{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "IMDb_baseline.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o9LbB0krq6z2",
"colab_type": "text"
},
"source": [
"# Prepare"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jMnqAd1HJ1Br",
"colab_type": "text"
},
"source": [
"## References\n",
"* [fastai/course-v3/nbs/dl1/lesson3-imdb.ipynb](https://nbviewer.jupyter.org/github/fastai/course-v3/blob/master/nbs/dl1/lesson3-imdb.ipynb) (2019-05-03)\n",
"\n",
"learner|bs|lr |bptt|wd |drop_mult|`to_fp16(clip)`|fw acc%\n",
"-- |-:|--: |--: |--: |--: |-- |--:\n",
"lm |48|1e-2|70 |0.01|0.3 |No |34.1371\n",
"cf |48|2e-2|70 |0.01|0.5 |No |94.3960\n",
"\n",
"* [fastai/fastai/examples/ULMFit.ipynb](https://nbviewer.jupyter.org/github/fastai/fastai/blob/master/examples/ULMFit.ipynb) (2019-06-11)\n",
"\n",
" > Fine-tuning a forward and backward langauge model to get to 95.4% accuracy on the IMDB movie reviews dataset. This tutorial is done with fastai v1.0.53.\n",
"\n",
" > The example was run on a Titan RTX (24 GB of RAM) so you will probably need to adjust the batch size accordinly. If you divide it by 2, don't forget to divide the learning rate by 2 as well in the following cells. You can also reduce a little bit the bptt to gain a bit of memory.\n",
"\n",
"learner|bs |lr |bptt|wd |drop_mult|`to_fp16(clip)`|fw acc%|bw acc%|avg acc%\n",
"-- |--:|--: |--: |--: |--: |-- |--: |--: |--:\n",
"lm |256|2e-2| 80|0.1 |1.0 |Yes; clip=0.1 |34.0075|37.2268|N/A\n",
"cf |128|1e-1| 70†|0.1‡|0.5 |No |94.9560|94.7400|95.39\n",
"\n",
" † both cf's used default bptt=70\n",
" ‡ forward cf used default wd=0.01\n",
"\n",
"* [fastai/course-nlp/nn-imdb-more.ipynb](https://nbviewer.jupyter.org/github/fastai/course-nlp/blob/master/nn-imdb-more.ipynb) (2019-06-12)\n",
"\n",
"learner|bs |lr |bptt|wd |drop_mult|`to_fp16(clip)`|fw acc%\n",
"-- |--:|--: |--: |--: |--: |-- |--:\n",
"lm |128|1e-2*bs/48| 70|0.01|1.0 |Yes; clip=None |>27.8265†\n",
"cf |128|2e-2*bs/48| 70|0.01|0.5 |Yes; clip=None |94.8080\n",
"\n",
" † forward lm's final accuracy wasn't shown"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QF718toQqs83",
"colab_type": "code",
"colab": {}
},
"source": [
"# Ensure GPU spec; T4 is for colab and one can change it for another env.\n",
"gpu_list = !nvidia-smi -L\n",
"if gpu_list[0].startswith('NVIDIA-SMI has failed'):\n",
" print('Runtime type should be GPU.')\n",
"elif not gpu_list[0].startswith('GPU 0: Tesla T4'):\n",
" display(gpu_list)\n",
" print('Please reset all runtimes. We need a Tesla T4 to reproduce the experiments!')\n",
"else:\n",
" display(gpu_list)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0OcNDqMgrCca",
"colab_type": "text"
},
"source": [
"## Dependency"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jrjoIzboW09N",
"colab_type": "text"
},
"source": [
"### Install"
]
},
{
"cell_type": "code",
"metadata": {
"id": "2rwtTuykrEWy",
"colab_type": "code",
"colab": {}
},
"source": [
"# Ensure no surprises from conflict packages.\n",
"!pip check"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bymTjo9wrz5f",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture pip_logs\n",
"!pip install -U fastai==1.0.57 ipyexperiments jupyter-console==5.2.0 coverage==4.5.3 coveralls datascience albumentations"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ojbk71i1xaFi",
"colab_type": "code",
"colab": {}
},
"source": [
"# If package updates demands a restart, a colab_vnd generated button will show.\n",
"colab_vnd = 'application/vnd.colab-display-data+json'\n",
"for o in pip_logs.outputs:\n",
" if colab_vnd in o.data and 'pip_warning' in o.data[colab_vnd]:\n",
" o.display()\n",
"!pip check"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7z1_46NArGi_",
"colab_type": "text"
},
"source": [
"### Import"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LMm8qgQqD2eC",
"colab_type": "code",
"colab": {}
},
"source": [
"import gc\n",
"import math\n",
"from pathlib import Path\n",
"import pickle\n",
"import random\n",
"from shutil import copytree\n",
"from typing import Optional, Tuple\n",
"\n",
"import numpy as np\n",
"import torch\n",
"from google.colab import drive\n",
"\n",
"from fastai import basic_data, basic_train, core\n",
"from fastai import *\n",
"from fastai.callbacks import CSVLogger, MixedPrecision\n",
"from fastai.core import plt\n",
"from fastai.text import *\n",
"from fastprogress import fastprogress\n",
"\n",
"from ipyexperiments import *"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "DTVchfsRnLgI",
"colab_type": "text"
},
"source": [
"### Init\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QnxWkqafNxvA",
"colab_type": "code",
"colab": {}
},
"source": [
"# Not set earlier because pip may require a restart.\n",
"SESSN_START, = !date +%Y%m%dT%H%M"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iTX1BGNQN6VB",
"colab_type": "code",
"colab": {}
},
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%config InlineBackend.figure_formats = {'png', 'retina'}"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wGlH0nWchbZL",
"colab_type": "code",
"colab": {}
},
"source": [
"# Stylize the plot of `lr_find()`\n",
"plt.style.use(['dark_background','seaborn-poster','seaborn-deep'])\n",
"plt.rcParams['axes.grid'] = True\n",
"plt.rcParams['axes.grid.axis'] = 'x'\n",
"plt.rcParams['axes.grid.which'] = 'both'\n",
"plt.rcParams['grid.alpha'] = 0.5\n",
"plt.rcParams['grid.color'] = 'xkcd:lime green'\n",
"plt.rcParams['grid.linestyle'] = ':'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "db_RafAMN2mK",
"colab_type": "code",
"colab": {}
},
"source": [
"# A special treatment for colab to decrease network traffic.\n",
"fastprogress.NO_BAR = True\n",
"master_bar, progress_bar = fastprogress.force_console_behavior()\n",
"basic_train.master_bar, basic_train.progress_bar = master_bar, progress_bar\n",
"basic_data.master_bar, basic_data.progress_bar = master_bar, progress_bar\n",
"dataclass.master_bar, dataclass.progress_bar = master_bar, progress_bar\n",
"text.master_bar, text.progress_bar = master_bar, progress_bar\n",
"text.data.master_bar, text.data.progress_bar = master_bar, progress_bar\n",
"core.master_bar, core.progress_bar = master_bar, progress_bar"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iDMmtPemp8H3",
"colab_type": "code",
"colab": {}
},
"source": [
"COLAB_CONTENT_DIR = Path('/content')\n",
"GD_DIR = COLAB_CONTENT_DIR / 'gdrive'\n",
"drive.mount(str(GD_DIR), force_remount=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uDRg1_MUpXjy",
"colab_type": "code",
"colab": {}
},
"source": [
"CORPUS = 'imdb'\n",
"BASE_DIR = GD_DIR / 'My Drive' / CORPUS\n",
"BASE_DIR.mkdir(parents=True, exist_ok=True)\n",
"DATA_DIR = BASE_DIR / 'data'\n",
"DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
"MDLS_DIR = BASE_DIR / 'models'\n",
"MDLS_DIR.mkdir(parents=True, exist_ok=True)\n",
"LOGS_DIR = BASE_DIR / 'logs'\n",
"LOGS_DIR.mkdir(parents=True, exist_ok=True)\n",
"\n",
"FASTAI_DATA_DIR = Path('/root/.fastai/data')\n",
"FASTAI_DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
"COLAB_DATA_DIR = COLAB_CONTENT_DIR / 'data'\n",
"if not COLAB_DATA_DIR.is_symlink():\n",
" COLAB_DATA_DIR.symlink_to(FASTAI_DATA_DIR)\n",
"if (COLAB_CONTENT_DIR / 'sample_data').exists():\n",
" !rm -rf /content/sample_data/\n",
"\n",
"CORPUS_IN_COLAB_DATA_DIR = COLAB_DATA_DIR / CORPUS"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2GRWbysx7IRg",
"colab_type": "code",
"colab": {}
},
"source": [
"downloaded_corpus_dir = untar_data(URLs.IMDB, dest=COLAB_DATA_DIR)\n",
"assert downloaded_corpus_dir == CORPUS_IN_COLAB_DATA_DIR"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "O2RKejF7YdPR",
"colab_type": "text"
},
"source": [
"# Assign"
]
},
{
"cell_type": "code",
"metadata": {
"id": "y18xZgbnXVMo",
"colab_type": "code",
"colab": {}
},
"source": [
"#@title Hyper-parameters\n",
"\n",
"lm_bs = 64 #@param {type: \"number\"}\n",
"cf_bs = 64 #@param {type: \"number\"}\n",
"bptt = 70 #@param {type: \"number\"}\n",
"moms = (0.8, 0.7) #@param\n",
"\n",
"REFERRED_LM_LR = 1e-2 * lm_bs / 48\n",
"REFERRED_CF_LR = 2e-2 * cf_bs / 48\n",
"\n",
"#@markdown ---\n",
"\n",
"lm_wd = 0.01 #@param {type: \"number\"}\n",
"cf_wd = 0.01 #@param {type: \"number\"}\n",
"lm_drop_mult = 1.0 #@param {type: \"number\"}\n",
"cf_drop_mult = 0.5 #@param {type: \"number\"}\n",
"\n",
"FW_LM_DBNCH_FNAME = f'fw_lm_dbnch-b{lm_bs}-bptt{bptt}.pkl'\n",
"FW_CF_DBNCH_FNAME = f'fw_cf_dbnch-b{cf_bs}-bptt{bptt}.pkl'\n",
"\n",
"VOCAB_FILE = DATA_DIR / f'imdb_vocab.pkl'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ruGmJGQX9zh-",
"colab_type": "code",
"colab": {}
},
"source": [
"IMDB_CLASSES = ['neg', 'pos']"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "P381SP4eYxv8",
"colab_type": "code",
"colab": {}
},
"source": [
"# Set num_workers to main process since the training set will be shuffled.\n",
"N_DBNCH_WRKRS = 0"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1iOEouOl0WkK",
"colab_type": "code",
"colab": {}
},
"source": [
"# One seed to rule pseudo-random number generators all.\n",
"SEED = 42"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "CEfPdqf-I-bZ",
"colab_type": "text"
},
"source": [
"# Define"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3S70SGOwBDp_",
"colab_type": "text"
},
"source": [
"All those helpers are for demonstration. Ideal implementations would be decorators or callbacks.\n",
"\n",
"However, although fastai prefers callbacks, the order of callback invocations will be another issue."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tAhmWnGMZh7q",
"colab_type": "text"
},
"source": [
"## PRNG State Fixer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "2QAli-Wt0Z1M",
"colab_type": "code",
"colab": {}
},
"source": [
"@dataclass\n",
"class PseudoRandomStatesHolder:\n",
" py3_state: Tuple[int, Tuple[int], Optional[float]]\n",
" np_state: Tuple[str, np.ndarray, int, int, float]\n",
" torch_state: torch.ByteTensor\n",
" cuda_states: List[torch.ByteTensor]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "H6ETnToMbEuG",
"colab_type": "code",
"colab": {}
},
"source": [
"# fastai v1.0.57 does something similar but it won't suffice.\n",
"def reset_prng_states(seed=SEED):\n",
" random.seed(SEED)\n",
" np.random.seed(SEED)\n",
" torch.manual_seed(SEED) # This implies torch.cuda.manual_seed_all(SEED) now\n",
" # if torch.cuda.is_available(): torch.cuda.manual_seed_all(SEED)\n",
" torch.backends.cudnn.deterministic = True # About 15% slower but...\n",
" torch.backends.cudnn.benchmark = False"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ACszEIL30lzu",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_prng_states():\n",
" prng_states = PseudoRandomStatesHolder(random.getstate(),\n",
" np.random.get_state(),\n",
" torch.get_rng_state(),\n",
" torch.cuda.get_rng_state_all())\n",
" # print(f'Got prng_states:\\n'\n",
" # f' py3: {prng_states.py3_state}\\n'\n",
" # f' np: {prng_states.np_state}\\n'\n",
" # f' torch: {prng_states.torch_state}\\n'\n",
" # f' cuda: {prng_states.cuda_states}')\n",
" return prng_states"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "j7RHtjHM0n4u",
"colab_type": "code",
"colab": {}
},
"source": [
"def set_prng_states(prng_states: PseudoRandomStatesHolder):\n",
" random.setstate(prng_states.py3_state)\n",
" np.random.set_state(prng_states.np_state)\n",
" torch.set_rng_state(prng_states.torch_state)\n",
" torch.cuda.set_rng_state_all(prng_states.cuda_states)\n",
" # print(f'Set prng_states:\\n'\n",
" # f' py3: {prng_states.py3_state}\\n'\n",
" # f' np: {prng_states.np_state}\\n'\n",
" # f' torch: {prng_states.torch_state}\\n'\n",
" # f' cuda: {prng_states.cuda_states}')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JIOGaaQO0p1w",
"colab_type": "code",
"colab": {}
},
"source": [
"def save_prng_states(name, prng_states, data_dir=DATA_DIR):\n",
" prng_states_pkl_path = data_dir / f'ps-{name}.pkl'\n",
" with open(prng_states_pkl_path, 'wb') as f:\n",
" pickle.dump(prng_states, f)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JlbP4Ycg0rXu",
"colab_type": "code",
"colab": {}
},
"source": [
"def load_prng_states(name, data_dir=DATA_DIR):\n",
" prng_states_pkl_path = data_dir / f'ps-{name}.pkl'\n",
" if (prng_states_pkl_path.exists() and prng_states_pkl_path.is_file()):\n",
" with open(prng_states_pkl_path, 'rb') as f:\n",
" prng_states = pickle.load(f)\n",
" return prng_states\n",
" else:\n",
" raise FileNotFoundError(f'No {prng_states_pkl_path} to load!')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "WugYIc_SHgkC",
"colab_type": "text"
},
"source": [
"## Checkpoint Helpers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JrTTQsr2HwOB",
"colab_type": "code",
"colab": {}
},
"source": [
"@dataclass\n",
"class Checkpoint:\n",
" name: str\n",
" frozen_to: int\n",
" mp_loss_scale: float"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HTXpAmZhHn4Z",
"colab_type": "code",
"colab": {}
},
"source": [
"def preserve_event(name, learner, frozen_to=-1, data_dir=DATA_DIR):\n",
" save_prng_states(name, get_prng_states())\n",
"\n",
" learner.save(name, with_opt=True)\n",
"\n",
" mp_loss_scale = None\n",
" for i, cb in enumerate(learner.callbacks):\n",
" if isinstance(cb, MixedPrecision):\n",
" print(f'Found MixedPrecision loss_scale={cb.loss_scale}')\n",
" mp_loss_scale = cb.loss_scale\n",
" break\n",
"\n",
" checkpoint = Checkpoint(name, frozen_to, mp_loss_scale)\n",
" checkpoint_fpath = data_dir / f'cp-{name}.pkl'\n",
" with open(checkpoint_fpath, 'wb') as f:\n",
" pickle.dump(checkpoint, f)\n",
" print(f'{checkpoint} saved to {checkpoint_fpath}')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Gzg87e_AIeHj",
"colab_type": "code",
"colab": {}
},
"source": [
"def retain_event(name, learner, data_dir=DATA_DIR):\n",
" if name is None:\n",
" reset_prng_states()\n",
" else:\n",
" set_prng_states(load_prng_states(name))\n",
"\n",
" checkpoint_fpath = data_dir / f'cp-{name}.pkl'\n",
" if not (checkpoint_fpath.exists() and checkpoint_fpath.is_file()):\n",
" print(f'No {checkpoint_fpath} to load!')\n",
" return learner\n",
" with open(checkpoint_fpath, 'rb') as f:\n",
" checkpoint = pickle.load(f)\n",
" print(f'{checkpoint} loaded from {checkpoint_fpath}')\n",
"\n",
" learner.freeze_to(checkpoint.frozen_to) # This must be before `load()`.\n",
" print(f'Frozen to {checkpoint.frozen_to}')\n",
"\n",
" learner = learner.load(checkpoint.name, with_opt=True)\n",
"\n",
" if checkpoint.mp_loss_scale:\n",
" for i, cb in enumerate(learner.callbacks):\n",
" if isinstance(cb, MixedPrecision):\n",
" learner.callbacks[i].loss_scale = checkpoint.mp_loss_scale\n",
" print('Retained MixedPrecision loss_scale='\n",
" f'{learner.callbacks[i].loss_scale}')\n",
" break\n",
"\n",
" return learner"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UOZDR1xb5_-j",
"colab_type": "code",
"colab": {}
},
"source": [
"def fit_a_named_cycle(name, learner, lrs, moms, wd, clbks, cyc_len, freeze_to,\n",
" prev_event_name):\n",
" for i, cb in enumerate(learner.callbacks):\n",
" if isinstance(cb, MixedPrecision):\n",
" print(f'Found MixedPrecision loss_scale={cb.loss_scale}')\n",
" learner = retain_event(prev_event_name, learner)\n",
"\n",
" # `unfreeze()` does just `freeze_to(0)`\n",
" # `freeze()` does `freeze_to(-1)` with a size-assertion of layer groups\n",
" learner.freeze_to(freeze_to)\n",
" learner.fit_one_cycle(cyc_len=cyc_len, max_lr=lrs, moms=moms, wd=wd,\n",
" callbacks=clbks)\n",
"\n",
" preserve_event(name, learner, freeze_to)\n",
"\n",
" return learner"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "dJwkXFIyJlkB",
"colab_type": "text"
},
"source": [
"## LM-specific Helpers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Hjm3439wUJUE",
"colab_type": "code",
"colab": {}
},
"source": [
"def set_lm_databunch(fname, bs, bptt, seed=SEED, presort=True,\n",
" n_wrkrs=N_DBNCH_WRKRS, data_dir=DATA_DIR,\n",
" raw_data_dir=CORPUS_IN_COLAB_DATA_DIR):\n",
" reset_prng_states()\n",
" tl = TextList.from_folder(raw_data_dir, presort=presort)\n",
" il = tl.filter_by_folder(include=['train', 'test', 'unsup'])\n",
" ils = il.split_by_rand_pct(0.1, seed) # Set the seed again since in theory one may have called np.random before this.\n",
" lls = ils.label_for_lm()\n",
" dbnch = lls.databunch(bs=bs, bptt=bptt, num_workers=n_wrkrs)\n",
" dbnch.save(data_dir / fname)\n",
" return dbnch"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LcZ9A0JNfA7M",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_lm_databunch(fname, bs, bptt, backwards=False, n_wrkrs=N_DBNCH_WRKRS,\n",
" data_dir=DATA_DIR):\n",
" reset_prng_states()\n",
" return load_data(data_dir, fname, bs, num_workers=n_wrkrs,\n",
" backwards=backwards, bptt=bptt)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NJnkgQiJqCyZ",
"colab_type": "code",
"colab": {}
},
"source": [
"def new_lm_learner_with_ulmfit(name, dbnch, drop_mult, base_dir=BASE_DIR):\n",
" reset_prng_states()\n",
" lrnr = language_model_learner(dbnch, AWD_LSTM, drop_mult=drop_mult,\n",
" path=base_dir)\n",
" # lrnr = lrnr.to_fp16(clip=0.1) # 2x faster\n",
" lrnr = lrnr.to_fp16() # 2x faster\n",
" save_prng_states(name, get_prng_states())\n",
" return lrnr"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "k7ygpuHbJo0t",
"colab_type": "text"
},
"source": [
"## CF-specific Helpers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sPkphOtiXTHH",
"colab_type": "code",
"colab": {}
},
"source": [
"def set_cf_databunch(fname, bs, vocab, tags=IMDB_CLASSES, presort=True,\n",
" n_wrkrs=N_DBNCH_WRKRS, data_dir=DATA_DIR,\n",
" raw_data_dir=CORPUS_IN_COLAB_DATA_DIR):\n",
" reset_prng_states()\n",
" tl = TextList.from_folder(raw_data_dir, vocab=vocab, presort=presort)\n",
" ils = tl.split_by_folder(valid='test')\n",
" lls = ils.label_from_folder(classes=tags)\n",
" dbnch = lls.databunch(bs=bs, num_workers=n_wrkrs)\n",
" dbnch.save(data_dir / fname)\n",
" return dbnch"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mB5T78sWfQ8V",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_cf_databunch(fname, bs, backwards=False, n_wrkrs=N_DBNCH_WRKRS,\n",
" data_dir=DATA_DIR):\n",
" reset_prng_states()\n",
" return load_data(data_dir, fname, bs, num_workers=n_wrkrs,\n",
" backwards=backwards)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wxk03xtFYkk6",
"colab_type": "code",
"colab": {}
},
"source": [
"def new_cf_learner_with_encoder(name, dbnch, drop_mult, enc_name, bptt,\n",
" base_dir=BASE_DIR):\n",
" reset_prng_states()\n",
" lrnr = text_classifier_learner(dbnch, AWD_LSTM, drop_mult=drop_mult,\n",
" path=base_dir, bptt=bptt, pretrained=False)\n",
" lrnr = lrnr.to_fp16()\n",
" lrnr = lrnr.load_encoder(enc_name)\n",
" save_prng_states(name, get_prng_states())\n",
" return lrnr"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4puv1GrLOp5R",
"colab_type": "text"
},
"source": [
"# Fit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g3uudRrTXGq3",
"colab_type": "text"
},
"source": [
"## Forward LM"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wopKpHoLPRj5",
"colab_type": "text"
},
"source": [
"### Process Data Once"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aUayc5jQ-ugt",
"colab_type": "code",
"colab": {}
},
"source": [
"if not (DATA_DIR / FW_LM_DBNCH_FNAME).exists():\n",
" fw_lm_dbnch = set_lm_databunch(FW_LM_DBNCH_FNAME, lm_bs, bptt)\n",
" print(f'Built and saved {DATA_DIR / FW_LM_DBNCH_FNAME}')\n",
" # fw_lm_dbnch.show_batch()\n",
" if not VOCAB_FILE.exists():\n",
" fw_lm_dbnch.vocab.save(VOCAB_FILE)\n",
" print(f'Saved {VOCAB_FILE}')\n",
"\n",
"# fw_lm_dbnch = get_lm_databunch(FW_LM_DBNCH_FNAME, lm_bs, bptt)\n",
"# print(f'Loaded {DATA_DIR / FW_LM_DBNCH_FNAME}')\n",
"# fw_lm_dbnch.show_batch()\n",
"# IMDB_VOC = Vocab.load(VOCAB_FILE)\n",
"# print(f'Loaded {VOCAB_FILE}')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "M_IIOjBt8a0O",
"colab_type": "text"
},
"source": [
"### Find Learning Rate"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sbwGy8uACFp9",
"colab_type": "text"
},
"source": [
"I use a different method to approximate a good learning rate."
]
},
{
"cell_type": "code",
"metadata": {
"id": "2dflRYKkYtv2",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture lm_lr_find_scope_begin_log\n",
"lm_lr_find_scope = IPyExperimentsPytorch(cl_enable=False)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bSdFJ6neuGSi",
"colab_type": "code",
"outputId": "ae4767b7-64f7-40e9-abac-2da61dc89002",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 62
}
},
"source": [
"fw_lm_dbnch = get_lm_databunch(FW_LM_DBNCH_FNAME, lm_bs, bptt)\n",
"assert fw_lm_dbnch.train_dl.batch_size == lm_bs\n",
"lm_epoch_sz = math.ceil(len(fw_lm_dbnch.train_ds) / lm_bs)\n",
"ratio = 4 / 5\n",
"epoch_scale = ratio * 0.3 # OneCycleScheduler's pct_start=0.3\n",
"lm_num_it = math.ceil(lm_epoch_sz * epoch_scale)\n",
"print(\n",
" f'lm_epoch_sz = {lm_epoch_sz}\\n'\n",
" f'lm_num_it = {lm_num_it}')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"lm_epoch_sz = 1407\n",
"lm_num_it = 338\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EYtHdhyQOSZ6",
"colab_type": "code",
"colab": {}
},
"source": [
"fw_lm_lrnr = new_lm_learner_with_ulmfit('new-fw_lm', fw_lm_dbnch, lm_drop_mult)\n",
"fw_lm_lrnr.path = COLAB_DATA_DIR"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jiqmaW1FXGqN",
"colab_type": "code",
"outputId": "cb42a414-ad15-47e4-ecf5-8b622e3ee0f7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 150
}
},
"source": [
"start_lr = 4.5e-4\n",
"end_lr = round(start_lr * 20, 3)\n",
"fw_lm_lrnr.lr_find(start_lr, end_lr, lm_num_it, wd=lm_wd)\n",
"min_loss = np.min(fw_lm_lrnr.recorder.losses)\n",
"min_loss_it = np.argmin(fw_lm_lrnr.recorder.losses) + 1\n",
"end_loss = fw_lm_lrnr.recorder.losses[-1].item()\n",
"print(f'lr range = [{start_lr}, {end_lr}]\\n'\n",
" f'min_loss = {min_loss} @ {min_loss_it}\\n'\n",
" f'end_loss = {end_loss} @ {lm_num_it}')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"epoch train_loss valid_loss accuracy time \n",
"0 4.593526 #na# 00:55 \n",
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
"lr range = [0.00045, 0.009]\n",
"min_loss = 4.593526363372803 @ 338\n",
"end_loss = 4.593526363372803 @ 338\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UqTHVxFymu4G",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture lm_lr_find_log\n",
"fw_lm_lrnr.recorder.plot(skip_start=0, skip_end=0, suggestion=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9Ib1vF17DxLg",
"colab_type": "code",
"outputId": "6fac2c4b-c606-461e-b998-bdfdd0d65cbf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 606
}
},
"source": [
"lm_lr_find_log()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Min numerical gradient: 4.58E-04\n",
"Min loss divided by 10: 8.92E-04\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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Nroi45LeKx9/cvmkmJsYrDKgLo0uWR7Tb8Vtf/sO4+t6/W/DvLYmI/yMizoqIty9WcIdR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\ns84669C/77333q5jYLAdGN8R408/Nuf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j2wj9pZvFILN+Vpai6mxh0JJkoAAICRaqsOa9VzP9TG1/9TjXUnO92vobZKW/7+Gy3707dUcWhz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\nxBY9sI/aW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IF91M7ilVq4KU9TczE17kgxURKGz+fr1n5//OMf9cc//rHbx21qatKzzz6r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\nBG3dulXx8fF6/vnndeONN/Y6zuv1laSgoEAVFRUhi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