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| -rw-r--r-- | fine_tune/bert/tutorials/05_model_outputs.ipynb | 395 | ||||
| -rw-r--r-- | fine_tune/bert/tutorials/05_output.py | 25 |
2 files changed, 420 insertions, 0 deletions
diff --git a/fine_tune/bert/tutorials/05_model_outputs.ipynb b/fine_tune/bert/tutorials/05_model_outputs.ipynb new file mode 100644 index 0000000..40155ea --- /dev/null +++ b/fine_tune/bert/tutorials/05_model_outputs.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:29:24.616302Z", + "start_time": "2022-07-10T02:29:18.081012Z" + } + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from transformers import BertModel, BertTokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:29:41.979562Z", + "start_time": "2022-07-10T02:29:24.618327Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n", + "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" + ] + } + ], + "source": [ + "model_name = 'bert-base-uncased'\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained(model_name)\n", + "model = BertModel.from_pretrained(model_name, output_hidden_states=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:23:46.325851Z", + "start_time": "2022-07-10T02:23:46.322584Z" + } + }, + "source": [ + "### 1. input " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:43:56.693361Z", + "start_time": "2022-07-10T02:43:56.691053Z" + } + }, + "outputs": [], + "source": [ + "text = \"After stealing money from the bank vault, the bank robber was seen \" \\\n", + " \"fishing on the Mississippi river bank.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:44:09.053456Z", + "start_time": "2022-07-10T02:44:09.049462Z" + } + }, + "outputs": [], + "source": [ + "token_input = tokenizer(text, return_tensors='pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:44:10.582118Z", + "start_time": "2022-07-10T02:44:10.558526Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': tensor([[ 101, 2044, 11065, 2769, 2013, 1996, 2924, 11632, 1010, 1996,\n", + " 2924, 27307, 2001, 2464, 5645, 2006, 1996, 5900, 2314, 2924,\n", + " 1012, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_input" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:45:01.400030Z", + "start_time": "2022-07-10T02:45:01.395465Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([[ 101, 2044, 11065, 2769, 2013, 1996, 2924, 11632, 1010, 1996,\n", + " 2924, 27307, 2001, 2464, 5645, 2006, 1996, 5900, 2314, 2924,\n", + " 1012, 102]]), torch.Size([1, 22]))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_input['input_ids'], token_input['input_ids'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- batch_size = 1， 只有一个句子，序列长度为 22（未 truncate 及 padding）\n", + "- " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:23:36.420705Z", + "start_time": "2022-07-10T02:23:36.418239Z" + } + }, + "source": [ + "### 2. model forward" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- forward\n", + " - embedding => encoder => pooler" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:46:02.848067Z", + "start_time": "2022-07-10T02:46:02.703744Z" + } + }, + "outputs": [], + "source": [ + "model.eval()\n", + "with torch.no_grad():\n", + " outputs = model(**token_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:23:44.578754Z", + "start_time": "2022-07-10T02:23:44.576132Z" + } + }, + "source": [ + "### 3. output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:40:09.069362Z", + "start_time": "2022-07-10T02:40:09.065437Z" + } + }, + "source": [ + "- len(outputs) == 3\n", + "- outputs[0]\n", + " - last_hidden_state, shape: batch_size\\*seq_len\\*hidden_size(1\\*22\\*768)\n", + "- outputs[1]\n", + " - pooler_output, shape: batch_size\\*hidden_size(1\\*768)\n", + " - Last layer hidden-state of the first token of the sequence (classification token, [CLS])\n", + "- outputs[2] (model.config.output_hidden_states = True) \n", + " - type: tuple\n", + " - one for the output of the embeddings(1), if the model has an embedding layer(12), + one for the output of each layer) \n", + " - (1+12)\\*(batch_size\\*seq_len\\*hidden_size) = 13\\*1\\*22\\*768\n", + " \n", + " \n", + " \n", + "- outputs[0] == outputs[2][-1]\n", + "- outputs[1] == model.pooler(outputs[2][-1])\n", + "- outputs[2][0] == model.embeddings(token_input['input_ids'], token_input['token_type_ids'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:46:38.864887Z", + "start_time": "2022-07-10T02:46:38.861110Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(outputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:48:44.742132Z", + "start_time": "2022-07-10T02:48:44.736806Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tuple, 13)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(outputs[2]), len(outputs[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:50:23.068317Z", + "start_time": "2022-07-10T02:50:23.059660Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " ...,\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True]]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outputs[0] == outputs[2][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:54:20.023961Z", + "start_time": "2022-07-10T02:54:20.014364Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " ...,\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True],\n", + " [True, True, True, ..., True, True, True]]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outputs[2][0] == model.embeddings(token_input['input_ids'], token_input['token_type_ids'])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T02:56:10.311946Z", + "start_time": "2022-07-10T02:56:10.307742Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 torch.Size([1, 22, 768])\n", + "1 torch.Size([1, 22, 768])\n", + "2 torch.Size([1, 22, 768])\n", + "3 torch.Size([1, 22, 768])\n", + "4 torch.Size([1, 22, 768])\n", + "5 torch.Size([1, 22, 768])\n", + "6 torch.Size([1, 22, 768])\n", + "7 torch.Size([1, 22, 768])\n", + "8 torch.Size([1, 22, 768])\n", + "9 torch.Size([1, 22, 768])\n", + "10 torch.Size([1, 22, 768])\n", + "11 torch.Size([1, 22, 768])\n", + "12 torch.Size([1, 22, 768])\n" + ] + } + ], + "source": [ + "for i in range(len(outputs[2])):\n", + " print(i, outputs[2][i].shape)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fine_tune/bert/tutorials/05_output.py b/fine_tune/bert/tutorials/05_output.py new file mode 100644 index 0000000..1911641 --- /dev/null +++ b/fine_tune/bert/tutorials/05_output.py @@ -0,0 +1,25 @@ +from transformers import BertModel, BertTokenizer +from transformers.models.bert import BertModel +import torch +from torch import nn + + +if __name__ == '__main__': + + model_name = 'bert-base-uncased' + + tokenizer = BertTokenizer.from_pretrained(model_name) + model = BertModel.from_pretrained(model_name, output_hidden_states=True) + + text = "After stealing money from the bank vault, the bank robber was seen " \ + "fishing on the Mississippi river bank." + + + token_inputs = tokenizer(text, return_tensors='pt') + with torch.no_grad(): + outputs = model(**token_inputs) + + + + + |