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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import hashlib
import os
from enum import Enum
import torch
from diffusion_models import CLIP, VAE, CLIPWithProj, PipelineInfo, UNet, UNetXL
class EngineType(Enum):
ORT_CUDA = 0 # ONNX Runtime CUDA Execution Provider
ORT_TRT = 1 # ONNX Runtime TensorRT Execution Provider
TRT = 2 # TensorRT
TORCH = 3 # PyTorch
def get_engine_type(name: str) -> EngineType:
name_to_type = {
"ORT_CUDA": EngineType.ORT_CUDA,
"ORT_TRT": EngineType.ORT_TRT,
"TRT": EngineType.TRT,
"TORCH": EngineType.TORCH,
}
return name_to_type[name]
class EngineBuilder:
def __init__(
self,
engine_type: EngineType,
pipeline_info: PipelineInfo,
device="cuda",
max_batch_size=16,
use_cuda_graph=False,
):
"""
Initializes the Engine Builder.
Args:
pipeline_info (PipelineInfo):
Version and Type of pipeline.
device (str | torch.device):
device to run engine
max_batch_size (int):
Maximum batch size for dynamic batch engine.
use_cuda_graph (bool):
Use CUDA graph to capture engine execution and then launch inference
"""
self.engine_type = engine_type
self.pipeline_info = pipeline_info
self.max_batch_size = max_batch_size
self.use_cuda_graph = use_cuda_graph
self.device = torch.device(device)
self.torch_device = torch.device(device, torch.cuda.current_device())
self.stages = pipeline_info.stages()
self.vae_torch_fallback = self.pipeline_info.vae_torch_fallback() and self.engine_type != EngineType.TORCH
self.custom_fp16_vae = self.pipeline_info.custom_fp16_vae()
self.models = {}
self.engines = {}
self.torch_models = {}
self.use_vae_slicing = False
self.torch_sdpa = getattr(torch.nn.functional, "scaled_dot_product_attention", None)
def enable_vae_slicing(self):
self.use_vae_slicing = True
def disable_torch_spda(self):
if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
delattr(torch.nn.functional, "scaled_dot_product_attention")
def enable_torch_spda(self):
if (not hasattr(torch.nn.functional, "scaled_dot_product_attention")) and self.torch_sdpa:
torch.nn.functional.scaled_dot_product_attention = self.torch_sdpa
def teardown(self):
for engine in self.engines.values():
del engine
self.engines = {}
def get_diffusers_module_name(self, model_name):
name_mapping = {
"clip": "text_encoder",
"clip2": "text_encoder_2",
"unet": "unet",
"unetxl": "unet",
"vae": "vae_decoder",
}
return name_mapping.get(model_name, model_name)
def get_cached_model_name(self, model_name):
model_name = self.get_diffusers_module_name(model_name)
is_unet = model_name == "unet"
hash_source = []
if model_name in ["text_encoder", "text_encoder_2", "unet"] and self.pipeline_info.lora_weights:
if self.pipeline_info.lora_weights in [
"latent-consistency/lcm-lora-sdxl",
"latent-consistency/lcm-lora-sdv1-5",
]:
if is_unet:
model_name = "unet_lcm-lora"
else:
model_name = model_name + "_lora"
hash_source.append(self.pipeline_info.lora_weights)
# TODO(tianleiwu): save custom model to a directory named by its original model.
if is_unet and self.pipeline_info.custom_unet():
model_name = model_name + "_lcm"
if model_name in ["unet"] and self.pipeline_info.controlnet:
model_name = model_name + "_" + "_".join(self.pipeline_info.controlnet)
if hash_source:
model_name += "_" + hashlib.sha256("\t".join(hash_source).encode("utf-8")).hexdigest()[:8]
# TODO: When we support original VAE, we shall save custom VAE to another directory.
if self.pipeline_info.is_inpaint():
model_name += "_inpaint"
return model_name
def get_model_dir(self, model_name, root_dir, opt=True, suffix="", create=True):
engine_name = self.engine_type.name.lower()
if engine_name != "ort_cuda" and not suffix:
suffix = f".{engine_name}" if opt else ""
directory_name = self.get_cached_model_name(model_name) + suffix
onnx_model_dir = os.path.join(root_dir, directory_name)
if create:
os.makedirs(onnx_model_dir, exist_ok=True)
return onnx_model_dir
def get_onnx_path(self, model_name, onnx_dir, opt=True, suffix=""):
onnx_model_dir = self.get_model_dir(model_name, onnx_dir, opt=opt, suffix=suffix)
return os.path.join(onnx_model_dir, "model.onnx")
def get_engine_path(self, engine_dir, model_name, profile_id):
return os.path.join(engine_dir, self.get_cached_model_name(model_name) + profile_id)
def load_pipeline_with_lora(self):
"""Load text encoders and UNet with diffusers pipeline"""
from diffusers import DiffusionPipeline # noqa: PLC0415
pipeline = DiffusionPipeline.from_pretrained(
self.pipeline_info.name(),
variant="fp16",
torch_dtype=torch.float16,
)
pipeline.load_lora_weights(self.pipeline_info.lora_weights)
pipeline.fuse_lora(lora_scale=self.pipeline_info.lora_scale)
del pipeline.vae
pipeline.vae = None
return pipeline
def get_or_load_model(self, pipeline, model_name, model_obj, framework_model_dir):
if model_name in ["clip", "clip2", "unet", "unetxl"] and pipeline:
if model_name == "clip":
model = pipeline.text_encoder
pipeline.text_encoder = None
elif model_name == "clip2":
model = pipeline.text_encoder_2
pipeline.text_encoder_2 = None
else:
model = pipeline.unet
pipeline.unet = None
else:
model = model_obj.load_model(framework_model_dir)
return model.to(self.torch_device)
def load_models(self, framework_model_dir: str):
# For TRT or ORT_TRT, we will export fp16 torch model for UNet and VAE
# For ORT_CUDA, we export fp32 model first, then optimize to fp16.
export_fp16 = self.engine_type in [EngineType.ORT_TRT, EngineType.TRT]
if "clip" in self.stages:
self.models["clip"] = CLIP(
self.pipeline_info,
None, # not loaded yet
device=self.torch_device,
max_batch_size=self.max_batch_size,
clip_skip=0,
)
if "clip2" in self.stages:
self.models["clip2"] = CLIPWithProj(
self.pipeline_info,
None, # not loaded yet
device=self.torch_device,
max_batch_size=self.max_batch_size,
clip_skip=0,
)
if "unet" in self.stages:
self.models["unet"] = UNet(
self.pipeline_info,
None, # not loaded yet
device=self.torch_device,
fp16=export_fp16,
max_batch_size=self.max_batch_size,
unet_dim=(9 if self.pipeline_info.is_inpaint() else 4),
)
if "unetxl" in self.stages:
self.models["unetxl"] = UNetXL(
self.pipeline_info,
None, # not loaded yet
device=self.torch_device,
fp16=export_fp16,
max_batch_size=self.max_batch_size,
unet_dim=4,
time_dim=(5 if self.pipeline_info.is_xl_refiner() else 6),
)
# VAE Decoder
if "vae" in self.stages:
self.models["vae"] = VAE(
self.pipeline_info,
None, # not loaded yet
device=self.torch_device,
max_batch_size=self.max_batch_size,
fp16=export_fp16,
custom_fp16_vae=self.custom_fp16_vae,
)
if self.vae_torch_fallback:
self.torch_models["vae"] = self.models["vae"].load_model(framework_model_dir)
def load_resources(self, image_height, image_width, batch_size):
if self.engine_type == EngineType.TORCH:
return
# Allocate buffers for I/O bindings
for model_name, obj in self.models.items():
if model_name == "vae" and self.vae_torch_fallback:
continue
slice_size = 1 if (model_name == "vae" and self.use_vae_slicing) else batch_size
self.engines[model_name].allocate_buffers(
shape_dict=obj.get_shape_dict(slice_size, image_height, image_width), device=self.torch_device
)
def _vae_decode(self, latents):
if self.engine_type == EngineType.TORCH:
if self.pipeline_info.is_xl() and not self.custom_fp16_vae: # need upcast
latents = latents.to(dtype=torch.float32)
images = self.engines["vae"](latents)["sample"]
else:
images = self.engines["vae"](latents)["sample"]
elif self.vae_torch_fallback:
if not self.custom_fp16_vae:
latents = latents.to(dtype=torch.float32)
self.torch_models["vae"] = self.torch_models["vae"].to(dtype=torch.float32)
images = self.torch_models["vae"](latents)["sample"]
else:
if self.pipeline_info.is_xl() and not self.custom_fp16_vae: # need upcast
images = self.run_engine("vae", {"latent": latents.to(dtype=torch.float32)})["images"]
else:
images = self.run_engine("vae", {"latent": latents})["images"]
return images
def vae_decode(self, latents):
if self.use_vae_slicing:
# The output tensor points to same buffer. Need clone it to avoid overwritten.
decoded_slices = [self._vae_decode(z_slice).clone() for z_slice in latents.split(1)]
return torch.cat(decoded_slices)
return self._vae_decode(latents)
def get_engine_paths(
work_dir: str, pipeline_info: PipelineInfo, engine_type: EngineType, framework_model_dir: str | None = None
):
root_dir = work_dir or "."
short_name = pipeline_info.short_name()
# When both ORT_CUDA and ORT_TRT/TRT is used, we shall make sub directory for each engine since
# ORT_CUDA need fp32 torch model, while ORT_TRT/TRT use fp16 torch model.
onnx_dir = os.path.join(root_dir, engine_type.name, short_name, "onnx")
engine_dir = os.path.join(root_dir, engine_type.name, short_name, "engine")
output_dir = os.path.join(root_dir, engine_type.name, short_name, "output")
timing_cache = os.path.join(root_dir, engine_type.name, "timing_cache")
# Shared among ORT_CUDA, ORT_TRT and TRT engines, and need use load_model(..., always_download_fp16=True)
# So that the shared model is always fp16.
if framework_model_dir is None:
framework_model_dir = os.path.join(root_dir, "torch_model")
return onnx_dir, engine_dir, output_dir, framework_model_dir, timing_cache
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