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import time
import shutil
import json
from pathlib import Path
from logging import info, error
from collections import OrderedDict
from typing import List, Tuple
import torch
import torch.nn.functional as F
import numpy as np
import onnx
from onnx import numpy_helper
from optimum.onnx.utils import (
_get_onnx_external_data_tensors,
check_model_uses_external_data,
)
from modules import shared
from utilities import Engine
from datastructures import ProfileSettings
from model_helper import UNetModel
def apply_lora(model: torch.nn.Module, lora_path: str, inputs: Tuple[torch.Tensor]) -> torch.nn.Module:
try:
import sys
sys.path.append("extensions-builtin/Lora")
import importlib
networks = importlib.import_module("networks")
network = importlib.import_module("network")
lora_net = importlib.import_module("extra_networks_lora")
except Exception as e:
error(e)
error("LoRA not found. Please install LoRA extension first from ...")
model.forward(*inputs)
lora_name = os.path.splitext(os.path.basename(lora_path))[0]
networks.load_networks(
[lora_name], [1.0], [1.0], [None]
)
model.forward(*inputs)
return model
def get_refit_weights(
state_dict: dict, onnx_opt_path: str, weight_name_mapping: dict, weight_shape_mapping: dict
) -> dict:
refit_weights = OrderedDict()
onnx_opt_dir = os.path.dirname(onnx_opt_path)
onnx_opt_model = onnx.load(onnx_opt_path)
# Create initializer data hashes
initializer_hash_mapping = {}
onnx_data_mapping = {}
for initializer in onnx_opt_model.graph.initializer:
initializer_data = numpy_helper.to_array(
initializer, base_dir=onnx_opt_dir
).astype(np.float16)
initializer_hash = hash(initializer_data.data.tobytes())
initializer_hash_mapping[initializer.name] = initializer_hash
onnx_data_mapping[initializer.name] = initializer_data
for torch_name, initializer_name in weight_name_mapping.items():
initializer_hash = initializer_hash_mapping[initializer_name]
wt = state_dict[torch_name]
# get shape transform info
initializer_shape, is_transpose = weight_shape_mapping[torch_name]
if is_transpose:
wt = torch.transpose(wt, 0, 1)
else:
wt = torch.reshape(wt, initializer_shape)
# include weight if hashes differ
wt_hash = hash(wt.cpu().detach().numpy().astype(np.float16).data.tobytes())
if initializer_hash != wt_hash:
delta = wt - torch.tensor(onnx_data_mapping[initializer_name]).to(wt.device)
refit_weights[initializer_name] = delta.contiguous()
return refit_weights
def export_lora(
modelobj: UNetModel,
onnx_path: str,
weights_map_path: str,
lora_name: str,
profile: ProfileSettings,
) -> dict:
info("Exporting to ONNX...")
inputs = modelobj.get_sample_input(
profile.bs_opt * 2,
profile.h_opt // 8,
profile.w_opt // 8,
profile.t_opt,
)
with open(weights_map_path, "r") as fp_wts:
print(f"[I] Loading weights map: {weights_map_path} ")
[weights_name_mapping, weights_shape_mapping] = json.load(fp_wts)
with torch.inference_mode(), torch.autocast("cuda"):
modelobj.unet = apply_lora(
modelobj.unet, os.path.splitext(lora_name)[0], inputs
)
refit_dict = get_refit_weights(
modelobj.unet.state_dict(),
onnx_path,
weights_name_mapping,
weights_shape_mapping,
)
return refit_dict
def swap_sdpa(func):
def wrapper(*args, **kwargs):
swap_sdpa = hasattr(F, "scaled_dot_product_attention")
old_sdpa = (
getattr(F, "scaled_dot_product_attention", None) if swap_sdpa else None
)
if swap_sdpa:
delattr(F, "scaled_dot_product_attention")
ret = func(*args, **kwargs)
if swap_sdpa and old_sdpa:
setattr(F, "scaled_dot_product_attention", old_sdpa)
return ret
return wrapper
@swap_sdpa
def export_onnx(
onnx_path: str,
modelobj: UNetModel,
profile: ProfileSettings,
opset: int = 17,
diable_optimizations: bool = False,
):
info("Exporting to ONNX...")
inputs = modelobj.get_sample_input(
profile.bs_opt * 2,
profile.h_opt // 8,
profile.w_opt // 8,
profile.t_opt,
)
if not os.path.exists(onnx_path):
_export_onnx(
modelobj.unet,
inputs,
Path(onnx_path),
opset,
modelobj.get_input_names(),
modelobj.get_output_names(),
modelobj.get_dynamic_axes(),
modelobj.optimize if not diable_optimizations else None,
)
def _export_onnx(
model: torch.nn.Module, inputs: Tuple[torch.Tensor], path: str, opset: int, in_names: List[str], out_names: List[str], dyn_axes: dict, optimizer=None
):
tmp_dir = os.path.abspath("onnx_tmp")
os.makedirs(tmp_dir, exist_ok=True)
tmp_path = os.path.join(tmp_dir, "model.onnx")
try:
info("Exporting to ONNX...")
with torch.inference_mode(), torch.autocast("cuda"):
torch.onnx.export(
model,
inputs,
tmp_path,
export_params=True,
opset_version=opset,
do_constant_folding=True,
input_names=in_names,
output_names=out_names,
dynamic_axes=dyn_axes,
)
except Exception as e:
error("Exporting to ONNX failed. {}".format(e))
return
info("Optimize ONNX.")
os.makedirs(path.parent, exist_ok=True)
onnx_model = onnx.load(tmp_path, load_external_data=False)
model_uses_external_data = check_model_uses_external_data(onnx_model)
if model_uses_external_data:
info("ONNX model uses external data. Saving as external data.")
tensors_paths = _get_onnx_external_data_tensors(onnx_model)
onnx_model = onnx.load(tmp_path, load_external_data=True)
onnx.save(
onnx_model,
str(path),
save_as_external_data=True,
all_tensors_to_one_file=True,
location=path.name + "_data",
size_threshold=1024,
)
if optimizer is not None:
try:
onnx_opt_graph = optimizer("unet", onnx_model)
onnx.save(onnx_opt_graph, path)
except Exception as e:
error("Optimizing ONNX failed. {}".format(e))
return
if not model_uses_external_data and optimizer is None:
shutil.move(tmp_path, str(path))
shutil.rmtree(tmp_dir)
def export_trt(trt_path: str, onnx_path: str, timing_cache: str, profile: dict, use_fp16: bool):
engine = Engine(trt_path)
# TODO Still approx. 2gb of VRAM unaccounted for...
model = shared.sd_model.cpu()
torch.cuda.empty_cache()
s = time.time()
ret = engine.build(
onnx_path,
use_fp16,
enable_refit=True,
enable_preview=True,
timing_cache=timing_cache,
input_profile=[profile],
# hwCompatibility=hwCompatibility,
)
e = time.time()
info(f"Time taken to build: {(e-s)}s")
shared.sd_model = model.cuda()
return ret
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