Determine_batch_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kqOqTZuKeJoa",
    "outputId": "9f63819c-9bc1-4c15-e9cd-9c1121edd2a6"
   },
   "outputs": [],
   "source": [
    "#!pip install \"jax[tpu]>=0.2.16\" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SQ-lhEVFeY4d",
    "outputId": "7346c6b8-1848-4755-c114-94d6de50b50d"
   },
   "outputs": [],
   "source": [
    "#!git clone https://github.com/huggingface/transformers.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9qSTMLvFfBVs",
    "outputId": "40659f61-86d4-4ae5-9262-501557737705"
   },
   "outputs": [],
   "source": [
    "#!pip install ./transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "7Og7zRTrfm08"
   },
   "outputs": [],
   "source": [
    "#!pip install jaxlib>=0.2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "nmQv7VMaf1L8"
   },
   "outputs": [],
   "source": [
    "#!pip install flax>=0.3.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "MT6jpop-f4dc"
   },
   "outputs": [],
   "source": [
    "#!pip install optax>=0.0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# !pip install jupyterlab_widgets\n",
    "# !pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "-F5NIqDmfDLb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-08 10:11:37.310929: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "from transformers import (\n",
    "    CONFIG_MAPPING,\n",
    "    FLAX_MODEL_FOR_MASKED_LM_MAPPING,\n",
    "    BatchEncoding,\n",
    "    FlaxT5ForConditionalGeneration,\n",
    "    T5ForConditionalGeneration,\n",
    "    HfArgumentParser,\n",
    "    PreTrainedTokenizerBase,\n",
    "    T5Config,\n",
    "    T5TokenizerFast,\n",
    "    TrainingArguments,\n",
    "    is_tensorboard_available,\n",
    "    set_seed,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "aInICxY6gREQ"
   },
   "outputs": [],
   "source": [
    "import flax\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import optax\n",
    "from flax import jax_utils, traverse_util\n",
    "from flax.training import train_state\n",
    "from flax.training.common_utils import get_metrics, onehot, shard\n",
    "from transformers import (\n",
    "    CONFIG_MAPPING,\n",
    "    FLAX_MODEL_FOR_MASKED_LM_MAPPING,\n",
    "    BatchEncoding,\n",
    "    FlaxT5ForConditionalGeneration,\n",
    "    T5ForConditionalGeneration,\n",
    "    HfArgumentParser,\n",
    "    PreTrainedTokenizerBase,\n",
    "    T5Config,\n",
    "    T5TokenizerFast,\n",
    "    TrainingArguments,\n",
    "    is_tensorboard_available,\n",
    "    set_seed,\n",
    ")\n",
    "from transformers.models.t5.modeling_flax_t5 import shift_tokens_right\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "iEqVlHptfOCT"
   },
   "outputs": [],
   "source": [
    "tokenizer = T5TokenizerFast.from_pretrained(\"t5-small\")\n",
    "config = T5Config.from_pretrained(\"t5-small\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LNETw3cWfjbr",
    "outputId": "95c0e750-c087-46dd-92fa-39f8ff0238f2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Starting the local TPU driver.\n",
      "INFO:absl:Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://\n",
      "INFO:absl:Unable to initialize backend 'gpu': Not found: Could not find registered platform with name: \"cuda\". Available platform names are: TPU Interpreter Host\n"
     ]
    }
   ],
   "source": [
    "model = FlaxT5ForConditionalGeneration(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "T5F3BEA2f6xE"
   },
   "outputs": [],
   "source": [
    "input_ids = np.asarray(208 * [512 * [1]], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_forward(input_ids, params):\n",
    "    return model(input_ids, decoder_input_ids=input_ids).logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "jitted_forward = jax.jit(run_forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits = jitted_forward(input_ids, model.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "name": "Untitled1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}