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convert.py
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convert.py
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"""Convert Bark's GPT and Encodec checkpoints into the GGML format.
The bytes are packed in a binary file in the following order:
- Magic (`ggml` in binary format)
- Tensors
For each tensor, the bytes are packed as follows:
- Number of dimensions (int)
- Name length (int)
- Dimensions (int[n_dims])
- Name (char[name_length])
- Data (float[n_dims])
Note
----
Encodec uses weight normalization for its convolutional layers. All the weights are
decomposed into two tensors called with the suffixes _weight_v and _weight_g. A simple
call to the hook torch._weight_norm allows to get the final weight tensor of the
convolution from weight_v and weight_g. To drastically reduce the number of operations
at inference time, the ggml weights file only contain the final convolution weights but
does not store the decomposition into weight_v and weight_g.
Example
-------
```bash
python convert.py \
--dir-model ~/.cache/suno/bark_v0 \
--codec-path ~/Documents/encodec.cpp/ggml_weights \
--vocab-path ./ggml_weights/ \
--out-dir ./ggml_weights/ \
--use-f16
```
"""
import argparse
from pathlib import Path
import re
import struct
import numpy as np
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--dir-model", type=str, required=True)
parser.add_argument("--codec-path", type=str, required=True)
parser.add_argument("--vocab-path", type=str, required=True)
parser.add_argument("--out-dir", type=str, required=True)
parser.add_argument("--use-f16", action="store_true")
def parse_codec_model(checkpoint, out_dir):
"""Load encodec model checkpoint."""
outfile = open(out_dir, "wb")
outfile.write(struct.pack("i", 0x67676d6c)) # ggml magic
for name in checkpoint.keys():
if "encoder." in name:
# bark only uses Encodec's quantizer and decoder.
continue
if "weight_g" in name:
# the tensor has already been parsed with the corresponding "weight_v"
# tensor to form the final weights tensor of the convolution, therefore
# we skip it
continue
if "inited" in name or "cluster_size" in name or "embed_avg" in name:
# "inited", "cluster_size" and "embed_avg" tensors in quantizer are not used
# for the forward pass
continue
var_data = checkpoint[name]
if not "weight_v" in name:
# if conv kernel, do not squeeze because 3d tensor
var_data = var_data.numpy().squeeze()
else:
# weight_v has its corresponding magnitude tensor to rescale the weights
# of the convolutional layers. We parse both kinds of weights jointly to
# build the final weight tensor of the convolution.
base_name = name.split(".")[:-1]
weight_g_name = ".".join(base_name + ["weight_g"])
var_data_g = checkpoint[weight_g_name]
final_var_data = torch._weight_norm(var_data, var_data_g, dim=0)
var_data = final_var_data.numpy()
name = ".".join(base_name + ["weight"])
print(f"Processing variable: {name} with shape: {var_data.shape}")
if var_data.dtype != np.float32:
print(" Converting to float32")
var_data = var_data.astype(np.float32)
n_dims = len(var_data.shape)
encoded_name = name.encode("utf-8")
ftype = 0 # float32
outfile.write(struct.pack("iii", n_dims, len(encoded_name), ftype))
for i in range(n_dims):
outfile.write(struct.pack("i", var_data.shape[n_dims - 1 - i]))
outfile.write(encoded_name)
var_data.tofile(outfile)
outfile.close()
def parse_hparams(hparams, outfile, use_f16):
"""Parse GPT hyperparameters."""
outfile.write(struct.pack("i", hparams["n_layer"]))
outfile.write(struct.pack("i", hparams["n_head"]))
outfile.write(struct.pack("i", hparams["n_embd"]))
outfile.write(struct.pack("i", hparams["block_size"]))
try:
outfile.write(struct.pack("ii", hparams["vocab_size"], hparams["vocab_size"]))
except KeyError:
outfile.write(
struct.pack("ii", hparams["input_vocab_size"], hparams["output_vocab_size"])
)
n_lm_heads, n_wtes = None, None
try:
# only for fine text model
n_lm_heads = hparams["n_codes_total"] - hparams["n_codes_given"]
n_wtes = hparams["n_codes_total"]
except KeyError:
n_lm_heads, n_wtes = 1, 1
ftype = int(use_f16)
outfile.write(struct.pack("iii", n_lm_heads, n_wtes, ftype))
def parse_text_models(checkpoint, outfile, use_f16):
"""Load GPT model checkpoint (text, fine, coarse)."""
for name in checkpoint.keys():
var_data = checkpoint[name].squeeze().numpy()
print(f"Processing variable: {name} with shape: {var_data.shape}")
n_dims = len(var_data.shape)
# ftype_cur = 0
# if var_data.dtype != np.float32:
# print(" Converting to float32")
# var_data = var_data.astype(np.float32)
# ftype_cur = 0
# strip `_orig_mod.transformer.` prefix
if name == "_orig_mod.lm_head.weight":
name = "lm_head.weight"
elif "lm_heads" in name:
name = ".".join(name.split(".")[1:])
else:
name = ".".join(name.split(".")[2:])
# rename headers to keep compatibility
if name == "ln_f.weight":
name = "model/ln_f/g"
elif name == "ln_f.bias":
name = "model/ln_f/b"
elif name == "wte.weight":
name = "model/wte/0"
elif name == "wpe.weight":
name = "model/wpe"
elif name == "lm_head.weight":
name = "model/lm_head/0"
elif re.match(r"wtes\.\d+\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/wte/{i}"
elif re.match(r"h\.\d+\.ln_1\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_1/g"
elif re.match(r"h\.\d+\.ln_1\.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_1/b"
elif re.match(r"h\.\d+\.attn\.c_attn\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_attn/w"
elif re.match(r"h\.\d+\.attn\.c_attn\.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_attn/b"
elif re.match(r"h\.\d+\.attn\.c_proj\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_proj/w"
elif re.match(r"h.\d+.attn.c_proj.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/attn/c_proj/b"
elif re.match(r"h.\d+.ln_2.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_2/g"
elif re.match(r"h.\d+.ln_2.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/ln_2/b"
elif re.match(r"h.\d+.mlp.c_fc.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_fc/w"
elif re.match(r"h.\d+.mlp.c_fc.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_fc/b"
elif re.match(r"h.\d+.mlp.c_proj.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_proj/w"
elif re.match(r"h.\d+.mlp.c_proj.bias", name):
i = re.findall("\d+", name)[0]
name = f"model/h{i}/mlp/c_proj/b"
elif re.match(r"lm_heads\.\d+\.weight", name):
i = re.findall("\d+", name)[0]
name = f"model/lm_head/{i}"
else:
print(f"Unrecognized variable name: {name}")
if use_f16:
if (name[-2:] == "/w" or "wte" in name or "lm_head" in name) and n_dims == 2:
print(" Converting to float16")
var_data = var_data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
var_data = var_data.astype(np.float32)
ftype_cur = 0
else:
print(" Converting to float32")
var_data = var_data.astype(np.float32)
ftype_cur = 0
encoded_name = name.encode("utf-8")
outfile.write(struct.pack("iii", n_dims, len(encoded_name), ftype_cur))
for i in range(n_dims):
outfile.write(struct.pack("i", var_data.shape[n_dims - 1 - i]))
outfile.write(encoded_name)
var_data.tofile(outfile)
def generate_file(in_file, out_dir, use_f16):
with open(out_dir, "wb") as fout:
fout.write(struct.pack("i", 0x67676d6c)) # ggml magic
checkpoint = torch.load(in_file, map_location="cpu")
parse_hparams(checkpoint["model_args"], fout, use_f16)
parse_text_models(checkpoint["model"], fout, use_f16)
def generate_vocab_file(dir_model, out_dir):
"""Parse vocabulary."""
# Even if bark relies on GPT to encode text, it uses BertTokenizer (WordPiece)
with open(dir_model / "vocab.txt", "r", encoding="utf-8") as fin:
vocab = fin.readlines()
with open(out_dir, "wb") as fout:
fout.write(struct.pack("i", 0x67676d6c)) # ggml magic
fout.write(struct.pack("i", len(vocab)))
print("Vocab size:", len(vocab))
for token in vocab:
data = bytearray(token[:-1], "utf-8") # strip newline at the end
fout.write(struct.pack("i", len(data)))
fout.write(data)
if __name__ == "__main__":
args = parser.parse_args()
dir_model = Path(args.dir_model)
codec_path = Path(args.codec_path)
vocab_path = Path(args.vocab_path)
out_dir = Path(args.out_dir)
out_dir.mkdir(exist_ok=True, parents=True)
generate_vocab_file(vocab_path, out_dir / "ggml_vocab.bin")
print(" Vocab loaded.")
generate_file(dir_model / "text_2.pt", out_dir / "ggml_weights_text.bin", args.use_f16)
print(" Text model loaded.")
generate_file(dir_model / "coarse_2.pt", out_dir / "ggml_weights_coarse.bin", args.use_f16)
print(" Coarse model loaded.")
generate_file(dir_model / "fine_2.pt", out_dir / "ggml_weights_fine.bin", args.use_f16)
print(" Fine model loaded.")
codec_chkpt = torch.load(codec_path / "encodec_24khz-d7cc33bc.th", map_location="cpu")
parse_codec_model(codec_chkpt, out_dir / "ggml_weights_codec.bin")
print(" Codec model loaded.")
print("Done.")