зеркало из https://github.com/microsoft/FLAML.git
972 строки
37 KiB
Plaintext
972 строки
37 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook uses flaml to finetune a transformer model from Huggingface transformers library.\n",
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"\n",
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"**Requirements.** This notebook has additional requirements:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# %pip install torch transformers datasets ipywidgets flaml[blendsearch,ray]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tokenizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_CHECKPOINT = \"distilbert-base-uncased\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(MODEL_CHECKPOINT, use_fast=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'input_ids': [101, 2023, 2003, 1037, 3231, 102], 'attention_mask': [1, 1, 1, 1, 1, 1]}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer(\"this is a test\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"TASK = \"cola\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import datasets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Reusing dataset glue (/home/ec2-user/.cache/huggingface/datasets/glue/cola/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\n"
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]
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}
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],
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"source": "raw_dataset = datasets.load_dataset(\"glue\", TASK, trust_remote_code=True)"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# define tokenization function used to process data\n",
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"COLUMN_NAME = \"sentence\"\n",
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"def tokenize(examples):\n",
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" return tokenizer(examples[COLUMN_NAME], truncation=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0dcf9ca8ce024a2b832606a6a3219b17",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c58845729f0a4261830ad679891e7c77",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9716d177a40748008cc6089e3d52a1d5",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"encoded_dataset = raw_dataset.map(tokenize, batched=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
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" 'idx': 0,\n",
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" 'input_ids': [101,\n",
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" 2256,\n",
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" 2814,\n",
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" 2180,\n",
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" 1005,\n",
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" 1056,\n",
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" 4965,\n",
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" 2023,\n",
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" 4106,\n",
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" 1010,\n",
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" 2292,\n",
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" 2894,\n",
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" 1996,\n",
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" 2279,\n",
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" 2028,\n",
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" 2057,\n",
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" 16599,\n",
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" 1012,\n",
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" 102],\n",
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" 'label': 1,\n",
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" 'sentence': \"Our friends won't buy this analysis, let alone the next one we propose.\"}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"encoded_dataset[\"train\"][0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoModelForSequenceClassification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
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"- This IS expected if you are initializing DistilBertForSequenceClassification 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",
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"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
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"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
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}
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],
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"source": [
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"NUM_LABELS = 2\n",
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"model = AutoModelForSequenceClassification.from_pretrained(MODEL_CHECKPOINT, num_labels=NUM_LABELS)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"DistilBertForSequenceClassification(\n",
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" (distilbert): DistilBertModel(\n",
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" (embeddings): Embeddings(\n",
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" (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
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" (position_embeddings): Embedding(512, 768)\n",
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" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (transformer): Transformer(\n",
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" (layer): ModuleList(\n",
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" (0): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" (ffn): FFN(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" )\n",
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
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" (1): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" (ffn): FFN(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" )\n",
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
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" (2): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" (ffn): FFN(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" )\n",
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
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" (3): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" (ffn): FFN(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
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" (4): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
|
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" (ffn): FFN(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
|
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" (5): TransformerBlock(\n",
|
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" (attention): MultiHeadSelfAttention(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" )\n",
|
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" (ffn): FFN(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
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" (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
|
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" (dropout): Dropout(p=0.2, inplace=False)\n",
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")"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Metric"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": "metric = datasets.load_metric(\"glue\", TASK, trust_remote_code=True)"
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Metric(name: \"glue\", features: {'predictions': Value(dtype='int64', id=None), 'references': Value(dtype='int64', id=None)}, usage: \"\"\"\n",
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"Compute GLUE evaluation metric associated to each GLUE dataset.\n",
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"Args:\n",
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" predictions: list of predictions to score.\n",
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" Each translation should be tokenized into a list of tokens.\n",
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" references: list of lists of references for each translation.\n",
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" Each reference should be tokenized into a list of tokens.\n",
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"Returns: depending on the GLUE subset, one or several of:\n",
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" \"accuracy\": Accuracy\n",
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" \"f1\": F1 score\n",
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" \"pearson\": Pearson Correlation\n",
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" \"spearmanr\": Spearman Correlation\n",
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" \"matthews_correlation\": Matthew Correlation\n",
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"Examples:\n",
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"\n",
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" >>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n",
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" >>> references = [0, 1]\n",
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" >>> predictions = [0, 1]\n",
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" >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
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" >>> print(results)\n",
|
|
" {'accuracy': 1.0}\n",
|
|
"\n",
|
|
" >>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'\n",
|
|
" >>> references = [0, 1]\n",
|
|
" >>> predictions = [0, 1]\n",
|
|
" >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
|
|
" >>> print(results)\n",
|
|
" {'accuracy': 1.0, 'f1': 1.0}\n",
|
|
"\n",
|
|
" >>> glue_metric = datasets.load_metric('glue', 'stsb')\n",
|
|
" >>> references = [0., 1., 2., 3., 4., 5.]\n",
|
|
" >>> predictions = [0., 1., 2., 3., 4., 5.]\n",
|
|
" >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
|
|
" >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n",
|
|
" {'pearson': 1.0, 'spearmanr': 1.0}\n",
|
|
"\n",
|
|
" >>> glue_metric = datasets.load_metric('glue', 'cola')\n",
|
|
" >>> references = [0, 1]\n",
|
|
" >>> predictions = [0, 1]\n",
|
|
" >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
|
|
" >>> print(results)\n",
|
|
" {'matthews_correlation': 1.0}\n",
|
|
"\"\"\", stored examples: 0)"
|
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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"source": [
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"metric"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
|
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"metadata": {},
|
|
"outputs": [],
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"source": [
|
|
"import numpy as np\n",
|
|
"def compute_metrics(eval_pred):\n",
|
|
" predictions, labels = eval_pred\n",
|
|
" predictions = np.argmax(predictions, axis=1)\n",
|
|
" return metric.compute(predictions=predictions, references=labels)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Training (aka Finetuning)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
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"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import Trainer\n",
|
|
"from transformers import TrainingArguments"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"args = TrainingArguments(\n",
|
|
" output_dir='output',\n",
|
|
" do_eval=True,\n",
|
|
")"
|
|
]
|
|
},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"trainer = Trainer(\n",
|
|
" model=model,\n",
|
|
" args=args,\n",
|
|
" train_dataset=encoded_dataset[\"train\"],\n",
|
|
" eval_dataset=encoded_dataset[\"validation\"],\n",
|
|
" tokenizer=tokenizer,\n",
|
|
" compute_metrics=compute_metrics,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"\n",
|
|
" <div>\n",
|
|
" <style>\n",
|
|
" /* Turns off some styling */\n",
|
|
" progress {\n",
|
|
" /* gets rid of default border in Firefox and Opera. */\n",
|
|
" border: none;\n",
|
|
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
|
" background-size: auto;\n",
|
|
" }\n",
|
|
" </style>\n",
|
|
" \n",
|
|
" <progress value='1591' max='3207' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|
" [1591/3207 1:03:06 < 1:04:11, 0.42 it/s, Epoch 1.49/3]\n",
|
|
" </div>\n",
|
|
" <table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: left;\">\n",
|
|
" <th>Step</th>\n",
|
|
" <th>Training Loss</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <td>500</td>\n",
|
|
" <td>0.571000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>1000</td>\n",
|
|
" <td>0.515400</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>1500</td>\n",
|
|
" <td>0.356100</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table><p>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Hyperparameter Optimization\n",
|
|
"\n",
|
|
"`flaml.tune` is a module for economical hyperparameter tuning. It frees users from manually tuning many hyperparameters for a software, such as machine learning training procedures. \n",
|
|
"The API is compatible with ray tune.\n",
|
|
"\n",
|
|
"### Step 1. Define training method\n",
|
|
"\n",
|
|
"We define a function `train_distilbert(config: dict)` that accepts a hyperparameter configuration dict `config`. The specific configs will be generated by flaml's search algorithm in a given search space.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import flaml\n",
|
|
"\n",
|
|
"def train_distilbert(config: dict):\n",
|
|
"\n",
|
|
" # Load CoLA dataset and apply tokenizer\n",
|
|
" cola_raw = datasets.load_dataset(\"glue\", TASK, trust_remote_code=True)\n",
|
|
" cola_encoded = cola_raw.map(tokenize, batched=True)\n",
|
|
" train_dataset, eval_dataset = cola_encoded[\"train\"], cola_encoded[\"validation\"]\n",
|
|
"\n",
|
|
" model = AutoModelForSequenceClassification.from_pretrained(\n",
|
|
" MODEL_CHECKPOINT, num_labels=NUM_LABELS\n",
|
|
" )\n",
|
|
"\n",
|
|
" metric = datasets.load_metric(\"glue\", TASK, trust_remote_code=True)\n",
|
|
" def compute_metrics(eval_pred):\n",
|
|
" predictions, labels = eval_pred\n",
|
|
" predictions = np.argmax(predictions, axis=1)\n",
|
|
" return metric.compute(predictions=predictions, references=labels)\n",
|
|
"\n",
|
|
" training_args = TrainingArguments(\n",
|
|
" output_dir='.',\n",
|
|
" do_eval=False,\n",
|
|
" disable_tqdm=True,\n",
|
|
" logging_steps=20000,\n",
|
|
" save_total_limit=0,\n",
|
|
" **config,\n",
|
|
" )\n",
|
|
"\n",
|
|
" trainer = Trainer(\n",
|
|
" model,\n",
|
|
" training_args,\n",
|
|
" train_dataset=train_dataset,\n",
|
|
" eval_dataset=eval_dataset,\n",
|
|
" tokenizer=tokenizer,\n",
|
|
" compute_metrics=compute_metrics,\n",
|
|
" )\n",
|
|
"\n",
|
|
" # train model\n",
|
|
" trainer.train()\n",
|
|
"\n",
|
|
" # evaluate model\n",
|
|
" eval_output = trainer.evaluate()\n",
|
|
"\n",
|
|
" # report the metric to optimize\n",
|
|
" flaml.tune.report(\n",
|
|
" loss=eval_output[\"eval_loss\"],\n",
|
|
" matthews_correlation=eval_output[\"eval_matthews_correlation\"],\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Step 2. Define the search\n",
|
|
"\n",
|
|
"We are now ready to define our search. This includes:\n",
|
|
"\n",
|
|
"- The `search_space` for our hyperparameters\n",
|
|
"- The metric and the mode ('max' or 'min') for optimization\n",
|
|
"- The constraints (`n_cpus`, `n_gpus`, `num_samples`, and `time_budget_s`)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"max_num_epoch = 64\n",
|
|
"search_space = {\n",
|
|
" # You can mix constants with search space objects.\n",
|
|
" \"num_train_epochs\": flaml.tune.loguniform(1, max_num_epoch),\n",
|
|
" \"learning_rate\": flaml.tune.loguniform(1e-6, 1e-4),\n",
|
|
" \"adam_epsilon\": flaml.tune.loguniform(1e-9, 1e-7),\n",
|
|
" \"adam_beta1\": flaml.tune.uniform(0.8, 0.99),\n",
|
|
" \"adam_beta2\": flaml.tune.loguniform(98e-2, 9999e-4),\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# optimization objective\n",
|
|
"HP_METRIC, MODE = \"matthews_correlation\", \"max\"\n",
|
|
"\n",
|
|
"# resources\n",
|
|
"num_cpus = 4\n",
|
|
"num_gpus = 4\n",
|
|
"\n",
|
|
"# constraints\n",
|
|
"num_samples = -1 # number of trials, -1 means unlimited\n",
|
|
"time_budget_s = 3600 # time budget in seconds"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Step 3. Launch with `flaml.tune.run`\n",
|
|
"\n",
|
|
"We are now ready to launch the tuning using `flaml.tune.run`:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/ec2-user/miniconda3/envs/myflaml/lib/python3.8/site-packages/ray/_private/services.py:238: UserWarning: Not all Ray Dashboard dependencies were found. To use the dashboard please install Ray using `pip install ray[default]`. To disable this message, set RAY_DISABLE_IMPORT_WARNING env var to '1'.\n",
|
|
" warnings.warn(warning_message)\n",
|
|
"2021-12-01 23:35:54,348\tWARNING function_runner.py:558 -- Function checkpointing is disabled. This may result in unexpected behavior when using checkpointing features or certain schedulers. To enable, set the train function arguments to be `func(config, checkpoint_dir=None)`.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tuning started...\n",
|
|
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"To disable this warning, you can either:\n",
|
|
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"== Status ==<br>Memory usage on this node: 4.3/7.7 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 4.0/4 GPUs, 0.0/2.34 GiB heap, 0.0/1.17 GiB objects<br>Result logdir: /home/ec2-user/FLAML/notebook/logs/train_distilbert_2021-12-01_23-35-54<br>Number of trials: 1/infinite (1 RUNNING)<br><br>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
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{
|
|
"data": {
|
|
"text/html": [
|
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"== Status ==<br>Memory usage on this node: 4.5/7.7 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 4.0/4 GPUs, 0.0/2.34 GiB heap, 0.0/1.17 GiB objects<br>Result logdir: /home/ec2-user/FLAML/notebook/logs/train_distilbert_2021-12-01_23-35-54<br>Number of trials: 2/infinite (1 PENDING, 1 RUNNING)<br><br>"
|
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],
|
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"output_type": "display_data"
|
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},
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{
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"data": {
|
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"text/html": [
|
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"== Status ==<br>Memory usage on this node: 4.6/7.7 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 4.0/4 GPUs, 0.0/2.34 GiB heap, 0.0/1.17 GiB objects<br>Result logdir: /home/ec2-user/FLAML/notebook/logs/train_distilbert_2021-12-01_23-35-54<br>Number of trials: 2/infinite (1 PENDING, 1 RUNNING)<br><br>"
|
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m Reusing dataset glue (/home/ec2-user/.cache/huggingface/datasets/glue/cola/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\n",
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" 0%| | 0/9 [00:00<?, ?ba/s]\n",
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" 22%|██▏ | 2/9 [00:00<00:00, 19.41ba/s]\n",
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" 56%|█████▌ | 5/9 [00:00<00:00, 20.98ba/s]\n",
|
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" 89%|████████▉ | 8/9 [00:00<00:00, 21.75ba/s]\n",
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"100%|██████████| 9/9 [00:00<00:00, 24.49ba/s]\n",
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"100%|██████████| 2/2 [00:00<00:00, 42.79ba/s]\n",
|
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" 0%| | 0/2 [00:00<?, ?ba/s]\n",
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"100%|██████████| 2/2 [00:00<00:00, 41.48ba/s]\n",
|
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"\u001B[2m\u001B[36m(pid=11344)\u001B[0m Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m - This IS expected if you are initializing DistilBertForSequenceClassification 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",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m - This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|
]
|
|
},
|
|
{
|
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"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m To disable this warning, you can either:\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m To disable this warning, you can either:\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Avoid using `tokenizers` before the fork if possible\n",
|
|
"\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import time\n",
|
|
"import ray\n",
|
|
"start_time = time.time()\n",
|
|
"ray.shutdown()\n",
|
|
"ray.init(num_cpus=num_cpus, num_gpus=num_gpus)\n",
|
|
"\n",
|
|
"print(\"Tuning started...\")\n",
|
|
"analysis = flaml.tune.run(\n",
|
|
" train_distilbert,\n",
|
|
" search_alg=flaml.CFO(\n",
|
|
" space=search_space,\n",
|
|
" metric=HP_METRIC,\n",
|
|
" mode=MODE,\n",
|
|
" low_cost_partial_config={\"num_train_epochs\": 1}),\n",
|
|
" # uncomment the following if scheduler = 'asha',\n",
|
|
" # max_resource=max_num_epoch, min_resource=1,\n",
|
|
" resources_per_trial={\"gpu\": num_gpus, \"cpu\": num_cpus},\n",
|
|
" local_dir='logs/',\n",
|
|
" num_samples=num_samples,\n",
|
|
" time_budget_s=time_budget_s,\n",
|
|
" use_ray=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"ray.shutdown()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"n_trials=22\n",
|
|
"time=3999.769361972809\n",
|
|
"Best model eval matthews_correlation: 0.5699\n",
|
|
"Best model parameters: {'num_train_epochs': 15.580684188655825, 'learning_rate': 1.2851507818900338e-05, 'adam_epsilon': 8.134982521948352e-08, 'adam_beta1': 0.99, 'adam_beta2': 0.9971094424784387}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"best_trial = analysis.get_best_trial(HP_METRIC, MODE, \"all\")\n",
|
|
"metric = best_trial.metric_analysis[HP_METRIC][MODE]\n",
|
|
"print(f\"n_trials={len(analysis.trials)}\")\n",
|
|
"print(f\"time={time.time()-start_time}\")\n",
|
|
"print(f\"Best model eval {HP_METRIC}: {metric:.4f}\")\n",
|
|
"print(f\"Best model parameters: {best_trial.config}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Next Steps\n",
|
|
"\n",
|
|
"Notice that we only reported the metric with `flaml.tune.report` at the end of full training loop. It is possible to enable reporting of intermediate performance - allowing early stopping - as follows:\n",
|
|
"\n",
|
|
"- Huggingface provides _Callbacks_ which can be used to insert the `flaml.tune.report` call inside the training loop\n",
|
|
"- Make sure to set `do_eval=True` in the `TrainingArguments` provided to `Trainer` and adjust the evaluation frequency accordingly"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"interpreter": {
|
|
"hash": "1cfcceddaeccda27c3cce104660d474924e2ba82887c0e8e481b6ede3743c483"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.5 64-bit",
|
|
"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": "3.8.12"
|
|
},
|
|
"metadata": {
|
|
"interpreter": {
|
|
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|