Comparing TF and PT models.ipynb

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   "metadata": {},
   "source": [
    "# TensorFlow code"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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     "end_time": "2018-11-02T13:05:56.692585Z",
     "start_time": "2018-11-02T13:05:55.699169Z"
    }
   },
   "outputs": [],
   "source": [
    "from extract_features import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:18:23.944585Z",
     "start_time": "2018-11-02T13:18:23.821309Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Example ***\n",
      "INFO:tensorflow:unique_id: 0\n",
      "INFO:tensorflow:tokens: [CLS] who was jim henson ? [SEP] jim henson was a puppet ##eer [SEP]\n",
      "INFO:tensorflow:input_ids: 101 2040 2001 3958 27227 1029 102 3958 27227 2001 1037 13997 11510 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "INFO:tensorflow:input_type_ids: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"
     ]
    }
   ],
   "source": [
    "data_dir=\"/Users/thomaswolf/Documents/Thomas/Code/HF/BERT/data/glue_data/MRPC/\"\n",
    "vocab_file=\"/Users/thomaswolf/Documents/Thomas/Code/HF/BERT/google_models/uncased_L-12_H-768_A-12/vocab.txt\"\n",
    "bert_config_file=\"/Users/thomaswolf/Documents/Thomas/Code/HF/BERT/google_models/uncased_L-12_H-768_A-12/bert_config.json\"\n",
    "init_checkpoint=\"/Users/thomaswolf/Documents/Thomas/Code/HF/BERT/google_models/uncased_L-12_H-768_A-12/bert_model.ckpt\"\n",
    "max_seq_length=128\n",
    "input_file=\"/Users/thomaswolf/Documents/Thomas/Code/HF/BERT/pytorch-pretrained-BERT/input.txt\"\n",
    "\n",
    "layer_indexes = [-1]\n",
    "bert_config = modeling.BertConfig.from_json_file(bert_config_file)\n",
    "tokenizer = tokenization.FullTokenizer(\n",
    "    vocab_file=vocab_file, do_lower_case=True)\n",
    "examples = read_examples(input_file)\n",
    "\n",
    "features = convert_examples_to_features(\n",
    "    examples=examples, seq_length=max_seq_length, tokenizer=tokenizer)\n",
    "unique_id_to_feature = {}\n",
    "for feature in features:\n",
    "    unique_id_to_feature[feature.unique_id] = feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:18:24.802620Z",
     "start_time": "2018-11-02T13:18:24.764474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Estimator's model_fn (<function model_fn_builder.<locals>.model_fn at 0x128feb7b8>) includes params argument, but params are not passed to Estimator.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmpp9hntmfs\n",
      "INFO:tensorflow:Using config: {'_model_dir': '/var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmpp9hntmfs', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1263809e8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=2, num_shards=1, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None), '_cluster': None}\n",
      "WARNING:tensorflow:Setting TPUConfig.num_shards==1 is an unsupported behavior. Please fix as soon as possible (leaving num_shards as None.\n",
      "INFO:tensorflow:_TPUContext: eval_on_tpu True\n",
      "WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False.\n"
     ]
    }
   ],
   "source": [
    "is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2\n",
    "run_config = tf.contrib.tpu.RunConfig(\n",
    "    master=None,\n",
    "    tpu_config=tf.contrib.tpu.TPUConfig(\n",
    "        num_shards=1,\n",
    "        per_host_input_for_training=is_per_host))\n",
    "\n",
    "model_fn = model_fn_builder(\n",
    "    bert_config=bert_config,\n",
    "    init_checkpoint=init_checkpoint,\n",
    "    layer_indexes=layer_indexes,\n",
    "    use_tpu=False,\n",
    "    use_one_hot_embeddings=False)\n",
    "\n",
    "# If TPU is not available, this will fall back to normal Estimator on CPU\n",
    "# or GPU.\n",
    "estimator = tf.contrib.tpu.TPUEstimator(\n",
    "    use_tpu=False,\n",
    "    model_fn=model_fn,\n",
    "    config=run_config,\n",
    "    predict_batch_size=1)\n",
    "\n",
    "input_fn = input_fn_builder(\n",
    "    features=features, seq_length=max_seq_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:19:20.060587Z",
     "start_time": "2018-11-02T13:19:14.804525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Could not find trained model in model_dir: /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmpp9hntmfs, running initialization to predict.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Running infer on CPU\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:prediction_loop marked as finished\n",
      "INFO:tensorflow:prediction_loop marked as finished\n"
     ]
    }
   ],
   "source": [
    "all_out = []\n",
    "for result in estimator.predict(input_fn, yield_single_examples=True):\n",
    "    unique_id = int(result[\"unique_id\"])\n",
    "    feature = unique_id_to_feature[unique_id]\n",
    "    output_json = collections.OrderedDict()\n",
    "    output_json[\"linex_index\"] = unique_id\n",
    "    all_features = []\n",
    "    for (i, token) in enumerate(feature.tokens):\n",
    "        all_layers = []\n",
    "        for (j, layer_index) in enumerate(layer_indexes):\n",
    "            layer_output = result[\"layer_output_%d\" % j]\n",
    "            layers = collections.OrderedDict()\n",
    "            layers[\"index\"] = layer_index\n",
    "            layers[\"values\"] = [\n",
    "                round(float(x), 6) for x in layer_output[i:(i + 1)].flat\n",
    "            ]\n",
    "            all_layers.append(layers)\n",
    "        features = collections.OrderedDict()\n",
    "        features[\"token\"] = token\n",
    "        features[\"layers\"] = all_layers\n",
    "        all_features.append(features)\n",
    "    output_json[\"features\"] = all_features\n",
    "    all_out.append(output_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:22:39.694206Z",
     "start_time": "2018-11-02T13:22:39.663432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_out)\n",
    "len(all_out[0])\n",
    "all_out[0].keys()\n",
    "len(all_out[0]['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:23:05.752981Z",
     "start_time": "2018-11-02T13:23:05.723891Z"
    }
   },
   "outputs": [],
   "source": [
    "tensorflow_output = all_out[0]['features'][0]['layers'][0]['values']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyTorch code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-02T13:24:27.644785Z",
     "start_time": "2018-11-02T13:24:27.611996Z"
    }
   },
   "outputs": [],
   "source": [
    "from extract_features_pytorch import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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