forked from ggerganov/llama.cpp
-
Notifications
You must be signed in to change notification settings - Fork 9
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
llm : add MPT support (ggerganov#3417)
* CUDA: added support for ggml_clamp (see also: ggerganov/ggml#545) * mpt : added an implementation based (mostly) on falcon integration, modified with deltas from ggml/examples/mpt * mpt : protect against "clip_qkv": null in mpt-7b * mpt : quick fix to avoid "Strange model" warning when quantizing MPT models * mpt : addendum to changeset:84e30e8 - leave parameter clamp_kqv out from metadata rather than use 0.0 to indicate "no clamping" (more compliant with the current GGUF spec?) * mpt : standardized all tensor names to follow GGUF spec * mpt : addendum to changeset:1be89c40 - use "req" parameter of GGUF_GET_KEY macro instead of duplicate code * mpt : fixed comment s/gptneox/mpt/ * mpt : remove tabs, trailing whitespace * mpt : removed ne01 + n_past == ne00 assertion from alibi (cuda/f32) and rope_shift from build_mpt * mpt : updated convert-mpt-hf-to-gguf.py to reflect changes made to convert-gptneox-hf-to-gguf.py in pr:3252 * comment out n_past instead of marking it unused * mpt : removed hardcoded +178 from convert script in favor of utilizing hparams["vocab_size"] * mpt : remove unused tokenizer_json in convert script * ggml : remove obsolete n_past assert in ggml_alibi * llama : print clam_kqv and max_alibi_bias hparams --------- Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
- Loading branch information
1 parent
11ea5c7
commit f5f9121
Showing
5 changed files
with
685 additions
and
9 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,216 @@ | ||
#!/usr/bin/env python3 | ||
# HF mpt--> gguf conversion | ||
|
||
from __future__ import annotations | ||
|
||
import argparse | ||
import json | ||
import os | ||
import struct | ||
import sys | ||
from pathlib import Path | ||
from typing import Any | ||
|
||
import numpy as np | ||
import torch | ||
from transformers import AutoTokenizer # type: ignore[import] | ||
|
||
if 'NO_LOCAL_GGUF' not in os.environ: | ||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) | ||
import gguf | ||
|
||
|
||
def count_model_parts(dir_model: Path) -> int: | ||
num_parts = 0 | ||
for filename in os.listdir(dir_model): | ||
if filename.startswith("pytorch_model-"): | ||
num_parts += 1 | ||
|
||
if num_parts > 0: | ||
print("gguf: found " + str(num_parts) + " model parts") | ||
return num_parts | ||
|
||
|
||
def parse_args() -> argparse.Namespace: | ||
parser = argparse.ArgumentParser(description="Convert an MPT model to a GGML compatible file") | ||
parser.add_argument( | ||
"--vocab-only", action="store_true", | ||
help="extract only the vocab", | ||
) | ||
parser.add_argument( | ||
"--outfile", type=Path, | ||
help="path to write to; default: based on input", | ||
) | ||
parser.add_argument( | ||
"model", type=Path, | ||
help="directory containing model file, or model file itself (*.bin)", | ||
) | ||
parser.add_argument( | ||
"ftype", type=int, choices=[0, 1], default=1, nargs='?', | ||
help="output format - use 0 for float32, 1 for float16", | ||
) | ||
return parser.parse_args() | ||
|
||
args = parse_args() | ||
|
||
dir_model = args.model | ||
ftype = args.ftype | ||
if not dir_model.is_dir(): | ||
print(f'Error: {args.model} is not a directory', file = sys.stderr) | ||
sys.exit(1) | ||
|
||
# possible tensor data types | ||
# ftype == 0 -> float32 | ||
# ftype == 1 -> float16 | ||
|
||
# map from ftype to string | ||
ftype_str = ["f32", "f16"] | ||
|
||
if args.outfile is not None: | ||
fname_out = args.outfile | ||
else: | ||
# output in the same directory as the model by default | ||
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' | ||
|
||
print("gguf: loading model "+dir_model.name) | ||
|
||
with open(dir_model / "config.json", "r", encoding="utf-8") as f: | ||
hparams = json.load(f) | ||
|
||
if hparams["architectures"][0] != "MPTForCausalLM": | ||
print("Model architecture not supported: " + hparams["architectures"][0]) | ||
|
||
sys.exit() | ||
|
||
# get number of model parts | ||
num_parts = count_model_parts(dir_model) | ||
|
||
ARCH=gguf.MODEL_ARCH.MPT | ||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) | ||
|
||
print("gguf: get model metadata") | ||
|
||
block_count = hparams["n_layers"] | ||
|
||
gguf_writer.add_name(dir_model.name) | ||
gguf_writer.add_context_length(hparams["max_seq_len"]) | ||
gguf_writer.add_embedding_length(hparams["d_model"]) | ||
gguf_writer.add_block_count(block_count) | ||
gguf_writer.add_feed_forward_length(4 * hparams["d_model"]) | ||
gguf_writer.add_head_count(hparams["n_heads"]) | ||
gguf_writer.add_layer_norm_eps(1e-05) | ||
if hparams["attn_config"]["clip_qkv"] is not None: | ||
gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"]) | ||
gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"]) | ||
|
||
# TOKENIZATION | ||
|
||
print("gguf: get tokenizer metadata") | ||
|
||
tokens: list[bytearray] = [] | ||
scores: list[float] = [] | ||
toktypes: list[int] = [] | ||
|
||
# gpt2 tokenizer | ||
gguf_writer.add_tokenizer_model("gpt2") | ||
|
||
print("gguf: get gpt2 tokenizer vocab") | ||
|
||
# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but | ||
# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to | ||
# accomodate some "reserved" tokens; this is causing problems down the line in | ||
# llama.cpp, so we pad the vocab with dummy tokens: | ||
|
||
vocab_size = hparams["vocab_size"] | ||
|
||
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py | ||
tokenizer = AutoTokenizer.from_pretrained(dir_model) | ||
|
||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} | ||
|
||
for i in range(vocab_size): | ||
tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]") | ||
scores.append(0.0) # dummy | ||
toktypes.append(gguf.TokenType.NORMAL) | ||
|
||
gguf_writer.add_token_list(tokens) | ||
gguf_writer.add_token_scores(scores) | ||
gguf_writer.add_token_types(toktypes) | ||
|
||
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) | ||
special_vocab.add_to_gguf(gguf_writer) | ||
|
||
# TENSORS | ||
|
||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count) | ||
|
||
# tensor info | ||
print("gguf: get tensor metadata") | ||
|
||
if num_parts == 0: | ||
part_names = iter(("pytorch_model.bin",)) | ||
else: | ||
part_names = ( | ||
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) | ||
) | ||
|
||
for part_name in part_names: | ||
if args.vocab_only: | ||
break | ||
print("gguf: loading model part '" + part_name + "'") | ||
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") | ||
|
||
for name in model_part.keys(): | ||
data = model_part[name] | ||
|
||
old_dtype = data.dtype | ||
|
||
# convert any unsupported data types to float32 | ||
if data.dtype != torch.float16 and data.dtype != torch.float32: | ||
data = data.to(torch.float32) | ||
|
||
data = data.squeeze().numpy() | ||
|
||
# map tensor names | ||
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) | ||
if new_name is None: | ||
print("Cannot map tensor '" + name + "'") | ||
continue # for the sake of compatibility with some old published models, don't quit | ||
sys.exit() | ||
|
||
n_dims = len(data.shape) | ||
data_dtype = data.dtype | ||
|
||
# if f32 desired, convert any float16 to float32 | ||
if ftype == 0 and data_dtype == np.float16: | ||
data = data.astype(np.float32) | ||
|
||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 | ||
if ftype == 1 and data_dtype == np.float16 and n_dims == 1: | ||
data = data.astype(np.float32) | ||
|
||
# if f16 desired, convert any float32 2-dim weight tensors to float16 | ||
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: | ||
data = data.astype(np.float16) | ||
|
||
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
|
||
gguf_writer.add_tensor(new_name, data) | ||
|
||
# note: MPT output is tied to (same as) wte in original model; | ||
# for easier implementation in llama.cpp it's duplicated in GGUF, though :/ | ||
if new_name == "token_embd.weight": | ||
gguf_writer.add_tensor("output.weight", data) | ||
|
||
print("gguf: write header") | ||
gguf_writer.write_header_to_file() | ||
print("gguf: write metadata") | ||
gguf_writer.write_kv_data_to_file() | ||
if not args.vocab_only: | ||
print("gguf: write tensors") | ||
gguf_writer.write_tensors_to_file() | ||
|
||
gguf_writer.close() | ||
|
||
print(f"gguf: model successfully exported to '{fname_out}'") | ||
print("") |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.