Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2025-01-06 02:48:57 +01:00
Code Issues Packages Projects Releases Wiki Activity

gguf : single pass for writing tensors + refactoring writer

Browse Source
This commit is contained in:
M. Yusuf Sarıgöz 2023-08-17 15:19:30 +03:00
parent 42f8fe1927
commit f31e9230ad
2 changed files with 225 additions and 192 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

55
convert-llama-h5-to-gguf.py
View File

@ -18,12 +18,16 @@ 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))
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
@ -34,6 +38,7 @@ 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")
@ -74,12 +79,11 @@ if hparams["architectures"][0] != "LlamaForCausalLM":
# 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, architecture="llama")
print("gguf: get model metadata")
llm_arch = "llama"
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
@ -102,19 +106,19 @@ else:
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
@ -140,15 +144,19 @@ if Path(dir_model + "/tokenizer.model").is_file():
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
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)
@ -243,6 +251,7 @@ for part_name in part_names:
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:
@ -256,17 +265,19 @@ 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 = " + 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()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
# tensor data
print("gguf: convert and write tensor data")
@ -325,8 +336,6 @@ for part_name in part_names:
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)
gguf_writer.close()

134
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
@ -70,6 +66,7 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
# recommended mapping of model tensor names for storage in gguf
#
def get_tensor_name_map(n_blocks: int):
tensor_map = {}
# Token embeddings
@ -168,6 +165,7 @@ def get_tensor_name_map(n_blocks : int):
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
@ -203,8 +201,9 @@ class GGUFValueType(IntEnum):
class GGUFWriter:
def __init__(self, fout: IO):
self.fout = fout
def __init__(self, path: str, architecture: str):
self.fout = open(path, "wb")
self.arch = architecture
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = b""
@ -228,11 +227,6 @@ class GGUFWriter:
self.fout.write(self.ti_data)
self.flush()
@classmethod
def open(cls, path: str) -> "GGUFWriter":
f = open(path, "wb")
return cls(f)
def add_key(self, key: str):
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
@ -269,7 +263,8 @@ class GGUFWriter:
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str):
if len(val) == 0: return
if len(val) == 0:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
@ -323,6 +318,8 @@ class GGUFWriter:
return ((x + n - 1) // n) * n
def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<I", len(encoded_name))
self.ti_data += encoded_name
@ -331,13 +328,25 @@ class GGUFWriter:
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
self.ti_data += struct.pack("<I", dtype)
self.ti_data += struct.pack("<Q", self.offset_tensor)
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_to_file(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
@ -349,21 +358,33 @@ 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()
def close(self):
self.fout.close()
def add_architecture(self, architecture: str):
self.add_string(KEY_GENERAL_ARCHITECTURE,
architecture)
def add_architecture(self):
self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
def add_author(self, author: str):
self.add_string(KEY_GENERAL_AUTHOR, author)
def add_tensor_data_layout(self, layout: str):
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT , layout)
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
def add_url(self, url: str):
self.add_string(KEY_GENERAL_URL, url)
@ -391,60 +412,60 @@ class GGUFWriter:
self.data_alignment = alignment
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
def add_context_length(self, llm: str, length: int):
def add_context_length(self, length: int):
self.add_uint32(
KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
KEY_LLM_CONTEXT_LENGTH.format(llm=self.arch), length)
def add_embedding_length(self, llm: str, length: int):
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
KEY_LLM_EMBEDDING_LENGTH.format(llm=self.arch), length)
def add_block_count(self, llm: str, length: int):
def add_block_count(self, length: int):
self.add_uint32(
KEY_LLM_BLOCK_COUNT.format(llm=llm), length)
KEY_LLM_BLOCK_COUNT.format(llm=self.arch), length)
def add_feed_forward_length(self, llm: str, length: int):
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
KEY_LLM_FEED_FORWARD_LENGTH.format(llm=self.arch), length)
def add_parallel_residual(self, llm: str, use: bool):
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=self.arch), use)
def add_tensor_data_layout(self, llm: str, layout: str):
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
def add_head_count(self, llm: str, count: int):
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT.format(llm=self.arch), count)
def add_head_count_kv(self, llm: str, count: int):
def add_head_count_kv(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT_KV.format(llm=self.arch), count)
def add_max_alibi_bias(self, llm: str, bias: float):
def add_max_alibi_bias(self, bias: float):
self.add_float32(
KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=self.arch), bias)
def add_clamp_kqv(self, llm: str, value: float):
def add_clamp_kqv(self, value: float):
self.add_float32(
KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
KEY_ATTENTION_CLAMP_KQV.format(llm=self.arch), value)
def add_layer_norm_eps(self, llm: str, value: float):
def add_layer_norm_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_EPS.format(llm=self.arch), value)
def add_layer_norm_rms_eps(self, llm: str, value: float):
def add_layer_norm_rms_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=self.arch), value)
def add_rope_dimension_count(self, llm: str, count: int):
def add_rope_dimension_count(self, count: int):
self.add_uint32(
KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
KEY_ROPE_DIMENSION_COUNT.format(llm=self.arch), count)
def add_rope_scale(self, llm: str, value: float):
self.add_float32(KEY_ROPE_SCALE.format(llm=llm), value)
def add_rope_scale(self, value: float):
self.add_float32(KEY_ROPE_SCALE.format(llm=self.arch), value)
def add_tokenizer_model(self, model: str):
self.add_string(KEY_TOKENIZER_MODEL, model)
@ -476,24 +497,27 @@ 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.open("example.gguf")
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture("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_to_file(tensor1)
gguf_writer.write_tensor_to_file(tensor2)
gguf_writer.write_tensors_to_file()
gguf_writer.close()