prepack_generation_demo.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Imports**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!git clone https://github.com/siyan-zhao/prepacking.git\n",
        "%cd prepacking/\n",
        "!pip install ortools==9.9.3963\n",
        "!pip install binpacking==1.5.2\n",
        "!pip install datasets==2.18.0\n",
        "!pip install -i https://pypi.org/simple/ bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UYWdWPXytfLG"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from processor import PrePackProcessor\n",
        "from model import CustomCausalLlamaModel\n",
        "from transformers import AutoTokenizer\n",
        "from transformers.trainer_utils import set_seed\n",
        "from dataset_utils import unpack_kv\n",
        "from transformers import BitsAndBytesConfig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW6yOlJxzCVw"
      },
      "source": [
        "**Load Model**"
      ]
    },
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      "source": [
        "SEED = 42\n",
        "set_seed(SEED)\n",
        "model_path = \"princeton-nlp/Sheared-LLaMA-1.3B\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "tokenizer.pad_token = \"[PAD]\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "custom_model = CustomCausalLlamaModel.from_pretrained(model_path)\n",
        "custom_model.to(device)\n",
        "custom_model.eval()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dy590zUqzHeV"
      },
      "source": [
        "**Prepacking Generation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZiRs_hLetkCB"
      },
      "outputs": [],
      "source": [
        "\n",
        "processor = PrePackProcessor(tokenizer)\n",
        "\n",
        "# Change to any prompts\n",
        "sentences = [\n",
        "    \"Rescuers are searching for multiple people in the water after Baltimore bridge collapse, report says\",\n",
        "    \"Major bridge in Maryland collapses after being hit by a ship\",\n",
        "    \"The capital of Germany is\",\n",
        "    \"The capital of Spain is\",\n",
        "    \"The capital of Greece is\",\n",
        "    \"Today I'm going to the\",\n",
        "    \"Baltimore Police Department told NBC\",\n",
        "    \"My\",\n",
        "    \"It\",\n",
        "]\n",
        "\n",
        "packed_tokens, restart_positions, independent_mask, restart_dict, original_ids = processor.batch_process(sentences)\n",
        "\n",
        "\n",
        "with torch.no_grad():\n",
        "    packed_outputs = custom_model(\n",
        "        input_ids=packed_tokens.to(device),\n",
        "        attention_mask=independent_mask.to(device),\n",
        "        position_ids=restart_positions.to(device),\n",
        "        return_dict=True,\n",
        "        output_hidden_states=True,\n",
        "    )\n",
        "\n",
        "cache, final_tokens, attention_mask = unpack_kv(\n",
        "    packed_outputs[\"past_key_values\"], restart_dict, original_ids, device\n",
        ")\n",
        "\n",
        "prepack_generated_output = custom_model.generate(\n",
        "    input_ids=final_tokens.to(device),\n",
        "    attention_mask=attention_mask.to(device),\n",
        "    max_new_tokens=20,\n",
        "    use_cache=True,\n",
        "    do_sample=False,\n",
        "    past_key_values=cache,\n",
        "    num_return_sequences=1,\n",
        "    output_scores=True,\n",
        "    return_dict_in_generate=True,\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2Q5miklzaDH"
      },
      "source": [
        "**Default Generation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "bdgfwCkbxCos"
      },
      "outputs": [],
      "source": [
        "\n",
        "with torch.no_grad():\n",
        "    normal_tokens_id = tokenizer(sentences, return_tensors=\"pt\", padding=True, truncation=False).to(\n",
        "        device\n",
        "    )\n",
        "    normal_outputs = custom_model(**normal_tokens_id, return_dict=True, output_hidden_states=True)\n",
        "\n",
        "default_generated_output = custom_model.generate(\n",
        "    **normal_tokens_id,\n",
        "    max_new_tokens=20,\n",
        "    use_cache=True,\n",
        "    do_sample=False,\n",
        "    num_return_sequences=1,\n",
        "    output_scores=True,\n",
        "    return_dict_in_generate=True\n",
        ")\n",
        "\n",
        "attention_mask = normal_tokens_id[\"attention_mask\"]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Compare Generations**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_unN3rS1pNQ",
        "outputId": "ecaae29d-5b29-4160-921c-46a009313e93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Asserting Same Tokens\n",
            "--------------- comparing ---------------\n",
            "Prepacked 0 : \n",
            "The Baltimore Sun reports that the collapse of a bridge in Baltimore on Monday morning has left at least\n",
            "Default 0 : \n",
            "The Baltimore Sun reports that the collapse of a bridge in Baltimore on Monday morning has left at least\n",
            "--------------- comparing ---------------\n",
            "Prepacked 1 : \n",
            "The bridge was built in 1968 and was the first of its kind in the\n",
            "Default 1 : \n",
            "The bridge was built in 1968 and was the first of its kind in the\n",
            "--------------- comparing ---------------\n",
            "Prepacked 2 : Berlin. Berlin is a city of contrasts. Berlin is a city of contrasts. It is\n",
            "Default 2 : Berlin. Berlin is a city of contrasts. Berlin is a city of contrasts. It is\n",
            "--------------- comparing ---------------\n",
            "Prepacked 3 : Madrid, and it is the largest city in the country. The city is located in the central part\n",
            "Default 3 : Madrid, and it is the largest city in the country. The city is located in the central part\n",
            "--------------- comparing ---------------\n",
            "Prepacked 4 : Athens, the largest city in Greece and the second largest in Europe. The city is located on\n",
            "Default 4 : Athens, the largest city in Greece and the second largest in Europe. The city is located on\n",
            "--------------- comparing ---------------\n",
            "Prepacked 5 : gym. I'm going to do a 30 minute workout. I'm\n",
            "Default 5 : gym. I'm going to do a 30 minute workout. I'm\n",
            "--------------- comparing ---------------\n",
            "Prepacked 6 : 10 that the suspect was arrested on a warrant for a probation violation.\n",
            "The\n",
            "Default 6 : 10 that the suspect was arrested on a warrant for a probation violation.\n",
            "The\n",
            "--------------- comparing ---------------\n",
            "Prepacked 7 : name is Katie and I am a 20 year old student from the UK. I am\n",
            "Default 7 : name is Katie and I am a 20 year old student from the UK. I am\n",
            "--------------- comparing ---------------\n",
            "Prepacked 8 : ’s been a while since I’ve posted anything on this blog. I’ve been busy\n",
            "Default 8 : ’s been a while since I’ve posted anything on this blog. I’ve been busy\n"
          ]
        }
      ],
      "source": [
        "print(\"Asserting Same Tokens\")\n",
        "\n",
        "# Check tokens\n",
        "# Note that it is possible to have different generations due to numerical instability\n",
        "for i, (prepack_token, default_token) in enumerate(\n",
        "    zip(prepack_generated_output.sequences, default_generated_output.sequences)\n",
        "):\n",
        "\n",
        "    prepack = tokenizer.decode(prepack_token[1:])\n",
        "    default = tokenizer.decode(default_token[attention_mask.shape[-1] :])\n",
        "    print(\"-\" * 15, \"comparing\", \"-\" * 15)\n",
        "    print(\"Prepacked\", i, \":\", prepack)\n",
        "    print(\"Default\", i, \":\", default)\n",
        "\n",
        "    assert prepack == default"
      ]
    }
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