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onnxruntime/python/tools/transformers/models/stable_diffusion/engine_builder_torch.py
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# ------------------------------------------------------------------------- | ||
# Copyright (c) Microsoft Corporation. All rights reserved. | ||
# Licensed under the MIT License. | ||
# -------------------------------------------------------------------------- | ||
import logging | ||
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from diffusion_models import PipelineInfo | ||
from engine_builder import EngineBuilder, EngineType | ||
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logger = logging.getLogger(__name__) | ||
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class TorchEngineBuilder(EngineBuilder): | ||
def __init__( | ||
self, | ||
pipeline_info: PipelineInfo, | ||
max_batch_size=16, | ||
device="cuda", | ||
use_cuda_graph=False, | ||
): | ||
""" | ||
Initializes the ONNX Runtime TensorRT ExecutionProvider Engine Builder. | ||
Args: | ||
pipeline_info (PipelineInfo): | ||
Version and Type of pipeline. | ||
max_batch_size (int): | ||
Maximum batch size for dynamic batch engine. | ||
device (str): | ||
device to run. | ||
use_cuda_graph (bool): | ||
Use CUDA graph to capture engine execution and then launch inference | ||
""" | ||
super().__init__( | ||
EngineType.TORCH, | ||
pipeline_info, | ||
max_batch_size=max_batch_size, | ||
device=device, | ||
use_cuda_graph=use_cuda_graph, | ||
) | ||
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self.compile_config = { | ||
"clip": {"mode": "reduce-overhead", "dynamic": False}, | ||
"clip2": {"mode": "reduce-overhead", "dynamic": False}, | ||
"unet": {"mode": "reduce-overhead", "fullgraph": True, "dynamic": False}, | ||
"unetxl": {"mode": "reduce-overhead", "fullgraph": True, "dynamic": False}, | ||
} | ||
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self.compile_config["vae"] = {"mode": "reduce-overhead", "fullgraph": False, "dynamic": False} | ||
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def build_engines( | ||
self, | ||
framework_model_dir: str, | ||
): | ||
import torch | ||
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self.torch_device = torch.device("cuda", torch.cuda.current_device()) | ||
self.load_models(framework_model_dir) | ||
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pipe = self.load_pipeline_with_lora() if self.pipeline_info.lora_weights else None | ||
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built_engines = {} | ||
for model_name, model_obj in self.models.items(): | ||
model = self.get_or_load_model(pipe, model_name, model_obj, framework_model_dir) | ||
if self.pipeline_info.is_xl() and not self.custom_fp16_vae: | ||
model = model.to(device=self.torch_device, dtype=torch.float32) | ||
else: | ||
model = model.to(device=self.torch_device, dtype=torch.float16) | ||
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if model_name in self.compile_config: | ||
compile_config = self.compile_config[model_name] | ||
if model_name in ["unet", "unetxl"]: | ||
model.to(memory_format=torch.channels_last) | ||
engine = torch.compile(model, **compile_config) | ||
built_engines[model_name] = engine | ||
else: | ||
built_engines[model_name] = model | ||
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self.engines = built_engines | ||
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def run_engine(self, model_name, feed_dict): | ||
if model_name in ["unet", "unetxl"]: | ||
if "controlnet_images" in feed_dict: | ||
return {"latent": self.engines[model_name](**feed_dict)} | ||
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if model_name == "unetxl": | ||
added_cond_kwargs = {k: feed_dict[k] for k in feed_dict if k in ["text_embeds", "time_ids"]} | ||
return { | ||
"latent": self.engines[model_name]( | ||
feed_dict["sample"], | ||
feed_dict["timestep"], | ||
feed_dict["encoder_hidden_states"], | ||
added_cond_kwargs=added_cond_kwargs, | ||
return_dict=False, | ||
)[0] | ||
} | ||
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return { | ||
"latent": self.engines[model_name]( | ||
feed_dict["sample"], feed_dict["timestep"], feed_dict["encoder_hidden_states"], return_dict=False | ||
)[0] | ||
} | ||
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if model_name in ["vae_encoder"]: | ||
return {"latent": self.engines[model_name](feed_dict["images"])} | ||
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raise RuntimeError(f"Shall not reach here: {model_name}") |