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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import types
from pathlib import Path
import tensorrt as trt
import torch
from cache_diffusion.cachify import CACHED_PIPE, get_model
from cuda import cudart
from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from pipe.config import ONNX_CONFIG
from pipe.models.sd3 import sd3_forward
from pipe.models.sdxl import (
cachecrossattnupblock2d_forward,
cacheunet_forward,
cacheupblock2d_forward,
)
from polygraphy.backend.trt import (
CreateConfig,
Profile,
engine_from_network,
network_from_onnx_path,
save_engine,
)
from torch.onnx import export as onnx_export
from .utils import Engine
def replace_new_forward(backbone):
if backbone.__class__ == UNet2DConditionModel:
backbone.forward = types.MethodType(cacheunet_forward, backbone)
for upsample_block in backbone.up_blocks:
if (
hasattr(upsample_block, "has_cross_attention")
and upsample_block.has_cross_attention
):
upsample_block.forward = types.MethodType(
cachecrossattnupblock2d_forward, upsample_block
)
else:
upsample_block.forward = types.MethodType(cacheupblock2d_forward, upsample_block)
elif backbone.__class__ == SD3Transformer2DModel:
backbone.forward = types.MethodType(sd3_forward, backbone)
def get_input_info(dummy_dict, info: str = None, batch_size: int = 1):
return_val = [] if info == "profile_shapes" or info == "input_names" else {}
def collect_leaf_keys(d):
for key, value in d.items():
if isinstance(value, dict):
collect_leaf_keys(value)
else:
value = (value[0] * batch_size,) + value[1:]
if info == "profile_shapes":
return_val.append((key, value)) # type: ignore
elif info == "profile_shapes_dict":
return_val[key] = value # type: ignore
elif info == "dummy_input":
return_val[key] = torch.ones(value).half().cuda() # type: ignore
elif info == "input_names":
return_val.append(key) # type: ignore
collect_leaf_keys(dummy_dict)
return return_val
def complie2trt(cls, onnx_path: Path, engine_path: Path, batch_size: int = 1):
subdirs = [f for f in onnx_path.iterdir() if f.is_dir()]
for subdir in subdirs:
if subdir.name not in ONNX_CONFIG[cls].keys():
continue
model_path = subdir / "model.onnx"
plan_path = engine_path / f"{subdir.name}.plan"
if not plan_path.exists():
print(f"Building {str(model_path)}")
build_profile = Profile()
profile_shapes = get_input_info(
ONNX_CONFIG[cls][subdir.name]["dummy_input"], "profile_shapes", batch_size
)
for input_name, input_shape in profile_shapes:
min_input_shape = (2,) + input_shape[1:]
build_profile.add(input_name, min_input_shape, input_shape, input_shape)
block_network = network_from_onnx_path(
str(model_path), flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM], strongly_typed=True
)
build_config = CreateConfig(
builder_optimization_level=6,
tf32=True,
#bf16=True,
profiles=[build_profile],
)
engine = engine_from_network(
block_network,
config=build_config,
)
save_engine(engine, path=plan_path)
else:
print(f"{str(model_path)} already exists!")
def get_total_device_memory(backbone):
max_device_memory = 0
for _, engine in backbone.engines.items():
max_device_memory = max(max_device_memory, engine.engine.device_memory_size)
return max_device_memory
def load_engines(backbone, engine_path: Path, batch_size: int = 1):
backbone.engines = {}
for f in engine_path.iterdir():
if f.is_file():
eng = Engine()
eng.load(str(f))
backbone.engines[f"{f.stem}"] = eng
_, shared_device_memory = cudart.cudaMalloc(get_total_device_memory(backbone))
for engine in backbone.engines.values():
engine.activate(shared_device_memory)
backbone.cuda_stream = cudart.cudaStreamCreate()[1]
for block_name in backbone.engines.keys():
backbone.engines[block_name].allocate_buffers(
shape_dict=get_input_info(
ONNX_CONFIG[backbone.__class__][block_name]["dummy_input"],
"profile_shapes_dict",
batch_size,
),
device=backbone.device,
batch_size=batch_size,
)
# TODO: Free and clean up the origin pytorch cuda memory
def export_onnx(backbone, onnx_path: Path):
for name, module in backbone.named_modules():
if isinstance(module, CACHED_PIPE[backbone.__class__]):
_onnx_dir = onnx_path.joinpath(f"{name}")
_onnx_file = _onnx_dir.joinpath("model.onnx")
if not _onnx_file.exists():
_onnx_dir.mkdir(parents=True, exist_ok=True)
dummy_input = get_input_info(
ONNX_CONFIG[backbone.__class__][f"{name}"]["dummy_input"], "dummy_input"
)
input_names = get_input_info(
ONNX_CONFIG[backbone.__class__][f"{name}"]["dummy_input"], "input_names"
)
output_names = ONNX_CONFIG[backbone.__class__][f"{name}"]["output_names"]
onnx_export(
module,
args=dummy_input,
f=_onnx_file.as_posix(),
input_names=input_names,
output_names=output_names,
dynamic_axes=ONNX_CONFIG[backbone.__class__][f"{name}"]["dynamic_axes"],
do_constant_folding=True,
opset_version=17,
)
else:
print(f"{str(_onnx_file)} alread exists!")
def warm_up(backbone, batch_size: int = 1):
print("Warming-up TensorRT engines...")
for name, engine in backbone.engines.items():
dummy_input = get_input_info(
ONNX_CONFIG[backbone.__class__][name]["dummy_input"], "dummy_input", batch_size
)
_ = engine(dummy_input, backbone.cuda_stream)
def teardown(pipe):
backbone = get_model(pipe)
for engine in backbone.engines.values():
del engine
cudart.cudaStreamDestroy(backbone.cuda_stream)
del backbone.cuda_stream
def compile(pipe, onnx_path: Path, engine_path: Path, batch_size: int = 1):
backbone = get_model(pipe)
onnx_path.mkdir(parents=True, exist_ok=True)
engine_path.mkdir(parents=True, exist_ok=True)
replace_new_forward(backbone)
export_onnx(backbone, onnx_path)
complie2trt(backbone.__class__, onnx_path, engine_path, batch_size)
load_engines(backbone, engine_path, batch_size)
warm_up(backbone, batch_size)
backbone.use_trt_infer = True
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