convert : write tensors in parallel

convert_hf_to_gguf.py

@@ -73,7 +73,7 @@ class Model:
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
                  split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
-                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None):
+                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, thread_count: int = 2):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
 
@@ -109,7 +109,8 @@ class Model:
 
         # Configure GGUF Writer
         self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
-                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
+                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard,
+                                           thread_count=thread_count)
 
     @classmethod
     def __init_subclass__(cls):
@@ -5470,6 +5471,10 @@ def parse_args() -> argparse.Namespace:
         "--print-supported-models", action="store_true",
         help="Print the supported models"
     )
+    parser.add_argument(
+        "-t", "--threads", type=int, default=2,
+        help="Number of threads to use when writing the tensors. Make sure you have enough RAM for at least THREADS of the biggest tensors in the model when setting this.",
+    )
 
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
@@ -5554,7 +5559,7 @@ def main() -> None:
                                      metadata_override=args.metadata, model_name=args.model_name,
                                      split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
-                                     small_first_shard=args.no_tensor_first_split)
+                                     small_first_shard=args.no_tensor_first_split, thread_count=args.threads)
 
         if args.vocab_only:
             logger.info("Exporting model vocab...")

gguf-py/gguf/gguf_writer.py

@@ -5,10 +5,12 @@ import os
 import shutil
 import struct
 import tempfile
+import threading
 from dataclasses import dataclass
 from enum import Enum, auto
 from math import prod
 from pathlib import Path
+from queue import Empty, Queue
 from io import BufferedWriter
 from typing import IO, Any, Sequence, Mapping
 from string import ascii_letters, digits
@@ -60,8 +62,31 @@ class WriterState(Enum):
     WEIGHTS = auto()
 
 
+@dataclass
+class TensorWriteInfo:
+    filename: Path
+    offset: int
+    post_pad: int
+    tensor: np.ndarray
+    bar: Any | None
+
+    def write_chunk(self, open_files: dict[Path, BufferedWriter]):
+        if self.filename not in open_files:
+            open_files[self.filename] = open(self.filename, "r+b")
+        f = open_files[self.filename]
+
+        f.seek(self.offset)
+        f.write(self.tensor.data)
+        if self.post_pad > 0:
+            f.write(bytes([0] * self.post_pad))
+        if self.bar is not None:
+            self.bar.update(self.tensor.nbytes)
+
+
 class GGUFWriter:
     fout: list[BufferedWriter] | None
+    filenames: list[Path] | None
+    thread_count: int
     path: Path | None
     temp_file: tempfile.SpooledTemporaryFile[bytes] | None
     tensors: list[dict[str, TensorInfo]]
@@ -83,7 +108,8 @@ class GGUFWriter:
 
     def __init__(
         self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE,
-        split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False
+        split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False,
+        thread_count: int = 2,
     ):
         self.fout = None
         self.path = Path(path) if path else None
@@ -98,6 +124,7 @@ class GGUFWriter:
         self.split_max_size = split_max_size
         self.dry_run = dry_run
         self.small_first_shard = small_first_shard
+        self.thread_count = thread_count
         logger.info("gguf: This GGUF file is for {0} Endian only".format(
             "Big" if self.endianess == GGUFEndian.BIG else "Little",
         ))
@@ -173,6 +200,7 @@ class GGUFWriter:
 
         if self.path is not None:
             filenames = self.print_plan()
+            self.filenames = filenames
             self.fout = [open(filename, "wb") for filename in filenames]
             self.state = WriterState.EMPTY
 
@@ -424,40 +452,78 @@ class GGUFWriter:
         self.write_ti_data_to_file()
 
         assert self.fout is not None
+        assert self.filenames is not None
 
         for fout in self.fout:
             self.write_padding(fout, fout.tell())
 
         if self.temp_file is None:
-            shard_bar = None
             bar = None
+            # Distribute writing the tensors between multiple threads
+            tensor_queue: Queue[TensorWriteInfo] = Queue()
+
+            offsets: list[int] = [fout.tell() for fout in self.fout]
 
             if progress:
+                # TODO: add back the shard bar to show which shard is being written when single-threaded
                 from tqdm import tqdm
 
                 total_bytes = sum(ti.nbytes for t in self.tensors for ti in t.values())
 
-                if len(self.fout) > 1:
-                    shard_bar = tqdm(desc=f"Shard (0/{len(self.fout)})", total=None, unit="byte", unit_scale=True)
                 bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
 
-            for i, (fout, tensors) in enumerate(zip(self.fout, self.tensors)):
-                if shard_bar is not None:
-                    shard_bar.set_description(f"Shard ({i + 1}/{len(self.fout)})")
-                    total = sum(ti.nbytes for ti in tensors.values())
-                    shard_bar.reset(total=(total if total > 0 else None))
+            for i, (filename, tensors) in enumerate(zip(self.filenames, self.tensors)):
+                offset = offsets[i]
 
                 # relying on the fact that Python dicts preserve insertion order (since 3.7)
                 for ti in tensors.values():
                     assert ti.tensor is not None  # can only iterate once over the tensors
                     assert ti.tensor.nbytes == ti.nbytes
-                    ti.tensor.tofile(fout)
-                    if shard_bar is not None:
-                        shard_bar.update(ti.nbytes)
-                    if bar is not None:
-                        bar.update(ti.nbytes)
-                    self.write_padding(fout, ti.nbytes)
-                    ti.tensor = None
+                    start_offset = offset
+                    nbytes = ti.tensor.nbytes
+                    offset = self.ggml_pad(start_offset + nbytes, self.data_alignment)
+                    padding = offset - (start_offset + nbytes)
+                    tensor_queue.put(
+                        TensorWriteInfo(
+                            filename=filename,
+                            offset=start_offset,
+                            post_pad=padding,
+                            tensor=ti.tensor,
+                            bar=bar,
+                        )
+                    )
+                    ti.tensor = None  # avoid keeping a reference to written tensors
+
+            # Write tensors in parallel
+            # TODO: total tensor size limit for the running threads
+            def write_tensors_from_thread(queue: Queue[TensorWriteInfo]):
+                open_files: dict[Path, BufferedWriter] = {}
+                try:
+                    while t := queue.get_nowait():
+                        t.write_chunk(open_files)
+                        del t
+                        queue.task_done()
+                except Empty:
+                    pass
+
+                for f in open_files.values():
+                    f.close()
+
+            threads = [
+                threading.Thread(target=write_tensors_from_thread, args=(tensor_queue,))
+                for _ in range(self.thread_count)
+            ]
+
+            for t in threads:
+                t.start()
+
+            # NOTE: thread joining has weird interactions with KeyboardInterrupt,
+            #       so waiting for the queue to be "done" first.
+            tensor_queue.join()
+
+            for t in threads:
+                t.join()
+
         else:
             self.temp_file.seek(0)
 

gguf-py/gguf/lazy.py

@@ -220,4 +220,9 @@ class LazyNumpyTensor(LazyBase):
         eager = LazyNumpyTensor.to_eager(self)
         return eager.tofile(*args, **kwargs)
 
+    @property
+    def data(self):
+        eager = LazyNumpyTensor.to_eager(self)
+        return eager.data
+
     # TODO: __array_function__