tentative push of convert-hf-to-gguf support

convert-hf-to-gguf.py

@@ -21,7 +21,9 @@ if TYPE_CHECKING:
 
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
+import importlib
+gguf = importlib.import_module("gguf-py.gguf")
+# import gguf
 
 from convert import LlamaHfVocab, permute
 
@@ -43,18 +45,18 @@ AnyModel = TypeVar("AnyModel", bound="type[Model]")
 class Model(ABC):
     _model_classes: dict[str, type[Model]] = {}
 
-    def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool):
+    def __init__(self, dir_model: Path, ftype: int, fname_out: Path, args: argparse.Namespace):
         self.dir_model = dir_model
         self.ftype = ftype
         self.fname_out = fname_out
-        self.is_big_endian = is_big_endian
-        self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
-        self.use_temp_file = use_temp_file
+        self.is_big_endian = args.bigendian
+        self.endianess = gguf.GGUFEndian.BIG if args.bigendian else gguf.GGUFEndian.LITTLE
+        self.use_temp_file = args.use_temp_file
         self.is_safetensors = self._is_model_safetensors()
         self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
         self.part_names = self._get_part_names()
         self.hparams = Model.load_hparams(self.dir_model)
-        self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
+        self.gguf_writer = gguf.GGUFManager(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], args, endianess=self.endianess, use_temp_file=self.use_temp_file)
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
 
     @property
@@ -174,14 +176,11 @@ class Model(ABC):
 
     def write(self):
         self.write_tensors()
-        self.gguf_writer.write_header_to_file()
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.write_tensors_to_file()
+        self.gguf_writer.write_to_file()
         self.gguf_writer.close()
 
     def write_vocab(self):
-        self.gguf_writer.write_header_to_file()
-        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.write_to_file(meta_only=True)
         self.gguf_writer.close()
 
     @staticmethod
@@ -1711,7 +1710,7 @@ class MiniCPMModel(Model):
 
             self.gguf_writer.add_tensor(new_name, data)
 
-
+# TODO what the hell is this?
 @Model.register("QWenLMHeadModel")
 class QwenModel(Model):
     model_arch = gguf.MODEL_ARCH.QWEN
@@ -2843,6 +2842,11 @@ def parse_args() -> argparse.Namespace:
         help="directory containing model file",
     )
     parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
+    parser.add_argument("--split", action="store_true", help="split the converted model into multiple files")
+    parser.add_argument("--split-max-tensors", type=int, help="max tensors in each split")
+    parser.add_argument("--split-max-size", type=str, help="max size per split N(M|G)")
+    parser.add_argument("--dry-run", action="store_true", help="only print out a split plan and exit, without writing any new files")
+    parser.add_argument("--large-first-shard", action="store_true", help="include tensors in the first shard when splitting (default: metadata only)")
 
     return parser.parse_args()
 
@@ -2869,6 +2873,15 @@ def main() -> None:
         print(f'Error: {args.model} is not a directory', file=sys.stderr)
         sys.exit(1)
 
+    if args.split and not (args.split_max_tensors or args.split_max_size):
+        raise ValueError("Need to specify one of --split-max-tensors or --split-max-size when splitting")
+
+    if args.split_max_tensors and args.split_max_size:
+        raise ValueError("Can't specify both --split-max-tensors and --split-max-size")
+
+    if args.split_max_size:
+        args.split_max_size = gguf.SplitStrategy.split_str_to_n_bytes(args.split_max_size)
+
     ftype_map = {
         "f32": gguf.GGMLQuantizationType.F32,
         "f16": gguf.GGMLQuantizationType.F16,
@@ -2886,7 +2899,7 @@ def main() -> None:
 
     with torch.inference_mode():
         model_class = Model.from_model_architecture(hparams["architectures"][0])
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file)
+        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args)
 
         print("Set model parameters")
         model_instance.set_gguf_parameters()

convert.py

@@ -1465,7 +1465,7 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
     parser.add_argument("--split", action="store_true", help="split the converted model into multiple files")
     parser.add_argument("--split-max-tensors", type=int, help="max tensors in each split")
-    parser.add_argument("--split-max-size", type=str, help="max size per split N(M|G)+")
+    parser.add_argument("--split-max-size", type=str, help="max size per split N(M|G)")
     parser.add_argument("--dry-run", action="store_true", help="only print out a split plan and exit, without writing any new files")
     parser.add_argument("--large-first-shard", action="store_true", help="include tensors in the first shard when splitting (default: metadata only)")
 

gguf-py/gguf/gguf_manager.py

@@ -4,6 +4,7 @@ import os
 import shutil
 import struct
 import tempfile
+import time
 from enum import IntEnum
 from typing import TYPE_CHECKING, Any, Sequence, Mapping
 from string import ascii_letters, digits
@@ -174,7 +175,9 @@ class GGUFManager:
     # have to consolidate because we need to know kv data count and tensor count before we can write the header
     # and we need to write tensor info before we can write metadata
     # these all kinda show up around the same places anyway so it's not a huge deal?
-    def write_to_file(self, meta_only: bool = False, ftype: int = 0, concurrency: int = 8, write_tensor_data: function = None) -> None:
+    def write_to_file(self, meta_only: bool = False, ftype: int = 0, concurrency: int = 8,
+                      write_tensor_data: function = None
+    ) -> None:
 
         # here is the first place you can assume you have all tensors written and you can establish the size of the file - so logic goes here
         self.total_tensors = len(self.tensors)
@@ -218,19 +221,37 @@ class GGUFManager:
 
         if self.args.dry_run:
             print("\nDry run, not writing files")
+            # instantiating GGUFWriters creates files
+            for name, _, _ in self.split_strategy:
+                os.remove(name)
             return
 
         # run add_tensor_info, write data, then write_tensor_data - taken from convert.py
         running_total = self.total_tensors
+        start = time.time()
         for i, (_, tensors, writer) in enumerate(self.split_strategy):
 
             if tensors:
-                for name, tensor in tensors:
+                for j, (name, tensor) in enumerate(tensors):
                     n_elements = int(np.prod(tensor.shape))
+                    # logic from convert.py
+                    if getattr(tensor, 'data_type', None):
                         raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
                         data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
                         data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
                         writer.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype)
+                    # logic from convert-hf-to-gguf.py
+                    else:
+                        # stolen from write_tensor_data because that doesn't get called with this logic
+                        elapsed = time.time() - start
+                        size = ' x '.join(f"{dim:6d}" for dim in tensor.shape)
+                        padi = len(str(self.total_tensors))
+                        dtype = str(tensor.dtype)
+                        print(
+                            f"[{j + 1:{padi}d}/{len(tensors)}] Writing tensor {name:38s} | size {size:16} | type {dtype:8} | T+{int(elapsed):4}"
+                        )
+                        writer.add_tensor(name, tensor)
+
 
             writer.write_header_to_file()
             writer.write_kv_data_to_file()
@@ -240,6 +261,7 @@ class GGUFManager:
                 print(f"\nWriting to shard {i + 1}/{self.total_shards} with {len(tensors)}/{running_total} remaining tensors (of {self.total_tensors} total)")
                 running_total -= len(tensors)
 
+                if write_tensor_data:
                     # convert.py's write_tensor_data is dependent on so many objects in convert.py itself that it's easier to pass the function as a parameter and call it here
                     write_tensor_data(ftype, dict(tensors), concurrency, writer)
 
@@ -295,8 +317,23 @@ class GGUFManager:
         self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
         raw_dtype: GGMLQuantizationType | None = None,
     ) -> None:
-        # TODO WRITE
-        pass
+        if self.endianess == GGUFEndian.BIG:
+            tensor.byteswap(inplace=True)
+
+        # TODO reimplement temp file
+        #if self.use_temp_file and self.temp_file is None:
+        #    fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024)
+        #    fp.seek(0)
+        #    self.temp_file = fp
+
+        self.add_tensor_info(name, tensor)
+
+        #if self.temp_file is None:
+        #    self.tensors.append(tensor)
+        #    return
+
+        #tensor.tofile(self.temp_file)
+        #self.write_padding(self.temp_file, tensor.nbytes)
 
     def write_tensors_to_file(self) -> None:
         # TODO WRITE