19.HQQ_1bit_ipynb.ipynb

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            "\u001b[?25h  Building wheel for hqq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "source": [
        "#@title Install HQQ\n",
        "!pip install -qqqU git+https://github.com/mobiusml/hqq.git\n",
        "!pip install -qqqU accelerate einops\n"
      ]
    },
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      "source": [
        "If you want to compare with the original LLaMA 2 from Meta's Hugging Face page, you will need your Hugging Face access token. However, if you do not wish to make the comparison, you can skip this step."
      ],
      "metadata": {
        "id": "pgfkMNqaSngt"
      }
    },
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        "from huggingface_hub import login\n",
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        "outputId": "eca97710-773d-4fc9-ca65-256b8d2108d7"
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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      "cell_type": "code",
      "source": [
        "#@title Load both the 1-bit and full-precision models. It runs on a T4 instance.\n",
        "from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "\n",
        "# Load the quantized and full models along with their tokenizers\n",
        "quantized_model_id = 'mobiuslabsgmbh/Llama-2-7b-chat-hf_1bitgs8_hqq'\n",
        "full_model_id = 'meta-llama/Llama-2-7b-chat-hf'\n",
        "\n",
        "# Specify the data type for the model to be loaded in PyTorch\n",
        "torch_dtype     = torch.float16\n",
        "device          = 'cuda'\n",
        "load_full_model = False\n",
        "\n",
        "\n",
        "#################################################################################\n",
        "# Load the quantized model using HQQModelForCausalLM\n",
        "one_bit_model = HQQModelForCausalLM.from_quantized(quantized_model_id, adapter='adapter_v0.1.lora')\n",
        "one_bit_model.config.use_cache = True\n",
        "one_bit_model.eval()\n",
        "\n",
        "#################################################################################\n",
        "# Load the full model using AutoModelForCausalLM with automatic device mapping\n",
        "if(load_full_model):\n",
        "  fullbit_model = AutoModelForCausalLM.from_pretrained(\n",
        "      full_model_id,\n",
        "      device_map=device,\n",
        "      trust_remote_code=True,\n",
        "      torch_dtype=torch_dtype\n",
        "  )\n",
        "\n",
        "  fullbit_model.config.use_cache = True\n",
        "  fullbit_model.eval()\n",
        "else:\n",
        "  fullbit_model = None\n",
        "#################################################################################\n",
        "\n",
        "# Load tokenizer from the quantized model\n",
        "tokenizer = AutoTokenizer.from_pretrained(quantized_model_id)\n",
        "\n",
        "# Set up inference mode by configuring the tokenizer and model\n",
        "tokenizer.add_bos_token = False\n",
        "tokenizer.add_eos_token = False\n",
        "if not tokenizer.pad_token:\n",
        "    tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "\n",
        "# Define a function for streaming inference with chat interaction\n",
        "import torch, transformers\n",
        "from threading import Thread\n",
        "\n",
        "def chat_processor(chat, current_model, current_tokenizer, max_new_tokens=100, do_sample=True, device=device):\n",
        "    current_tokenizer.use_default_system_prompt = False\n",
        "    streamer = transformers.TextIteratorStreamer(current_tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)\n",
        "\n",
        "    generate_params = dict(\n",
        "        current_tokenizer(\"<s> [INST] \" + chat + \" [/INST] \", return_tensors=\"pt\").to(device),\n",
        "        streamer=streamer,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=do_sample,\n",
        "        pad_token_id=current_tokenizer.pad_token_id,\n",
        "        top_p=0.90 if do_sample else None,\n",
        "        top_k=50 if do_sample else None,\n",
        "        temperature= 0.6 if do_sample else None,\n",
        "        num_beams=1,\n",
        "        repetition_penalty=1.2,\n",
        "    )\n",
        "\n",
        "    t = Thread(target=current_model.generate, kwargs=generate_params)\n",
        "    t.start()\n",
        "\n",
        "    print(\"User: \", chat);\n",
        "    print(\"Assistant: \");\n",
        "    outputs = \"\"\n",
        "    for text in streamer:\n",
        "        outputs += text\n",
        "        print(text, end=\"\", flush=True)\n",
        "\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    return outputs\n"
      ],
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        "id": "DMbxd7Rib_bP",
        "outputId": "7d0938c6-efbf-49a0-f52a-15e1d757da53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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          "data": {
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          "text": [
            "100%|██████████| 32/32 [00:00<00:00, 602.10it/s]\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "import random, gc\n",
        "\n",
        "# List of questions for the quiz\n",
        "questions = [\n",
        "    \"What happens to you if you eat watermelon seeds?\",\n",
        "    \"Can I give grapes to a dog?\",\n",
        "    \"What is the solution to x^2 - 1 = 0\",\n",
        "    \"Are rats good at cooking?\",\n",
        "    \"What movie does this emoji describe? 👦👓⚡️\\n  choice: harry potter\\n. choice: shutter island\\n. choice: inglourious basterds\\n. choice: die hard\\n. choice: moonlight\\nA:\",\n",
        "    \"In what follows, we provide a brief narrative along with a choice of proverbs. Please select the one that best fits the narrative.\\n\\nNarrative: David really wanted a better job than his current one. He couldn't manage his bills with his existing salary. Despite his extensive search, it seemed futile. However, he wouldn’t settle for less. He believed that the right job was out there. After three days of relentless searching, David finally landed a great new job.\\n\\nChoices:\\n1. A golden key can open any door.\\n2. Look before you leap.\\n3. Strike while the iron is hot.\\n4. Seek and you shall find.\\n5. The best things in life are free.\\n\\nA:\",\n",
        "    \"Which of the following sentences makes more sense? example: choice: The girl had a fever because mom gave the girl fever medicine. choice: Mom gave the girl fever medicine because the girl had a fever.\",\n",
        "    \"Is the given example sentence an example of irony? Respond 'ironic' or 'not ironic' and give your reason. Example: Honesty is the best policy, but insanity is the best defense. Ironic/Not ironic?\",\n",
        "    \"If you had 3 apples and you ate 2 yesterday, how many apples do you have now?\",\n",
        "]\n",
        "\n",
        "# Select a random question from the list\n",
        "curr_question = random.choice(questions)\n",
        "\n",
        "print(\"-------------------------\")\n",
        "print(\"Output as per 1-bit model\")\n",
        "print(\"-------------------------\")\n",
        "one_bit_outputs = chat_processor(curr_question,\n",
        "                                 one_bit_model,\n",
        "                                 tokenizer,\n",
        "                                 max_new_tokens=256,\n",
        "                                 do_sample=False)\n",
        "print(\"\\n\\n\")\n",
        "if(fullbit_model is not None):\n",
        "  print(\"----------------------------------\")\n",
        "  print(\"Output as per full precision model\")\n",
        "  print(\"----------------------------------\")\n",
        "  fullbit_outputs = chat_processor(curr_question,\n",
        "                                  fullbit_model,\n",
        "                                  tokenizer,\n",
        "                                  max_new_tokens=256,\n",
        "                                  do_sample=False)\n",
        "\n",
        "torch.cuda.empty_cache();\n",
        "gc.collect();\n"
      ],
      "metadata": {
        "id": "bKnu6uTxdFJL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77a59c14-f59b-4934-bdef-ece480542bdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------\n",
            "Output as per 1-bit model\n",
            "-------------------------\n",
            "User:  What is the solution to x^2 - 1 = 0\n",
            "Assistant: \n",
            "The equation $x^2 - 1 = 0$ can be factored as $(x-1)(x+1) = 0$.\n",
            "You want to find a value of $x$ that makes this true for all values of $x$. This means that either $x=1$ or $-1$, or $x=-1$. So, there are two solutions: $x=\\boxed{1}$ and $x=\\boxed{-1}$. The answer is: 1\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}