epwalsh

import numpy as np


def num_bits(max_i: int) -> int:
    return len(bin(max_i)[2:])


def int_to_bits(i: int, n: int, dtype=int) -> list[int]:
    return [dtype(int(b)) for b in bin(i)[2:].zfill(n)]

epwalsh

# Step 1: Create and activate a new virtual environment (need Python 3.7 or newer)
virtualenv .venv
. .venv/bin/activate

# Step 2: Install latest PyTorch
# This assumes your drivers are compatable with CUDA 11.*. If not, see https://pytorch.org/
# for alternate install instructions.
pip install torch==1.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

# Step 3: Clone and install the "tango" repo which has the GPT-J example.

epwalsh

import signal
import logging
import time

from transformers import AutoTokenizer

from allennlp.data.instance import Instance
from allennlp.data.dataset_readers import DatasetReader
from allennlp.data.data_loaders import MultiProcessDataLoader
from allennlp.data.fields import TransformerTextField

epwalsh

# Requires allennlp>=2.4.0, allennlp_models>=2.4.0

from allennlp_models.generation.predictors import Seq2SeqPredictor

ARTICLE_TO_SUMMARIZE = '''
summarize: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building,
and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side.
During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest
man-made structure in the world, a title it held for 41 years until the Chrysler Building in
New York City was finished in 1930. It was the first structure to reach a height of 300 metres.

epwalsh

local target_namespace = "target_tokens";

{
    "dataset_reader": {
        "target_namespace": target_namespace,
        "type": "copynet_seq2seq",
        "source_token_indexers": {
            "tokens": {
                "type": "single_id",
                "namespace": "source_tokens"

epwalsh

from typing import List, Tuple, Dict, Any

import torch

from allennlp.common.lazy import Lazy
from allennlp.common.params import Params
from allennlp.training.optimizers import Optimizer


ParameterGroupType = List[Tuple[List[str], Dict[str, Any]]]

epwalsh

- uses: actions/cache@v2
  with:
    path: ${{ env.pythonLocation }}
    key: ${{ env.pythonLocation }}-${{ hashFiles('setup.py') }}-${{ hashFiles('dev-requirements.txt') }}

epwalsh

    steps:
    - uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v1
      with:
        python-version: 3.7

    - uses: actions/cache@v2
      with:

epwalsh

name: PR
on:
  pull_request:
    branches:
    - master
jobs:
  checks:
    name: Checks
    runs-on: ubuntu-latest
    steps:

epwalsh

local transformer_model = 'bert-base-cased';
local epochs = 3;
local batch_size = 8;
local train_path = "https://allennlp.s3.amazonaws.com/datasets/squad/squad-train-v1.1.json";
local dev_path = "https://allennlp.s3.amazonaws.com/datasets/squad/squad-dev-v1.1.json";

{
  "dataset_reader": {
    "type": "transformer_squad",
    "transformer_model_name": transformer_model,