Fine-Tuning GPT2.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Fine-Tuning GPT2.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOnI1AodsqAe7WXpGfR3DCS"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"QWmtbdYHI9HN","colab_type":"text"},"source":["# Fine-Tuning GPT2 on Colab GPU… For Free!\n","\n","This is a colab notebook for the [associated Medium article](https://medium.com/p/340468c92ed)"]},{"cell_type":"markdown","metadata":{"id":"1ov2wQbRIs8J","colab_type":"text"},"source":["## Installing Dependencies\n","We would run pip3 install transformers normally in Bash, but because this is in Colab, we have to run it with !"]},{"cell_type":"code","metadata":{"id":"GVQ5Le5kF7CV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":615},"outputId":"54d7b8af-1cf4-4db7-8d3c-511a2a7d0a35","executionInfo":{"status":"ok","timestamp":1591992162918,"user_tz":240,"elapsed":8096,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["!pip3 install transformers"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting transformers\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/35/ad2c5b1b8f99feaaf9d7cdadaeef261f098c6e1a6a2935d4d07662a6b780/transformers-2.11.0-py3-none-any.whl (674kB)\n","\u001b[K     |████████████████████████████████| 675kB 9.5MB/s \n","\u001b[?25hCollecting sacremoses\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n","\u001b[K     |████████████████████████████████| 890kB 30.1MB/s \n","\u001b[?25hCollecting tokenizers==0.7.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/e5/a26eb4716523808bb0a799fcfdceb6ebf77a18169d9591b2f46a9adb87d9/tokenizers-0.7.0-cp36-cp36m-manylinux1_x86_64.whl (3.8MB)\n","\u001b[K     |████████████████████████████████| 3.8MB 56.6MB/s \n","\u001b[?25hCollecting sentencepiece\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n","\u001b[K     |████████████████████████████████| 1.1MB 55.9MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.5)\n","Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n","Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.12.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.15.1)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.4.5.1)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.9)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n","Building wheels for collected packages: sacremoses\n","  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=d41ca9d8d3385b1a6acd81e9bbe1f948ead27bca4956d7049191c9de32047d49\n","  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n","Successfully built sacremoses\n","Installing collected packages: sacremoses, tokenizers, sentencepiece, transformers\n","Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.7.0 transformers-2.11.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Wdfc4qUVKYLj","colab_type":"text"},"source":["## Getting WikiText Data\n","\n","You can read more about WikiText data here. Overall, there's WikiText-2 and WikiText-103. We're going to use WikiText-2 because it's smaller, and we have limits in terms of how long we can run on GPU, and how much data we can load into memory in Colab. To download and run"]},{"cell_type":"code","metadata":{"id":"C4RMT_FQIrGi","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"f9a6dec5-ac0e-4641-d875-6309dca0ee9e","executionInfo":{"status":"ok","timestamp":1591992710757,"user_tz":240,"elapsed":2352,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["%%bash\n","wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip\n","unzip wikitext-2-raw-v1.zip"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Archive:  wikitext-2-raw-v1.zip\n","   creating: wikitext-2-raw/\n","  inflating: wikitext-2-raw/wiki.test.raw  \n","  inflating: wikitext-2-raw/wiki.valid.raw  \n","  inflating: wikitext-2-raw/wiki.train.raw  \n"],"name":"stdout"},{"output_type":"stream","text":["--2020-06-12 20:11:48--  https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 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fine tune models here. We can just download the script by running"]},{"cell_type":"code","metadata":{"id":"E-L4LiHiKdr1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":224},"outputId":"fd458970-8959-4c8c-ae40-cac5df9a91c5","executionInfo":{"status":"ok","timestamp":1591993064226,"user_tz":240,"elapsed":2229,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["! wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/language-modeling/run_language_modeling.py"],"execution_count":6,"outputs":[{"output_type":"stream","text":["--2020-06-12 20:17:42--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/language-modeling/run_language_modeling.py\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 10399 (10K) [text/plain]\n","Saving to: ‘run_language_modeling.py’\n","\n","\rrun_language_modeli   0%[                    ]       0  --.-KB/s               \rrun_language_modeli 100%[===================>]  10.16K  --.-KB/s    in 0s      \n","\n","2020-06-12 20:17:43 (105 MB/s) - ‘run_language_modeling.py’ saved [10399/10399]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"sklH4LNoMxRC","colab_type":"text"},"source":["Now we are ready to fine tune.\n","\n","There are many parameters to the script, and you can understand them by reading the manual. I'm just going to go over the important ones for basic training.\n","\n","- `output_dir` is where the model will be output\n","- `model_type` is what model you want to use. In our case, it's gpt2 \n","- `model_name_or_path` is the path to the model. If you want to train from scratch, you can leave this blank. In our case, it's also gpt2 \n","- `do_train` tells it to train\n","- `train_data_file` points to the training file\n","- `do_eval` tells it to evaluate afterwards. Not always required, but good to have\n","- `eval_data_file` points to the evaluation file\n","\n","Some extra ones you MAY care about, but you can also skip this.\n","- `save_steps` is when to save checkpoints. If you have limited memory, you can set this to -1 so it'll skip saving until the end\n","- `per_gpu_train_batch_size` is batch size for GPU. You can increase this if your GPU has enough memory. To be safe, you can start with 1 and ramp it up if you still have memory\n","- `num_train_epochs` is the number of epochs to train. Since we're fine-tuning, I'm going to set this to 2\n"]},{"cell_type":"code","metadata":{"id":"Y6fspISiMx5V","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"43a1cbe6-e5a9-4ce3-d7b8-ee5a7eb5b95a","executionInfo":{"status":"ok","timestamp":1591994754510,"user_tz":240,"elapsed":1031973,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["%%bash\n","export TRAIN_FILE=wikitext-2-raw/wiki.train.raw\n","export TEST_FILE=wikitext-2-raw/wiki.test.raw\n","export MODEL_NAME=gpt2\n","export OUTPUT_DIR=output\n","\n","python run_language_modeling.py \\\n","    --output_dir=$OUTPUT_DIR \\\n","    --model_type=$MODEL_NAME \\\n","    --model_name_or_path=$MODEL_NAME \\\n","    --do_train \\\n","    --train_data_file=$TRAIN_FILE \\\n","    --do_eval \\\n","    --eval_data_file=$TEST_FILE \\\n","    --per_gpu_train_batch_size=1 \\\n","    --save_steps=-1 \\\n","    --num_train_epochs=2"],"execution_count":11,"outputs":[{"output_type":"stream","text":["{\"loss\": 3.3095970368385315, \"learning_rate\": 4.470563320626853e-05, \"epoch\": 0.2117746717492588, \"step\": 500}\n","{\"loss\": 3.165794837236404, \"learning_rate\": 3.9411266412537063e-05, \"epoch\": 0.4235493434985176, \"step\": 1000}\n","{\"loss\": 3.146680611371994, \"learning_rate\": 3.4116899618805594e-05, \"epoch\": 0.6353240152477764, \"step\": 1500}\n","{\"loss\": 3.1375980377197266, \"learning_rate\": 2.882253282507412e-05, \"epoch\": 0.8470986869970352, \"step\": 2000}\n","{\"loss\": 3.0865356652736664, \"learning_rate\": 2.352816603134265e-05, \"epoch\": 1.058873358746294, \"step\": 2500}\n","{\"loss\": 2.963025534391403, \"learning_rate\": 1.8233799237611182e-05, \"epoch\": 1.2706480304955527, \"step\": 3000}\n","{\"loss\": 2.9398239650726317, \"learning_rate\": 1.2939432443879713e-05, \"epoch\": 1.4824227022448115, \"step\": 3500}\n","{\"loss\": 2.9378042299747467, \"learning_rate\": 7.645065650148241e-06, \"epoch\": 1.6941973739940703, \"step\": 4000}\n","{\"loss\": 2.9477706546783446, \"learning_rate\": 2.3506988564167727e-06, \"epoch\": 1.9059720457433291, \"step\": 4500}\n","{\"eval_loss\": 2.985287094116211, \"epoch\": 2.0, \"step\": 4722}\n"],"name":"stdout"},{"output_type":"stream","text":["2020-06-12 20:28:43.643915: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n","06/12/2020 20:28:45 - INFO - transformers.training_args -   PyTorch: setting up devices\n","06/12/2020 20:28:45 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n","06/12/2020 20:28:45 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(output_dir='output', overwrite_output_dir=False, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, per_gpu_train_batch_size=1, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=2.0, max_steps=-1, warmup_steps=0, logging_dir=None, logging_first_step=False, logging_steps=500, save_steps=-1, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False)\n","06/12/2020 20:28:45 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json from cache at /root/.cache/torch/transformers/4be02c5697d91738003fb1685c9872f284166aa32e061576bbe6aaeb95649fcf.db13c9bc9c7bdd738ec89e069621d88e05dc670366092d809a9cbcac6798e24e\n","06/12/2020 20:28:45 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n","  \"activation_function\": \"gelu_new\",\n","  \"architectures\": [\n","    \"GPT2LMHeadModel\"\n","  ],\n","  \"attn_pdrop\": 0.1,\n","  \"bos_token_id\": 50256,\n","  \"embd_pdrop\": 0.1,\n","  \"eos_token_id\": 50256,\n","  \"initializer_range\": 0.02,\n","  \"layer_norm_epsilon\": 1e-05,\n","  \"model_type\": \"gpt2\",\n","  \"n_ctx\": 1024,\n","  \"n_embd\": 768,\n","  \"n_head\": 12,\n","  \"n_layer\": 12,\n","  \"n_positions\": 1024,\n","  \"resid_pdrop\": 0.1,\n","  \"summary_activation\": null,\n","  \"summary_first_dropout\": 0.1,\n","  \"summary_proj_to_labels\": true,\n","  \"summary_type\": \"cls_index\",\n","  \"summary_use_proj\": true,\n","  \"task_specific_params\": {\n","    \"text-generation\": {\n","      \"do_sample\": true,\n","      \"max_length\": 50\n","    }\n","  },\n","  \"vocab_size\": 50257\n","}\n","\n","06/12/2020 20:28:46 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json from cache at /root/.cache/torch/transformers/4be02c5697d91738003fb1685c9872f284166aa32e061576bbe6aaeb95649fcf.db13c9bc9c7bdd738ec89e069621d88e05dc670366092d809a9cbcac6798e24e\n","06/12/2020 20:28:46 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n","  \"activation_function\": \"gelu_new\",\n","  \"architectures\": [\n","    \"GPT2LMHeadModel\"\n","  ],\n","  \"attn_pdrop\": 0.1,\n","  \"bos_token_id\": 50256,\n","  \"embd_pdrop\": 0.1,\n","  \"eos_token_id\": 50256,\n","  \"initializer_range\": 0.02,\n","  \"layer_norm_epsilon\": 1e-05,\n","  \"model_type\": \"gpt2\",\n","  \"n_ctx\": 1024,\n","  \"n_embd\": 768,\n","  \"n_head\": 12,\n","  \"n_layer\": 12,\n","  \"n_positions\": 1024,\n","  \"resid_pdrop\": 0.1,\n","  \"summary_activation\": null,\n","  \"summary_first_dropout\": 0.1,\n","  \"summary_proj_to_labels\": true,\n","  \"summary_type\": \"cls_index\",\n","  \"summary_use_proj\": true,\n","  \"task_specific_params\": {\n","    \"text-generation\": {\n","      \"do_sample\": true,\n","      \"max_length\": 50\n","    }\n","  },\n","  \"vocab_size\": 50257\n","}\n","\n","06/12/2020 20:28:46 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json from cache at /root/.cache/torch/transformers/f2808208f9bec2320371a9f5f891c184ae0b674ef866b79c58177067d15732dd.1512018be4ba4e8726e41b9145129dc30651ea4fec86aa61f4b9f40bf94eac71\n","06/12/2020 20:28:46 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt from cache at /root/.cache/torch/transformers/d629f792e430b3c76a1291bb2766b0a047e36fae0588f9dbc1ae51decdff691b.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda\n","06/12/2020 20:28:47 - INFO - transformers.modeling_utils -   loading weights file https://cdn.huggingface.co/gpt2-pytorch_model.bin from cache at /root/.cache/torch/transformers/d71fd633e58263bd5e91dd3bde9f658bafd81e11ece622be6a3c2e4d42d8fd89.778cf36f5c4e5d94c8cd9cefcf2a580c8643570eb327f0d4a1f007fab2acbdf1\n","06/12/2020 20:28:51 - INFO - transformers.modeling_utils -   Weights of GPT2LMHeadModel not initialized from pretrained model: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias', 'lm_head.weight']\n","06/12/2020 20:28:51 - INFO - filelock -   Lock 139626457554896 acquired on wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.train.raw.lock\n","06/12/2020 20:28:51 - INFO - transformers.data.datasets.language_modeling -   Loading features from cached file wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.train.raw [took 0.099 s]\n","06/12/2020 20:28:51 - INFO - filelock -   Lock 139626457554896 released on wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.train.raw.lock\n","06/12/2020 20:28:51 - INFO - filelock -   Lock 139626457554728 acquired on wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.test.raw.lock\n","06/12/2020 20:28:51 - INFO - transformers.data.datasets.language_modeling -   Loading features from cached file wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.test.raw [took 0.010 s]\n","06/12/2020 20:28:51 - INFO - filelock -   Lock 139626457554728 released on wikitext-2-raw/cached_lm_GPT2Tokenizer_1024_wiki.test.raw.lock\n","06/12/2020 20:28:54 - INFO - transformers.trainer -   You are instantiating a Trainer but W&B is not installed. To use wandb logging, run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface.\n","06/12/2020 20:28:54 - WARNING - transformers.training_args -   Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future version. Using `--per_device_train_batch_size` is preferred.\n","06/12/2020 20:28:55 - WARNING - transformers.training_args -   Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future version. Using `--per_device_train_batch_size` is preferred.\n","06/12/2020 20:28:55 - INFO - transformers.trainer -   ***** Running training *****\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Num examples = 2361\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Num Epochs = 2\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Instantaneous batch size per device = 8\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 1\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Gradient Accumulation steps = 1\n","06/12/2020 20:28:55 - INFO - transformers.trainer -     Total optimization steps = 4722\n","\rEpoch:   0%|          | 0/2 [00:00<?, ?it/s]\n","\rIteration:   0%|          | 0/2361 [00:00<?, ?it/s]\u001b[A\n","\rIteration:   0%|          | 1/2361 [00:00<14:08,  2.78it/s]\u001b[A\n","\rIteration:   0%|          | 2/2361 [00:00<12:21,  3.18it/s]\u001b[A\n","\rIteration:   0%|       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Do not forget to share your model on huggingface.co/models =)\n","\n","\n","06/12/2020 20:45:35 - INFO - transformers.trainer -   Saving model checkpoint to output\n","06/12/2020 20:45:35 - INFO - transformers.configuration_utils -   Configuration saved in output/config.json\n","06/12/2020 20:45:36 - INFO - transformers.modeling_utils -   Model weights saved in output/pytorch_model.bin\n","06/12/2020 20:45:37 - INFO - __main__ -   *** Evaluate ***\n","06/12/2020 20:45:37 - INFO - transformers.trainer -   ***** Running Evaluation *****\n","06/12/2020 20:45:37 - INFO - transformers.trainer -     Num examples = 279\n","06/12/2020 20:45:37 - INFO - transformers.trainer -     Batch size = 8\n","\rEvaluation:   0%|          | 0/35 [00:00<?, ?it/s]\rEvaluation:   3%|▎         | 1/35 [00:00<00:15,  2.18it/s]\rEvaluation:   6%|▌         | 2/35 [00:00<00:15,  2.18it/s]\rEvaluation:   9%|▊         | 3/35 [00:01<00:14,  2.18it/s]\rEvaluation:  11%|█▏        | 4/35 [00:01<00:14,  2.19it/s]\rEvaluation:  14%|█▍        | 5/35 [00:02<00:13,  2.19it/s]\rEvaluation:  17%|█▋        | 6/35 [00:02<00:13,  2.19it/s]\rEvaluation:  20%|██        | 7/35 [00:03<00:12,  2.19it/s]\rEvaluation:  23%|██▎       | 8/35 [00:03<00:12,  2.19it/s]\rEvaluation:  26%|██▌       | 9/35 [00:04<00:11,  2.19it/s]\rEvaluation:  29%|██▊       | 10/35 [00:04<00:11,  2.19it/s]\rEvaluation:  31%|███▏      | 11/35 [00:05<00:10,  2.19it/s]\rEvaluation:  34%|███▍      | 12/35 [00:05<00:10,  2.19it/s]\rEvaluation:  37%|███▋      | 13/35 [00:05<00:10,  2.19it/s]\rEvaluation:  40%|████      | 14/35 [00:06<00:09,  2.19it/s]\rEvaluation:  43%|████▎     | 15/35 [00:06<00:09,  2.19it/s]\rEvaluation:  46%|████▌     | 16/35 [00:07<00:08,  2.19it/s]\rEvaluation:  49%|████▊     | 17/35 [00:07<00:08,  2.19it/s]\rEvaluation:  51%|█████▏    | 18/35 [00:08<00:07,  2.19it/s]\rEvaluation:  54%|█████▍    | 19/35 [00:08<00:07,  2.19it/s]\rEvaluation:  57%|█████▋    | 20/35 [00:09<00:06,  2.19it/s]\rEvaluation:  60%|██████    | 21/35 [00:09<00:06,  2.19it/s]\rEvaluation:  63%|██████▎   | 22/35 [00:10<00:05,  2.19it/s]\rEvaluation:  66%|██████▌   | 23/35 [00:10<00:05,  2.19it/s]\rEvaluation:  69%|██████▊   | 24/35 [00:10<00:05,  2.19it/s]\rEvaluation:  71%|███████▏  | 25/35 [00:11<00:04,  2.19it/s]\rEvaluation:  74%|███████▍  | 26/35 [00:11<00:04,  2.19it/s]\rEvaluation:  77%|███████▋  | 27/35 [00:12<00:03,  2.19it/s]\rEvaluation:  80%|████████  | 28/35 [00:12<00:03,  2.19it/s]\rEvaluation:  83%|████████▎ | 29/35 [00:13<00:02,  2.19it/s]\rEvaluation:  86%|████████▌ | 30/35 [00:13<00:02,  2.19it/s]\rEvaluation:  89%|████████▊ | 31/35 [00:14<00:01,  2.19it/s]\rEvaluation:  91%|█████████▏| 32/35 [00:14<00:01,  2.19it/s]\rEvaluation:  94%|█████████▍| 33/35 [00:15<00:00,  2.19it/s]\rEvaluation:  97%|█████████▋| 34/35 [00:15<00:00,  2.19it/s]\rEvaluation: 100%|██████████| 35/35 [00:15<00:00,  2.29it/s]\rEvaluation: 100%|██████████| 35/35 [00:15<00:00,  2.20it/s]\n","06/12/2020 20:45:53 - INFO - __main__ -   ***** Eval results *****\n","06/12/2020 20:45:53 - INFO - __main__ -     perplexity = 19.79218364025258\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"s0Fwb6TEUgj1","colab_type":"text"},"source":["## Results\n","\n","To use it, you can run something like"]},{"cell_type":"code","metadata":{"id":"EGAlyaB3Sfiu","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":156},"outputId":"ebb32a7a-7351-4f09-8c2f-0a82e72aa0f8","executionInfo":{"status":"ok","timestamp":1591995232282,"user_tz":240,"elapsed":8491,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["from transformers import GPT2Tokenizer, GPT2LMHeadModel\n","import torch\n","import numpy as np\n","\n","OUTPUT_DIR = \"./output\"\n","device = 'cpu'\n","if torch.cuda.is_available():\n","    device = 'cuda'\n","\n","tokenizer = GPT2Tokenizer.from_pretrained(OUTPUT_DIR)\n","model = GPT2LMHeadModel.from_pretrained(OUTPUT_DIR)\n","model = model.to(device)\n","                                        \n","def generate(input_str, length=250, n=5):\n","  cur_ids = torch.tensor(tokenizer.encode(input_str)).unsqueeze(0).long().to(device)\n","  model.eval()\n","  with torch.no_grad():\n","    for i in range(length):\n","      outputs = model(cur_ids[:, -1024:], labels=cur_ids[:, -1024:])\n","      loss, logits = outputs[:2]\n","      softmax_logits = torch.softmax(logits[0,-1], dim=0)\n","      next_token_id = choose_from_top(softmax_logits.to('cpu').numpy(), n=n)\n","      cur_ids = torch.cat([cur_ids, torch.ones((1,1)).long().to(device) * next_token_id], dim=1)\n","    output_list = list(cur_ids.squeeze().to('cpu').numpy())\n","    output_text = tokenizer.decode(output_list)\n","    return output_text\n","\n","def choose_from_top(probs, n=5):\n","    ind = np.argpartition(probs, -n)[-n:]\n","    top_prob = probs[ind]\n","    top_prob = top_prob / np.sum(top_prob) # Normalize\n","    choice = np.random.choice(n, 1, p = top_prob)\n","    token_id = ind[choice][0]\n","    return int(token_id)\n","\n","generated_text = generate(\" = Toronto Raptors = \\n\")\n","print(generated_text)"],"execution_count":17,"outputs":[{"output_type":"stream","text":[" = Toronto Raptors = \n"," \n"," Toronto's first @-@ round draft pick in 2006 was selected by the Toronto Raptors with the seventh overall pick. He played in all 82 games, averaging 18 points per game and 6 assists. The Raptors won their third straight NBA championship in 2007, and won the 2009 NBA All @-@ Star Game. He played in a record 16 games for Toronto, averaging 19 points on 5 @.@ 6 rebounds and 6 assists in a season that saw Toronto win the Eastern Conference finals. He also played in the 2008 All @-@ Star Game and was named to the All @-@ Star Game MVP for the first time. He also was named to the All @-@ Star Game's all @-@ time career scoring list, and was the first player to accomplish the feat. He finished the season with an assist and an assist in eight games, and was the first player in NBA history to score in double figures. He was named to the All @-@ Star Game's All @-@ time scoring list in 2011, and was the first player to do this in consecutive seasons. \n"," \n"," = = Draft = = \n"," \n"," Toronto selected Jordan Matthews with the seventh overall pick in\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KvLfcwTOUnlt","colab_type":"text"},"source":["## Compressing/Zipping Model\n","\n","In order for us to preserve this model, we should compress it and save it somewhere. This can be done easily with"]},{"cell_type":"code","metadata":{"id":"W4zDMZZdUkW2","colab_type":"code","colab":{}},"source":["! tar -czf gpt2-tuned.tar.gz output/"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LT8ffxEsVL0-","colab_type":"text"},"source":["which creates a file called `gpt2-tuned.tar.gz`"]},{"cell_type":"markdown","metadata":{"id":"8vGGDE3bVO6s","colab_type":"text"},"source":["## Saving it to Google Drive"]},{"cell_type":"code","metadata":{"id":"X9J6NUCkVPpn","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"92ecdae4-67e4-4d90-e91d-2cea4e5f1411","executionInfo":{"status":"ok","timestamp":1591995754331,"user_tz":240,"elapsed":1229,"user":{"displayName":"Joey Sham","photoUrl":"","userId":"07509763358611462220"}}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_MmLCVxSXDc-","colab_type":"text"},"source":["Now you can copy your output model to your Google Drive by running"]},{"cell_type":"code","metadata":{"id":"XyUwAAqJXMxS","colab_type":"code","colab":{}},"source":["!cp gpt2-tuned.tar.gz /content/drive/My\\ Drive/"],"execution_count":0,"outputs":[]}]}