vbatts/llama.cpp
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This commit is contained in:
Dmitry Wolf 2023-03-15 22:57:59 +03:00
parent bb997650be
commit 0deb075a3f
1 changed files with 44 additions and 46 deletions
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90
convert-pth-to-ggml.py
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@ -84,6 +84,50 @@ print(f'Model params.json: {hparams}')
print(f'Parts to process: {n_parts}')
def load_model(fname):
class Tensor():
def __init__(self, shape, dtype, loadinfo):
self.shape = shape
self.dtype = dtype
self.loadinfo = loadinfo
def numpy(self):
myzip, base_name, storage_offset, k, shape, dtype = self.loadinfo
with myzip.open(f'{base_name}/data/{k}') as myfile:
bytes_size = np.dtype(self.dtype).itemsize
myfile.seek(storage_offset * bytes_size, 1)
ret = np.empty(shape, dtype=dtype)
myfile.readinto(ret.data)
return ret
def my_unpickle(datapkl, myzip, base_name):
def my_rebuild_tensor(storage, storage_offset, size, stride, requires_grad, backward_hooks, metadata=None):
storage_type = storage[1]
obj_key = storage[2]
return Tensor(shape=size, dtype=storage_type, loadinfo=(
myzip, base_name, storage_offset,
obj_key, size, storage_type
))
class MyUnpickler(pickle.Unpickler):
def find_class(self, *p):
if p == ('torch', 'HalfStorage'): return np.float16
if p == ('torch', 'FloatStorage'): return np.float32
if p == ('torch._utils', '_rebuild_tensor_v2'): return my_rebuild_tensor
if p == ('collections', 'OrderedDict'): return dict
raise ValueError(f'Unrecognized pickle {p}')
def persistent_load(self, pid):
return pid
return MyUnpickler(datapkl).load()
myzip = zipfile.ZipFile(fname, 'r')
base_name = myzip.namelist()[0].split('/', 1)[0]
with myzip.open(f'{base_name}/data.pkl') as myfile:
model = my_unpickle(myfile, myzip, base_name)
return model
def get_fname(p):
fname = "/consolidated.0" + str(p) + ".pth"
return fname
@ -133,51 +177,6 @@ def process_part(p):
fout.write(struct.pack("i", len(text)))
fout.write(text)
def load_model(fname):
class Tensor():
def __init__(self, shape, dtype, loadinfo):
self.shape = shape
self.dtype = dtype
self.loadinfo = loadinfo
def numpy(self):
myzip, base_name, storage_offset, k, shape, dtype = self.loadinfo
with myzip.open(f'{base_name}/data/{k}') as myfile:
bytes_size = np.dtype(self.dtype).itemsize
myfile.seek(storage_offset * bytes_size, 1)
ret = np.empty(shape, dtype=dtype)
myfile.readinto(ret.data)
return ret
def my_unpickle(datapkl, myzip, base_name):
def my_rebuild_tensor(storage, storage_offset, size, stride, requires_grad, backward_hooks, metadata=None):
storage_type = storage[1]
obj_key = storage[2]
return Tensor(shape=size, dtype=storage_type, loadinfo=(
myzip, base_name, storage_offset,
obj_key, size, storage_type
))
class MyUnpickler(pickle.Unpickler):
def find_class(self, *p):
if p == ('torch', 'HalfStorage'): return np.float16
if p == ('torch', 'FloatStorage'): return np.float32
if p == ('torch._utils', '_rebuild_tensor_v2'): return my_rebuild_tensor
if p == ('collections', 'OrderedDict'): return dict
raise ValueError(f'Unrecognized pickle {p}')
def persistent_load(self, pid):
return pid
return MyUnpickler(datapkl).load()
myzip = zipfile.ZipFile(fname, 'r')
base_name = myzip.namelist()[0].split('/', 1)[0]
with myzip.open(f'{base_name}/data.pkl') as myfile:
model = my_unpickle(myfile, myzip, base_name)
return model
model = load_model(fname_model)
q = queue.Queue(maxsize=2)
@ -234,7 +233,6 @@ def process_part(p):
# data
memout.write(data.tobytes())
# data.tofile(memout)
q.put(memout)
q.join()