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Keval Morabia
1ef1d72a1b
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
1232 lines
52 KiB
Python
1232 lines
52 KiB
Python
# NOTE: This is adapted from run_qa_no_trainer.py and utils_qa.py from
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# https://github.com/huggingface/transformers/blob/c52b515e/examples/pytorch/question-answering
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#
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Example showcasing how to do end-to-end optimization of a BERT model on SQuAD using Model Optimizer.
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This includes GradNAS pruning, INT8 quantization, fine-tuning / QAT with distillation, and ONNX export.
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"""
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import argparse
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import collections
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import json
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import logging
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import math
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import os
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import random
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from typing import Any
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import datasets
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import evaluate
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import numpy as np
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import torch
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import torch.nn as nn
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import set_seed
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from torch.utils.data import DataLoader
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from tqdm.auto import tqdm
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from transformers import (
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AutoModelForQuestionAnswering,
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AutoTokenizer,
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DataCollatorWithPadding,
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EvalPrediction,
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PreTrainedTokenizer,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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)
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# Model Optimizer: imports
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import modelopt.torch.distill as mtd
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import modelopt.torch.opt as mto
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import modelopt.torch.prune as mtp
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import modelopt.torch.quantization as mtq
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from modelopt.torch._deploy.utils import get_onnx_bytes
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# Enable automatic save/load of modelopt_state with huggingface checkpointing
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mto.enable_huggingface_checkpointing()
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logger = get_logger(__name__)
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SEED = 123
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def parse_args(input_args: list[str] | None = None):
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parser = argparse.ArgumentParser(
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description="Finetune a transformers model on a Question Answering task"
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)
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# Training arguments
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parser.add_argument(
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"--model_name_or_path",
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type=str,
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default="bert-large-uncased-whole-word-masking-finetuned-squad",
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--do_train", action="store_true", help="Whether to run training / fine-tuning."
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)
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=16,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=64,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=[
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"linear",
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"cosine",
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"cosine_with_restarts",
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"polynomial",
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"constant",
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"constant_with_warmup",
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],
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)
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parser.add_argument(
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"--num_warmup_steps",
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type=int,
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default=0,
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help="Number of steps for the warmup in the lr scheduler.",
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)
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parser.add_argument(
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"--num_train_epochs",
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type=float,
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default=2.0,
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help="Total number of training epochs to perform.",
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)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--preprocessing_num_workers",
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type=int,
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default=4,
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help="The number of processes to use for preprocessing the dataset.",
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)
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# Logging and checkpointing arguments
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parser.add_argument(
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"--finetuned_model_path",
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type=str,
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default=None,
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help="Path to save the finetuned (pruned or quantized) model for restoring later with `.from_pretrained()`.",
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)
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to enable experiment trackers for logging.",
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)
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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default="epoch",
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help=(
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"Whether the various states should be saved at the end of every n steps, or 'epoch' for"
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" each epoch."
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),
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)
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parser.add_argument(
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"--resume_from_last_ckpt",
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action="store_true",
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help="If the training should continue from the latest checkpoint in model_name_or_path.",
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)
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parser.add_argument(
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"--onnx_export_path", type=str, default=None, help="Path to export the ONNX model to."
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)
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# Misc arguments for Bert (should not be modified in most cases)
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parser.add_argument(
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"--max_seq_length",
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type=int,
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default=384,
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help=(
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"The maximum total input sequence length after tokenization. Sequences longer than this"
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" will be truncated, and shorter will be padded if `--pad_to_max_length` is passed."
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),
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)
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parser.add_argument(
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"--pad_to_max_length",
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action="store_true",
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help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.",
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)
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parser.add_argument(
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"--doc_stride",
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type=int,
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default=128,
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help=(
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"When splitting up a long document into chunks how much stride to take between chunks."
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),
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)
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parser.add_argument(
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"--n_best_size",
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type=int,
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default=20,
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help="The total number of n-best predictions to generate when looking for an answer.",
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)
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parser.add_argument(
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"--max_answer_length",
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type=int,
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default=30,
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help=(
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"The maximum length of an answer that can be generated. This is needed because the"
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" start and end predictions are not conditioned on one another."
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),
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)
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# Debugging arguments
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help="For debugging purposes or quicker training.",
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)
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parser.add_argument(
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"--max_eval_samples",
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type=int,
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default=None,
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help="For debugging purposes or quicker training.",
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)
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# Model Optimizer: pruning arguments
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parser.add_argument(
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"--do_modelopt_prune",
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action="store_true",
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help="Whether or not to use Model Optimizer pruning.",
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)
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parser.add_argument(
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"--modelopt_prune_flops_percent",
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type=float,
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default=None,
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help="The percentage (between 0 and 100) of FLOPs to retain in the pruned model.",
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)
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parser.add_argument(
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"--pruned_model_path",
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type=str,
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default=None,
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help="Path to save the pruned model for further finetuning.",
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)
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# Model Optimizer: quantization arguments
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parser.add_argument(
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"--modelopt_quantize_cfg",
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help="Model Optimizer quantization config.",
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choices=mtq.config.choices,
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)
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# Model Optimizer: Distillation arguments
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parser.add_argument(
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"--do_modelopt_distill",
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action="store_true",
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help="Whether or not to use distillation. A teacher model must be specified.",
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)
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parser.add_argument(
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"--temperature", type=float, default=2.0, help="The temperature to use when distilling."
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)
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parser.add_argument(
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"--ptq_model_path",
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type=str,
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default=None,
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help="Path to save the PTQ quantized model for further QAT.",
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)
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args = parser.parse_args(input_args)
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# Sanity checks
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if args.do_train and not args.finetuned_model_path:
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raise ValueError("`finetuned_model_path` required when `do_train` is passed.")
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if args.do_modelopt_prune and not (
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args.modelopt_prune_flops_percent and args.pruned_model_path
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):
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raise ValueError(
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"`modelopt_prune_flops_percent` and `pruned_model_path` required when `do_modelopt_prune` is passed."
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)
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if args.modelopt_quantize_cfg and not args.ptq_model_path:
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raise ValueError("`ptq_model_path` required when `modelopt_quantize_cfg` is passed.")
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return args
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def get_datasets_and_dataloaders(args, tokenizer: PreTrainedTokenizer, accelerator: Accelerator):
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"""Get the examples, dataset, dataloader, answer_column_name
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You can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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(the dataset will be downloaded automatically from the datasets Hub).
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For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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'text' is found. You can easily tweak this behavior (see below).
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"""
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def prepare_train_features(examples):
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# Some of the questions have lots of whitespace on the left, which is not useful and will make the
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# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
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# left whitespace
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examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
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# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
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# in one example possible giving several features when a context is long, each of those features having a
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# context that overlaps a bit the context of the previous feature.
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tokenized_examples = tokenizer(
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examples[question_column_name if pad_on_right else context_column_name],
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examples[context_column_name if pad_on_right else question_column_name],
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truncation="only_second" if pad_on_right else "only_first",
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max_length=max_seq_length,
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stride=args.doc_stride,
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return_overflowing_tokens=True,
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return_offsets_mapping=True,
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padding="max_length" if args.pad_to_max_length else False,
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)
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# Since one example might give us several features if it has a long context, we need a map from a feature to
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# its corresponding example. This key gives us just that.
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sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
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# The offset mappings will give us a map from token to character position in the original context. This will
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# help us compute the start_positions and end_positions.
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offset_mapping = tokenized_examples.pop("offset_mapping")
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# Let's label those examples!
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tokenized_examples["start_positions"] = []
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tokenized_examples["end_positions"] = []
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for i, offsets in enumerate(offset_mapping):
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# We will label impossible answers with the index of the CLS token.
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input_ids = tokenized_examples["input_ids"][i]
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cls_index = input_ids.index(tokenizer.cls_token_id)
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# Grab the sequence corresponding to that example (to know what is the context and what is the question).
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sequence_ids = tokenized_examples.sequence_ids(i)
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# One example can give several spans, this is the index of the example containing this span of text.
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sample_index = sample_mapping[i]
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answers = examples[answer_column_name][sample_index]
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# If no answers are given, set the cls_index as answer.
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if len(answers["answer_start"]) == 0:
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tokenized_examples["start_positions"].append(cls_index)
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tokenized_examples["end_positions"].append(cls_index)
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else:
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# Start/end character index of the answer in the text.
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start_char = answers["answer_start"][0]
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end_char = start_char + len(answers["text"][0])
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# Start token index of the current span in the text.
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token_start_index = 0
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while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
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token_start_index += 1
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# End token index of the current span in the text.
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token_end_index = len(input_ids) - 1
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while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
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token_end_index -= 1
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# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
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if not (
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offsets[token_start_index][0] <= start_char
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and offsets[token_end_index][1] >= end_char
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):
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tokenized_examples["start_positions"].append(cls_index)
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tokenized_examples["end_positions"].append(cls_index)
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else:
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# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
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# Note: we could go after the last offset if the answer is the last word (edge case).
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while (
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token_start_index < len(offsets)
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and offsets[token_start_index][0] <= start_char
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):
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token_start_index += 1
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tokenized_examples["start_positions"].append(token_start_index - 1)
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while offsets[token_end_index][1] >= end_char:
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token_end_index -= 1
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tokenized_examples["end_positions"].append(token_end_index + 1)
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return tokenized_examples
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def prepare_validation_features(examples):
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# Some of the questions have lots of whitespace on the left, which is not useful and will make the
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# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
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# left whitespace
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examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
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# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
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# in one example possible giving several features when a context is long, each of those features having a
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# context that overlaps a bit the context of the previous feature.
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tokenized_examples = tokenizer(
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examples[question_column_name if pad_on_right else context_column_name],
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examples[context_column_name if pad_on_right else question_column_name],
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truncation="only_second" if pad_on_right else "only_first",
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max_length=max_seq_length,
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stride=args.doc_stride,
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return_overflowing_tokens=True,
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return_offsets_mapping=True,
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padding="max_length" if args.pad_to_max_length else False,
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)
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# Since one example might give us several features if it has a long context, we need a map from a feature to
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# its corresponding example. This key gives us just that.
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sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
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# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
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# corresponding example_id and we will store the offset mappings.
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tokenized_examples["example_id"] = []
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for i in range(len(tokenized_examples["input_ids"])):
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# Grab the sequence corresponding to that example (to know what is the context and what is the question).
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sequence_ids = tokenized_examples.sequence_ids(i)
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context_index = 1 if pad_on_right else 0
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# One example can give several spans, this is the index of the example containing this span of text.
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sample_index = sample_mapping[i]
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tokenized_examples["example_id"].append(examples["id"][sample_index])
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# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
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# position is part of the context or not.
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tokenized_examples["offset_mapping"][i] = [
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(o if sequence_ids[k] == context_index else None)
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for k, o in enumerate(tokenized_examples["offset_mapping"][i])
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]
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return tokenized_examples
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examples, dataset, dataloader = {}, {}, {}
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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# Downloading and loading a dataset from the hub.
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raw_datasets = datasets.load_dataset("squad")
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.
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# Preprocessing the datasets.
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# Preprocessing is slightly different for training and evaluation.
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column_names = raw_datasets["train"].column_names
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question_column_name = "question" if "question" in column_names else column_names[0]
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context_column_name = "context" if "context" in column_names else column_names[1]
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answer_column_name = "answers" if "answers" in column_names else column_names[2]
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# Padding side determines if we do (question|context) or (context|question).
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pad_on_right = tokenizer.padding_side == "right"
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if args.max_seq_length > tokenizer.model_max_length:
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logger.warning(
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f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length"
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f" for the model ({tokenizer.model_max_length}). Using"
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f" max_seq_length={tokenizer.model_max_length}."
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)
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max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
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examples["train"] = raw_datasets["train"]
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if args.max_train_samples is not None:
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# We will select sample from whole data if argument is specified
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examples["train"] = examples["train"].select(range(args.max_train_samples))
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|
|
# Create train feature from dataset
|
|
with accelerator.main_process_first():
|
|
dataset["train"] = examples["train"].map(
|
|
prepare_train_features,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=True,
|
|
desc="Running tokenizer on train dataset",
|
|
)
|
|
# if args.max_train_samples is not None:
|
|
# # Number of samples might increase during Feature Creation, We select only specified max samples
|
|
# dataset["train"] = dataset["train"].select(range(args.max_train_samples))
|
|
|
|
examples["eval"] = raw_datasets["validation"]
|
|
if args.max_eval_samples is not None:
|
|
# We will select sample from whole data
|
|
examples["eval"] = examples["eval"].select(range(args.max_eval_samples))
|
|
# Validation Feature Creation
|
|
with accelerator.main_process_first():
|
|
dataset["eval"] = examples["eval"].map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=True,
|
|
desc="Running tokenizer on validation dataset",
|
|
)
|
|
# if args.max_eval_samples is not None:
|
|
# # During Feature creation dataset samples might increase, we will select required samples again
|
|
# dataset["eval"] = dataset["eval"].select(range(args.max_eval_samples))
|
|
|
|
# Log a random sample from the training set:
|
|
for index in random.sample(range(len(dataset["train"])), 1):
|
|
logger.info(f"Sample {index} of the training set: {dataset['train'][index]}.")
|
|
|
|
# DataLoaders creation:
|
|
if args.pad_to_max_length:
|
|
# If padding was already done to max length, we use the default data collator that will just convert everything
|
|
# to tensors.
|
|
data_collator = default_data_collator
|
|
else:
|
|
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
|
|
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
|
|
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
|
|
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
|
|
|
|
dataloader["train"] = DataLoader(
|
|
dataset["train"],
|
|
shuffle=True,
|
|
collate_fn=data_collator,
|
|
batch_size=args.per_device_train_batch_size,
|
|
)
|
|
|
|
dataloader["eval"] = DataLoader(
|
|
dataset["eval"].remove_columns(["example_id", "offset_mapping"]),
|
|
collate_fn=data_collator,
|
|
batch_size=args.per_device_eval_batch_size,
|
|
)
|
|
|
|
return examples, dataset, dataloader, answer_column_name
|
|
|
|
|
|
def evaluate_model(
|
|
args,
|
|
model: nn.Module,
|
|
accelerator: Accelerator,
|
|
eval_examples: Any,
|
|
eval_dataset: Any,
|
|
eval_dataloader: DataLoader,
|
|
answer_column_name: str,
|
|
prefix: str = "Eval",
|
|
):
|
|
def create_and_fill_np_array(start_or_end_logits, max_len):
|
|
"""Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
|
|
|
|
Args:
|
|
start_or_end_logits: This is the output predictions of the model.
|
|
We can only enter either start or end logits.
|
|
max_len: The maximum length of the output tensor. (See the model.eval() part for more details)
|
|
"""
|
|
step = 0
|
|
# create a numpy array and fill it with -100.
|
|
logits_concat = np.full((len(eval_dataset), max_len), -100, dtype=np.float64)
|
|
# Now since we have create an array we will populate it with the outputs using accelerator.gather_for_metrics
|
|
for i, output_logit in enumerate(start_or_end_logits): # populate columns
|
|
# We have to fill it such that we have to take the whole tensor and replace it on the newly created array
|
|
# And after every iteration we have to change the step
|
|
batch_size = output_logit.shape[0]
|
|
cols = output_logit.shape[1]
|
|
|
|
if step + batch_size < len(eval_dataset):
|
|
logits_concat[step : step + batch_size, :cols] = output_logit
|
|
else:
|
|
logits_concat[step:, :cols] = output_logit[: len(eval_dataset) - step]
|
|
|
|
step += batch_size
|
|
|
|
return logits_concat
|
|
|
|
def postprocess_qa_predictions(
|
|
examples,
|
|
features,
|
|
predictions: tuple[np.ndarray, np.ndarray],
|
|
version_2_with_negative: bool = False,
|
|
n_best_size: int = 20,
|
|
max_answer_length: int = 30,
|
|
null_score_diff_threshold: float = 0.0,
|
|
output_dir: str | None = None,
|
|
prefix: str | None = None,
|
|
) -> EvalPrediction:
|
|
"""Post-processes the predictions of a question-answering model to convert them to answers
|
|
that are substrings of the original contexts. This is the base postprocessing functions for
|
|
models that only return start and end logits.
|
|
|
|
Args:
|
|
examples: The non-preprocessed dataset.
|
|
features: The processed dataset.
|
|
predictions: The predictions of the model: two arrays containing the start logits and the end logits
|
|
respectively. Its first dimension must match the number of elements of `features`.
|
|
version_2_with_negative: Whether or not the underlying dataset contains examples with no answers.
|
|
n_best_size: The total number of n-best predictions to generate when looking for an answer.
|
|
max_answer_length: The maximum length of an answer that can be generated. This is needed
|
|
because the start and end predictions are not conditioned on one another.
|
|
null_score_diff_threshold: The threshold used to select the null answer: if the best answer
|
|
has a score that is less than the score of the null answer minus this threshold, the
|
|
null answer is selected for this example (note that the score of the null answer for
|
|
an example giving several features is the minimum of the scores for the null answer on
|
|
each feature: all features must be aligned on the fact they `want` to predict a null answer).
|
|
Only useful when `version_2_with_negative` is `True`.
|
|
output_dir: If provided, the dictionaries of predictions, n_best predictions (with their scores and logits)
|
|
and, if `version_2_with_negative=True`, the dictionary of the scores differences between best and null
|
|
answers, are saved in `output_dir`.
|
|
prefix: If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
|
|
"""
|
|
if len(predictions) != 2:
|
|
raise ValueError(
|
|
"`predictions` should be a tuple with two elements (start_logits, end_logits)."
|
|
)
|
|
all_start_logits, all_end_logits = predictions
|
|
|
|
if len(predictions[0]) != len(features):
|
|
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
|
|
|
|
# Build a map example to its corresponding features.
|
|
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
|
|
features_per_example = collections.defaultdict(list)
|
|
for i, feature in enumerate(features):
|
|
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
|
|
|
|
# The dictionaries we have to fill.
|
|
all_predictions = collections.OrderedDict()
|
|
all_nbest_json = collections.OrderedDict()
|
|
if version_2_with_negative:
|
|
scores_diff_json = collections.OrderedDict()
|
|
|
|
logger.debug(
|
|
f"Post-processing {len(examples)} example predictions split into"
|
|
f" {len(features)} features."
|
|
)
|
|
|
|
# Let's loop over all the examples!
|
|
for example_index, example in enumerate(examples):
|
|
# Those are the indices of the features associated to the current example.
|
|
feature_indices = features_per_example[example_index]
|
|
|
|
min_null_prediction = None
|
|
prelim_predictions = []
|
|
|
|
# Looping through all the features associated to the current example.
|
|
for feature_index in feature_indices:
|
|
# We grab the predictions of the model for this feature.
|
|
start_logits = all_start_logits[feature_index]
|
|
end_logits = all_end_logits[feature_index]
|
|
# This is what will allow us to map some the positions in our logits to span of texts in the original
|
|
# context.
|
|
offset_mapping = features[feature_index]["offset_mapping"]
|
|
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum
|
|
# context available in the current feature.
|
|
token_is_max_context = features[feature_index].get("token_is_max_context", None)
|
|
|
|
# Update minimum null prediction.
|
|
feature_null_score = start_logits[0] + end_logits[0]
|
|
if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
|
|
min_null_prediction = {
|
|
"offsets": (0, 0),
|
|
"score": feature_null_score,
|
|
"start_logit": start_logits[0],
|
|
"end_logit": end_logits[0],
|
|
}
|
|
|
|
# Go through all possibilities for the `n_best_size` greater start and end logits.
|
|
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
|
|
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
|
|
for start_index in start_indexes:
|
|
for end_index in end_indexes:
|
|
# Don't consider out-of-scope answers, either because the indices are out of bounds or
|
|
# correspond to part of the input_ids that are not in the context.
|
|
if (
|
|
start_index >= len(offset_mapping)
|
|
or end_index >= len(offset_mapping)
|
|
or offset_mapping[start_index] is None
|
|
or len(offset_mapping[start_index]) < 2
|
|
or offset_mapping[end_index] is None
|
|
or len(offset_mapping[end_index]) < 2
|
|
):
|
|
continue
|
|
# Don't consider answers with a length that is either < 0 or > max_answer_length.
|
|
if (
|
|
end_index < start_index
|
|
or end_index - start_index + 1 > max_answer_length
|
|
):
|
|
continue
|
|
# Don't consider answer that don't have the maximum context available (if such information is
|
|
# provided).
|
|
if token_is_max_context is not None and not token_is_max_context.get(
|
|
str(start_index), False
|
|
):
|
|
continue
|
|
|
|
prelim_predictions.append(
|
|
{
|
|
"offsets": (
|
|
offset_mapping[start_index][0],
|
|
offset_mapping[end_index][1],
|
|
),
|
|
"score": start_logits[start_index] + end_logits[end_index],
|
|
"start_logit": start_logits[start_index],
|
|
"end_logit": end_logits[end_index],
|
|
}
|
|
)
|
|
if version_2_with_negative and min_null_prediction is not None:
|
|
# Add the minimum null prediction
|
|
prelim_predictions.append(min_null_prediction)
|
|
null_score = min_null_prediction["score"]
|
|
|
|
# Only keep the best `n_best_size` predictions.
|
|
n_best_preds = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[
|
|
:n_best_size
|
|
]
|
|
|
|
# Add back the minimum null prediction if it was removed because of its low score.
|
|
if (
|
|
version_2_with_negative
|
|
and min_null_prediction is not None
|
|
and not any(p["offsets"] == (0, 0) for p in n_best_preds)
|
|
):
|
|
n_best_preds.append(min_null_prediction)
|
|
|
|
# Use the offsets to gather the answer text in the original context.
|
|
context = example["context"]
|
|
for pred in n_best_preds:
|
|
offsets = pred.pop("offsets")
|
|
pred["text"] = context[offsets[0] : offsets[1]]
|
|
|
|
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
|
|
# failure.
|
|
if len(n_best_preds) == 0 or (len(n_best_preds) == 1 and n_best_preds[0]["text"] == ""):
|
|
n_best_preds.insert(
|
|
0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0}
|
|
)
|
|
|
|
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file,
|
|
# using the LogSumExp trick).
|
|
scores = np.array([pred.pop("score") for pred in n_best_preds])
|
|
exp_scores = np.exp(scores - np.max(scores))
|
|
probs = exp_scores / exp_scores.sum()
|
|
|
|
# Include the probabilities in our n_best_preds.
|
|
for prob, pred in zip(probs, n_best_preds):
|
|
pred["probability"] = prob
|
|
|
|
# Pick the best prediction. If the null answer is not possible, this is easy.
|
|
if not version_2_with_negative:
|
|
all_predictions[example["id"]] = n_best_preds[0]["text"]
|
|
else:
|
|
# Otherwise we first need to find the best non-empty prediction.
|
|
i = 0
|
|
while n_best_preds[i]["text"] == "":
|
|
i += 1
|
|
best_non_null_pred = n_best_preds[i]
|
|
|
|
# Then we compare to the null prediction using the threshold.
|
|
score_diff = (
|
|
null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
|
|
)
|
|
scores_diff_json[example["id"]] = float(score_diff) # To be JSON-serializable.
|
|
if score_diff > null_score_diff_threshold:
|
|
all_predictions[example["id"]] = ""
|
|
else:
|
|
all_predictions[example["id"]] = best_non_null_pred["text"]
|
|
|
|
# Make `n_best_preds` JSON-serializable by casting np.float back to float.
|
|
all_nbest_json[example["id"]] = [
|
|
{
|
|
k: float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v
|
|
for k, v in pred.items()
|
|
}
|
|
for pred in n_best_preds
|
|
]
|
|
|
|
# If we have an output_dir, let's save all those dicts.
|
|
if output_dir is not None:
|
|
if not os.path.isdir(output_dir):
|
|
raise OSError(f"{output_dir} is not a directory.")
|
|
|
|
prediction_file = os.path.join(
|
|
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
|
|
)
|
|
nbest_file = os.path.join(
|
|
output_dir,
|
|
"nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json",
|
|
)
|
|
if version_2_with_negative:
|
|
null_odds_file = os.path.join(
|
|
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
|
|
)
|
|
|
|
logger.info(f"Saving predictions to {prediction_file}.")
|
|
with open(prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
logger.info(f"Saving nbest_preds to {nbest_file}.")
|
|
with open(nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
if version_2_with_negative:
|
|
logger.info(f"Saving null_odds to {null_odds_file}.")
|
|
with open(null_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
|
|
|
|
# Format the result to the format the metric expects.
|
|
formatted_predictions = [
|
|
{"id": k, "prediction_text": v} for k, v in all_predictions.items()
|
|
]
|
|
|
|
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
|
|
return EvalPrediction(predictions=formatted_predictions, label_ids=references)
|
|
|
|
logger.info(f"***** Running {prefix} *****")
|
|
logger.info(f" Num examples = {len(eval_dataset)}")
|
|
logger.info(f" Batch size = {args.per_device_eval_batch_size}")
|
|
|
|
model.eval()
|
|
all_start_logits = []
|
|
all_end_logits = []
|
|
for _, batch in enumerate(eval_dataloader):
|
|
with torch.no_grad():
|
|
outputs = model(**batch)
|
|
start_logits = outputs.start_logits
|
|
end_logits = outputs.end_logits
|
|
|
|
if (
|
|
not args.pad_to_max_length
|
|
): # necessary to pad predictions and labels for being gathered
|
|
start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
|
|
end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
|
|
|
|
all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy())
|
|
all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy())
|
|
|
|
max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor
|
|
|
|
# concatenate the numpy array
|
|
start_logits_concat = create_and_fill_np_array(all_start_logits, max_len)
|
|
end_logits_concat = create_and_fill_np_array(all_end_logits, max_len)
|
|
|
|
outputs_numpy = (start_logits_concat, end_logits_concat)
|
|
prediction = postprocess_qa_predictions(
|
|
examples=eval_examples,
|
|
features=eval_dataset,
|
|
predictions=outputs_numpy,
|
|
n_best_size=args.n_best_size,
|
|
max_answer_length=args.max_answer_length,
|
|
# output_dir=args.finetuned_model_path,
|
|
prefix=prefix,
|
|
)
|
|
|
|
metric = evaluate.load("squad")
|
|
eval_metric = metric.compute(
|
|
predictions=prediction.predictions, references=prediction.label_ids
|
|
)
|
|
logger.info(f"{prefix} metrics: {eval_metric}\n")
|
|
return eval_metric
|
|
|
|
|
|
# Model Optimizer: Define a teacher factory for initializing the distillation model
|
|
def teacher_factory(model_name_or_path):
|
|
return AutoModelForQuestionAnswering.from_pretrained(model_name_or_path)
|
|
|
|
|
|
# Model Optimizer: Define a custom distillation loss function that uses start and end logits
|
|
class StartEndLogitsDistillationLoss(mtd.LogitsDistillationLoss):
|
|
def forward(self, outputs_s, outputs_t):
|
|
loss_start = super().forward(outputs_s.start_logits, outputs_t.start_logits)
|
|
loss_end = super().forward(outputs_s.end_logits, outputs_t.end_logits)
|
|
loss = (loss_start + loss_end) / 2.0
|
|
|
|
return loss
|
|
|
|
|
|
def train_and_evaluate_model(
|
|
args,
|
|
model: nn.Module,
|
|
accelerator: Accelerator,
|
|
examples: dict,
|
|
dataset: dict,
|
|
dataloader: dict[str, DataLoader],
|
|
answer_column_name,
|
|
):
|
|
# Optimizer
|
|
# Split weights in two groups, one with weight decay and the other not.
|
|
no_decay = ["bias", "LayerNorm.weight"]
|
|
optimizer_grouped_parameters = [
|
|
{
|
|
"params": [
|
|
p
|
|
for n, p in model.named_parameters()
|
|
if p.requires_grad and not any(nd in n for nd in no_decay)
|
|
],
|
|
"weight_decay": args.weight_decay,
|
|
},
|
|
{
|
|
"params": [
|
|
p
|
|
for n, p in model.named_parameters()
|
|
if p.requires_grad and any(nd in n for nd in no_decay)
|
|
],
|
|
"weight_decay": 0.0,
|
|
},
|
|
]
|
|
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(
|
|
len(dataloader["train"]) / args.gradient_accumulation_steps
|
|
)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = int(args.num_train_epochs * num_update_steps_per_epoch)
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
name=args.lr_scheduler_type,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
model, optimizer, dataloader["train"], dataloader["eval"], lr_scheduler = accelerator.prepare(
|
|
model, optimizer, dataloader["train"], dataloader["eval"], lr_scheduler
|
|
)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(
|
|
len(dataloader["train"]) / args.gradient_accumulation_steps
|
|
)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = int(args.num_train_epochs * num_update_steps_per_epoch)
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# Figure out how many steps we should save the Accelerator states
|
|
checkpointing_steps = args.checkpointing_steps
|
|
if checkpointing_steps is not None and checkpointing_steps.isdigit():
|
|
checkpointing_steps = int(checkpointing_steps)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if args.with_tracking:
|
|
experiment_config = vars(args)
|
|
# TensorBoard cannot log Enums, need the raw value
|
|
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
|
|
accelerator.init_trackers("tensorboard", experiment_config)
|
|
|
|
# Train!
|
|
total_batch_size = (
|
|
args.per_device_train_batch_size
|
|
* accelerator.num_processes
|
|
* args.gradient_accumulation_steps
|
|
)
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(dataset['train'])}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
|
|
logger.info(
|
|
f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
|
|
)
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
completed_steps = 0
|
|
starting_epoch = 0
|
|
resume_step = None
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_last_ckpt:
|
|
# Get the most recent checkpoint
|
|
dirs = [
|
|
f.path
|
|
for f in os.scandir(args.finetuned_model_path)
|
|
if f.is_dir() and (f.name.startswith("epoch_") or f.name.startswith("step_"))
|
|
]
|
|
if len(dirs) == 0:
|
|
logger.warning(
|
|
f"No checkpoint found in {args.finetuned_model_path}. Training from scratch!"
|
|
)
|
|
else:
|
|
latest_dir = max(dirs, key=os.path.getctime)
|
|
accelerator.load_state(latest_dir)
|
|
|
|
# Extract `epoch_{i}` or `step_{i}`
|
|
latest_dir = os.path.basename(latest_dir)
|
|
if "epoch" in latest_dir:
|
|
starting_epoch = int(latest_dir.replace("epoch_", "")) + 1
|
|
completed_steps = starting_epoch * num_update_steps_per_epoch
|
|
else:
|
|
# need to multiply `gradient_accumulation_steps` to reflect real steps
|
|
resume_step = (
|
|
int(latest_dir.replace("step_", "")) * args.gradient_accumulation_steps
|
|
)
|
|
starting_epoch = resume_step // len(dataloader["train"])
|
|
completed_steps = resume_step // args.gradient_accumulation_steps
|
|
resume_step -= starting_epoch * len(dataloader["train"])
|
|
|
|
# update the progress_bar if load from checkpoint
|
|
progress_bar.update(completed_steps)
|
|
|
|
# Evaluate before training (e.g. PTQ accuracy before QAT)
|
|
eval_metric = evaluate_model(
|
|
args,
|
|
model,
|
|
accelerator,
|
|
examples["eval"],
|
|
dataset["eval"],
|
|
dataloader["eval"],
|
|
answer_column_name,
|
|
)
|
|
for epoch in range(starting_epoch, args.num_train_epochs):
|
|
model.train()
|
|
if args.with_tracking:
|
|
total_loss = 0
|
|
if args.resume_from_last_ckpt and epoch == starting_epoch and resume_step is not None:
|
|
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
|
|
active_dataloader = accelerator.skip_first_batches(dataloader["train"], resume_step)
|
|
else:
|
|
active_dataloader = dataloader["train"]
|
|
for batch in active_dataloader:
|
|
optimizer.zero_grad()
|
|
outputs = model(**batch)
|
|
|
|
# Model Optimizer: If using distillation, we unwrap the model and extract the custom loss function
|
|
if args.do_modelopt_distill:
|
|
loss = model.module.compute_kd_loss()
|
|
else:
|
|
loss, _, _ = outputs.to_tuple()
|
|
|
|
# We keep track of the loss at each epoch
|
|
if args.with_tracking:
|
|
total_loss += loss.detach().float()
|
|
|
|
accelerator.backward(loss)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
completed_steps += 1
|
|
|
|
if isinstance(checkpointing_steps, int) and completed_steps % checkpointing_steps == 0:
|
|
accelerator.save_state(
|
|
os.path.join(args.finetuned_model_path, f"step_{completed_steps}")
|
|
)
|
|
|
|
if completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
if args.checkpointing_steps == "epoch":
|
|
accelerator.save_state(os.path.join(args.finetuned_model_path, f"epoch_{epoch}"))
|
|
|
|
eval_metric = evaluate_model(
|
|
args,
|
|
model,
|
|
accelerator,
|
|
examples["eval"],
|
|
dataset["eval"],
|
|
dataloader["eval"],
|
|
answer_column_name,
|
|
)
|
|
|
|
if args.with_tracking:
|
|
log = {
|
|
"squad": eval_metric,
|
|
"train_loss": total_loss.item() / len(dataloader["train"]),
|
|
"epoch": epoch,
|
|
"step": completed_steps,
|
|
}
|
|
accelerator.log(log, step=completed_steps)
|
|
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process and eval_metric:
|
|
logger.info(json.dumps(eval_metric, indent=4))
|
|
# Prefix all keys with prefix + '_'
|
|
for key in list(eval_metric.keys()):
|
|
eval_metric[f"Eval_{key}"] = eval_metric.pop(key)
|
|
|
|
with open(os.path.join(args.finetuned_model_path, "results.json"), "w") as f:
|
|
json.dump(eval_metric, f, indent=4)
|
|
|
|
|
|
def main(input_args: list[str] | None = None) -> None:
|
|
args = parse_args(input_args)
|
|
|
|
# Initialize the accelerator
|
|
accelerator_log_kwargs = {}
|
|
if args.with_tracking:
|
|
accelerator_log_kwargs["log_with"] = "tensorboard"
|
|
accelerator_log_kwargs["project_dir"] = args.finetuned_model_path
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs
|
|
)
|
|
|
|
# Setup logging
|
|
logging.basicConfig(
|
|
format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
force=True,
|
|
)
|
|
logger.info(accelerator.state, main_process_only=False)
|
|
if accelerator.is_local_main_process:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
|
|
# Set the training seed
|
|
set_seed(SEED)
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
# Load pretrained model and tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True)
|
|
model = AutoModelForQuestionAnswering.from_pretrained(args.model_name_or_path)
|
|
dummy_input = model.dummy_inputs["input_ids"]
|
|
|
|
# Get datasets
|
|
examples, dataset, dataloader, answer_column_name = get_datasets_and_dataloaders(
|
|
args, tokenizer, accelerator
|
|
)
|
|
|
|
def save(model, output_path):
|
|
if accelerator.is_main_process:
|
|
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
|
model = accelerator.unwrap_model(model)
|
|
model.save_pretrained(output_path)
|
|
tokenizer.save_pretrained(output_path)
|
|
logger.info(f"Saved model and tokenizer to {output_path}")
|
|
|
|
# Model Optimizer: Prune the model to given FLOPS target using GradNAS algorithm
|
|
if args.do_modelopt_prune:
|
|
logger.info(f"Pruning model to {args.modelopt_prune_flops_percent}% FLOPS")
|
|
|
|
# NOTE: gradnas does not perform synchronization across data parallel groups
|
|
# Use unwrapped model & non-distributed dataloader for gradnas so that all the processes
|
|
# in the data parallel group have the same gradients for pruning
|
|
model = model.to(accelerator.device)
|
|
dummy_input = dummy_input.to(accelerator.device)
|
|
|
|
# Search for the best pruned model
|
|
# To use other NAS algorithms, you can use `mtn.convert` + `mtn.search` here.
|
|
model, _ = mtp.prune(
|
|
model=model,
|
|
mode="gradnas",
|
|
constraints={"flops": f"{args.modelopt_prune_flops_percent}%"},
|
|
dummy_input=dummy_input,
|
|
config={
|
|
"data_loader": dataloader["train"],
|
|
"collect_func": lambda batch: (batch,),
|
|
"loss_func": lambda output, batch: (
|
|
output["loss"] if isinstance(output, dict) else output[0]
|
|
),
|
|
},
|
|
)
|
|
save(model, args.pruned_model_path)
|
|
|
|
# Model Optimizer: Quantize the model to INT8 precision
|
|
if args.modelopt_quantize_cfg:
|
|
logger.info(f"Quantizing model with {args.modelopt_quantize_cfg} config")
|
|
|
|
# NOTE: `mtq.quantize` does not perform synchronization across data parallel groups
|
|
# Use unwrapped model & non-distributed dataloader for PTQ calibration so that all the processes
|
|
# in the data parallel group have same calibration statistics
|
|
model = model.to(accelerator.device)
|
|
|
|
def forward_loop(model):
|
|
num_samples = 256 # Use only 256 samples for PTQ calibration
|
|
num_batches = num_samples // args.per_device_train_batch_size
|
|
for idx, batch in tqdm(enumerate(dataloader["train"]), total=num_batches):
|
|
batch = {k: v.to(accelerator.device) for k, v in batch.items()}
|
|
model(**batch)
|
|
if idx >= num_batches:
|
|
break
|
|
|
|
model = mtq.quantize(model, getattr(mtq, args.modelopt_quantize_cfg), forward_loop)
|
|
torch.cuda.empty_cache()
|
|
save(model, args.ptq_model_path)
|
|
|
|
if args.do_train:
|
|
# Handle the finetuned_model_path creation
|
|
if accelerator.is_main_process:
|
|
os.makedirs(args.finetuned_model_path, exist_ok=True)
|
|
|
|
# Model Optimizer: Convert to a DistillationModel containing teacher to train with distillation
|
|
if args.do_modelopt_distill:
|
|
logger.info(f"Using distillation with teacher {args.model_name_or_path}")
|
|
|
|
kd_config = {
|
|
"teacher_model": (teacher_factory, (args.model_name_or_path,), {}),
|
|
"criterion": StartEndLogitsDistillationLoss(args.temperature),
|
|
}
|
|
model = mtd.convert(model, mode=[("kd_loss", kd_config)])
|
|
|
|
train_and_evaluate_model(
|
|
args, model, accelerator, examples, dataset, dataloader, answer_column_name
|
|
)
|
|
|
|
# Model Optimizer: Export the distilled model
|
|
if args.do_modelopt_distill:
|
|
model = mtd.export(model)
|
|
|
|
save(model, args.finetuned_model_path)
|
|
|
|
if accelerator.is_main_process and args.onnx_export_path is not None:
|
|
logger.info(f"Exporting ONNX model to {args.onnx_export_path}")
|
|
|
|
# Move the model and dummy_input to the device
|
|
model = model.to(accelerator.device)
|
|
dummy_input = dummy_input.to(accelerator.device)
|
|
|
|
with open(args.onnx_export_path, "wb") as f:
|
|
f.write(get_onnx_bytes(model, dummy_input, onnx_opset=14))
|
|
|
|
logger.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|