alphard/stable-diffusion.cpp
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fix: correct the handling of weight loading

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This commit is contained in:
leejet
2023-08-30 21:44:06 +08:00
parent 1b5a868296
commit c542a77a3f
2 changed files with 23 additions and 9 deletions
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26
models/convert.py
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@@ -31,7 +31,7 @@ ggml_ttype_str_to_int = {
QK4_0 = 32
def quantize_q4_0(x):
assert x.shape[-1] % QK4_0 == 0
assert x.shape[-1] % QK4_0 == 0 and x.shape[-1] > QK4_0
x = x.reshape(-1, QK4_0)
max = np.take_along_axis(x, np.argmax(np.abs(x), axis=-1)[:, np.newaxis], axis=-1)
d = max / -8
@@ -44,7 +44,7 @@ def quantize_q4_0(x):
QK4_1 = 32
def quantize_q4_1(x):
assert x.shape[-1] % QK4_1 == 0
assert x.shape[-1] % QK4_1 == 0 and x.shape[-1] > QK4_1
x = x.reshape(-1, QK4_1)
min = np.min(x, axis=-1, keepdims=True)
max = np.max(x, axis=-1, keepdims=True)
@@ -59,7 +59,7 @@ def quantize_q4_1(x):
QK5_0 = 32
def quantize_q5_0(x):
assert x.shape[1] % QK5_0 == 0
assert x.shape[-1] % QK5_0 == 0 and x.shape[-1] > QK5_0
x = x.reshape(-1, QK5_0)
max = np.take_along_axis(x, np.argmax(np.abs(x), axis=-1)[:, np.newaxis], axis=-1)
d = max / -16
@@ -76,7 +76,7 @@ def quantize_q5_0(x):
QK5_1 = 32
def quantize_q5_1(x):
assert x.shape[-1] % QK5_1 == 0
assert x.shape[-1] % QK5_1 == 0 and x.shape[-1] > QK5_1
x = x.reshape(-1, QK5_1)
min = np.min(x, axis=-1, keepdims=True)
max = np.max(x, axis=-1, keepdims=True)
@@ -95,7 +95,7 @@ def quantize_q5_1(x):
QK8_0 = 32
def quantize_q8_0(x):
assert x.shape[-1] % QK8_0 == 0
assert x.shape[-1] % QK8_0 == 0 and x.shape[-1] > QK8_0
x = x.reshape(-1, QK8_0)
amax = np.max(np.abs(x), axis=-1, keepdims=True)
d = amax / ((1 << 7) - 1)
@@ -156,7 +156,10 @@ unused_tensors = [
"posterior_mean_coef2",
"cond_stage_model.transformer.text_model.embeddings.position_ids",
"model_ema.decay",
"model_ema.num_updates"
"model_ema.num_updates",
"control_model",
"lora_te_text_model",
"embedding_manager"
]
def convert(model_path, out_type = None, out_file=None):
@@ -182,6 +185,10 @@ def convert(model_path, out_type = None, out_file=None):
out_type = "f32"
elif weight.dtype == np.float16:
out_type = "f16"
elif weight.dtype == np.float64:
out_type = "f32"
else:
raise Exception("unsupported weight type %s" % weight.dtype)
if out_file == None:
out_file = os.path.splitext(os.path.basename(model_path))[0] + f"-ggml-model-{out_type}.bin"
out_file = os.path.join(os.getcwd(), out_file)
@@ -207,6 +214,13 @@ def convert(model_path, out_type = None, out_file=None):
for name in state_dict.keys():
if not isinstance(state_dict[name], torch.Tensor):
continue
skip = False
for unused_tensor in unused_tensors:
if name.startswith(unused_tensor):
skip = True
break
if skip:
continue
if name in unused_tensors:
continue
data = state_dict[name].numpy()