colab/Llama_2_7b_Chat_GPTQ.ipynb

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      "source": [
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        "id": "L-vkqBJPTq__",
        "outputId": "5a95ba1a-450b-42da-b3b5-f697009d92db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on GPU with backend torch transformers.\n",
            "Model path is empty.\n",
            "Use default gptq model path: ./models/Llama-2-7b-Chat-GPTQ\n",
            "Start downloading model to: ./models/Llama-2-7b-Chat-GPTQ\n"
          ]
        },
        {
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          "metadata": {}
        },
        {
          "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": "display_data",
          "data": {
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              "0it [00:00, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:accelerate.utils.modeling:The safetensors archive passed at ./models/Llama-2-7b-Chat-GPTQ/gptq_model-4bit-128g.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to 'pt' metadata.\n",
            "WARNING:auto_gptq.nn_modules.fused_llama_mlp:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = get_prompt(\"Hi do you know Pytorch?\")\n",
        "print(llama2_wrapper(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yr2IwMY9WmN8",
        "outputId": "ed23c4ea-d486-4d7a-c47f-fa9594d8cf80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Yes, I'm familiar with PyTorch! PyTorch is an open-source machine learning library developed by Facebook that provides a dynamic computation graph and is particularly well-suited for deep learning tasks. It is built on top of the Torch library and is primarily used for developing neural networks and their associated algorithms.\n",
            "Here are some key features of PyTorch:\n",
            "\n",
            "1. **Dynamic Computation Graph**: PyTorch's computation graph is dynamic, which means that it can be built and modified at runtime. This allows for more flexible and efficient computation, especially in deep learning tasks.\n",
            "2. **Tensor Algebra**: PyTorch provides a powerful tensor algebra system that allows for easy manipulation of tensors. This makes it easy to define and optimize neural networks.\n",
            "3. **Autograd**: PyTorch's autograd system automatically computes gradients, which saves time and effort compared to manual computation.\n",
            "4. **Modules**: PyTorch provides a variety of pre-built modules that can be easily integrated into neural networks, such as convolutional layers, recurrent layers, and more.\n",
            "5. **JIT**: PyTorch has a just-in-time (JIT) compiler that can compile Python code into C++ code, which can lead to significant performance improvements.\n",
            "6. **CUDA and cuDNN Support**: PyTorch has built-in support for NVIDIA GPUs and cuDNN, which can significantly accelerate training times for deep learning models.\n",
            "7. **Pythonic API**: PyTorch's Pythonic API makes it easy to define and train neural networks using Python, which is a popular and easy-to-learn language.\n",
            "8. **Extensive Community Support**: PyTorch has a large and active community of developers and users, which can be a great resource for getting help and staying up-to-date with the latest developments.\n",
            "\n",
            "These are just a few of the many features and benefits of PyTorch. It's a powerful tool for deep learning and neural networks, and I'm sure you'll find it useful in your work!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content\n",
        "!git clone https://github.com/liltom-eth/llama2-webui\n",
        "%cd /content/llama2-webui\n",
        "!python benchmark.py --backend_type gptq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6A7bJdkmzY8",
        "outputId": "0b38b55b-8139-4e66-b1a8-ecb5b9fde5a0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "fatal: destination path 'llama2-webui' already exists and is not an empty directory.\n",
            "/content/llama2-webui\n",
            "Running on GPU with backend torch transformers.\n",
            "Model path is empty.\n",
            "Use default gptq model path: ./models/Llama-2-7b-Chat-GPTQ\n",
            "Model exists in ./models/Llama-2-7b-Chat-GPTQ\n",
            "2023-08-15 23:50:49.173114: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "The safetensors archive passed at ./models/Llama-2-7b-Chat-GPTQ/gptq_model-4bit-128g.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to 'pt' metadata.\n",
            "skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.\n",
            "Performing cold run...\n",
            "Performing 5 timed runs...\n",
            "  0% 0/5 [00:00<?, ?it/s]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            " 20% 1/5 [00:12<00:50, 12.73s/it]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            " 40% 2/5 [00:26<00:40, 13.55s/it]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            " 60% 3/5 [00:38<00:25, 12.88s/it]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            " 80% 4/5 [00:51<00:12, 12.61s/it]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "100% 5/5 [01:03<00:00, 12.68s/it]\n",
            "Last model response:  Of course! Python is a high-level, interpreted programming language that is widely used for various purposes, including web development, scientific computing, data analysis, artificial intelligence, and more. It is known for its simplicity, readability, and ease of use, making it a popular choice among developers and beginners alike. Python is also known for its large and active community, which has contributed to its growth and development over the years.\n",
            "Python has a syntax that is easy to read and write, with a focus on readability. It supports a wide range of programming paradigms, including object-oriented, imperative, and functional programming. Python is also known for its extensive standard library, which provides a wide range of modules and functions for various tasks, such as file I/O, networking, and data structures.\n",
            "Overall, Python is a versatile and powerful language that is widely used in many different fields, and is a great language for beginners to learn.\n",
            "Initialization time: 37.4424 seconds.\n",
            "Average generation time over 5 iterations: 12.6785 seconds.\n",
            "Average speed over 5 iterations: 15.9851 tokens/sec.\n",
            "Average memory usage during generation: 1881.52 MiB\n"
          ]
        }
      ]
    }
  ]
}