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gguf.py : write tensors in a single pass (#2644)

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* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : style fixes in simple conversion script

* gguf : refactor gptneox conversion script

* gguf : rename h5 to hf (for HuggingFace)

* gguf : refactor pth to gguf conversion script

* gguf : rm file_type key and method

* gguf.py : fix vertical alignment

* gguf.py : indentation

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
M. Yusuf Sarıgöz 2023-08-17 21:57:39 +03:00 committed by GitHub
parent 5484737d58
commit fc3a523211
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 301 additions and 405 deletions
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117
convert-gptneox-h5-to-gguf.py → convert-gptneox-hf-to-gguf.py
View File

@ -13,6 +13,8 @@ from pathlib import Path
from transformers import AutoTokenizer
# 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.
@ -34,6 +36,7 @@ def bytes_to_unicode():
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
@ -44,6 +47,7 @@ def count_model_parts(dir_model: str) -> int:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
@ -58,7 +62,7 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
@ -67,6 +71,7 @@ if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
@ -77,30 +82,30 @@ with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "GPTNeoXForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0] )
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
gguf_writer = gguf.GGUFWriter.open(fname_out)
llm_arch = "gptneox"
gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch)
print("gguf: get model metadata")
llm_arch = "gptneox"
block_count = hparams["num_hidden_layers"]
gguf_writer.add_architecture(llm_arch)
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_file_type( "All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
gguf_writer.add_block_count(llm_arch, block_count)
gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(llm_arch, int( hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])) )
gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"])
gguf_writer.add_parallel_residual(llm_arch, hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
gguf_writer.add_layer_norm_eps(llm_arch, hparams["layer_norm_eps"])
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])))
gguf_writer.add_head_count(hparams["num_attention_heads"])
gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
# TOKENIZATION
@ -124,14 +129,14 @@ if Path(dir_model + "/tokenizer.json").is_file():
print("gguf: get gpt2 tokenizer vocab")
vocab_size = len( tokenizer_json["model"]["vocab"] )
vocab_size = len(tokenizer_json["model"]["vocab"])
# 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()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
byte_decoder = {v: k for k, v in byte_encoder.items()}
for i in range(vocab_size):
if i in reverse_vocab:
@ -146,8 +151,9 @@ if Path(dir_model + "/tokenizer.json").is_file():
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
padding_token = f"[PAD{i}]".encode("utf8")
text = bytearray(padding_token)
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
tokens.append(text)
gguf_writer.add_token_list(tokens)
@ -201,7 +207,7 @@ else:
)
for part_name in part_names:
print("gguf: loading model part '"+ part_name + "'")
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():
@ -223,11 +229,12 @@ for part_name in part_names:
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
@ -241,77 +248,21 @@ for part_name in part_names:
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype))
gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
data = data.astype(data_dtype)
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensor metadata")
gguf_writer.write_ti_data_to_file()
# tensor data
print("gguf: convert and write tensor data")
if num_parts == 0:
part_names = ("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:
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
# we don't need these
if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
continue
# 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
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
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( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.write_tensor_to_file(data)
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'" )
print("gguf: model successfully exported to '" + fname_out + "'")
print("")

120
convert-llama-7b-pth-to-gguf.py
View File

@ -18,6 +18,7 @@ from sentencepiece import SentencePieceProcessor
# compatible with python < 3.9
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
@ -28,10 +29,12 @@ def count_model_parts(dir_model: str) -> int:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
@ -43,7 +46,7 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
@ -52,6 +55,7 @@ if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
@ -70,14 +74,14 @@ num_parts = count_model_parts(dir_model)
if num_parts > 1:
print("gguf: Only models with a single datafile are supported.")
sys.exit()
gguf_writer = gguf.GGUFWriter.open(fname_out)
sys.exit()
llm_arch = "llama"
gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch)
print("gguf: get model metadata")
llm_arch = "llama"
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
@ -89,21 +93,20 @@ else:
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo=""
hf_repo = ""
gguf_writer.add_architecture(llm_arch)
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_file_type( "All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout(llm_arch, "Meta AI original pth")
gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
gguf_writer.add_block_count(llm_arch, block_count)
gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(llm_arch, head_count)
gguf_writer.add_head_count_kv(llm_arch, head_count_kv)
gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
# TOKENIZATION
@ -125,19 +128,23 @@ if Path(dir_model + "/tokenizer.model").is_file():
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i): toktype = 2
if tokenizer.is_control(i): toktype = 3
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# TODO: How to determinate if a token is user defined?
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = 4
if tokenizer.is_unused(i): toktype = 5
if tokenizer.is_byte(i): toktype = 6
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
@ -193,10 +200,10 @@ tensor_map = gguf.get_tensor_name_map(block_count)
# tensor info
print("gguf: get tensor metadata")
part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) )
part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
for part_name in part_names:
print("gguf: loading model part '"+ part_name + "'")
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():
@ -218,11 +225,12 @@ for part_name in part_names:
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
@ -236,69 +244,19 @@ for part_name in part_names:
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype))
gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
data = data.astype(data_dtype)
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensor metadata")
gguf_writer.write_ti_data_to_file()
# tensor data
print("gguf: convert and write tensor data")
part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) )
for part_name in part_names:
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
# we don't need these
if name == "rope.freqs":
continue
# 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
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
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( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.write_tensor_data(data)
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()

140
convert-llama-h5-to-gguf.py → convert-llama-hf-to-gguf.py
View File

@ -18,26 +18,35 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
# reverse HF permute back to original pth layout
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
.swapaxes(1, 2)
.reshape(weights.shape))
def count_model_parts(dir_model: str) -> 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
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
@ -49,7 +58,8 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
@ -58,6 +68,7 @@ if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
@ -69,17 +80,17 @@ with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
if hparams["architectures"][0] != "LlamaForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
gguf_writer = gguf.GGUFWriter.open(fname_out)
gguf_writer = gguf.GGUFWriter(fname_out, arch="llama")
print("gguf: get model metadata")
llm_arch = "llama"
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
@ -91,7 +102,7 @@ else:
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo=""
hf_repo = ""
if "max_sequence_length" in hparams:
ctx_length = hparams["max_sequence_length"]
@ -99,22 +110,22 @@ elif "max_position_embeddings" in hparams:
ctx_length = hparams["max_position_embeddings"]
else:
print("gguf: can not find ctx length parameter.")
sys.exit()
gguf_writer.add_architecture(llm_arch)
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_file_type("All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout(llm_arch, "Meta AI original pth")
gguf_writer.add_context_length(llm_arch, ctx_length)
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
gguf_writer.add_block_count(llm_arch, block_count)
gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(llm_arch, head_count)
gguf_writer.add_head_count_kv(llm_arch, head_count_kv)
gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
# TOKENIZATION
@ -136,19 +147,23 @@ if Path(dir_model + "/tokenizer.model").is_file():
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i): toktype = 2
if tokenizer.is_control(i): toktype = 3
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# TODO: How to determinate if a token is user defined?
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = 4
if tokenizer.is_unused(i): toktype = 5
if tokenizer.is_byte(i): toktype = 6
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
@ -212,7 +227,7 @@ else:
)
for part_name in part_names:
print("gguf: loading model part '"+ part_name + "'")
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():
@ -238,11 +253,13 @@ for part_name in part_names:
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
@ -256,78 +273,19 @@ for part_name in part_names:
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
data = data.astype(data_dtype)
gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensor metadata")
gguf_writer.write_ti_data_to_file()
# tensor data
print("gguf: convert and write tensor data")
if num_parts == 0:
part_names = ("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:
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
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
# 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()
# reverse permute these
if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
data = reverse_hf_permute(data, head_count, head_count_kv)
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
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(name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.write_tensor_to_file(data)
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()

329
gguf.py
View File

@ -1,11 +1,7 @@
"""TODOs
1. Implement writers for known architectures, LLaMA in particular.
2. Add docstrings from the format specs.
3. After development is done, Convert it to a proper pip-installable Python package, and possibly move it to its own repo under ggml-org.
"""
import shutil
import sys
import struct
import tempfile
import numpy as np
from enum import IntEnum, auto
@ -27,30 +23,29 @@ KEY_GENERAL_NAME = "general.name"
KEY_GENERAL_AUTHOR = "general.author"
KEY_GENERAL_URL = "general.url"
KEY_GENERAL_DESCRIPTION = "general.description"
KEY_GENERAL_FILE_TYPE = "general.file_type"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
# LLM
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
# attention
KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
# RoPE
KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
KEY_ROPE_SCALE = "{arch}.rope.scale"
KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
KEY_ROPE_SCALE = "{arch}.rope.scale"
# tokenization
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
@ -70,6 +65,7 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
# recommended mapping of model tensor names for storage in gguf
#
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
@ -78,81 +74,84 @@ class MODEL_ARCH(IntEnum):
GPTNEOX = auto()
MPT = auto()
class MODEL_TENSOR(IntEnum):
TOKEN_EMBD = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
TOKEN_EMBD = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
MODEL_ARCH_NAMES = {
MODEL_ARCH.LLAMA : "llama",
MODEL_ARCH.FALCON : "falcon",
MODEL_ARCH.GPT2 : "gpt2",
MODEL_ARCH.GPTJ : "gptj",
MODEL_ARCH.GPTNEOX : "gptneox",
MODEL_ARCH.MPT : "mpt",
}
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.GPT2: "gpt2",
MODEL_ARCH.GPTJ: "gptj",
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
}
MODEL_TENSOR_NAMES = {
MODEL_ARCH.LLAMA : {
MODEL_TENSOR.TOKEN_EMBD : "token_embd",
MODEL_TENSOR.OUTPUT_NORM : "output_norm",
MODEL_TENSOR.OUTPUT : "output",
MODEL_TENSOR.ROPE_FREQS : "rope_freqs",
MODEL_TENSOR.ATTN_NORM : "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_Q : "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K : "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V : "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT : "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD : "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.FFN_NORM : "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE : "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN : "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP : "blk.{bid}.ffn_up",
},
MODEL_ARCH.FALCON : {
MODEL_TENSOR.TOKEN_EMBD : "token_embd",
MODEL_TENSOR.OUTPUT_NORM : "output_norm",
MODEL_TENSOR.OUTPUT : "output",
MODEL_TENSOR.ATTN_NORM : "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2 : "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV : "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_OUT : "blk.{bid}.attn_output",
MODEL_TENSOR.FFN_DOWN : "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP : "blk.{bid}.ffn_up",
},
MODEL_ARCH.GPT2 : {
MODEL_ARCH.LLAMA: {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
},
MODEL_ARCH.FALCON: {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
},
MODEL_ARCH.GPT2: {
# TODO
},
},
# TODO
}
}
# tensors that will not be serialized
MODEL_TENSOR_SKIP = {
MODEL_ARCH.LLAMA : [
MODEL_ARCH.LLAMA: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
],
}
# TODO: the following helper functions should be removed
# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
# REMOVE
def should_skip_tensor_TMP(arch : MODEL_ARCH, n_blocks : int, name : str) -> bool:
def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
for skip in MODEL_TENSOR_SKIP.get(arch, []):
for i in range(n_blocks):
if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
@ -160,151 +159,152 @@ def should_skip_tensor_TMP(arch : MODEL_ARCH, n_blocks : int, name : str) -> boo
return False
def get_tensor_name_map(arch : MODEL_ARCH, n_blocks : int) -> dict:
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
tensor_map = {}
# Token embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
# Position embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
tensor_map["transformer.wpe"] = mapped_to # gpt2
tensor_map["transformer.wpe"] = mapped_to # gpt2
# Output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Output norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
# Rope frequencies
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
tensor_map["rope.freqs"] = mapped_to # llama-pth
tensor_map["rope.freqs"] = mapped_to # llama-pth
# Attention and feed-forward blocks
for i in range(0,n_blocks):
for i in range(0, n_blocks):
# Attention norm
# TODO: is there are simpler way to write these 2 lines in Python?
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to else None
tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
# Attention norm 2
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
# Attention query-key-value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
# Attention query
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
# Attention key
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
# Attention value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
# Attention output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
# Rotary embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
# Feed-forward norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
# Feed-forward up
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
# Feed-forward gate
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
# Feed-forward down
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
return tensor_map
@ -312,6 +312,7 @@ def get_tensor_name_map(arch : MODEL_ARCH, n_blocks : int) -> dict:
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
@ -481,6 +482,19 @@ class GGUFWriter:
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def add_tensor(self, name: str, tensor: np.ndarray):
if not hasattr(self, "temp_file"):
self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
self.temp_file.seek(0)
self.add_tensor_info(name, tensor.shape, tensor.dtype, tensor.nbytes)
tensor.tofile(self.temp_file)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.temp_file.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
@ -492,6 +506,19 @@ class GGUFWriter:
if pad != 0:
self.fout.write(bytes([0] * pad))
def write_tensors_to_file(self):
self.write_ti_data_to_file()
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
self.temp_file.seek(0)
shutil.copyfileobj(self.temp_file, self.fout)
self.flush()
self.temp_file.close()
def flush(self):
self.fout.flush()
@ -513,9 +540,6 @@ class GGUFWriter:
def add_description(self, description: str):
self.add_string(KEY_GENERAL_DESCRIPTION, description)
def add_file_type(self, file_type: str):
self.add_string(KEY_GENERAL_FILE_TYPE, file_type)
def add_source_url(self, url: str):
self.add_string(KEY_GENERAL_SOURCE_URL, url)
@ -618,23 +642,28 @@ class GGUFWriter:
def add_pad_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
# Example usage:
if __name__ == "__main__":
# Example usage with a file
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture()
gguf_writer.add_block_count(12)
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
gguf_writer.add_custom_alignment(64)
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
tensor2 = np.ones((32,), dtype=np.float32) * 101.0
gguf_writer.add_tensor_info("tensor0", tensor1)
gguf_writer.add_tensor_info("tensor1", tensor2)
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
gguf_writer.add_tensor("tensor1", tensor1)
gguf_writer.add_tensor("tensor2", tensor2)
gguf_writer.add_tensor("tensor3", tensor3)
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_ti_data_to_file()
gguf_writer.write_tensor_data(tensor1)
gguf_writer.write_tensor_data(tensor2)
gguf_writer.write_tensors_to_file()
gguf_writer.close()