:
-            # reassemble multifile tensor containing some of the cols
-            cols_per_chunk = partshape[1]
-            current_col = part_id * cols_per_chunk
-            bytes_per_row = fullshape[1] // blck_size * type_size
-            offset_current_col = current_col // blck_size * type_size
-            for row in range(partshape[0]):
-                offset_row = row * bytes_per_row
-                offset = offset_row + offset_current_col
-                fout.seek(tensor_data_offset + offset)
-                data[row].tofile(fout)
-
-        # advance file position to next tensor
-        fout.seek(tensor_data_offset + ggml_nbytes(fullshape, ftype_cur))
 
 def main():
+
     args = parse_args()
     dir_model = args.dir_model
     ftype = args.ftype
     ftype_str = ["f32", "f16"]
+
     hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
 
     print(args)
 
     # if only writing vocab to file
     if args.vocab_only:
+
         fname_model = f"{dir_model}/consolidated.00.pth"
         fname_out = f"{dir_model}/ggml-vocab.bin"
+
         print(f"Extracting only the vocab from '{fname_model}'\n")
-        model = torch.load(fname_model, map_location="cpu")
+
+
         with open(fname_out, "wb") as fout:
             write_header(fout, hparams, ftype)
             write_tokens(fout, tokenizer)
-        del model
+
+
         print(f"Done. Output file: {fname_out}\n")
+
         return
 
     n_parts = get_n_parts(hparams["dim"])
-    fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin"
 
-    # we output a single file for ggml
+    for p in range(n_parts):
+
+        print(f"Processing part {p+1} of {n_parts}\n")
+
+        fname_model = f"{dir_model}/consolidated.0{p}.pth"
+        fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
+
+        model = torch.load(fname_model, map_location="cpu")
+
         with open(fname_out, "wb") as fout:
             write_header(fout, hparams, ftype)
             write_tokens(fout, tokenizer)
-        offset_of_tensors = fout.tell()
+            process_and_write_variables(fout, model, ftype)
-        # the tensors we load could be split across multiple files
+
-        for part_id in range(n_parts):
-            fout.seek(offset_of_tensors)
-            print(f"Processing part {part_id+1} of {n_parts}\n")
-            fname_model = f"{dir_model}/consolidated.0{part_id}.pth"
-            model = torch.load(fname_model, map_location="cpu")
-            process_and_write_variables(fout, model, ftype, part_id, n_parts)
         del model
 
-    print(f"Done. Output file: {fname_out}\n")
+        print(f"Done. Output file: {fname_out}, (part {p})\n")
 
 if __name__ == "__main__":
     main()

llama.cpp

@@ -12,19 +12,17 @@
 #include <cassert>
 #include <cstring>
 
-#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
+// mmap
+#if defined (__unix__) || defined (__APPLE__)
+#   include <sys/mman.h>
+#   include <fcntl.h>
+#   include <unistd.h>
+#elif defined(_WIN32)
 #   define WIN32_LEAN_AND_MEAN
 #   include <Windows.h>
-#else
+//#include <Memoryapi.h>
-#include <sys/types.h>
-#include <sys/mman.h>
-#include <unistd.h>
-#include <fcntl.h>
 #endif
 
-#define Min(X, Y) ((Y) > (X) ? (X) : (Y))
-#define Max(X, Y) ((Y) < (X) ? (X) : (Y))
-
 #define LLAMA_USE_SCRATCH
 #define LLAMA_MAX_SCRATCH_BUFFERS 16
 
@@ -157,7 +155,7 @@ struct llama_model {
 
     // model memory mapped file
     void * mm_addr = NULL;
-    uint64_t mm_length = 0;
+    size_t mm_length = 0;
 
     // tensors
     int n_loaded;
@@ -182,7 +180,6 @@ struct llama_context {
 
     int64_t t_load_us = 0;
     int64_t t_start_us = 0;
-    bool has_evaluated_once = false;
 
     int64_t t_sample_us = 0;
     int64_t t_eval_us   = 0;
@@ -224,7 +221,7 @@ struct llama_context {
         }
 
         if (buf_last >= 0) {
-            buf_max_size[buf_last] = Max(buf_max_size[buf_last], last_size);
+            buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
         }
 
         buf_last = i;
@@ -307,57 +304,59 @@ struct llama_context_params llama_context_default_params() {
 // model loading
 //
 
-static void *mmap_file(const char *fname, uint64_t *mm_length) {
+static void mmap_file(const char* fname, void * &mm_addr, size_t &mm_length) {
-#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
+#if defined(MAP_FAILED)
-    HANDLE hFile = CreateFileA(fname,
+    // POSIX
-                               GENERIC_READ,
+    int fd = open(fname, O_RDONLY);
-                               FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE,
+    mm_length = lseek(fd, 0, SEEK_END);
-                               NULL,
+    mm_addr = mmap(NULL, mm_length, PROT_READ, MAP_SHARED, fd, 0);
-                               OPEN_EXISTING,
+    close(fd);
-                               FILE_ATTRIBUTE_NORMAL | FILE_ATTRIBUTE_NOT_CONTENT_INDEXED,
+    if (mm_addr == MAP_FAILED) {
-                               NULL);
+        perror("mmap failed");
-    if (hFile == INVALID_HANDLE_VALUE) return 0;
+        mm_addr = NULL;
+        mm_length = 0;
+    }
+#elif defined(_WIN32)
+    mm_addr = NULL;
+    
+    HANDLE hFile = CreateFileA(filename, GENERIC_READ, FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL);
+    if (hFile == INVALID_HANDLE_VALUE) {
+        return;
+    }
+
+    // not really necessary
     LARGE_INTEGER fileSize;
-    fileSize.QuadPart = -1;
     GetFileSizeEx(hFile, &fileSize);
-    int64_t length = fileSize.QuadPart;
+    mm_length = fileSize;
+
     HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
     CloseHandle(hFile);
-    if (!hMapping) return 0;
+
-    void *addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+    if (hMapping == NULL) {
+        return;
+    }
+
+    mm_addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
     CloseHandle(hMapping);
-    if (!addr) return 0;
 #else
-    int fd = open(fname, O_RDONLY);
+    mm_addr = NULL;
-    if (fd == -1) return 0;
+    mm_length = 0;
-    int64_t length = lseek(fd, 0, SEEK_END);
+    (void)(fname); // suppress warnings
-    void *addr = mmap(NULL, length, PROT_READ, MAP_SHARED, fd, 0);
-    close(fd);
-    if (addr == MAP_FAILED) return 0;
 #endif
-    *mm_length = length;
-    return addr;
 }
 
 static void munmap_file(void * addr, size_t length) {
-#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
+#if defined(MAP_FAILED)
+    // POSIX
+    munmap(addr, length);
+#elif defined(_WIN32)
     UnmapViewOfFile(addr);
 #else
-    munmap(addr, length);
+    (void)(addr); // suppress warnings
+    (void)(length);
 #endif
 }
 
-static bool report_bad_magic(const char *path) {
-    fprintf(stderr,
-            "%s: invalid model file (bad magic)\n"
-            "you most likely need to regenerate your ggml files\n"
-            "the benefit is you'll get 10-100x faster load times\n"
-            "see https://github.com/ggerganov/llama.cpp/issues/91\n"
-            "use convert-pth-to-ggml.py on your llama model files\n",
-            path);
-    return false;
-}
-
 static bool llama_model_load(
         const std::string & fname,
         llama_context & lctx,
@@ -369,24 +368,23 @@ static bool llama_model_load(
         void *progress_callback_user_data) {
     fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
 
-    lctx.t_start_us = ggml_time_us();
+    const int64_t t_start_us = ggml_time_us();
+
+    lctx.t_start_us = t_start_us;
+
+    // TODO: this could probably be smaller when using mmap
+    std::vector<char> f_buf(1024*1024);
 
     auto & model = lctx.model;
     auto & vocab = lctx.vocab;
 
     auto fin = std::ifstream(fname, std::ios::binary);
+    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
     if (!fin) {
         fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
         return false;
     }
 
-    std::vector<char> f_buf(1024*1024);
-    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
-
-    fin.seekg(0, fin.end);
-    const size_t file_size = fin.tellg();
-    fin.seekg(0);
-
     // verify magic
     {
         uint32_t magic;
@@ -397,7 +395,8 @@ static bool llama_model_load(
             return false;
         }
         if (magic != LLAMA_FILE_MAGIC) {
-            return report_bad_magic(fname.c_str());
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
         }
 
         uint32_t format_version;
@@ -520,24 +519,54 @@ static bool llama_model_load(
                 }
     }
 
-    // map model into memory
+    bool use_mmap = (n_parts == 1);
-    char *mm_addr = NULL;
+
-    model.mm_addr = mmap_file(fname.c_str(), &model.mm_length);
+    // try to memory map the model file
+    void * mm_addr = NULL;
+    if (use_mmap) {
+        mmap_file(fname.c_str(), model.mm_addr, model.mm_length);
         if (model.mm_addr == NULL) {
-        fprintf(stderr, "%s: failed to mmap '%s'\n", __func__, fname.c_str());
+            use_mmap = false;
-        return false;
+        }
+        else {
+            mm_addr = model.mm_addr;
+        }
     }
-    mm_addr = (char *)model.mm_addr;
-    fprintf(stderr, "%s: ggml map size = %6.2f MB\n", __func__, model.mm_length/(1024.0*1024.0));
 
     auto & ctx = model.ctx;
 
     size_t ctx_size = 0;
     {
         const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
         const int n_layer = hparams.n_layer;
+        const int n_vocab = hparams.n_vocab;
+
+        if (!use_mmap) {
+            ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
+
+            ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
+
+            ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
+
+            ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
+
+            ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
+            ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
+            ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
+            ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo
+
+            ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm
+
+            ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
+            ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
+            ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
+        }
+
         ctx_size += (5 + 10*n_layer)*256; // object overhead
-        fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
+
+        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
     }
 
     // print memory requirements
@@ -547,7 +576,6 @@ static bool llama_model_load(
         // this is the total memory required to run the inference
         const size_t mem_required =
             ctx_size +
-            model.mm_length +
             MEM_REQ_SCRATCH0.at(model.type) +
             MEM_REQ_SCRATCH1.at(model.type) +
             MEM_REQ_EVAL.at    (model.type);
@@ -567,7 +595,7 @@ static bool llama_model_load(
         struct ggml_init_params params = {
             /*.mem_size   =*/ lctx.model.buf.size(),
             /*.mem_buffer =*/ lctx.model.buf.data(),
-            /*.no_alloc   =*/ true,
+            /*.no_alloc   =*/ use_mmap,
         };
 
         model.ctx = ggml_init(params);
@@ -630,19 +658,43 @@ static bool llama_model_load(
         }
     }
 
+    const size_t file_offset = fin.tellg();
+
+    fin.close();
+
     std::vector<uint8_t> tmp;
 
     if (progress_callback) {
         progress_callback(0.0, progress_callback_user_data);
     }
 
-    fprintf(stderr, "%s: loading tensors from '%s'\n", __func__, fname.c_str());
+    for (int i = 0; i < n_parts; ++i) {
+        const int part_id = i;
+        //const int part_id = n_parts - i - 1;
+
+        std::string fname_part = fname;
+        if (i > 0) {
+            fname_part += "." + std::to_string(i);
+        }
+
+        fprintf(stderr, "%s: loading model part %d/%d from '%s'%s\n", __func__, i+1, n_parts, fname_part.c_str(), use_mmap ? " (memory mapped)" : "");
+
+        fin = std::ifstream(fname_part, std::ios::binary);
+        fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
+
+        fin.seekg(0, fin.end);
+        const size_t file_size = fin.tellg();
+
+        fin.seekg(file_offset);
 
         // load weights
         {
             size_t total_size = 0;
+
             model.n_loaded = 0;
 
+            fprintf(stderr, "%s: ", __func__);
+
             while (true) {
                 int32_t n_dims;
                 int32_t length;
@@ -671,52 +723,162 @@ static bool llama_model_load(
                     return false;
                 }
 
+                // split_type = 0: split by columns
+                // split_type = 1: split by rows
+                int split_type = 0;
+
+                // split_type = 0:
+                // regex:
+                //   - tok_embeddings.*
+                //   - layers.*.attention.wo.weight
+                //   - layers.*.feed_forward.w2.weight
+
+                // split_type = 1:
+                // regex:
+                //   - output.*
+                //   - layers.*.attention.wq.weight
+                //   - layers.*.attention.wk.weight
+                //   - layers.*.attention.wv.weight
+                //   - layers.*.feed_forward.w1.weight
+                //   - layers.*.feed_forward.w3.weight
+                if (name.find("tok_embeddings") != std::string::npos) {
+                    split_type = 0;
+                } else if (name.find("layers") != std::string::npos) {
+                    if (name.find("attention.wo.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else {
+                        split_type = 1;
+                    }
+                } else if (name.find("output") != std::string::npos) {
+                    split_type = 1;
+                }
+
                 auto tensor = model.tensors[name.data()];
 
+                if (n_dims == 1) {
                     if (ggml_nelements(tensor) != nelements) {
                         fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                         return false;
                     }
+                } else {
+                    if (ggml_nelements(tensor)/n_parts != nelements) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                        return false;
+                    }
+                }
+
+                if (n_dims == 1) {
                     if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%" PRId64 ", %" PRId64 "], expected [%d, %d]\n",
+                        fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
                                 __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
                         return false;
                     }
-            if (0) {
+                } else {
-                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+                    if (split_type == 0) {
-                fprintf(stderr, "%24s - [%5d, %5d], type = %6s\n", name.data(), ne[0], ne[1], ftype_str[ftype]);
+                        if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
+                            return false;
+                        }
+                    } else {
+                        if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
+                            return false;
+                        }
+                    }
                 }
 
+                if (0) {
+                    static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
+                }
+
+                size_t bpe = 0;
+
                 switch (ftype) {
-                case 0:  // f32
+                    case 0: bpe = ggml_type_size(GGML_TYPE_F32);  break;
-                case 1:  // f16
+                    case 1: bpe = ggml_type_size(GGML_TYPE_F16);  break;
-                    break;
+                    case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
-                case 2:  // q4_0
+                    case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
-                case 3:  // q4_1
-                    assert(ne[0] % 64 == 0);
-                    break;
                     default:
+                            {
                                 fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
                                 return false;
+                            }
                 };
 
-            // load the tensor data into memory without copying or reading it
+                if (n_dims == 1 || n_parts == 1) {
-            size_t offset = fin.tellg();
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
-            size_t tensor_data_size = ggml_nbytes(tensor);
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-            offset = (offset + 31) & -32;
+                                __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
-            tensor->data = mm_addr + offset;
+                        return false;
-            fin.seekg(offset + tensor_data_size);
+                    }
-            total_size += tensor_data_size;
+
+                    if (part_id == 0) {
+                        if (mm_addr) {
+                            off_t offset = fin.tellg();
+                            tensor->data = (char *) mm_addr + offset;
+                            fin.seekg(ggml_nbytes(tensor), std::ios::cur);
+                        }
+                        else {
+                            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+                        }
+                    } else {
+                        fin.seekg(ggml_nbytes(tensor), std::ios::cur);
+                    }
+
+                    total_size += ggml_nbytes(tensor);
+                } else {
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                                __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
+                        return false;
+                    }
+
+                    if (split_type == 0) {
+                        const int np0 = ne[0];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                        assert(row_size == tensor->nb[1]);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = i1*row_size;
+                            const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
+                        }
+                    } else {
+                        const int np1 = ne[1];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = (i1 + part_id*np1)*row_size;
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
+                        }
+                    }
+
+                    total_size += ggml_nbytes(tensor)/n_parts;
+                }
+
+                //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
                 model.n_loaded++;
 
                 // progress
                 if (progress_callback) {
-                double current_progress = size_t(fin.tellg()) / double(file_size);
+                    float current_file_progress = float(size_t(fin.tellg()) - file_offset) / float(file_size - file_offset);
+                    float current_progress = (float(i) + current_file_progress) / float(n_parts);
                     progress_callback(current_progress, progress_callback_user_data);
                 }
+                if (model.n_loaded % 8 == 0) {
+                    fprintf(stderr, ".");
+                    fflush(stderr);
+                }
             }
 
-        fin.close();
+            fprintf(stderr, " done\n");
 
             fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
             if (model.n_loaded == 0) {
@@ -727,9 +889,10 @@ static bool llama_model_load(
             }
         }
 
-    // loading time will be recalculate after the first eval, so
+        fin.close();
-    // we take page faults deferred by mmap() into consideration
+    }
-    lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
+
+    lctx.t_load_us = ggml_time_us() - t_start_us;
 
     if (progress_callback) {
         progress_callback(1.0, progress_callback_user_data);
@@ -1053,7 +1216,7 @@ struct llama_tokenizer {
         size_t offs = 0;
         while (offs < text.size()) {
             llama_sp_symbol sym;
-            size_t char_len = Min(text.size() - offs, utf8_len(text[offs]));
+            size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
             sym.text = text.c_str() + offs;
             sym.n = char_len;
             offs += char_len;
@@ -1218,7 +1381,7 @@ static llama_vocab::id llama_sample_top_p_top_k(
 
     float maxl = -std::numeric_limits<float>::infinity();
     for (const auto & kv : logits_id) {
-        maxl = Max(maxl, kv.first);
+        maxl = std::max(maxl, kv.first);
     }
 
     // compute probs for the top k tokens
@@ -1312,7 +1475,8 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
             return false;
         }
         if (magic != LLAMA_FILE_MAGIC) {
-            return report_bad_magic(fname_inp.c_str());
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
+            return false;
         }
 
         fout.write((char *) &magic, sizeof(magic));
@@ -1378,8 +1542,8 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
             fout.write((char *) &len, sizeof(len));
 
             word.resize(len);
-            finp.read ((char *) &word[0], len);
+            finp.read ((char *) word.data(), len);
-            fout.write((char *) &word[0], len);
+            fout.write((char *) word.data(), len);
 
             float score;
             finp.read ((char *) &score, sizeof(score));
@@ -1429,13 +1593,6 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
             std::string name(length, 0);
             finp.read (&name[0], length);
 
-            {
-                // ensure tensor data is aligned
-                uint64_t offset = finp.tellg();
-                offset = (offset + 31) & -32;
-                finp.seekg(offset);
-            }
-
             {
                 static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
                 printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
@@ -1491,13 +1648,6 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
             }
             fout.write(&name[0], length);
 
-            {
-                // ensure tensor data is aligned
-                uint64_t offset = fout.tellp();
-                offset = (offset + 31) & -32;
-                fout.seekp(offset);
-            }
-
             if (quantize) {
                 printf("quantizing .. ");
                 work.resize(nelements); // for quantization
@@ -1701,11 +1851,7 @@ int llama_eval(
         fprintf(stderr, "%s: failed to eval\n", __func__);
         return 1;
     }
-    // get a more accurate load time, upon first eval
+
-    if (!ctx->has_evaluated_once) {
-        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
-        ctx->has_evaluated_once = true;
-    }
     return 0;
 }
 
@@ -1798,9 +1944,9 @@ llama_token llama_sample_top_p_top_k(
 void llama_print_timings(struct llama_context * ctx) {
     const int64_t t_end_us = ggml_time_us();
 
-    const int32_t n_sample = Max(1, ctx->n_sample);
+    const int32_t n_sample = std::max(1, ctx->n_sample);
-    const int32_t n_eval   = Max(1, ctx->n_eval);
+    const int32_t n_eval   = std::max(1, ctx->n_eval);
-    const int32_t n_p_eval = Max(1, ctx->n_p_eval);
+    const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
 
     fprintf(stderr, "\n");
     fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
@@ -1812,6 +1958,7 @@ void llama_print_timings(struct llama_context * ctx) {
 
 void llama_reset_timings(struct llama_context * ctx) {
     ctx->t_start_us = ggml_time_us();
+
     ctx->t_sample_us = ctx->n_sample = 0;
     ctx->t_eval_us   = ctx->n_eval   = 0;
     ctx->t_p_eval_us = ctx->n_p_eval = 0;

llama.h

@@ -20,7 +20,7 @@
 #endif
 
 #define LLAMA_FILE_VERSION 1
-#define LLAMA_FILE_MAGIC 0x67676a74 // 'ggjt' in hex
+#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
 #define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
 
 #ifdef __cplusplus