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  "cells": [
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      "cell_type": "code",
      "source": [
        "!pip install onnx diffusers transformers huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_Qwed4hiWul",
        "outputId": "e6940bc5-6955-49ec-902f-74097538a848"
      },
      "execution_count": null,
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: onnx in /usr/local/lib/python3.7/dist-packages (1.12.0)\n",
            "Requirement already satisfied: diffusers in /usr/local/lib/python3.7/dist-packages (0.5.1)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.23.1)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.10.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx) (4.1.1)\n",
            "Requirement already satisfied: protobuf<=3.20.1,>=3.12.2 in /usr/local/lib/python3.7/dist-packages (from onnx) (3.17.3)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx) (1.21.6)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf<=3.20.1,>=3.12.2->onnx) (1.15.0)\n",
            "Requirement already satisfied: Pillow<10.0 in /usr/local/lib/python3.7/dist-packages (from diffusers) (7.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from diffusers) (2.23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from diffusers) (3.8.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from diffusers) (2022.6.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from diffusers) (5.0.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->diffusers) (3.9.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtX0gEVRjLRR",
        "outputId": "57a708be-208d-42c0-d4f0-36dadea129d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pmFzJ4r3hphb"
      },
      "outputs": [],
      "source": [
        "# Copyright 2022 The HuggingFace Team. All rights reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "\n",
        "import argparse\n",
        "import os\n",
        "import shutil\n",
        "from pathlib import Path\n",
        "\n",
        "import torch\n",
        "from torch.onnx import export\n",
        "\n",
        "import onnx\n",
        "from diffusers import StableDiffusionOnnxPipeline, StableDiffusionPipeline\n",
        "from diffusers.onnx_utils import OnnxRuntimeModel\n",
        "from packaging import version\n",
        "\n",
        "\n",
        "is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse(\"1.11\")\n",
        "\n",
        "\n",
        "def onnx_export(\n",
        "    model,\n",
        "    model_args: tuple,\n",
        "    output_path: Path,\n",
        "    ordered_input_names,\n",
        "    output_names,\n",
        "    dynamic_axes,\n",
        "    opset,\n",
        "    use_external_data_format=False,\n",
        "):\n",
        "    output_path.parent.mkdir(parents=True, exist_ok=True)\n",
        "    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,\n",
        "    # so we check the torch version for backwards compatibility\n",
        "    if is_torch_less_than_1_11:\n",
        "        export(\n",
        "            model,\n",
        "            model_args,\n",
        "            f=output_path.as_posix(),\n",
        "            input_names=ordered_input_names,\n",
        "            output_names=output_names,\n",
        "            dynamic_axes=dynamic_axes,\n",
        "            do_constant_folding=True,\n",
        "            use_external_data_format=use_external_data_format,\n",
        "            enable_onnx_checker=True,\n",
        "            opset_version=opset,\n",
        "        )\n",
        "    else:\n",
        "        export(\n",
        "            model,\n",
        "            model_args,\n",
        "            f=output_path.as_posix(),\n",
        "            input_names=ordered_input_names,\n",
        "            output_names=output_names,\n",
        "            dynamic_axes=dynamic_axes,\n",
        "            do_constant_folding=True,\n",
        "            opset_version=opset,\n",
        "        )\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def convert_models(model_path: str, output_path: str, opset: int):\n",
        "    pipeline = StableDiffusionPipeline.from_pretrained(model_path)\n",
        "    output_path = Path(output_path)\n",
        "\n",
        "    # TEXT ENCODER\n",
        "    text_input = pipeline.tokenizer(\n",
        "        \"A sample prompt\",\n",
        "        padding=\"max_length\",\n",
        "        max_length=pipeline.tokenizer.model_max_length,\n",
        "        truncation=True,\n",
        "        return_tensors=\"pt\",\n",
        "    )\n",
        "    onnx_export(\n",
        "        pipeline.text_encoder,\n",
        "        # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files\n",
        "        model_args=(text_input.input_ids.to(torch.int32)),\n",
        "        output_path=output_path / \"text_encoder\" / \"model.onnx\",\n",
        "        ordered_input_names=[\"input_ids\"],\n",
        "        output_names=[\"last_hidden_state\", \"pooler_output\"],\n",
        "        dynamic_axes={\n",
        "            \"input_ids\": {0: \"batch\", 1: \"sequence\"},\n",
        "        },\n",
        "        opset=opset,\n",
        "    )\n",
        "\n",
        "    # UNET\n",
        "    unet_path = output_path / \"unet\" / \"model.onnx\"\n",
        "    onnx_export(\n",
        "        pipeline.unet,\n",
        "        model_args=(torch.randn(2, 4, 64, 64), torch.LongTensor([0, 1]), torch.randn(2, 77, 768), False),\n",
        "        output_path=unet_path,\n",
        "        ordered_input_names=[\"sample\", \"timestep\", \"encoder_hidden_states\", \"return_dict\"],\n",
        "        output_names=[\"out_sample\"],  # has to be different from \"sample\" for correct tracing\n",
        "        dynamic_axes={\n",
        "            \"sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n",
        "            \"timestep\": {0: \"batch\"},\n",
        "            \"encoder_hidden_states\": {0: \"batch\", 1: \"sequence\"},\n",
        "        },\n",
        "        opset=opset,\n",
        "        use_external_data_format=True,  # UNet is > 2GB, so the weights need to be split\n",
        "    )\n",
        "    unet_model_path = str(unet_path.absolute().as_posix())\n",
        "    unet_dir = os.path.dirname(unet_model_path)\n",
        "    unet = onnx.load(unet_model_path)\n",
        "    # clean up existing tensor files\n",
        "    shutil.rmtree(unet_dir)\n",
        "    os.mkdir(unet_dir)\n",
        "    # collate external tensor files into one\n",
        "    onnx.save_model(\n",
        "        unet,\n",
        "        unet_model_path,\n",
        "        save_as_external_data=True,\n",
        "        all_tensors_to_one_file=True,\n",
        "        location=\"weights.pb\",\n",
        "        convert_attribute=False,\n",
        "    )\n",
        "\n",
        "    # VAE ENCODER\n",
        "    vae_encoder = pipeline.vae\n",
        "    # need to get the raw tensor output (sample) from the encoder\n",
        "    vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()\n",
        "    onnx_export(\n",
        "        vae_encoder,\n",
        "        model_args=(torch.randn(1, 3, 512, 512), False),\n",
        "        output_path=output_path / \"vae_encoder\" / \"model.onnx\",\n",
        "        ordered_input_names=[\"sample\", \"return_dict\"],\n",
        "        output_names=[\"latent_sample\"],\n",
        "        dynamic_axes={\n",
        "            \"sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n",
        "        },\n",
        "        opset=opset,\n",
        "    )\n",
        "\n",
        "    # VAE DECODER\n",
        "    vae_decoder = pipeline.vae\n",
        "    # forward only through the decoder part\n",
        "    vae_decoder.forward = vae_encoder.decode\n",
        "    onnx_export(\n",
        "        vae_decoder,\n",
        "        model_args=(torch.randn(1, 4, 64, 64), False),\n",
        "        output_path=output_path / \"vae_decoder\" / \"model.onnx\",\n",
        "        ordered_input_names=[\"latent_sample\", \"return_dict\"],\n",
        "        output_names=[\"sample\"],\n",
        "        dynamic_axes={\n",
        "            \"latent_sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n",
        "        },\n",
        "        opset=opset,\n",
        "    )\n",
        "\n",
        "    # SAFETY CHECKER\n",
        "    safety_checker = pipeline.safety_checker\n",
        "    safety_checker.forward = safety_checker.forward_onnx\n",
        "    onnx_export(\n",
        "        pipeline.safety_checker,\n",
        "        model_args=(torch.randn(1, 3, 224, 224), torch.randn(1, 512, 512, 3)),\n",
        "        output_path=output_path / \"safety_checker\" / \"model.onnx\",\n",
        "        ordered_input_names=[\"clip_input\", \"images\"],\n",
        "        output_names=[\"out_images\", \"has_nsfw_concepts\"],\n",
        "        dynamic_axes={\n",
        "            \"clip_input\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n",
        "            \"images\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n",
        "        },\n",
        "        opset=opset,\n",
        "    )\n",
        "\n",
        "    onnx_pipeline = StableDiffusionOnnxPipeline(\n",
        "        vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / \"vae_decoder\"),\n",
        "        text_encoder=OnnxRuntimeModel.from_pretrained(output_path / \"text_encoder\"),\n",
        "        tokenizer=pipeline.tokenizer,\n",
        "        unet=OnnxRuntimeModel.from_pretrained(output_path / \"unet\"),\n",
        "        scheduler=pipeline.scheduler,\n",
        "        safety_checker=OnnxRuntimeModel.from_pretrained(output_path / \"safety_checker\"),\n",
        "        feature_extractor=pipeline.feature_extractor,\n",
        "    )\n",
        "\n",
        "    onnx_pipeline.save_pretrained(output_path)\n",
        "    print(\"ONNX pipeline saved to\", output_path)\n",
        "\n",
        "    _ = StableDiffusionOnnxPipeline.from_pretrained(output_path, provider=\"CPUExecutionProvider\")\n",
        "    print(\"ONNX pipeline is loadable\")\n",
        "\n",
        "\n",
        "\n",
        "    \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "convert_models(\"CompVis/stable-diffusion-v1-4\", \"drive/MyDrive/exported_models/stable_diffusion_onnx\", 14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "a51d588a-56d7-4720-8b10-783a0cdc9215"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stderr",
          "text": [
            "ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/clip/modeling_clip.py:682: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
            "  mask.fill_(torch.tensor(torch.finfo(dtype).min))\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/clip/modeling_clip.py:222: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/clip/modeling_clip.py:230: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/clip/modeling_clip.py:262: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):\n",
            "/usr/local/lib/python3.7/dist-packages/torch/onnx/symbolic_opset9.py:4194: UserWarning: Exporting aten::index operator of advanced indexing in opset 14 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.\n",
            "  + \"If indices include negative values, the exported graph will produce incorrect results.\"\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/unet_2d_condition.py:262: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:2515: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  _verify_batch_size([input.size(0) * input.size(1) // num_groups, num_groups] + list(input.size()[2:]))\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py:253: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py:254: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py:260: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py:261: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/resnet.py:107: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  assert hidden_states.shape[1] == self.channels\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/resnet.py:112: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  assert hidden_states.shape[1] == self.channels\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/resnet.py:39: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  assert hidden_states.shape[1] == self.channels\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/unet_2d_condition.py:338: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if not return_dict:\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/vae.py:570: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if not return_dict:\n",
            "/usr/local/lib/python3.7/dist-packages/diffusers/models/vae.py:579: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if not return_dict:\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-75819ee14a25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconvert_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CompVis/stable-diffusion-v1-4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"drive/MyDrive/exported_models/stable_diffusion_onnx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-24c82ed83612>\u001b[0m in \u001b[0;36mconvert_models\u001b[0;34m(model_path, output_path, opset)\u001b[0m\n\u001b[1;32m    156\u001b[0m             \u001b[0;34m\"latent_sample\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"batch\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"channels\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"height\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"width\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m         },\n\u001b[0;32m--> 158\u001b[0;31m         \u001b[0mopset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    159\u001b[0m     )\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-24c82ed83612>\u001b[0m in \u001b[0;36monnx_export\u001b[0;34m(model, model_args, output_path, ordered_input_names, output_names, dynamic_axes, opset, use_external_data_format)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mdynamic_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdynamic_axes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mdo_constant_folding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mopset_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         )\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/onnx/__init__.py\u001b[0m in \u001b[0;36mexport\u001b[0;34m(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0mkeep_initializers_as_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0mcustom_opsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m         \u001b[0mexport_modules_as_functions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    366\u001b[0m     )\n\u001b[1;32m    367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py\u001b[0m in \u001b[0;36mexport\u001b[0;34m(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mkeep_initializers_as_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_initializers_as_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mcustom_opsets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_opsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mexport_modules_as_functions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexport_modules_as_functions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m     )\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py\u001b[0m in \u001b[0;36m_export\u001b[0;34m(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions)\u001b[0m\n\u001b[1;32m   1082\u001b[0m                 \u001b[0mfixed_batch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfixed_batch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m                 \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m                 \u001b[0mdynamic_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdynamic_axes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1085\u001b[0m             )\n\u001b[1;32m   1086\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py\u001b[0m in \u001b[0;36m_model_to_graph\u001b[0;34m(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)\u001b[0m\n\u001b[1;32m    737\u001b[0m             \u001b[0mdynamic_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdynamic_axes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m             \u001b[0minput_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 739\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    740\u001b[0m         )\n\u001b[1;32m    741\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py\u001b[0m in \u001b[0;36m_optimize_graph\u001b[0;34m(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_pass_onnx_lint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m     \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_pass_onnx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator_export_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m     \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_pass_onnx_lint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m     \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_pass_lint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Unable to cast from non-held to held instance (T& to Holder<T>) (compile in debug mode for type information)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install onnxruntime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XSuVT40mmIl",
        "outputId": "2d1d85a7-096a-4937-f528-44f65e253298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting onnxruntime\n",
            "  Downloading onnxruntime-1.12.1-cp37-cp37m-manylinux_2_27_x86_64.whl (4.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.21.6)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.7.1)\n",
            "Collecting coloredlogs\n",
            "  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
            "\u001b[K     |████████████████████████████████| 46 kB 3.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.17.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (21.3)\n",
            "Collecting humanfriendly>=9.1\n",
            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
            "\u001b[K     |████████████████████████████████| 86 kB 4.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxruntime) (3.0.9)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (1.15.0)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime) (1.2.1)\n",
            "Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from onnx import numpy_helper\n",
        "import onnxruntime as rt\n",
        "import numpy as np\n",
        "import shutil\n",
        "import onnx\n",
        "import time\n",
        "import glob\n",
        "import sys\n",
        "import os\n",
        "\n",
        "def _try_load_and_check_model(path_to_onnx):\n",
        "    \"\"\"\n",
        "    Loads and checks the ONNX model, or returns an error code.\n",
        "    \"\"\"\n",
        "    try:\n",
        "        onnx_model = onnx.load(path_to_onnx)\n",
        "        onnx.checker.check_model(onnx_model)\n",
        "    except FileNotFoundError as e:\n",
        "        print(e)\n",
        "        return False\n",
        "    except onnx.checker.ValidationError as e:\n",
        "        print(e)\n",
        "        return False\n",
        "    else:\n",
        "        return True\n",
        "\n",
        "def _try_load_and_test_model(path_to_onnx):\n",
        "    \"\"\"\n",
        "    Loads and tests the ONNX model, returning list of test results.\n",
        "    \"\"\"\n",
        "    # Load session\n",
        "    sess = rt.InferenceSession(path_to_onnx)\n",
        "    input_name = sess.get_inputs()[0].name\n",
        "    output_name = sess.get_outputs()[0].name\n",
        "    # Test folders should lie in same directory\n",
        "    top_level_dir = os.path.dirname(path_to_onnx)\n",
        "    test_dir_pattern = os.path.join(top_level_dir, \"test_data_set_*\")\n",
        "    results = []\n",
        "    for test_dir in glob.glob(test_dir_pattern):\n",
        "        input_pattern = os.path.join(test_dir, \"input_*.pb\")\n",
        "        output_pattern = os.path.join(test_dir, \"output_*.pb\")\n",
        "        # glob ordering is arbitrary, sorting them will ensure correct\n",
        "        # pairings of inputs and outputs\n",
        "        inputs = sorted(glob.glob(input_pattern))\n",
        "        outputs = sorted(glob.glob(output_pattern))\n",
        "        for inp, expected_out in zip(inputs, outputs):\n",
        "            # Initialize TensorProto\n",
        "            inp_tensor = onnx.TensorProto()\n",
        "            expected_out_tensor = onnx.TensorProto()\n",
        "            # Parse protobuf\n",
        "            with open(inp, 'rb') as inp_f, open(expected_out, 'rb') as exp_o_f:\n",
        "                inp_tensor.ParseFromString(inp_f.read())\n",
        "                expected_out_tensor.ParseFromString(exp_o_f.read())\n",
        "            # Retrieve actual value\n",
        "            actual_out_tensor = sess.run([output_name], {\n",
        "                input_name: numpy_helper.to_array(inp_tensor)\n",
        "            })[0]\n",
        "            # Compare the results and output result\n",
        "            try:\n",
        "                np.testing.assert_allclose(\n",
        "                    actual_out_tensor,\n",
        "                    numpy_helper.to_array(expected_out_tensor),\n",
        "                    rtol=5e-4,\n",
        "                    atol=1e-3\n",
        "                )\n",
        "                sys.stdout.write('.')\n",
        "                # Mutability :(\n",
        "                results.append(1)\n",
        "            except AssertionError as e:\n",
        "                print(f'Input: {numpy_helper.to_array(inp_tensor)}')\n",
        "                print(e)\n",
        "                results.append(0)\n",
        "    # If we've made it this far then everything went well\n",
        "    return results\n",
        "\n",
        "\n",
        "path_to_onnx = 'drive/MyDrive/exported_models/stable_diffusion_onnx/text_encoder/model.onnx'\n",
        "term_size = shutil.get_terminal_size((80, 20)).columns\n",
        "title_str = f' {path_to_onnx} '\n",
        "title_str = title_str.center(term_size, '=')\n",
        "print(title_str)\n",
        "start = time.time()\n",
        "# This must pass in order for the next suite to make\n",
        "# sense, so assert here and fail early\n",
        "assert _try_load_and_check_model(path_to_onnx) == True\n",
        "# Now run the actual suite of tests\n",
        "results = _try_load_and_test_model(path_to_onnx)\n",
        "end = time.time()\n",
        "total = end - start\n",
        "# Print a summary\n",
        "print('\\n\\nFinished in {:.3f} seconds'.format(total))\n",
        "print(f'{len(results)} tests, {len(results) - sum(results)} failures')\n",
        "print('='*term_size)\n",
        "# Exit code is number of failures\n",
        "exit(len(results) - sum(results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNTcKjN4mQ3L",
        "outputId": "b62fc103-8376-4ac4-93f2-43d750a4c512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "= drive/MyDrive/exported_models/stable_diffusion_onnx/text_encoder/model.onnx ==\n",
            "\n",
            "\n",
            "Finished in 10.779 seconds\n",
            "0 tests, 0 failures\n",
            "================================================================================\n"
          ]
        }
      ]
    }
  ]
}