ebeyabraham · July 14, 2023 17:12
diff --git a/convert-hf-to-ggml.py b/convert-hf-to-ggml.py
 # Convert GPT-2 huggingface transformer model to ggml format
 #
 # Load the model using GPT2Model.
 # Iterate over all variables and write them to a binary file.
 #
 # For each variable, write the following:
 #   - Number of dimensions (int)
 #   - Name length (int)
 #   - Dimensions (int[n_dims])
 #   - Name (char[name_length])
 #   - Data (float[n_dims])
 #
 # By default, the bigger matrices are converted to 16-bit floats.
 # This can be disabled by adding the "use-f32" CLI argument.
 #
 # At the start of the ggml file we write the model parameters
 # and vocabulary.
 #

 import sys
 import struct
 import json
 import numpy as np
 import re

 from transformers import GPT2Model

 # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
 def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))

 if len(sys.argv) < 2:
    print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
    sys.exit(1)

 # output in the same directory as the model
 dir_model = sys.argv[1]
 fname_out = sys.argv[1] + "/ggml-model.bin"

 with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
    encoder = json.load(f)

 with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
    encoder_added = json.load(f)

 with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
    hparams = json.load(f)

 # use 16-bit or 32-bit floats
 use_f16 = True
 if len(sys.argv) > 2:
    use_f16 = False
    fname_out = sys.argv[1] + "/ggml-model-f32.bin"

 model = GPT2Model.from_pretrained(dir_model, low_cpu_mem_usage=True)
 #print (model)

 list_vars = model.state_dict()
 #print (list_vars)

 fout = open(fname_out, "wb")

 fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
 fout.write(struct.pack("i", hparams["vocab_size"]))
 fout.write(struct.pack("i", hparams["n_positions"]))
 fout.write(struct.pack("i", hparams["n_embd"]))
 fout.write(struct.pack("i", hparams["n_head"]))
 fout.write(struct.pack("i", hparams["n_layer"]))
 #fout.write(struct.pack("i", hparams["rotary_dim"]))
 fout.write(struct.pack("i", use_f16))

 byte_encoder = bytes_to_unicode()
 byte_decoder = {v:k for k, v in byte_encoder.items()}

 fout.write(struct.pack("i", len(encoder) + len(encoder_added)))

 for key in encoder:
    text = bytearray([byte_decoder[c] for c in key])
    fout.write(struct.pack("i", len(text)))
    fout.write(text)

 for key in encoder_added:
    text = bytearray([byte_decoder[c] for c in key])
    fout.write(struct.pack("i", len(text)))
    fout.write(text)

 for name in list_vars.keys():
    data = list_vars[name].squeeze().numpy()
    print("Processing variable: " + name + " with shape: ", data.shape)

    # we don't need these
    if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
        print("  Skipping variable: " + name)
        continue

    n_dims = len(data.shape);

    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype = 0;
    if use_f16:
        if name != "wpe.weight" and (name[-7:] == ".weight" and n_dims == 2):
            print("  Converting to float16")
            data = data.astype(np.float16)
            ftype = 1
        else:
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype = 0

    # for efficiency - transpose these matrices:
    #  "transformer.h.*.mlp.c_proj.weight
    if name.endswith(".mlp.c_proj.weight") or name.endswith("c_attn.weight") or name.endswith("c_fc.weight") or name.endswith("c_proj.weight"):
        print("  Transposing")
        data = data.transpose()

    # 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"
    elif name == "wpe.weight":
        name = "model/wpe"
    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"
    else:
        print("Unrecognized variable name. %s", name)

    str = name.encode('utf-8')

    fout.write(struct.pack("iii", n_dims, len(str), ftype))
    for i in range(n_dims):
        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
    fout.write(str);

    # data
    data.tofile(fout)

 fout.close()

 print("Done. Output file: " + fname_out)
 print("")
	# Convert GPT-2 huggingface transformer model to ggml format
	#
	# Load the model using GPT2Model.
	# Iterate over all variables and write them to a binary file.
	#
	# For each variable, write the following:
	# - Number of dimensions (int)
	# - Name length (int)
	# - Dimensions (int[n_dims])
	# - Name (char[name_length])
	# - Data (float[n_dims])
	#
	# By default, the bigger matrices are converted to 16-bit floats.
	# This can be disabled by adding the "use-f32" CLI argument.
	#
	# At the start of the ggml file we write the model parameters
	# and vocabulary.
	#

	import sys
	import struct
	import json
	import numpy as np
	import re

	from transformers import GPT2Model

	# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
	def bytes_to_unicode():
	"""
	Returns list of utf-8 byte and a corresponding list of unicode strings.
	The reversible bpe codes work on unicode strings.
	This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
	When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
	This is a signficant percentage of your normal, say, 32K bpe vocab.
	To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
	And avoids mapping to whitespace/control characters the bpe code barfs on.
	"""
	bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
	cs = bs[:]
	n = 0
	for b in range(2**8):
	if b not in bs:
	bs.append(b)
	cs.append(2**8+n)
	n += 1
	cs = [chr(n) for n in cs]
	return dict(zip(bs, cs))

	if len(sys.argv) < 2:
	print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
	sys.exit(1)

	# output in the same directory as the model
	dir_model = sys.argv[1]
	fname_out = sys.argv[1] + "/ggml-model.bin"

	with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
	encoder = json.load(f)

	with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
	encoder_added = json.load(f)

	with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
	hparams = json.load(f)

	# use 16-bit or 32-bit floats
	use_f16 = True
	if len(sys.argv) > 2:
	use_f16 = False
	fname_out = sys.argv[1] + "/ggml-model-f32.bin"

	model = GPT2Model.from_pretrained(dir_model, low_cpu_mem_usage=True)
	#print (model)

	list_vars = model.state_dict()
	#print (list_vars)

	fout = open(fname_out, "wb")

	fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
	fout.write(struct.pack("i", hparams["vocab_size"]))
	fout.write(struct.pack("i", hparams["n_positions"]))
	fout.write(struct.pack("i", hparams["n_embd"]))
	fout.write(struct.pack("i", hparams["n_head"]))
	fout.write(struct.pack("i", hparams["n_layer"]))
	#fout.write(struct.pack("i", hparams["rotary_dim"]))
	fout.write(struct.pack("i", use_f16))

	byte_encoder = bytes_to_unicode()
	byte_decoder = {v:k for k, v in byte_encoder.items()}

	fout.write(struct.pack("i", len(encoder) + len(encoder_added)))

	for key in encoder:
	text = bytearray([byte_decoder[c] for c in key])
	fout.write(struct.pack("i", len(text)))
	fout.write(text)

	for key in encoder_added:
	text = bytearray([byte_decoder[c] for c in key])
	fout.write(struct.pack("i", len(text)))
	fout.write(text)

	for name in list_vars.keys():
	data = list_vars[name].squeeze().numpy()
	print("Processing variable: " + name + " with shape: ", data.shape)

	# we don't need these
	if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
	print(" Skipping variable: " + name)
	continue

	n_dims = len(data.shape);

	# ftype == 0 -> float32, ftype == 1 -> float16
	ftype = 0;
	if use_f16:
	if name != "wpe.weight" and (name[-7:] == ".weight" and n_dims == 2):
	print(" Converting to float16")
	data = data.astype(np.float16)
	ftype = 1
	else:
	print(" Converting to float32")
	data = data.astype(np.float32)
	ftype = 0

	# for efficiency - transpose these matrices:
	# "transformer.h.*.mlp.c_proj.weight
	if name.endswith(".mlp.c_proj.weight") or name.endswith("c_attn.weight") or name.endswith("c_fc.weight") or name.endswith("c_proj.weight"):
	print(" Transposing")
	data = data.transpose()

	# 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"
	elif name == "wpe.weight":
	name = "model/wpe"
	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"
	else:
	print("Unrecognized variable name. %s", name)

	str = name.encode('utf-8')

	fout.write(struct.pack("iii", n_dims, len(str), ftype))
	for i in range(n_dims):
	fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
	fout.write(str);

	# data
	data.tofile(fout)

	fout.close()

	print("Done. Output file: " + fname_out)
	print("")