apply lint
This commit is contained in:
parent
91210ee236
commit
45166f281e
|
@ -1,7 +1,7 @@
|
|||
from argparse import ArgumentParser
|
||||
from logging import getLogger
|
||||
from typing import Dict, List, Literal, Tuple
|
||||
from os import path
|
||||
from typing import Dict, List, Literal, Tuple
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
@ -12,7 +12,7 @@ from onnx.external_data_helper import (
|
|||
set_external_data,
|
||||
write_external_data_tensors,
|
||||
)
|
||||
from onnxruntime import OrtValue, InferenceSession, SessionOptions
|
||||
from onnxruntime import InferenceSession, OrtValue, SessionOptions
|
||||
from safetensors.torch import load_file
|
||||
|
||||
from onnx_web.convert.utils import ConversionContext
|
||||
|
@ -25,7 +25,9 @@ logger = getLogger(__name__)
|
|||
###
|
||||
|
||||
|
||||
def buffer_external_data_tensors(model: ModelProto) -> Tuple[ModelProto, List[Tuple[str, OrtValue]]]:
|
||||
def buffer_external_data_tensors(
|
||||
model: ModelProto,
|
||||
) -> Tuple[ModelProto, List[Tuple[str, OrtValue]]]:
|
||||
external_data = []
|
||||
for tensor in model.graph.initializer:
|
||||
name = tensor.name
|
||||
|
@ -74,17 +76,19 @@ def merge_lora(
|
|||
lora_prefix = f"lora_{dest_type}_"
|
||||
|
||||
blended: Dict[str, np.ndarray] = {}
|
||||
for lora_name, lora_model, lora_weight in zip(lora_names, lora_models, lora_weights):
|
||||
for lora_name, lora_model, lora_weight in zip(
|
||||
lora_names, lora_models, lora_weights
|
||||
):
|
||||
logger.info("blending LoRA from %s with weight of %s", lora_name, lora_weight)
|
||||
for key in lora_model.keys():
|
||||
if ".lora_down" in key and lora_prefix in key:
|
||||
base_key = key[: key.index(".lora_down")].replace(
|
||||
lora_prefix, ""
|
||||
)
|
||||
base_key = key[: key.index(".lora_down")].replace(lora_prefix, "")
|
||||
|
||||
up_key = key.replace("lora_down", "lora_up")
|
||||
alpha_key = key[: key.index("lora_down")] + "alpha"
|
||||
logger.info("blending weights for keys: %s, %s, %s", key, up_key, alpha_key)
|
||||
logger.info(
|
||||
"blending weights for keys: %s, %s, %s", key, up_key, alpha_key
|
||||
)
|
||||
|
||||
down_weight = lora_model[key].to(dtype=torch.float32)
|
||||
up_weight = lora_model[up_key].to(dtype=torch.float32)
|
||||
|
@ -95,12 +99,22 @@ def merge_lora(
|
|||
try:
|
||||
if len(up_weight.size()) == 2:
|
||||
# blend for nn.Linear
|
||||
logger.info("blending weights for Linear node: %s, %s, %s", down_weight.shape, up_weight.shape, alpha)
|
||||
logger.info(
|
||||
"blending weights for Linear node: %s, %s, %s",
|
||||
down_weight.shape,
|
||||
up_weight.shape,
|
||||
alpha,
|
||||
)
|
||||
weights = up_weight @ down_weight
|
||||
np_weights = (weights.numpy() * (alpha / dim))
|
||||
np_weights = weights.numpy() * (alpha / dim)
|
||||
elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (1, 1):
|
||||
# blend for nn.Conv2d 1x1
|
||||
logger.info("blending weights for Conv node: %s, %s, %s", down_weight.shape, up_weight.shape, alpha)
|
||||
logger.info(
|
||||
"blending weights for Conv node: %s, %s, %s",
|
||||
down_weight.shape,
|
||||
up_weight.shape,
|
||||
alpha,
|
||||
)
|
||||
weights = (
|
||||
(
|
||||
up_weight.squeeze(3).squeeze(2)
|
||||
|
@ -109,10 +123,14 @@ def merge_lora(
|
|||
.unsqueeze(2)
|
||||
.unsqueeze(3)
|
||||
)
|
||||
np_weights = (weights.numpy() * (alpha / dim))
|
||||
np_weights = weights.numpy() * (alpha / dim)
|
||||
else:
|
||||
# TODO: add support for Conv2d 3x3
|
||||
logger.warning("unknown LoRA node type at %s: %s", base_key, up_weight.shape[-2:])
|
||||
logger.warning(
|
||||
"unknown LoRA node type at %s: %s",
|
||||
base_key,
|
||||
up_weight.shape[-2:],
|
||||
)
|
||||
continue
|
||||
|
||||
np_weights *= lora_weight
|
||||
|
@ -122,15 +140,13 @@ def merge_lora(
|
|||
blended[base_key] = np_weights
|
||||
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"error blending weights for key %s", base_key
|
||||
)
|
||||
logger.exception("error blending weights for key %s", base_key)
|
||||
|
||||
logger.info(
|
||||
"updating %s of %s initializers: %s",
|
||||
len(blended.keys()),
|
||||
len(base_model.graph.initializer),
|
||||
list(blended.keys())
|
||||
list(blended.keys()),
|
||||
)
|
||||
|
||||
fixed_initializer_names = [
|
||||
|
@ -138,17 +154,19 @@ def merge_lora(
|
|||
]
|
||||
# logger.info("fixed initializer names: %s", fixed_initializer_names)
|
||||
|
||||
fixed_node_names = [
|
||||
fix_node_name(node.name) for node in base_model.graph.node
|
||||
]
|
||||
fixed_node_names = [fix_node_name(node.name) for node in base_model.graph.node]
|
||||
# logger.info("fixed node names: %s", fixed_node_names)
|
||||
|
||||
|
||||
for base_key, weights in blended.items():
|
||||
conv_key = base_key + "_Conv"
|
||||
matmul_key = base_key + "_MatMul"
|
||||
|
||||
logger.info("key %s has conv: %s, matmul: %s", base_key, conv_key in fixed_node_names, matmul_key in fixed_node_names)
|
||||
logger.info(
|
||||
"key %s has conv: %s, matmul: %s",
|
||||
base_key,
|
||||
conv_key in fixed_node_names,
|
||||
matmul_key in fixed_node_names,
|
||||
)
|
||||
|
||||
if conv_key in fixed_node_names:
|
||||
conv_idx = fixed_node_names.index(conv_key)
|
||||
|
@ -166,7 +184,11 @@ def merge_lora(
|
|||
|
||||
# blending
|
||||
base_weights = numpy_helper.to_array(weight_node)
|
||||
logger.info("found blended weights for conv: %s, %s", weights.shape, base_weights.shape)
|
||||
logger.info(
|
||||
"found blended weights for conv: %s, %s",
|
||||
weights.shape,
|
||||
base_weights.shape,
|
||||
)
|
||||
|
||||
blended = base_weights.squeeze((3, 2)) + weights.squeeze((3, 2))
|
||||
blended = np.expand_dims(blended, (2, 3))
|
||||
|
@ -191,7 +213,11 @@ def merge_lora(
|
|||
|
||||
# blending
|
||||
base_weights = numpy_helper.to_array(matmul_node)
|
||||
logger.info("found blended weights for matmul: %s, %s", weights.shape, base_weights.shape)
|
||||
logger.info(
|
||||
"found blended weights for matmul: %s, %s",
|
||||
weights.shape,
|
||||
base_weights.shape,
|
||||
)
|
||||
|
||||
blended = base_weights + weights.transpose()
|
||||
logger.info("blended weight shape: %s", blended.shape)
|
||||
|
@ -208,7 +234,7 @@ def merge_lora(
|
|||
len(fixed_initializer_names),
|
||||
len(base_model.graph.initializer),
|
||||
len(fixed_node_names),
|
||||
len(base_model.graph.node)
|
||||
len(base_model.graph.node),
|
||||
)
|
||||
|
||||
return base_model
|
||||
|
@ -219,11 +245,16 @@ if __name__ == "__main__":
|
|||
parser.add_argument("--base", type=str)
|
||||
parser.add_argument("--dest", type=str)
|
||||
parser.add_argument("--type", type=str, choices=["text_encoder", "unet"])
|
||||
parser.add_argument("--lora_models", nargs='+', type=str)
|
||||
parser.add_argument("--lora_weights", nargs='+', type=float)
|
||||
parser.add_argument("--lora_models", nargs="+", type=str)
|
||||
parser.add_argument("--lora_weights", nargs="+", type=float)
|
||||
|
||||
args = parser.parse_args()
|
||||
logger.info("merging %s with %s with weights: %s", args.lora_models, args.base, args.lora_weights)
|
||||
logger.info(
|
||||
"merging %s with %s with weights: %s",
|
||||
args.lora_models,
|
||||
args.base,
|
||||
args.lora_weights,
|
||||
)
|
||||
|
||||
blend_model = merge_lora(args.base, args.lora_models, args.type, args.lora_weights)
|
||||
if args.dest is None or args.dest == "" or args.dest == "ort":
|
||||
|
@ -234,10 +265,18 @@ if __name__ == "__main__":
|
|||
external_names, external_values = zip(*external_data)
|
||||
opts = SessionOptions()
|
||||
opts.add_external_initializers(list(external_names), list(external_values))
|
||||
sess = InferenceSession(bare_model.SerializeToString(), sess_options=opts, providers=["CPUExecutionProvider"])
|
||||
logger.info("successfully loaded blended model: %s", [i.name for i in sess.get_inputs()])
|
||||
sess = InferenceSession(
|
||||
bare_model.SerializeToString(),
|
||||
sess_options=opts,
|
||||
providers=["CPUExecutionProvider"],
|
||||
)
|
||||
logger.info(
|
||||
"successfully loaded blended model: %s", [i.name for i in sess.get_inputs()]
|
||||
)
|
||||
else:
|
||||
convert_model_to_external_data(blend_model, all_tensors_to_one_file=True, location=f"lora-{args.type}.pb")
|
||||
convert_model_to_external_data(
|
||||
blend_model, all_tensors_to_one_file=True, location=f"lora-{args.type}.pb"
|
||||
)
|
||||
bare_model = write_external_data_tensors(blend_model, args.dest)
|
||||
dest_file = path.join(args.dest, f"lora-{args.type}.onnx")
|
||||
|
||||
|
|
|
@ -37,7 +37,7 @@ try:
|
|||
except ImportError:
|
||||
from ..diffusers.stub_scheduler import StubScheduler as UniPCMultistepScheduler
|
||||
|
||||
from ..convert.diffusion.lora import merge_lora, buffer_external_data_tensors
|
||||
from ..convert.diffusion.lora import buffer_external_data_tensors, merge_lora
|
||||
from ..params import DeviceParams, Size
|
||||
from ..server import ServerContext
|
||||
from ..utils import run_gc
|
||||
|
@ -118,7 +118,10 @@ def get_loras_from_prompt(prompt: str) -> Tuple[str, List[str]]:
|
|||
name, weight = next_match.groups()
|
||||
loras.append(name)
|
||||
# remove this match and look for another
|
||||
remaining_prompt = remaining_prompt[:next_match.start()] + remaining_prompt[next_match.end():]
|
||||
remaining_prompt = (
|
||||
remaining_prompt[: next_match.start()]
|
||||
+ remaining_prompt[next_match.end() :]
|
||||
)
|
||||
next_match = lora_expr.search(remaining_prompt)
|
||||
|
||||
return (remaining_prompt, loras)
|
||||
|
@ -244,15 +247,23 @@ def load_pipeline(
|
|||
)
|
||||
|
||||
# test LoRA blending
|
||||
lora_models = [path.join(server.model_path, "lora", f"{i}.safetensors") for i in loras]
|
||||
lora_models = [
|
||||
path.join(server.model_path, "lora", f"{i}.safetensors") for i in loras
|
||||
]
|
||||
logger.info("blending base model %s with LoRA models: %s", model, lora_models)
|
||||
|
||||
# blend and load text encoder
|
||||
blended_text_encoder = merge_lora(path.join(model, "text_encoder", "model.onnx"), lora_models, "text_encoder")
|
||||
(text_encoder_model, text_encoder_data) = buffer_external_data_tensors(blended_text_encoder)
|
||||
blended_text_encoder = merge_lora(
|
||||
path.join(model, "text_encoder", "model.onnx"), lora_models, "text_encoder"
|
||||
)
|
||||
(text_encoder_model, text_encoder_data) = buffer_external_data_tensors(
|
||||
blended_text_encoder
|
||||
)
|
||||
text_encoder_names, text_encoder_values = zip(*text_encoder_data)
|
||||
text_encoder_opts = SessionOptions()
|
||||
text_encoder_opts.add_external_initializers(list(text_encoder_names), list(text_encoder_values))
|
||||
text_encoder_opts.add_external_initializers(
|
||||
list(text_encoder_names), list(text_encoder_values)
|
||||
)
|
||||
components["text_encoder"] = OnnxRuntimeModel(
|
||||
OnnxRuntimeModel.load_model(
|
||||
text_encoder_model.SerializeToString(),
|
||||
|
@ -262,7 +273,9 @@ def load_pipeline(
|
|||
)
|
||||
|
||||
# blend and load unet
|
||||
blended_unet = merge_lora(path.join(model, "unet", "model.onnx"), lora_models, "unet")
|
||||
blended_unet = merge_lora(
|
||||
path.join(model, "unet", "model.onnx"), lora_models, "unet"
|
||||
)
|
||||
(unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
|
||||
unet_names, unet_values = zip(*unet_data)
|
||||
unet_opts = SessionOptions()
|
||||
|
|
Loading…
Reference in New Issue