Faster_TrOCR_with_ONNX+AutoRegressive_Hack.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/IlyasMoutawwakil/ONNX-TrOCR/blob/main/Faster_TrOCR_with_ONNX%2BAutoRegressive_Hack.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So you want a faster TrOCR ? You're in the right place."
      ],
      "metadata": {
        "id": "TcU71HDSa8DC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "BsfvSXH8RYEf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title This should be taken into account because we are going to experiment with different configurations\n",
        "#@markdown Lucky shot !! We got the A100-40GB xD\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "MGGSEXXGTAyU",
        "outputId": "a3e11ec5-84d5-4c4d-a019-cc908c745f92"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Oct 22 13:33:46 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    61W / 400W |      0MiB / 40536MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "qbQUiDaeFvWR",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Run this to pip install everything needed and wget an image with some text in it\n",
        "%%capture\n",
        "\n",
        "!pip install craft-text-detector\n",
        "!pip install transformers\n",
        "!pip install sentencepiece\n",
        "!pip install onnxruntime-gpu\n",
        "!pip install protobuf\n",
        "!pip install torch\n",
        "!pip install onnx\n",
        "\n",
        "!wget https://www.t4help.uhi.ac.uk/t4-media/one-web/t4-help/landing-pages/content-type-text.JPG"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Detect some textlines to work our experiments on\n",
        "\n",
        "%%capture\n",
        "\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from craft_text_detector import Craft\n",
        "\n",
        "# set image path and export folder directory\n",
        "filename = 'content-type-text.JPG'\n",
        "image = cv2.imread(filename)\n",
        "\n",
        "# create a craft instance\n",
        "craft = Craft(crop_type=\"poly\", cuda=True)\n",
        "\n",
        "# apply craft text detection and export detected regions to output directory\n",
        "prediction_result = craft.detect_text(image)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "nB52e34-I3nT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Print some examples\n",
        "#@markdown Notice that the length is usually not the same when working textlines coming from real scripts.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "textlines = []\n",
        "\n",
        "for i,j in enumerate(prediction_result['boxes']): \n",
        "    roi = image[int(prediction_result['boxes'][i][0][1]): int(prediction_result['boxes'][i][2][1]), \n",
        "              int(prediction_result['boxes'][i][0][0]): int(prediction_result['boxes'][i][2][0])]\n",
        "    textlines.append(Image.fromarray(roi).convert(\"RGB\"))\n",
        "\n",
        "    if i<5:\n",
        "      plt.figure(figsize=(15, 60))\n",
        "      plt.imshow(textlines[-1])\n",
        "      plt.show()\n",
        "\n",
        "print('Number of textlines in total :', len(textlines))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "cellView": "form",
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        "outputId": "9c547255-2210-4788-a674-8c2e8b9526f7"
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      "execution_count": 3,
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1080x4320 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x4320 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x4320 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x4320 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x4320 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of textlines in total : 17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HF model"
      ],
      "metadata": {
        "id": "ZsAvzylPGRry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Create HF TrOCR models for both CPU and GPU\n",
        "\n",
        "from transformers import VisionEncoderDecoderModel, TrOCRProcessor\n",
        "import torch\n",
        "\n",
        "preprocessor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-stage1')\n",
        "\n",
        "cuda_hf_model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-stage1').to('cuda')\n",
        "cpu_hf_model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-stage1').to('cpu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "d6e05892102740c8ac3978d7a577f605",
            "c58cdab9ba14482682048fd2d82dd6c9",
            "af41e9c7235942db97e045d88cf7acc8",
            "c3b3fb4c62134f3394756c6674010f93",
            "375fecf9eaaf4a49bbeb5bf8e672c456",
            "3fe6eb78ed9e4e7d92f97a151aa45572",
            "460407e67d3e42a38f37e7dafabf6c72",
            "d2735ca816494cafbc133e2fe9865f35",
            "0812a1fc4f7b429b9a0e588160330bc4",
            "0b116d21a8fe4502a16dba372665592a",
            "be98bdd7806349f2a392835fbe054806"
          ]
        },
        "id": "4fN0-oHlGN1d",
        "outputId": "b3a345db-7ca5-481d-a3c8-4b1f66b28b12"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6e05892102740c8ac3978d7a577f605"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Generation configuration\n",
        "\n",
        "def configure_generation(model, beams=1):\n",
        "    # model.config.decoder_start_token_id = preprocessor.tokenizer.cls_token_id # only if you're gonna train the model\n",
        "    model.config.pad_token_id = model.config.decoder.pad_token_id = preprocessor.tokenizer.pad_token_id\n",
        "    model.config.eos_token_id = model.config.decoder.eos_token_id = preprocessor.tokenizer.sep_token_id\n",
        "    # make sure vocab size is set correctly\n",
        "    model.config.vocab_size = model.config.decoder.vocab_size\n",
        "    # set beam search parameters\n",
        "    model.config.decoder.early_stopping = True\n",
        "    model.config.decoder.no_repeat_ngram_size = 3\n",
        "    model.config.decoder.length_penalty = 2.0\n",
        "    model.config.decoder.num_beams = beams\n",
        "\n",
        "configure_generation(cuda_hf_model, 1)\n",
        "configure_generation(cpu_hf_model, 1)"
      ],
      "metadata": {
        "id": "zYMHaLuTGfhJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_values = preprocessor.feature_extractor(\n",
        "  textlines,\n",
        "  return_tensors=\"pt\"\n",
        ").pixel_values"
      ],
      "metadata": {
        "id": "HbVlRcNqWssw"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HF Inference"
      ],
      "metadata": {
        "id": "G7TjcxcB78KK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit preprocessor.tokenizer.batch_decode( \\\n",
        "                                            cuda_hf_model.generate( \\\n",
        "                                                                   pixel_values.to('cuda'), \\\n",
        "                                                                   max_length=96 \\\n",
        "                                                                   ), \\\n",
        "                                            skip_special_tokens=True \\\n",
        "                                            )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPRNvi0dJ3HI",
        "outputId": "7b87cb34-1a08-4df9-fd8b-8c7b23ce6e46"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.68 s ± 2.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time preprocessor.tokenizer.batch_decode( \\\n",
        "                                          cpu_hf_model.generate( \\\n",
        "                                                                pixel_values.to('cpu'), \\\n",
        "                                                                max_length=96 \\\n",
        "                                                                ), \\\n",
        "                                          skip_special_tokens=True \\\n",
        "                                          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UO0aAKEJkStJ",
        "outputId": "37caa9c6-62da-4b1c-f19d-29f8495eeced"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4min 7s, sys: 17.5 s, total: 4min 24s\n",
            "Wall time: 44.8 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This text is generated by Landing Page Text positioned at Top. In the Banner Ribbon and Action Button item above, Display',\n",
              " 'Search Box replaces the Ribbon text, and there is no Action button. In this 2-1-1 layout, there is a Content Butes item in the',\n",
              " 'double-width left column. The other two columns each contain Landing Page Text items, positioned at Left and Right. Return to',\n",
              " 'the Landing Page overview.',\n",
              " 'This is Landing Page Text which is',\n",
              " 'positioned at Right. It is automatically',\n",
              " 'right-aligned.',\n",
              " 'University of the Highlands and Islands',\n",
              " 'Executive Office',\n",
              " '12b Ness Walk',\n",
              " 'Inverness',\n",
              " 'Scotland',\n",
              " 'IV3 5SO',\n",
              " 'Linking to Media Library documents, or',\n",
              " 'to Sections is possible. The usual text',\n",
              " 'formatting options are available in the',\n",
              " 'toolbar.']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Container models"
      ],
      "metadata": {
        "id": "BfWdJcHjqqLC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class TrOCREncoder(torch.nn.Module):\n",
        "    \"\"\" Creation of a class to output only the last hidden state from the encoder \"\"\"\n",
        "\n",
        "    def __init__(self, encoder, config):\n",
        "        super().__init__()\n",
        "        self.model = encoder\n",
        "        self.config = config\n",
        "        \n",
        "    def forward(self, *inputs, **kwargs):\n",
        "        return self.model(*inputs, **kwargs)\n",
        "\n",
        "class TrOCRDecoder(torch.nn.Module):\n",
        "    \"\"\" Creation of a class to combine the decoder and the lm head \"\"\"\n",
        "\n",
        "    def __init__(self, decoder, config):\n",
        "        super().__init__()\n",
        "        self.model = decoder\n",
        "        self.config = config\n",
        "\n",
        "    def forward(self, *inputs, **kwargs):\n",
        "        return self.model(*inputs, **kwargs) # I tried unrolling the decoder but it didn't work"
      ],
      "metadata": {
        "id": "eFpJyL9jXb1X"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "simplified_encoder = TrOCREncoder(cuda_hf_model.encoder, cuda_hf_model.encoder.config)\n",
        "simplified_decoder = TrOCRDecoder(cuda_hf_model.decoder, cuda_hf_model.decoder.config)"
      ],
      "metadata": {
        "id": "k0lEEDhqXfqg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ONNXization"
      ],
      "metadata": {
        "id": "PN5hPbQOXjXP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "with torch.no_grad():\n",
        "    torch.onnx.export(\n",
        "        simplified_encoder,\n",
        "        args=torch.ones([64, 3, 384, 384]).to('cuda'),\n",
        "        f=Path('/content/trocr_encoder.onnx'),\n",
        "        opset_version=15,\n",
        "        input_names=[\"pixel_values\"],\n",
        "        output_names=[\"last_hidden_state\"],\n",
        "        dynamic_axes={\n",
        "            \"pixel_values\": {0:'batch'},\n",
        "            \"last_hidden_state\": {0: 'batch'},\n",
        "        },\n",
        "        do_constant_folding=True,\n",
        "    )"
      ],
      "metadata": {
        "id": "F3-woCZUX-AK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aba6d5fe-ca94-4b87-a2d6-1d14137688c1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/vit/modeling_vit.py:172: 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 num_channels != self.num_channels:\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/vit/modeling_vit.py:177: 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 height != self.image_size[0] or width != self.image_size[1]:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids = torch.ones(64, 96, dtype=torch.int64).to('cuda')\n",
        "attention_mask = torch.ones(64, 96, dtype=torch.int64).to('cuda')\n",
        "encoder_hidden_states  = torch.ones([64, 577, 768]).to('cuda')\n",
        "\n",
        "with torch.no_grad():\n",
        "    torch.onnx.export(\n",
        "        simplified_decoder,\n",
        "        args=(input_ids, attention_mask, encoder_hidden_states),\n",
        "        f=Path('/content/trocr_decoder.onnx'),\n",
        "        opset_version=15,\n",
        "        input_names=[\n",
        "            \"input_ids\",\n",
        "            \"attention_mask\",\n",
        "            \"encoder_hidden_states\",\n",
        "        ],\n",
        "        output_names=[\"logits\"],\n",
        "        dynamic_axes={\n",
        "            \"input_ids\": {0:'batch', 1:'sequence_length'},\n",
        "            \"attention_mask\": {0:'batch', 1:'sequence_length'},\n",
        "            \"encoder_hidden_states\": {0:'batch'},\n",
        "            \"logits\": {0:'batch'},\n",
        "        },\n",
        "        do_constant_folding=True,\n",
        "    )"
      ],
      "metadata": {
        "id": "ZSgNlATgaosW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9670bea0-be41-44c8-a24c-96589ed6c1f8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/trocr/modeling_trocr.py:141: 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 self.weights is None or max_pos > self.weights.size(0):\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/trocr/modeling_trocr.py:526: 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 input_shape[-1] > 1:\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/trocr/modeling_trocr.py:53: 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 = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/trocr/modeling_trocr.py:258: 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/trocr/modeling_trocr.py:265: 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 attention_mask.size() != (bsz, 1, tgt_len, src_len):\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/trocr/modeling_trocr.py:297: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "onnx_models = (Path('/content/trocr_encoder.onnx'),\n",
        "               Path('/content/trocr_decoder.onnx'))"
      ],
      "metadata": {
        "id": "4JVDqn2aa-s1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Quantization \n",
        "I guess some operations used in TrOCR are not implemented for quantization so let's just skip it."
      ],
      "metadata": {
        "id": "GwzjJaOseAEH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from onnxruntime.quantization import quantize_dynamic, QuantType\n",
        "# from pathlib import Path\n",
        "\n",
        "# def quantize(models_paths):\n",
        "#     \"\"\"\n",
        "#     Quantize the weights of the model from float32 to in8 to allow very efficient inference on modern CPU\n",
        "#     Uses unsigned ints for activation values, signed ints for weights, per\n",
        "#     https://onnxruntime.ai/docs/performance/quantization.html#data-type-selection\n",
        "#     it is faster on most CPU architectures\n",
        "#     Args:\n",
        "#         onnx_model_path: Path to location the exported ONNX model is stored\n",
        "#     Returns: The Path generated for the quantized\n",
        "#     \"\"\"\n",
        "\n",
        "#     quant_models_paths = []\n",
        "#     for model_path in models_paths:\n",
        "      \n",
        "#         model_path = model_path.as_posix()\n",
        "#         output_model_path = f\"{model_path[:-5]}_quantized.onnx\"\n",
        "        \n",
        "#         quantize_dynamic(\n",
        "#             model_input=model_path,\n",
        "#             model_output=output_model_path,\n",
        "#             per_channel=True,\n",
        "#             reduce_range=True, # should be the same as per_channel\n",
        "#             weight_type=QuantType.QUInt8, # per docs, signed is faster on most CPUs\n",
        "#             optimize_model=True,\n",
        "#         )\n",
        "#         quant_models_paths.append(output_model_path)\n",
        "\n",
        "#     return tuple(quant_models_paths)\n",
        "\n",
        "# quantized_models_paths = quantize(models_paths=onnx_models)\n",
        "# quantized_models_paths"
      ],
      "metadata": {
        "id": "CJsV5pU4d-ZY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ONNX Runtime Sessions"
      ],
      "metadata": {
        "id": "7ln-CHwDraNb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from onnxruntime import (\n",
        "    GraphOptimizationLevel,\n",
        "    InferenceSession,\n",
        "    SessionOptions,\n",
        "    ExecutionMode,\n",
        ")\n",
        "\n",
        "def get_onnx_runtime_sessions(\n",
        "    model_paths,\n",
        "    default: bool = True,\n",
        "    opt_level: int = 99,\n",
        "    parallel_exe_mode: bool = True,\n",
        "    provider=[\"TensorrtExecutionProvider\", \"CPUExecutionProvider\", \"CUDAExecutionProvider\"]\n",
        "):\n",
        "    path_to_encoder, path_to_decoder = model_paths\n",
        "    \n",
        "    if default:\n",
        "        encoder_sess = InferenceSession(str(path_to_encoder), providers=provider)\n",
        "        decoder_sess = InferenceSession(str(path_to_decoder), providers=provider)\n",
        "    else:\n",
        "        # Few properties that might have an impact on performances\n",
        "        options = SessionOptions()\n",
        "        if opt_level == 1:\n",
        "            options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC\n",
        "        elif opt_level == 2:\n",
        "            options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_EXTENDED\n",
        "        else:\n",
        "            assert opt_level == 99\n",
        "            options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL\n",
        "\n",
        "        # set this true for better performance\n",
        "        if parallel_exe_mode == True:\n",
        "            options.execution_mode = ExecutionMode.ORT_PARALLEL\n",
        "        else:\n",
        "            options.execution_mode = ExecutionMode.ORT_SEQUENTIAL\n",
        "\n",
        "        encoder_sess = InferenceSession(str(path_to_encoder), options, providers=provider)\n",
        "        decoder_sess = InferenceSession(str(path_to_decoder), options, providers=provider)\n",
        "\n",
        "    return encoder_sess, decoder_sess\n",
        "  \n",
        "cuda_encoder_sess, cuda_decoder_sess = get_onnx_runtime_sessions(\n",
        "  model_paths=onnx_models,\n",
        "  default=True,\n",
        "  provider=['CUDAExecutionProvider']\n",
        ")\n",
        "\n",
        "cpu_encoder_sess, cpu_decoder_sess = get_onnx_runtime_sessions(\n",
        "  model_paths=onnx_models,\n",
        "  default=True,\n",
        "  provider=['CPUExecutionProvider']\n",
        ")"
      ],
      "metadata": {
        "id": "QDf7y8rsfk-O"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference Models Testing"
      ],
      "metadata": {
        "id": "9y62DFchhFUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from transformers.modeling_outputs import BaseModelOutput\n",
        "\n",
        "class ONNXTrOCREncoder(torch.nn.Module):\n",
        "    def __init__(self, encoder_sess, encoder_config={}):\n",
        "        super().__init__()\n",
        "        self.encoder = encoder_sess\n",
        "        self.config = encoder_config\n",
        "        self.main_input_name = 'pixel_values'\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        pixel_values,\n",
        "        *args, **kwargs\n",
        "    ):\n",
        "        last_hidden_state = torch.from_numpy(\n",
        "            self.encoder.run(\n",
        "                [\"last_hidden_state\"],\n",
        "                {\n",
        "                    \"pixel_values\": pixel_values.cpu().numpy(),\n",
        "                },\n",
        "            )[0])\n",
        "        \n",
        "        return BaseModelOutput(last_hidden_state=last_hidden_state)\n",
        "\n",
        "cuda_onnx_encoder = ONNXTrOCREncoder(cuda_encoder_sess, cuda_hf_model.encoder.config)\n",
        "cpu_onnx_encoder = ONNXTrOCREncoder(cpu_encoder_sess, cpu_hf_model.encoder.config)\n",
        "\n",
        "hidden_state = cuda_onnx_encoder(torch.ones([2, 3, 384, 384]).to('cuda'))\n",
        "hidden_state = cpu_onnx_encoder(torch.ones([2, 3, 384, 384]).to('cpu'))\n",
        "\n",
        "hidden_state[0].shape"
      ],
      "metadata": {
        "id": "YQowydG-hBvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05166518-9d3c-469a-a519-fb0c371ee0ee"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 577, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.modeling_outputs import Seq2SeqLMOutput\n",
        "\n",
        "class ONNXTrOCRDecoder(torch.nn.Module):\n",
        "    def __init__(self, decoder_sess, decoder_config={}):\n",
        "        super().__init__()\n",
        "        self.decoder = decoder_sess\n",
        "        self.config = decoder_config\n",
        "    \n",
        "    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):\n",
        "        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\n",
        "        if attention_mask is None:\n",
        "            attention_mask = input_ids.new_ones(input_ids.shape)\n",
        "\n",
        "        return {\n",
        "            \"input_ids\": input_ids,  # encoder_outputs is defined. input_ids not needed\n",
        "            \"attention_mask\": attention_mask,\n",
        "            \"past_key_values\": past,\n",
        "            \"use_cache\": use_cache,\n",
        "        }\n",
        "    \n",
        "    def forward(self, input_ids, attention_mask, encoder_hidden_states, *args, **kwargs):\n",
        "        decoder_outputs = torch.from_numpy(self.decoder.run(\n",
        "            [\"logits\"],\n",
        "            {\n",
        "                \"input_ids\": input_ids.cpu().numpy(),\n",
        "                \"attention_mask\": attention_mask.cpu().numpy(),\n",
        "                \"encoder_hidden_states\": encoder_hidden_states.cpu().numpy(),\n",
        "            },\n",
        "        )[0])\n",
        "        \n",
        "        return Seq2SeqLMOutput(logits=decoder_outputs)\n",
        "\n",
        "input_ids = torch.ones(2, 96, dtype=torch.int64)\n",
        "attention_mask = torch.ones(2, 96, dtype=torch.int64)\n",
        "encoder_hidden_states  = torch.ones([2, 577, 768]).to('cuda')\n",
        "\n",
        "cuda_onnx_decoder = ONNXTrOCRDecoder(cuda_decoder_sess, cuda_hf_model.decoder.config)\n",
        "cpu_onnx_decoder = ONNXTrOCRDecoder(cpu_decoder_sess, cpu_hf_model.decoder.config)\n",
        "\n",
        "decoder_logits = cuda_onnx_decoder(input_ids.to('cuda'), \n",
        "                                   attention_mask.to('cuda'),\n",
        "                                   encoder_hidden_states.to('cuda'))\n",
        "\n",
        "decoder_logits = cpu_onnx_decoder(input_ids.to('cpu'), \n",
        "                                  attention_mask.to('cpu'),\n",
        "                                  encoder_hidden_states.to('cpu'))\n",
        "\n",
        "decoder_logits[0].shape"
      ],
      "metadata": {
        "id": "FEK4l4-37rt3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "347ad20b-bb27-419c-bbb9-773c9c134cea"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 96, 50265])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic ONNX Inference"
      ],
      "metadata": {
        "id": "R2BMaLncs3V0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoConfig\n",
        "\n",
        "class ONNXTrOCR(VisionEncoderDecoderModel):\n",
        "    \"\"\"creates a T5 model using onnx sessions (encode, decoder & init_decoder)\"\"\"\n",
        "\n",
        "    def __init__(self, model_path, onnx_model_sessions):\n",
        "        config = AutoConfig.from_pretrained(model_path)\n",
        "        super().__init__(config)\n",
        "        \n",
        "        encoder_sess, decoder_sess = onnx_model_sessions\n",
        "\n",
        "        self.encoder = ONNXTrOCREncoder(encoder_sess)\n",
        "        self.decoder = ONNXTrOCRDecoder(decoder_sess)\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        pixel_values=None,\n",
        "        decoder_input_ids=None,\n",
        "        decoder_attention_mask=None,\n",
        "        encoder_outputs=None,\n",
        "        past_key_values=None,\n",
        "        decoder_inputs_embeds=None,\n",
        "        labels=None,\n",
        "        use_cache=None,\n",
        "        output_attentions=None,\n",
        "        output_hidden_states=None,\n",
        "        return_dict=None,\n",
        "        **kwargs,\n",
        "    ):\n",
        "        encoder_hidden_states = encoder_outputs['last_hidden_state'] \\\n",
        "                                 if type(encoder_outputs)==dict \\\n",
        "                                 else encoder_outputs[0]\n",
        "\n",
        "        # else:\n",
        "        encoder_attention_mask = None\n",
        "        \n",
        "        # Decode\n",
        "        decoder_outputs = self.decoder(\n",
        "            input_ids=decoder_input_ids,\n",
        "            attention_mask=decoder_attention_mask,\n",
        "            encoder_hidden_states=encoder_hidden_states,\n",
        "            encoder_attention_mask=encoder_attention_mask,\n",
        "            inputs_embeds=decoder_inputs_embeds,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            use_cache=use_cache,\n",
        "            past_key_values=past_key_values,\n",
        "        )\n",
        "\n",
        "        return Seq2SeqLMOutput(\n",
        "            logits=decoder_outputs.logits,\n",
        "        )\n",
        "\n",
        "cuda_onnx_trocr = ONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(cuda_encoder_sess, cuda_decoder_sess)\n",
        "    ).to('cuda')\n",
        "\n",
        "cpu_onnx_trocr = ONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(cpu_encoder_sess, cpu_decoder_sess)\n",
        "    ).to('cpu')\n",
        "\n",
        "configure_generation(cuda_onnx_trocr)\n",
        "configure_generation(cpu_onnx_trocr)"
      ],
      "metadata": {
        "id": "kz9BHX8H7vN-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit preprocessor.tokenizer.batch_decode( \\\n",
        "                                            cuda_onnx_trocr.generate( \\\n",
        "                                                                     pixel_values.to('cpu'), \\\n",
        "                                                                     max_length=96 \\\n",
        "                                                                     ), \\\n",
        "                                            skip_special_tokens=True, \\\n",
        "                                            )"
      ],
      "metadata": {
        "id": "FBDiP_NE70h-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8671934d-5661-4537-8447-fb0ef73bb487"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.95 s ± 6.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time preprocessor.tokenizer.batch_decode( \\\n",
        "                                          cpu_onnx_trocr.generate( \\\n",
        "                                                                  pixel_values.to('cpu'), \\\n",
        "                                                                  max_length=96 \\\n",
        "                                                                  ), \\\n",
        "                                          skip_special_tokens=True, \\\n",
        "                                          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvbGW4xvqYVk",
        "outputId": "c9f2f82d-5ac4-412b-bf5f-e986d9d80b21"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5min 3s, sys: 114 ms, total: 5min 4s\n",
            "Wall time: 51.1 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This text is generated by Landing Page Text positioned at Top. In the Banner Ribbon and Action Button item above, Display',\n",
              " 'Search Box replaces the Ribbon text, and there is no Action button. In this 2-1-1 layout, there is a Content Butes item in the',\n",
              " 'double-width left column. The other two columns each contain Landing Page Text items, positioned at Left and Right. Return to',\n",
              " 'the Landing Page overview.',\n",
              " 'This is Landing Page Text which is',\n",
              " 'positioned at Right. It is automatically',\n",
              " 'right-aligned.',\n",
              " 'University of the Highlands and Islands',\n",
              " 'Executive Office',\n",
              " '12b Ness Walk',\n",
              " 'Inverness',\n",
              " 'Scotland',\n",
              " 'IV3 5SO',\n",
              " 'Linking to Media Library documents, or',\n",
              " 'to Sections is possible. The usual text',\n",
              " 'formatting options are available in the',\n",
              " 'toolbar.']"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yeeeep it's slower and that's because ONNX needs to send the tensors from its runtime to numpy and viceversa each time it passes them through the auto regressive decoder."
      ],
      "metadata": {
        "id": "gbPib4xfX5HX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optimized ONNX Infrence"
      ],
      "metadata": {
        "id": "mVOzT33D4Qjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "opt_cuda_encoder_sess, opt_cuda_decoder_sess = get_onnx_runtime_sessions(\n",
        "  model_paths=onnx_models,\n",
        "  default=False,\n",
        "  provider=['CUDAExecutionProvider']\n",
        ")\n",
        "\n",
        "opt_cpu_encoder_sess, opt_cpu_decoder_sess = get_onnx_runtime_sessions(\n",
        "  model_paths=onnx_models,\n",
        "  default=False,\n",
        "  provider=['CPUExecutionProvider']\n",
        ")\n",
        "\n",
        "opt_cuda_onnx_trocr = ONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(opt_cuda_encoder_sess, opt_cuda_decoder_sess)\n",
        "    ).to('cuda')\n",
        "\n",
        "opt_cpu_onnx_trocr = ONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(opt_cpu_encoder_sess, opt_cpu_decoder_sess)\n",
        "    ).to('cpu')\n",
        "\n",
        "configure_generation(opt_cuda_onnx_trocr)\n",
        "configure_generation(opt_cpu_onnx_trocr)"
      ],
      "metadata": {
        "id": "LtzUg-QJ4Vz0"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit preprocessor.tokenizer.batch_decode( \\\n",
        "                                            opt_cuda_onnx_trocr.generate( \\\n",
        "                                                                         pixel_values.to('cpu'), \\\n",
        "                                                                         max_length=96 \\\n",
        "                                                                         ), \\\n",
        "                                            skip_special_tokens=True, \\\n",
        "                                            )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zM1SZua04XM-",
        "outputId": "9d9727e1-f4ae-45b8-a9b7-62997a8b1eb4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.92 s ± 7.53 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time preprocessor.tokenizer.batch_decode( \\\n",
        "                                          opt_cpu_onnx_trocr.generate( \\\n",
        "                                                                      pixel_values.to('cpu'), \\\n",
        "                                                                      max_length=96 \\\n",
        "                                                                      ), \\\n",
        "                                          skip_special_tokens=True, \\\n",
        "                                          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n68SPoGd_nsI",
        "outputId": "c2164ab3-b299-480d-9882-ea86a5822bf4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7min 59s, sys: 14.1 s, total: 8min 13s\n",
            "Wall time: 56.3 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This text is generated by Landing Page Text positioned at Top. In the Banner Ribbon and Action Button item above, Display',\n",
              " 'Search Box replaces the Ribbon text, and there is no Action button. In this 2-1-1 layout, there is a Content Butes item in the',\n",
              " 'double-width left column. The other two columns each contain Landing Page Text items, positioned at Left and Right. Return to',\n",
              " 'the Landing Page overview.',\n",
              " 'This is Landing Page Text which is',\n",
              " 'positioned at Right. It is automatically',\n",
              " 'right-aligned.',\n",
              " 'University of the Highlands and Islands',\n",
              " 'Executive Office',\n",
              " '12b Ness Walk',\n",
              " 'Inverness',\n",
              " 'Scotland',\n",
              " 'IV3 5SO',\n",
              " 'Linking to Media Library documents, or',\n",
              " 'to Sections is possible. The usual text',\n",
              " 'formatting options are available in the',\n",
              " 'toolbar.']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No gain at all LMAO."
      ],
      "metadata": {
        "id": "ossV4EcPAKAj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ONNX Inference With an Auto-Regressive Hack"
      ],
      "metadata": {
        "id": "wYnVAkxvPQwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoConfig\n",
        "\n",
        "class BetterONNXTrOCR(VisionEncoderDecoderModel):\n",
        "    \"\"\"creates a T5 model using onnx sessions (encode, decoder & init_decoder)\"\"\"\n",
        "\n",
        "    def __init__(self, model_path, onnx_model_sessions):\n",
        "        config = AutoConfig.from_pretrained(model_path)\n",
        "        super().__init__(config)\n",
        "        \n",
        "        encoder_sess, decoder_sess = onnx_model_sessions\n",
        "\n",
        "        self.encoder = ONNXTrOCREncoder(encoder_sess)\n",
        "        self.decoder = ONNXTrOCRDecoder(decoder_sess)\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        pixel_values=None,\n",
        "        decoder_input_ids=None,\n",
        "        decoder_attention_mask=None,\n",
        "        encoder_outputs=None,\n",
        "        past_key_values=None,\n",
        "        decoder_inputs_embeds=None,\n",
        "        labels=None,\n",
        "        use_cache=None,\n",
        "        output_attentions=None,\n",
        "        output_hidden_states=None,\n",
        "        return_dict=None,\n",
        "        **kwargs,\n",
        "    ):\n",
        "        encoder_hidden_states = encoder_outputs['last_hidden_state'] \\\n",
        "                                 if type(encoder_outputs)==dict \\\n",
        "                                 else encoder_outputs[0]\n",
        "\n",
        "        encoder_attention_mask = None\n",
        "        \n",
        "        eos_mask = decoder_input_ids[:, -1] <= self.config.eos_token_id\n",
        "\n",
        "        # Decode        \n",
        "        if any(eos_mask) and (decoder_input_ids.shape[1]>1):\n",
        "            reduced_logits = self.decoder(\n",
        "                ### compute reduction ###\n",
        "                input_ids=decoder_input_ids[torch.logical_not(eos_mask), :],\n",
        "                attention_mask=decoder_attention_mask[torch.logical_not(eos_mask), :],\n",
        "                encoder_hidden_states=encoder_hidden_states[torch.logical_not(eos_mask), :, :],\n",
        "                #########################\n",
        "                encoder_attention_mask=encoder_attention_mask,\n",
        "                inputs_embeds=decoder_inputs_embeds,\n",
        "                output_attentions=output_attentions,\n",
        "                output_hidden_states=output_hidden_states,\n",
        "                use_cache=use_cache,\n",
        "                past_key_values=past_key_values,\n",
        "            ).logits\n",
        "\n",
        "            logits = torch.full((decoder_input_ids.shape[0], decoder_input_ids.shape[1], self.config.decoder.vocab_size), fill_value=self.config.pad_token_id, dtype=reduced_logits.dtype, device=reduced_logits.device)\n",
        "            logits[torch.logical_not(eos_mask), :, :] = reduced_logits\n",
        "            logits[eos_mask, :, :] = self.ids_to_logits(decoder_input_ids[eos_mask, 1:], reduced_logits)\n",
        "        else:\n",
        "            logits = self.decoder(\n",
        "                input_ids=decoder_input_ids,\n",
        "                attention_mask=decoder_attention_mask,\n",
        "                encoder_hidden_states=encoder_hidden_states,\n",
        "                encoder_attention_mask=encoder_attention_mask,\n",
        "                inputs_embeds=decoder_inputs_embeds,\n",
        "                output_attentions=output_attentions,\n",
        "                output_hidden_states=output_hidden_states,\n",
        "                use_cache=use_cache,\n",
        "                past_key_values=past_key_values,\n",
        "            ).logits\n",
        "        \n",
        "        return Seq2SeqLMOutput(\n",
        "          logits=logits,\n",
        "        )\n",
        "\n",
        "    def ids_to_logits(self, ids, reduced_logits):\n",
        "        logits = torch.zeros((ids.shape[0], ids.shape[1]+1, self.config.decoder.vocab_size), dtype=reduced_logits.dtype, device=reduced_logits.device)\n",
        "        logits[:, -1, 2] = 1 # max_pad_token\n",
        "        for i in range(ids.shape[1]):\n",
        "            logits[:, i, ids[:, i]] = 1\n",
        "        \n",
        "        return logits\n",
        "\n",
        "better_cuda_onnx_trocr = BetterONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(cuda_encoder_sess, cuda_decoder_sess)\n",
        "    ).to('cuda')\n",
        "\n",
        "better_cpu_onnx_trocr = BetterONNXTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    onnx_model_sessions=(cpu_encoder_sess, cpu_decoder_sess)\n",
        "    ).to('cpu')\n",
        "\n",
        "configure_generation(better_cuda_onnx_trocr)\n",
        "configure_generation(better_cpu_onnx_trocr)"
      ],
      "metadata": {
        "id": "QQjdFJ9O8xrP"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit preprocessor.tokenizer.batch_decode( \\\n",
        "                                            better_cuda_onnx_trocr.generate( \\\n",
        "                                                                            pixel_values.to('cpu'), \\\n",
        "                                                                            max_length=96, \\\n",
        "                                                                            ), \\\n",
        "                                            skip_special_tokens=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4UB2tOGzaPB",
        "outputId": "33f8c9ff-cb5c-48fd-f48d-7cd0823d4045"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "942 ms ± 6.85 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time preprocessor.tokenizer.batch_decode( \\\n",
        "                                          better_cpu_onnx_trocr.generate( \\\n",
        "                                                                         pixel_values.to('cpu'), \\\n",
        "                                                                         max_length=96, \\\n",
        "                                                                         ), \\\n",
        "                                          skip_special_tokens=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rllrn-87sTNo",
        "outputId": "146632de-f96c-49bb-e88a-ef376ef702ac"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2min 12s, sys: 67.8 ms, total: 2min 13s\n",
            "Wall time: 22.4 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This text is generated by Landing Page Text positioned at Top. In the Banner Ribbon and Action Button item above, Display',\n",
              " 'Search Box replaces the Ribbon text, and there is no Action button. In this 2-1-1 layout, there is a Content Butes item in the',\n",
              " 'double-width left column. The other two columns each contain Landing Page Text items, positioned at Left and Right. Return to',\n",
              " 'the Landing Page overview.',\n",
              " 'This is Landing Page Text which is',\n",
              " 'positioned at Right. It is automatically',\n",
              " 'right-aligned.',\n",
              " 'University of the Highlands and Islands',\n",
              " 'Executive Office',\n",
              " '12b Ness Walk',\n",
              " 'Inverness',\n",
              " 'Scotland',\n",
              " 'IV3 5SO',\n",
              " 'Linking to Media Library documents, or',\n",
              " 'to Sections is possible. The usual text',\n",
              " 'formatting options are available in the',\n",
              " 'toolbar.']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HF Inference With an Auto-Regressive Hack"
      ],
      "metadata": {
        "id": "sZurdsJEtB2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoConfig\n",
        "\n",
        "class BetterHFTrOCR(VisionEncoderDecoderModel):\n",
        "    \"\"\"creates a TrOCR model\"\"\"\n",
        "\n",
        "    def __init__(self, model_path):\n",
        "        model_ = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-stage1')\n",
        "\n",
        "        super().__init__(model_.config)\n",
        "\n",
        "        self.encoder = model_.encoder\n",
        "        self.decoder = model_.decoder\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        pixel_values=None,\n",
        "        decoder_input_ids=None,\n",
        "        decoder_attention_mask=None,\n",
        "        encoder_outputs=None,\n",
        "        past_key_values=None,\n",
        "        decoder_inputs_embeds=None,\n",
        "        labels=None,\n",
        "        use_cache=None,\n",
        "        output_attentions=None,\n",
        "        output_hidden_states=None,\n",
        "        return_dict=None,\n",
        "        **kwargs,\n",
        "    ):\n",
        "\n",
        "        encoder_hidden_states = encoder_outputs['last_hidden_state'] \\\n",
        "                                 if type(encoder_outputs)==dict \\\n",
        "                                 else encoder_outputs[0]\n",
        "\n",
        "        encoder_attention_mask = None\n",
        "        \n",
        "        eos_mask = decoder_input_ids[:, -1] <= self.config.eos_token_id\n",
        "\n",
        "        # Decode        \n",
        "        if any(eos_mask) and (decoder_input_ids.shape[1]>1):\n",
        "            reduced_logits = self.decoder(\n",
        "                ### compute reduction ###\n",
        "                input_ids=decoder_input_ids[torch.logical_not(eos_mask), :],\n",
        "                attention_mask=decoder_attention_mask[torch.logical_not(eos_mask), :],\n",
        "                encoder_hidden_states=encoder_hidden_states[torch.logical_not(eos_mask), :, :],\n",
        "                #########################\n",
        "                encoder_attention_mask=encoder_attention_mask,\n",
        "                inputs_embeds=decoder_inputs_embeds,\n",
        "                output_attentions=output_attentions,\n",
        "                output_hidden_states=output_hidden_states,\n",
        "                use_cache=use_cache,\n",
        "                past_key_values=past_key_values,\n",
        "            ).logits\n",
        "            \n",
        "            logits = torch.full((decoder_input_ids.shape[0], decoder_input_ids.shape[1], self.config.decoder.vocab_size), fill_value=self.config.pad_token_id, dtype=reduced_logits.dtype, device=reduced_logits.device)\n",
        "            logits[torch.logical_not(eos_mask), :, :] = reduced_logits\n",
        "            logits[eos_mask, :, :] = self.ids_to_logits(decoder_input_ids[eos_mask, 1:], reduced_logits)\n",
        "        else:\n",
        "            logits = self.decoder(\n",
        "                input_ids=decoder_input_ids,\n",
        "                attention_mask=decoder_attention_mask,\n",
        "                encoder_hidden_states=encoder_hidden_states,\n",
        "                encoder_attention_mask=encoder_attention_mask,\n",
        "                inputs_embeds=decoder_inputs_embeds,\n",
        "                output_attentions=output_attentions,\n",
        "                output_hidden_states=output_hidden_states,\n",
        "                use_cache=use_cache,\n",
        "                past_key_values=past_key_values,\n",
        "            ).logits\n",
        "\n",
        "        return Seq2SeqLMOutput(\n",
        "          logits=logits,\n",
        "        )\n",
        "\n",
        "    def ids_to_logits(self, ids, reduced_logits):\n",
        "        logits = torch.zeros((ids.shape[0], ids.shape[1]+1, self.config.decoder.vocab_size), dtype=reduced_logits.dtype, device=reduced_logits.device)\n",
        "        logits[:, -1, 2] = 1 # max_pad_token\n",
        "        for i in range(ids.shape[1]):\n",
        "            logits[:, i, ids[:, i]] = 1\n",
        "        \n",
        "        return logits\n",
        "\n",
        "better_cuda_hf_trocr = BetterHFTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    ).to('cuda')\n",
        "\n",
        "better_cpu_hf_trocr = BetterHFTrOCR(\n",
        "    model_path='microsoft/trocr-base-stage1', \n",
        "    ).to('cpu')\n",
        "\n",
        "configure_generation(better_cuda_hf_trocr)\n",
        "configure_generation(better_cpu_hf_trocr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNclmbSfPrGs",
        "outputId": "72d35786-47af-4080-c3d2-8cee438de95b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit preprocessor.tokenizer.batch_decode( \\\n",
        "                                            better_cuda_hf_trocr.generate( \\\n",
        "                                                                          pixel_values.to('cuda'), \\\n",
        "                                                                          max_length=96, \\\n",
        "                                                                          ), \\\n",
        "                                            skip_special_tokens=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e07805a8-83ec-4f6c-ea7f-fe3348cf2647",
        "id": "wP-xY0pL0mcU"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "894 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time preprocessor.tokenizer.batch_decode( \\\n",
        "                                          better_cpu_hf_trocr.generate( \\\n",
        "                                                                       pixel_values.to('cpu'), \\\n",
        "                                                                       max_length=96, \\\n",
        "                                                                       ), \\\n",
        "                                          skip_special_tokens=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71846be9-e2d8-4f2a-b669-fc4a830eec6c",
        "id": "0VVGiPsC0mcV"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 32s, sys: 1.46 s, total: 1min 33s\n",
            "Wall time: 15.6 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This text is generated by Landing Page Text positioned at Top. In the Banner Ribbon and Action Button item above, Display',\n",
              " 'Search Box replaces the Ribbon text, and there is no Action button. In this 2-1-1 layout, there is a Content Butes item in the',\n",
              " 'double-width left column. The other two columns each contain Landing Page Text items, positioned at Left and Right. Return to',\n",
              " 'the Landing Page overview.',\n",
              " 'This is Landing Page Text which is',\n",
              " 'positioned at Right. It is automatically',\n",
              " 'right-aligned.',\n",
              " 'University of the Highlands and Islands',\n",
              " 'Executive Office',\n",
              " '12b Ness Walk',\n",
              " 'Inverness',\n",
              " 'Scotland',\n",
              " 'IV3 5SO',\n",
              " 'Linking to Media Library documents, or',\n",
              " 'to Sections is possible. The usual text',\n",
              " 'formatting options are available in the',\n",
              " 'toolbar.']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tadaaaaaaaaaaaaaaaaaaa we devided inference time by 2 in the case of GPU and by 3 in the case of CPU."
      ],
      "metadata": {
        "id": "_OsQJ5Cf1nrP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Duh6_D610x4o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}