docstr: Trace a torch.nn.Module and convert it into an accelerated module for inference.
  
  For example, this function returns a PytorchOpenVINOModel when accelerator=='openvino'.
  
  :param low_memory: only valid when accelerator="jit" and ipex=True, model will use less memory during inference
  :cache_dir: the directory to save the converted model
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code: def convert_pipeline(
      self,
      accelerator="jit",
      ipex=True,
      precision="float32",
      device="CPU",
      samples=None,
      height=512,
      width=512,
      low_memory=False,
      cache=False,
      cache_dir=None,
      fail_if_no_cache=False,
      channels_last=False,
      num_inference_steps=50,
      accuracy_drop=3000,
  ):
      """
      Trace a torch.nn.Module and convert it into an accelerated module for inference.
  
      For example, this function returns a PytorchOpenVINOModel when accelerator=='openvino'.
  
      :param low_memory: only valid when accelerator="jit" and ipex=True, model will use less memory during inference
      :cache_dir: the directory to save the converted model
      """
      generator = torch.Generator(device="cpu")
      generator.manual_seed(1)
      loaded = False
  
      latent_shape = (2, self.unet.in_channels, height // 8, width // 8)
      image_latents = torch.randn(
          latent_shape,
          generator=generator,
          device="cpu",
          dtype=torch.float32,
      )
      encoder_hidden_states = self.get_encoder_hidden_states()
      input_sample = (
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
      )
  
      # vae decoder
      if (
          accelerator == "openvino"
          and precision == "float16"
          and device == "GPU"
      ):
          print(f"Start optimizing vae decoder...")
          decoder_loaded = False
          if cache:
              assert (
                  cache_dir is not None
              ), f"Please provide cache_dir if cache=True."
              vae_cache_path = self._get_cache_path(
                  cache_dir,
                  accelerator=accelerator,
                  precision=precision,
                  device=device,
                  vae=True,
              )
              if vae_cache_path and os.path.exists(vae_cache_path):
                  try:
                      print(
                          f"Loading the existing cache from {vae_cache_path}"
                      )
                      nano_vae_decoder = InferenceOptimizer.load(
                          vae_cache_path, device=device
                      )
                      decoder_loaded = True
                  except Exception as e:
                      decoder_loaded = False
                      print(
                          f"The cache path {vae_cache_path} exists, but failed to load. Error message: {str(e)}"
                      )
          if not decoder_loaded:
              vae_decoder = self.vae.decoder
              nano_vae_decoder = InferenceOptimizer.quantize(
                  vae_decoder,
                  accelerator=accelerator,
                  input_sample=torch.randn(
                      (
                          1,
                          self.unet.in_channels,
                          height // 8,
                          width // 8,
                      ),
                      generator=generator,
                      device="cpu",
                      dtype=torch.float32,
                  ),
                  precision="fp16",
                  dynamic_axes=False,
              )
          setattr(self.vae, "decoder", nano_vae_decoder)
          if cache:
              logger.info(
                  f"Caching the converted vae decoder model to {vae_cache_path}"
              )
              InferenceOptimizer.save(nano_vae_decoder, vae_cache_path)
  
      print(f"Start optimizing unet...")
      unet_input_names = ["sample", "timestep", "encoder_hidden_states"]
      unet_output_names = ["unet_output"]
      unet_dynamic_axes = {
          "sample": [0],
          "encoder_hidden_states": [0],
          "unet_output": [0],
      }
  
      if cache:
          assert (
              cache_dir is not None
          ), f"Please provide cache_dir if cache=True."
          cache_path = self._get_cache_path(
              cache_dir,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              low_memory=low_memory,
              device=device,
          )
          if precision == "bfloat16" and accelerator != "openvino":
              pass
          elif os.path.exists(cache_path):
              try:
                  print(f"Loading the existing cache from {cache_path}")
                  nano_unet = InferenceOptimizer.load(
                      cache_path, device=device
                  )
                  loaded = True
              except Exception as e:
                  loaded = False
                  print(
                      f"The cache path {cache_path} exists, but failed to load. Error message: {str(e)}"
                  )
  
      print("precision is", precision)
      if not loaded:
          if fail_if_no_cache:
              raise Exception(
                  "You have to download the model to nano_stable_diffusion folder"
              )
  
          extra_args = {}
          if precision == "float32":
              if accelerator == "jit":
                  weights_prepack = False if low_memory else None
                  extra_args["weights_prepack"] = weights_prepack
                  extra_args["use_ipex"] = ipex
                  extra_args["jit_strict"] = False
                  extra_args["enable_onednn"] = False
                  extra_args["channels_last"] = channels_last
              elif accelerator is None:
                  if ipex:
                      extra_args["use_ipex"] = ipex
                      extra_args["channels_last"] = channels_last
                  else:
                      raise ValueError(
                          "IPEX should be True if accelerator is None and precision is float32."
                      )
              elif accelerator == "openvino":
                  extra_args["input_names"] = unet_input_names
                  extra_args["output_names"] = unet_output_names
                  # Nano will deal with the GPU/VPU dynamic axes issue
                  extra_args["dynamic_axes"] = unet_dynamic_axes
                  extra_args["device"] = device
              else:
                  raise ValueError(
                      f"The accelerator can be one of `None`, `jit`, and `openvino` if the precision is float32, but got {accelerator}"
                  )
              nano_unet = InferenceOptimizer.trace(
                  self.unet,
                  accelerator=accelerator,
                  input_sample=input_sample,
                  **extra_args,
              )
          else:
              precision_map = {
                  "bfloat16": "bf16",
                  "int8": "int8",
                  "float16": "fp16",
              }
              precision_short = precision_map[precision]
  
              # prepare input samples, calib dataloader and eval functions
              if accelerator == "openvino":
                  # minimal UI integrates FP16 CPU and GPU options because there two models are the same
                  # so we hardcode here to avoid GPU not available issue
                  extra_args["device"] = "CPU"
                  extra_args["input_names"] = unet_input_names
                  extra_args["output_names"] = unet_output_names
                  # Nano will deal with the GPU/VPU dynamic axes issue
                  extra_args["dynamic_axes"] = False
  
                  if precision_short == "int8":
                      # TODO: openvino int8 here
                      raise ValueError(
                          "OpenVINO int8 quantization is not supported."
                      )
              elif accelerator == "onnxruntime":
                  raise ValueError(
                      f"Onnxruntime {precision_short} quantization is not supported."
                  )
              else:
                  # PyTorch bf16
                  if precision_short == "bf16":
                      # Ignore jit & ipex
                      if accelerator == "jit":
                          raise ValueError(
                              f"JIT {precision_short} quantization is not supported."
                          )
                      extra_args["channels_last"] = channels_last
                  elif precision_short == "int8":
                      # INC int8 or INC ipex int8
                      if samples is not None:
                          # get real calibration sample
                          input_sample = samples[0][0]
  
                      class CalibDataLoader(object):
                          def __init__(self, samples):
                              self.batch_size = 1
                              self.data = samples
                              self.len = len(samples)
  
                          def __iter__(self):
                              for i in range(self.len):
                                  data = self.data[i]
                                  input, output = data
                                  yield input, output
  
                      prompt = "a photo of an astronaut riding a horse on mars"
                      generator_eval = torch.Generator(
                          "cpu"
                      ).manual_seed(77)
                      eval_image = self(
                          prompt,
                          generator=generator_eval,
                          num_inference_steps=num_inference_steps,
                      )[0]
                      eval_image.save("eval_image.jpg")
  
                      def eval_func(model):
                          setattr(model, "in_channels", 4)
                          setattr(self, "unet", model)
                          with torch.no_grad():
                              loss = torch.nn.MSELoss()
                              generator_eval = torch.Generator(
                                  "cpu"
                              ).manual_seed(77)
                              new_image = self(
                                  prompt,
                                  guidance_scale=7.5,
                                  num_inference_steps=num_inference_steps,
                                  generator=generator_eval,
                              )[0]
                              new_image.save("new_image.jpg")
                              mse_score = 0
                              new = torch.from_numpy(
                                  np.array(new_image)
                              )
                              old = torch.from_numpy(
                                  np.array(eval_image)
                              )
                              new = new.to(dtype=torch.float32)
                              old = old.to(dtype=torch.float32)
                              mse_score += loss(new, old)
                              mse_score = mse_score.item()
                              return mse_score
  
                      if samples is None or len(samples) < 1:
                          raise ValueError(
                              "Calibration samples can't be None or empty for quantization."
                          )
  
                      dataloader = CalibDataLoader(samples)
                      dataloader.collate_fn = None
                      extra_args["calib_dataloader"] = dataloader
                      extra_args["eval_func"] = eval_func
                      extra_args["accuracy_criterion"] = {
                          "absolute": accuracy_drop,
                          "higher_is_better": False,
                      }
                      extra_args["max_trials"] = 10
                      extra_args["timeout"] = 0
                      extra_args["tuning_strategy"] = "basic"
                  else:
                      raise ValueError(
                          f"PyTorch {precision_short} quantization is not supported."
                      )
  
              # unet
              nano_unet = InferenceOptimizer.quantize(
                  self.unet,
                  accelerator=accelerator,
                  precision=precision_short,
                  input_sample=input_sample,
                  **extra_args,
              )
  
          # Save model if cache=True
          if cache:
              logger.info(
                  f"Caching the converted unet model to {cache_path}"
              )
              InferenceOptimizer.save(nano_unet, cache_path)
  
      setattr(nano_unet, "in_channels", 4)
      self.unet = nano_unet
      return self
  
imports: import os
  import torch
  import inspect
  import numpy
  from tqdm.auto import tqdm
  import multiprocessing
  from bigdl.nano.pytorch import InferenceOptimizer
  from typing import Callable, List, Optional, Union
  from diffusers import AutoencoderKL
  from transformers import CLIPTokenizer, CLIPTextModel
  from diffusers.schedulers import *
  from diffusers.utils import logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipeline_utils import DiffusionPipeline
  from diffusers.models import UNet2DConditionModel
label_desc: Use image node_id 4 for calling the NanoStableDiffusionPipeline obj's underlying member method code with example usage: obj.convert_pipeline(accelerator, ipex, precision, device, samples, height, width, low_memory, cache, cache_dir, fail_if_no_cache, channels_last, num_inference_steps, accuracy_drop) and returns: self, mse_score