{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "L4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qaj9T-0yEQPd", "outputId": "4638c732-b689-4f7d-c125-cecb80b77452" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'ai-edge-torch'...\n", "remote: Enumerating objects: 6174, done.\u001b[K\n", "remote: Counting objects: 100% (707/707), done.\u001b[K\n", "remote: Compressing objects: 100% (194/194), done.\u001b[K\n", "remote: Total 6174 (delta 613), reused 525 (delta 512), pack-reused 5467 (from 1)\u001b[K\n", "Receiving objects: 100% (6174/6174), 11.90 MiB | 18.41 MiB/s, done.\n", "Resolving deltas: 100% (4430/4430), done.\n" ] } ], "source": [ "!git clone https://github.com/google-ai-edge/ai-edge-torch.git" ] }, { "cell_type": "code", "source": [ "!pip install tensorflow==2.19.0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oPOsX9qaEktY", "outputId": "23765ef6-513e-45a7-f08e-1309aa682264" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting tensorflow==2.19.0\n", " Downloading tensorflow-2.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n", "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.19.0) (1.4.0)\n", "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.19.0) (1.6.3)\n", "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.19.0) (25.2.10)\n", "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in 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MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m644.9/644.9 MB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading ml_dtypes-0.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m119.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading tensorboard-2.19.0-py3-none-any.whl (5.5 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m130.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: ml-dtypes, tensorboard, tensorflow\n", " Attempting uninstall: ml-dtypes\n", " Found existing installation: ml-dtypes 0.4.1\n", " Uninstalling ml-dtypes-0.4.1:\n", " Successfully uninstalled ml-dtypes-0.4.1\n", " Attempting uninstall: tensorboard\n", " Found existing installation: tensorboard 2.18.0\n", " Uninstalling tensorboard-2.18.0:\n", " Successfully uninstalled tensorboard-2.18.0\n", " Attempting uninstall: tensorflow\n", " Found existing installation: tensorflow 2.18.0\n", " Uninstalling tensorflow-2.18.0:\n", " Successfully uninstalled tensorflow-2.18.0\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "tf-keras 2.18.0 requires tensorflow<2.19,>=2.18, but you have tensorflow 2.19.0 which is incompatible.\n", "tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.19.0 which is incompatible.\n", "tensorflow-decision-forests 1.11.0 requires tensorflow==2.18.0, but you have tensorflow 2.19.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed ml-dtypes-0.5.1 tensorboard-2.19.0 tensorflow-2.19.0\n" ] } ] }, { "cell_type": "code", "source": [ "!pip list" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_VMnnza5EnKy", "outputId": "b43b5a94-d51a-4984-e82f-e6426e1338d0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Package Version\n", "------------------------------------- ------------------\n", "absl-py 1.4.0\n", "accelerate 1.8.1\n", "aiofiles 24.1.0\n", "aiohappyeyeballs 2.6.1\n", 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1.0.5\n", "spanner-graph-notebook 1.1.7\n", "Sphinx 8.2.3\n", "sphinxcontrib-applehelp 2.0.0\n", "sphinxcontrib-devhelp 2.0.0\n", "sphinxcontrib-htmlhelp 2.1.0\n", "sphinxcontrib-jsmath 1.0.1\n", "sphinxcontrib-qthelp 2.0.0\n", "sphinxcontrib-serializinghtml 2.0.0\n", "SQLAlchemy 2.0.41\n", "sqlglot 25.20.2\n", "sqlparse 0.5.3\n", "srsly 2.5.1\n", "stanio 0.5.1\n", "starlette 0.46.2\n", "statsmodels 0.14.4\n", "stringzilla 3.12.5\n", "stumpy 1.13.0\n", "sympy 1.13.1\n", "tables 3.10.2\n", "tabulate 0.9.0\n", "tbb 2022.2.0\n", "tblib 3.1.0\n", "tcmlib 1.4.0\n", "tenacity 8.5.0\n", "tensorboard 2.19.0\n", "tensorboard-data-server 0.7.2\n", "tensorflow 2.19.0\n", "tensorflow-datasets 4.9.9\n", "tensorflow_decision_forests 1.11.0\n", "tensorflow-hub 0.16.1\n", "tensorflow-io-gcs-filesystem 0.37.1\n", "tensorflow-metadata 1.17.2\n", "tensorflow-probability 0.25.0\n", "tensorflow-text 2.18.1\n", "tensorstore 0.1.74\n", "termcolor 3.1.0\n", "terminado 0.18.1\n", "text-unidecode 1.3\n", "textblob 0.19.0\n", "tf_keras 2.18.0\n", "tf-slim 1.1.0\n", "thinc 8.3.6\n", "threadpoolctl 3.6.0\n", "tifffile 2025.6.11\n", "tiktoken 0.9.0\n", "timm 1.0.16\n", "tinycss2 1.4.0\n", "tokenizers 0.21.2\n", "toml 0.10.2\n", "tomlkit 0.13.3\n", "toolz 0.12.1\n", "torch 2.6.0+cu124\n", "torchao 0.10.0\n", "torchaudio 2.6.0+cu124\n", "torchdata 0.11.0\n", "torchsummary 1.5.1\n", "torchtune 0.6.1\n", "torchvision 0.21.0+cu124\n", "tornado 6.4.2\n", "tqdm 4.67.1\n", "traitlets 5.7.1\n", "traittypes 0.2.1\n", "transformers 4.53.1\n", "treelite 4.4.1\n", "treescope 0.1.9\n", "triton 3.2.0\n", "tsfresh 0.21.0\n", "tweepy 4.15.0\n", "typeguard 4.4.4\n", "typer 0.16.0\n", "types-pytz 2025.2.0.20250516\n", "types-setuptools 80.9.0.20250529\n", "typing_extensions 4.14.1\n", "typing-inspection 0.4.1\n", "tzdata 2025.2\n", "tzlocal 5.3.1\n", "uc-micro-py 1.0.3\n", "ucx-py-cu12 0.42.0\n", "ucxx-cu12 0.42.0\n", "umap-learn 0.5.9.post2\n", "umf 0.11.0\n", "uritemplate 4.2.0\n", "urllib3 2.4.0\n", "uvicorn 0.35.0\n", "vega-datasets 0.9.0\n", "wadllib 1.3.6\n", "wandb 0.21.0\n", "wasabi 1.1.3\n", "wcwidth 0.2.13\n", "weasel 0.4.1\n", "webcolors 24.11.1\n", "webencodings 0.5.1\n", "websocket-client 1.8.0\n", "websockets 15.0.1\n", "Werkzeug 3.1.3\n", "wheel 0.45.1\n", "widgetsnbextension 3.6.10\n", "wordcloud 1.9.4\n", "wrapt 1.17.2\n", "wurlitzer 3.1.1\n", "xarray 2025.3.1\n", "xarray-einstats 0.9.1\n", "xgboost 2.1.4\n", "xlrd 2.0.2\n", "xxhash 3.5.0\n", "xyzservices 2025.4.0\n", "yarl 1.20.1\n", "ydf 0.12.0\n", "yellowbrick 1.5\n", "yfinance 0.2.65\n", "zict 3.0.0\n", "zipp 3.23.0\n", "zstandard 0.23.0\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install torch-xla2\n", "!pip install ai_edge_quantizer\n", "!pip install ai_edge_litert #available latest 0.2.1 and 0.1.0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "wo7zRFbfEsYT", "outputId": "1a3acc29-2dce-4bc6-f5af-6494e81bf2b9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting torch-xla2\n", " Downloading torch_xla2-0.0.1.dev202412041639-py3-none-any.whl.metadata (2.5 kB)\n", "Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (from torch-xla2) (1.4.0)\n", "Requirement already satisfied: immutabledict in /usr/local/lib/python3.11/dist-packages (from torch-xla2) (4.2.1)\n", "Requirement already satisfied: pytest in /usr/local/lib/python3.11/dist-packages (from torch-xla2) (8.3.5)\n", "Requirement already satisfied: torch>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from torch-xla2) (2.6.0+cu124)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=2.3.0->torch-xla2) (3.18.0)\n", "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch>=2.3.0->torch-xla2) (4.14.1)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from 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"\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m108.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch-xla2\n", " Attempting uninstall: nvidia-nvjitlink-cu12\n", " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", " Attempting uninstall: nvidia-curand-cu12\n", " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", " Attempting uninstall: nvidia-cufft-cu12\n", " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", " Attempting uninstall: nvidia-cuda-runtime-cu12\n", " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-cupti-cu12\n", " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", " Attempting uninstall: nvidia-cublas-cu12\n", " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", " Attempting uninstall: nvidia-cusparse-cu12\n", " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", " Attempting uninstall: nvidia-cudnn-cu12\n", " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", " Attempting uninstall: nvidia-cusolver-cu12\n", " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 torch-xla2-0.0.1.dev202412041639\n", "Collecting ai_edge_quantizer\n", " Downloading ai_edge_quantizer-0.2.1-py3-none-any.whl.metadata (1.7 kB)\n", "Requirement already satisfied: immutabledict in /usr/local/lib/python3.11/dist-packages (from ai_edge_quantizer) (4.2.1)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from ai_edge_quantizer) (2.0.2)\n", "Collecting tf-nightly==2.20.0.dev20250515 (from ai_edge_quantizer)\n", " Downloading tf_nightly-2.20.0.dev20250515-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)\n", "Collecting ai-edge-litert==1.3.0 (from ai_edge_quantizer)\n", " Downloading ai_edge_litert-1.3.0-cp311-cp311-manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n", "Collecting backports.strenum (from ai-edge-litert==1.3.0->ai_edge_quantizer)\n", " Downloading backports_strenum-1.2.8-py3-none-any.whl.metadata (3.6 kB)\n", "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert==1.3.0->ai_edge_quantizer) (25.2.10)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert==1.3.0->ai_edge_quantizer) (4.67.1)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert==1.3.0->ai_edge_quantizer) (4.14.1)\n", "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tf-nightly==2.20.0.dev20250515->ai_edge_quantizer) (1.4.0)\n", "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages 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[ { "output_type": "stream", "name": "stdout", "text": [ "ai_edge_torch CONTRIBUTING.md\t LICENSE\t run_tests.sh\n", "bazel\t dev-requirements.txt MODULE.bazel setup.py\n", "build_config docs\t\t README.md\t test\n", "ci\t format.sh\t requirements.txt WORKSPACE\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install -U \"huggingface_hub[cli]\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "S8NOGLRqEz42", "outputId": "1e532c95-6440-4cc0-cd66-131ee5c826a4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: huggingface_hub[cli] in /usr/local/lib/python3.11/dist-packages (0.33.2)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub[cli]) (3.18.0)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub[cli]) (2025.3.2)\n", "Requirement already satisfied: 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Please save the token first.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hVUtajitF_aJ", "outputId": "d5588d84-649a-49a7-873f-3f64bb4e4515" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Successfully logged in to Hugging Face!\n" ] } ] }, { "cell_type": "code", "source": [ "!huggingface-cli download MadeAgents/Hammer2.1-0.5b --repo-type model --local-dir ./models/MadeAgents/Hammer2.1-0.5b" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lMKgeOA1E3a4", "outputId": "416f50b3-b692-430a-c3dd-2e9bc0c61804" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\rFetching 13 files: 0% 0/13 [00:00 0.5b" ], "metadata": { "id": "DrXtVp_K1ZDv" } }, { "cell_type": "code", "source": [], "metadata": { "id": "ED0CDmjS41Pi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# For CPU inference in android, use dynamic_int8\n", "# For GPU inference no set up is found to successful load in android GPU\n", "NONE = 'none'\n", "DYNAMIC_INT8 = 'dynamic_int8'\n", "WEIGHT_ONLY_INT8 = 'weight_only_int8'\n", "FP16 = 'fp16'\n", "DYNAMIC_INT4_BLOCK32 = 'dynamic_int4_block32'\n", "DYNAMIC_INT4_BLOCK128 = 'dynamic_int4_block128'\n", "\n", "!python -m ai_edge_torch.generative.examples.hammer.convert_to_tflite \\\n", " --quantize=\"dynamic_int8\" \\\n", " --checkpoint_path=/content/ai-edge-torch/models/MadeAgents/Hammer2.1-0.5b \\\n", " --output_path=\"/content/ai-edge-torch\" \\\n", " --prefill_seq_lens=128 \\\n", " --kv_cache_max_len=1280 \\\n", " --mask_as_input \\\n", " --transpose_kv_cache \\\n", " --model_size=0.5b \\\n", " --undefok=model_size" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 906 }, "id": "iZkgWLKsHUbu", "outputId": "f9ae1947-cb7b-4e65-b2c7-2c54348064f2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2025-07-10 06:50:41.687029: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2025-07-10 06:50:41.759915: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "/usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:354: UserWarning: Device capability of jax unspecified, assuming `cpu` and `cuda`. Please specify it via the `devices` argument of `register_backend`.\n", " warnings.warn(\n", "I0710 06:52:12.594704 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.view\n", "I0710 06:52:12.625253 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.embedding\n", "I0710 06:52:12.652188 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.arange.start_step\n", "I0710 06:52:12.657038 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.mul.Tensor\n", "I0710 06:52:12.662169 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.pow\n", "I0710 06:52:12.669392 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.clone.default\n", "I0710 06:52:12.673428 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.unsqueeze.default\n", "I0710 06:52:12.690506 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.div\n", "I0710 06:52:12.695722 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.cos\n", "I0710 06:52:12.700235 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.sin\n", "I0710 06:52:12.704801 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.full\n", "I0710 06:52:12.714902 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.mean\n", "I0710 06:52:12.721963 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.add.Tensor\n", "I0710 06:52:12.727718 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.rsqrt\n", "I0710 06:52:12.752371 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.permute\n", "I0710 06:52:12.756566 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.mm\n", "I0710 06:52:12.794465 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.split_with_sizes\n", "I0710 06:52:12.841455 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.sub.Tensor\n", "I0710 06:52:12.941104 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.select\n", "I0710 06:52:13.022526 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten._softmax\n", "I0710 06:52:13.125936 134034596196992 _jax_lowerings.py:32] Use JAX lowering: aten.sigmoid\n", "2025-07-10 06:52:45.497092: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1752130365.497227 2680 gpu_device.cc:2018] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20961 MB memory: -> device: 0, name: NVIDIA L4, pci bus id: 0000:00:03.0, compute capability: 8.9\n", "I0710 06:52:48.273987 134034596196992 signature_serialization.py:156] Function `inner` contains input name(s) resource with unsupported characters which will be renamed to xlacallmodule_readvariableop_192_resource in the SavedModel.\n", "I0710 06:52:48.417292 134034596196992 signature_serialization.py:156] Function `inner` contains input name(s) resource with unsupported characters which will be renamed to xlacallmodule_readvariableop_193_resource in the SavedModel.\n", "I0710 06:52:50.194184 134034596196992 functional_saver.py:440] Sharding callback duration: 145 microseconds\n", "I0710 06:52:52.274698 134034596196992 functional_saver.py:440] Sharding callback duration: 129 microseconds\n", "INFO:tensorflow:Assets written to: /tmp/tmpwltrmz55/assets\n", "I0710 06:52:57.939098 134034596196992 builder_impl.py:836] Assets written to: /tmp/tmpwltrmz55/assets\n", "I0710 06:52:57.947176 134034596196992 fingerprinting_utils.py:49] Writing fingerprint to /tmp/tmpwltrmz55/fingerprint.pb\n", "W0000 00:00:1752130383.092105 2680 tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.\n", "W0000 00:00:1752130383.092143 2680 tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.\n", "2025-07-10 06:53:03.093113: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmpwltrmz55\n", "2025-07-10 06:53:03.102923: I tensorflow/cc/saved_model/reader.cc:52] Reading meta graph with tags { serve }\n", "2025-07-10 06:53:03.102950: I tensorflow/cc/saved_model/reader.cc:147] Reading SavedModel debug info (if present) from: /tmp/tmpwltrmz55\n", "I0000 00:00:1752130383.166955 2680 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled\n", "2025-07-10 06:53:03.176475: I tensorflow/cc/saved_model/loader.cc:236] Restoring SavedModel bundle.\n", "2025-07-10 06:53:04.416172: I tensorflow/cc/saved_model/loader.cc:220] Running initialization op on SavedModel bundle at path: /tmp/tmpwltrmz55\n", "2025-07-10 06:53:04.542634: I tensorflow/cc/saved_model/loader.cc:471] SavedModel load for tags { serve }; Status: success: OK. Took 1449520 microseconds.\n", "2025-07-10 06:53:04.654622: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", "2025-07-10 06:55:05.854247: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:4096] Estimated count of arithmetic ops: 93.485 G ops, equivalently 46.743 G MACs\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'\\nblaze run //third_party/py/ai_edge_torch/generative/examples/hammer:convert_to_tflite -- \\n --checkpoint_path /tmp/snapshots/MadeAgents/Hammer2.1-1.5b \\n --output_path /tmp/tmps7np6qhp \\n --output_name_prefix Hammer2.1-1.5b_seq128 \\n --kv_cache_max_len 1280 \\n --mask_as_input \\n --transpose_kv_cache\\n --model_size 1.5b \\n --undefok=model_size \\n --quantize dynamic_int8 \\n --prefill_seq_lens 128\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "%cd ai_edge_torch/generative/tools\n", "\n", "!python tokenizer_to_sentencepiece.py \\\n", " --checkpoint=MadeAgents/Hammer2.1-0.5b \\\n", " --output_path=/content/tokenizer.model" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sRvr3KXIHuwO", "outputId": "2a25018b-34cb-492b-a6f3-eff1e05d24a7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/ai-edge-torch/ai_edge_torch/generative/tools\n", "I0710 06:57:41.739611 137180622922368 utils.py:164] NumExpr defaulting to 12 threads.\n", "tokenizer_config.json: 7.13kB [00:00, 23.0MB/s]\n", "vocab.json: 2.78MB [00:00, 48.8MB/s]\n", "merges.txt: 1.67MB [00:00, 127MB/s]\n", "tokenizer.json: 100% 11.4M/11.4M [00:00<00:00, 128MB/s]\n", "added_tokens.json: 100% 605/605 [00:00<00:00, 3.52MB/s]\n", "special_tokens_map.json: 100% 613/613 [00:00<00:00, 4.64MB/s]\n", "I0710 06:57:44.359609 137180622922368 tokenizer_to_sentencepiece.py:299] vocab_file does not exist. Try to build a new one.\n", "I0710 06:57:53.229321 137180622922368 tokenizer_to_sentencepiece.py:183] Found pad_id: 151643, pad_piece: <|endoftext|>\n", "I0710 06:57:53.229638 137180622922368 tokenizer_to_sentencepiece.py:179] Found eos_id: 151645, eos_piece: <|im_end|>\n", "I0710 06:57:53.230827 137180622922368 tokenizer_to_sentencepiece.py:204] number of tokens: 151665\n", "I0710 06:57:53.231059 137180622922368 tokenizer_to_sentencepiece.py:206] number of NORMAL: 150144\n", "I0710 06:57:53.231109 137180622922368 tokenizer_to_sentencepiece.py:206] number of UNUSED: 1499\n", "I0710 06:57:53.231164 137180622922368 tokenizer_to_sentencepiece.py:206] number of UNKNOWN: 1\n", "I0710 06:57:53.231228 137180622922368 tokenizer_to_sentencepiece.py:206] number of USER_DEFINED: 21\n", "I0710 06:57:53.352619 137180622922368 tokenizer_to_sentencepiece.py:257] String to verify: Hello, world! How are you?\n", "I0710 06:57:53.352900 137180622922368 tokenizer_to_sentencepiece.py:258] Token IDs by the oringal tokenizer: [9707, 11, 1879, 0, 2585, 525, 498, 30]\n", "I0710 06:57:53.352944 137180622922368 tokenizer_to_sentencepiece.py:259] Token IDs by the SentencePiece tokenizer: [9707, 11, 1879, 0, 2585, 525, 498, 30]\n", "I0710 06:57:53.352984 137180622922368 tokenizer_to_sentencepiece.py:261] PASS\n", "I0710 06:57:53.353320 137180622922368 tokenizer_to_sentencepiece.py:257] String to verify: Instruct: write a python program to add 2 and 3.\n", "I0710 06:57:53.353379 137180622922368 tokenizer_to_sentencepiece.py:258] Token IDs by the oringal tokenizer: [641, 1235, 25, 3270, 264, 10135, 2025, 311, 912, 220, 17, 323, 220, 18, 13]\n", "I0710 06:57:53.353415 137180622922368 tokenizer_to_sentencepiece.py:259] Token IDs by the SentencePiece tokenizer: [641, 1235, 25, 3270, 264, 10135, 2025, 311, 912, 220, 17, 323, 220, 18, 13]\n", "I0710 06:57:53.353448 137180622922368 tokenizer_to_sentencepiece.py:261] PASS\n", "I0710 06:58:04.615295 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 3/100 pairs: 3.00%, loosely 0/100 pairs: 0.00%\n", "I0710 06:58:16.108360 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 6/200 pairs: 3.00%, loosely 0/200 pairs: 0.00%\n", "I0710 06:58:27.580932 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 8/300 pairs: 2.67%, loosely 0/300 pairs: 0.00%\n", "I0710 06:58:39.014399 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 13/400 pairs: 3.25%, loosely 0/400 pairs: 0.00%\n", "I0710 06:58:50.537630 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 19/500 pairs: 3.80%, loosely 0/500 pairs: 0.00%\n", "I0710 06:59:01.968418 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 22/600 pairs: 3.67%, loosely 0/600 pairs: 0.00%\n", "I0710 06:59:13.855856 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 30/700 pairs: 4.29%, loosely 0/700 pairs: 0.00%\n", "I0710 06:59:24.913957 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 35/800 pairs: 4.38%, loosely 0/800 pairs: 0.00%\n", "I0710 06:59:35.834920 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 40/900 pairs: 4.44%, loosely 0/900 pairs: 0.00%\n", "I0710 06:59:46.792476 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 46/1000 pairs: 4.60%, loosely 0/1000 pairs: 0.00%\n", "I0710 06:59:46.792602 137180622922368 tokenizer_to_sentencepiece.py:225] Not matched strictly 46/1000 pairs: 4.60%, loosely 0/1000 pairs: 0.00%\n", "I0710 06:59:46.792666 137180622922368 tokenizer_to_sentencepiece.py:307] Writing the SentencePieceModel protobuf file to: /content/tokenizer.model\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install mediapipe" ], "metadata": { "id": "QiyP70vCH6bw", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "ab29eb7e-4b19-4f02-9831-42355ab1f2d1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting mediapipe\n", " Downloading mediapipe-0.10.21-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (9.7 kB)\n", "Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (from mediapipe) (1.4.0)\n", "Requirement already satisfied: attrs>=19.1.0 in /usr/local/lib/python3.11/dist-packages (from mediapipe) (25.3.0)\n", "Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.11/dist-packages (from mediapipe) (25.2.10)\n", "Requirement already satisfied: jax in /usr/local/lib/python3.11/dist-packages (from mediapipe) (0.5.2)\n", "Requirement already satisfied: jaxlib in /usr/local/lib/python3.11/dist-packages (from mediapipe) (0.5.1)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from mediapipe) (3.10.0)\n", "Collecting numpy<2 (from mediapipe)\n", " Downloading 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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "grpcio-status 1.71.2 requires protobuf<6.0dev,>=5.26.1, but you have protobuf 4.25.8 which is incompatible.\n", "tf-keras 2.18.0 requires tensorflow<2.19,>=2.18, but you have tensorflow 2.19.0 which is incompatible.\n", "tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.19.0 which is incompatible.\n", "ydf 0.12.0 requires protobuf<6.0.0,>=5.29.1, but you have protobuf 4.25.8 which is incompatible.\n", "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.\n", "tensorflow-decision-forests 1.11.0 requires tensorflow==2.18.0, but you have tensorflow 2.19.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed mediapipe-0.10.21 numpy-1.26.4 protobuf-4.25.8 sounddevice-0.5.2\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "google", "numpy" ] }, "id": "39abba471a0d4db1a62010247933bb32" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "'''\n", "# --- Hammer 2.1 0.5b Model Configuration ---\n", "\n", "# 1. Set the path to your converted Hammer TFLite model.\n", "HAMMER_TFLITE_MODEL = '/content/ai-edge-torch/hammer_q8_ekv1280.tflite'\n", "\n", "# 2. Set the path to the Hammer tokenizer model file (e.g., 'tokenizer.model').\n", "# This is the actual tokenizer file, not the JSON configuration file.\n", "HAMMER_TOKENIZER_MODEL = '/content/tokenizer.model'\n", "\n", "# --- Define Hammer's Special Tokens and Prompt Structure ---\n", "\n", "START_TOKEN = \"\"\n", "\n", "# Stop tokens signal the end of a turn ('<|im_end|>') or the end of the entire text ('<|endoftext|>').\n", "STOP_TOKENS = [\"<|im_end|>\", \"<|endoftext|>\"]\n", "\n", "# The prefix to be added before the user's input.\n", "# Format: <|im_start|>user\\n\n", "PROMPT_PREFIX = \"<|im_start|>user\\n\"\n", "\n", "# The suffix to be added after the user's input, which cues the model's response.\n", "# Format: <|im_end|>\\n<|im_start|>assistant\\n\n", "PROMPT_SUFFIX = \"<|im_end|>\\n<|im_start|>assistant\\n\"\n", "\n", "# --- Create the MediaPipe GenAI Task Bundle ---\n", "\n", "print(f\"Starting to bundle the Hammer 2.1 0.5b model...\")\n", "print(f\"TFLite Model: {HAMMER_TFLITE_MODEL}\")\n", "print(f\"Tokenizer Model: {HAMMER_TOKENIZER_MODEL}\")\n", "\n", "# Define the output file path for the bundled model.\n", "output_filename = \"/content/hammer.task\"\n", "\n", "# Configure the bundler with Hammer's specific settings.\n", "config = bundler.BundleConfig(\n", " tflite_model=HAMMER_TFLITE_MODEL,\n", " tokenizer_model=HAMMER_TOKENIZER_MODEL,\n", " start_token=START_TOKEN,\n", " stop_tokens=STOP_TOKENS,\n", " prompt_prefix=PROMPT_PREFIX,\n", " prompt_suffix=PROMPT_SUFFIX,\n", " output_filename=output_filename,\n", ")\n", "\n", "# Create the bundle.\n", "bundler.create_bundle(config)\n", "\n", "print(f\"\\nSuccessfully created the model bundle at: {os.path.abspath(output_filename)}\")\n", "'''" ], "metadata": { "id": "bB4q3Cp-fjXN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "TFLITE_MODEL = \"/content/ai-edge-torch/hammer_q8_ekv1280.tflite\"\n", "TOKENIZER_MODEL = \"/content/tokenizer.model\"\n", "\n", "from mediapipe.tasks.python.genai import bundler\n", "\n", "config = bundler.BundleConfig(\n", " tflite_model=TFLITE_MODEL,\n", " tokenizer_model=TOKENIZER_MODEL,\n", " start_token=\"<|im_start|>user\\n\", # begin user turn\n", " stop_tokens=[\"<|im_end|>\"], # end of any turn\n", " output_filename=\"/content/hammer2p1_05b.task\",\n", " prompt_prefix=\"\", # prefix is handled via start_token\n", " prompt_suffix=\"<|im_end|>\\n<|im_start|>assistant\\n\", # close user, open assistant\n", ")\n", "bundler.create_bundle(config)\n" ], "metadata": { "id": "UrpxPv6v4JXD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "0TBsfzyl9FSb" }, "execution_count": null, "outputs": [] } ] }