root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-29 04:44:34 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Merge branch 'gguf-write-tokenization' into gguf

Browse Source
This commit is contained in:
M. Yusuf Sarıgöz 2023-07-29 00:36:35 +03:00
commit 9475cdb7a3
1 changed files with 32 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

56
gguf.py
View File

@ -13,9 +13,9 @@ import numpy as np
class GGMLQuantizationType(IntEnum): class GGMLQuantizationType(IntEnum):
F32 = 0 F32 = 0
F16 = 1 F16 = 1
QR_0 = 2 Q4_0 = 2
Q4_1 = 3 Q4_1 = 3
# Q4_2 = 4 # support has been removed # Q4_2 = 4 # support has been removed
# Q4_3 = 5 # support has been removed # Q4_3 = 5 # support has been removed
@ -32,16 +32,16 @@ class GGMLQuantizationType(IntEnum):
class GGUFValueType(IntEnum): class GGUFValueType(IntEnum):
UINT8 = 0 UINT8 = 0
INT8 = 1 INT8 = 1
UINT16 = 2 UINT16 = 2
INT16 = 3 INT16 = 3
UINT32 = 4 UINT32 = 4
INT32 = 5 INT32 = 5
FLOAT32 = 6 FLOAT32 = 6
BOOL = 7 BOOL = 7
STRING = 8 STRING = 8
ARRAY = 9 ARRAY = 9
@staticmethod @staticmethod
def get_type(val): def get_type(val):
@ -75,7 +75,9 @@ class GGUFWriter:
return cls(f) return cls(f)
def write_key(self, key: str): def write_key(self, key: str):
self.write_val(key, GGUFValueType.STRING) encoded_key = key.encode("utf8")
self.fout.write(struct.pack("<I", len(encoded_key)))
self.fout.write(encoded_key)
def write_uint8(self, key: str, val: int): def write_uint8(self, key: str, val: int):
self.write_key(key) self.write_key(key)
@ -158,29 +160,35 @@ class GGUFWriter:
return ((x + n - 1) // n) * n return ((x + n - 1) // n) * n
def write_tensor_info(self, name: str, tensor: np.ndarray): def write_tensor_info(self, name: str, tensor: np.ndarray):
self.write_val(name, GGUFValueType.STRING) self.write_key(name)
n_dims = len(tensor.shape) n_dims = len(tensor.shape)
self.write_val(n_dims, GGUFValueType.INT32) self.fout.write(struct.pack("<i", n_dims))
for i in range(n_dims): for i in range(n_dims):
self.write_val(tensor.shape[n_dims - 1 - i], GGUFValueType.INT32) self.fout.write(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" 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 dtype = GGMLQuantizationType.F32 if tensor.dtype == np.float32 else GGMLQuantizationType.F16
self.write_val(dtype, GGUFValueType.INT32) self.fout.write(struct.pack("<i", dtype))
self.fout.write(struct.pack("<Q", self.offset_tensor)) self.fout.write(struct.pack("<Q", self.offset_tensor))
self.offset_tensor += GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) self.offset_tensor += GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT)
offset_data = GGUFWriter.ggml_pad(self.fout.tell(), constants.GGUF_DEFAULT_ALIGNMENT) self.flush()
pad = offset_data - self.fout.tell()
self.fout.write(bytes([0] * pad))
self.tensors.append(tensor) self.tensors.append(tensor)
def write_tensors(self): def write_tensors(self):
offset_data = GGUFWriter.ggml_pad(self.fout.tell(), constants.GGUF_DEFAULT_ALIGNMENT)
pad = offset_data - self.fout.tell()
print(f"pad: {pad}")
if pad != 0:
self.fout.write(bytes([0] * pad))
for tensor in self.tensors: for tensor in self.tensors:
tensor.tofile(self.fout) tensor.tofile(self.fout)
pad = GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) - tensor.nbytes pad = GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) - tensor.nbytes
self.fout.write(bytes([0] * pad)) print(f"pad: {pad}")
if pad != 0:
self.fout.write(bytes([0] * pad))
def flush(self): def flush(self):
self.fout.flush() self.fout.flush()
@ -274,10 +282,10 @@ if __name__ == "__main__":
gguf_writer.write_architecture("llama") gguf_writer.write_architecture("llama")
gguf_writer.write_uint32("answer", 42) # Write a 32-bit integer gguf_writer.write_uint32("answer", 42) # Write a 32-bit integer
gguf_writer.write_float32("answer_in_float", 42.0) # Write a 32-bit float gguf_writer.write_float32("answer_in_float", 42.0) # Write a 32-bit float
tensor1 = np.random.random(size=(7, 10)).astype(np.float32) tensor1 = np.ones((7, 8, 3), dtype=np.float32)
tensor2 = np.random.random(size=(16, 12)).astype(np.float16) tensor2 = np.ones((7, 8, 3), dtype=np.float32)
gguf_writer.write_tensor_info("tensor1", tensor1) gguf_writer.write_tensor_info("tensor1", tensor1)
gguf_writer.write_tensor_info("tensor2", tensor2) gguf_writer.write_tensor_info("tensor2", tensor2)
gguf_writer.write_tensors() gguf_writer.write_tensors()
gguf_writer.close() gguf_writer.close()