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https://github.com/huggingface/transformers.git
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1ab147d648
* Remove redundant `nn.log_softmax` in `run_flax_glue.py` `optax.softmax_cross_entropy` expects unnormalized logits, and so it already calls `nn.log_softmax`, so I believe it is not needed here. `nn.log_softmax` is idempotent so mathematically it shouldn't have made a difference. * Remove unused 'flax.linen' import
510 lines
20 KiB
Python
Executable File
510 lines
20 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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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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""" Finetuning a 🤗 Flax Transformers model for sequence classification on GLUE."""
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import argparse
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import logging
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import os
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import random
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import time
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from itertools import chain
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from typing import Any, Callable, Dict, Tuple
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import datasets
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from datasets import load_dataset, load_metric
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import jax
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import jax.numpy as jnp
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import optax
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import transformers
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from flax import struct, traverse_util
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from flax.jax_utils import replicate, unreplicate
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from flax.metrics import tensorboard
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard
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from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, PretrainedConfig
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logger = logging.getLogger(__name__)
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Array = Any
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Dataset = datasets.arrow_dataset.Dataset
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PRNGKey = Any
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task_to_keys = {
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"cola": ("sentence", None),
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"mnli": ("premise", "hypothesis"),
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"mrpc": ("sentence1", "sentence2"),
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"qnli": ("question", "sentence"),
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"qqp": ("question1", "question2"),
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"rte": ("sentence1", "sentence2"),
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"sst2": ("sentence", None),
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"stsb": ("sentence1", "sentence2"),
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"wnli": ("sentence1", "sentence2"),
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}
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def parse_args():
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parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
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parser.add_argument(
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"--task_name",
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type=str,
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default=None,
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help="The name of the glue task to train on.",
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choices=list(task_to_keys.keys()),
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)
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parser.add_argument(
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"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
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)
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parser.add_argument(
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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)
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parser.add_argument(
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"--max_length",
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type=int,
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default=128,
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help=(
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"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
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" sequences shorter will be padded."
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),
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)
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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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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=True,
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)
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parser.add_argument(
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"--use_slow_tokenizer",
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action="store_true",
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help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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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=8,
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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=8,
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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("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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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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"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=3, help="A seed for reproducible training.")
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args = parser.parse_args()
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# Sanity checks
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if args.task_name is None and args.train_file is None and args.validation_file is None:
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raise ValueError("Need either a task name or a training/validation file.")
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else:
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if args.train_file is not None:
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extension = args.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if args.validation_file is not None:
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extension = args.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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return args
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def create_train_state(
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model: FlaxAutoModelForSequenceClassification,
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learning_rate_fn: Callable[[int], float],
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is_regression: bool,
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num_labels: int,
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weight_decay: float,
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) -> train_state.TrainState:
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"""Create initial training state."""
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class TrainState(train_state.TrainState):
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"""Train state with an Optax optimizer.
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The two functions below differ depending on whether the task is classification
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or regression.
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Args:
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logits_fn: Applied to last layer to obtain the logits.
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loss_fn: Function to compute the loss.
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"""
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logits_fn: Callable = struct.field(pytree_node=False)
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loss_fn: Callable = struct.field(pytree_node=False)
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# Creates a multi-optimizer consisting of two "Adam with weight decay" optimizers.
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def adamw(decay):
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return optax.adamw(learning_rate=learning_rate_fn, b1=0.9, b2=0.999, eps=1e-6, weight_decay=decay)
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def traverse(fn):
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def mask(data):
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flat = traverse_util.flatten_dict(data)
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return traverse_util.unflatten_dict({k: fn(k, v) for k, v in flat.items()})
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return mask
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# We use Optax's "masking" functionality to create a multi-optimizer, one
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# with weight decay and the other without. Note masking means the optimizer
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# will ignore these paths.
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decay_path = lambda p: not any(x in p for x in ["bias", "LayerNorm.weight"]) # noqa: E731
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tx = optax.chain(
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optax.masked(adamw(0.0), mask=traverse(lambda path, _: decay_path(path))),
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optax.masked(adamw(weight_decay), mask=traverse(lambda path, _: not decay_path(path))),
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)
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if is_regression:
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def mse_loss(logits, labels):
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return jnp.mean((logits[..., 0] - labels) ** 2)
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return TrainState.create(
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apply_fn=model.__call__,
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params=model.params,
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tx=tx,
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logits_fn=lambda logits: logits[..., 0],
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loss_fn=mse_loss,
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)
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else: # Classification.
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def cross_entropy_loss(logits, labels):
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xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))
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return jnp.mean(xentropy)
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return TrainState.create(
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apply_fn=model.__call__,
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params=model.params,
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tx=tx,
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logits_fn=lambda logits: logits.argmax(-1),
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loss_fn=cross_entropy_loss,
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)
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def create_learning_rate_fn(
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train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
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) -> Callable[[int], jnp.array]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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steps_per_epoch = train_ds_size // train_batch_size
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num_train_steps = steps_per_epoch * num_train_epochs
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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def glue_train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
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"""Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
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steps_per_epoch = len(dataset) // batch_size
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perms = jax.random.permutation(rng, len(dataset))
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perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch.
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perms = perms.reshape((steps_per_epoch, batch_size))
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for perm in perms:
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batch = dataset[perm]
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batch = {k: jnp.array(v) for k, v in batch.items()}
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batch = shard(batch)
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yield batch
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def glue_eval_data_collator(dataset: Dataset, batch_size: int):
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"""Returns batches of size `batch_size` from `eval dataset`, sharded over all local devices."""
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for i in range(len(dataset) // batch_size):
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batch = dataset[i * batch_size : (i + 1) * batch_size]
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batch = {k: jnp.array(v) for k, v in batch.items()}
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batch = shard(batch)
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yield batch
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def main():
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args = parse_args()
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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# Setup logging, we only want one process per machine to log things on the screen.
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logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
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if jax.process_index() == 0:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
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# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
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# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
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# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
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# label if at least two columns are provided.
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# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
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# single column. You can easily tweak this behavior (see below)
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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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if args.task_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset("glue", args.task_name)
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else:
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# Loading the dataset from local csv or json file.
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data_files = {}
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if args.train_file is not None:
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data_files["train"] = args.train_file
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if args.validation_file is not None:
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data_files["validation"] = args.validation_file
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extension = (args.train_file if args.train_file is not None else args.valid_file).split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files)
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# See more about loading any type of standard or custom dataset at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Labels
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if args.task_name is not None:
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is_regression = args.task_name == "stsb"
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if not is_regression:
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label_list = raw_datasets["train"].features["label"].names
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num_labels = len(label_list)
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else:
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num_labels = 1
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else:
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# Trying to have good defaults here, don't hesitate to tweak to your needs.
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is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
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if is_regression:
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num_labels = 1
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else:
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# A useful fast method:
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# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
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label_list = raw_datasets["train"].unique("label")
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label_list.sort() # Let's sort it for determinism
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num_labels = len(label_list)
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# Load pretrained model and tokenizer
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config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
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model = FlaxAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, config=config)
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# Preprocessing the datasets
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if args.task_name is not None:
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sentence1_key, sentence2_key = task_to_keys[args.task_name]
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else:
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# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
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non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
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if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
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sentence1_key, sentence2_key = "sentence1", "sentence2"
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else:
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if len(non_label_column_names) >= 2:
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sentence1_key, sentence2_key = non_label_column_names[:2]
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else:
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sentence1_key, sentence2_key = non_label_column_names[0], None
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# Some models have set the order of the labels to use, so let's make sure we do use it.
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label_to_id = None
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if (
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model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
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and args.task_name is not None
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and not is_regression
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):
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# Some have all caps in their config, some don't.
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label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
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if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
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logger.info(
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f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
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"Using it!"
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)
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label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
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else:
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logger.warning(
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"Your model seems to have been trained with labels, but they don't match the dataset: ",
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f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
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"\nIgnoring the model labels as a result.",
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)
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elif args.task_name is None:
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label_to_id = {v: i for i, v in enumerate(label_list)}
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def preprocess_function(examples):
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# Tokenize the texts
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texts = (
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(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
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)
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result = tokenizer(*texts, padding="max_length", max_length=args.max_length, truncation=True)
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if "label" in examples:
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if label_to_id is not None:
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# Map labels to IDs (not necessary for GLUE tasks)
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result["labels"] = [label_to_id[l] for l in examples["label"]]
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else:
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# In all cases, rename the column to labels because the model will expect that.
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result["labels"] = examples["label"]
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return result
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processed_datasets = raw_datasets.map(
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preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
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)
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train_dataset = processed_datasets["train"]
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eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"]
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# Log a few random samples from the training set:
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for index in random.sample(range(len(train_dataset)), 3):
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logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
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# Define a summary writer
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summary_writer = tensorboard.SummaryWriter(args.output_dir)
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summary_writer.hparams(vars(args))
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def write_metric(train_metrics, eval_metrics, train_time, step):
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summary_writer.scalar("train_time", train_time, step)
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train_metrics = get_metrics(train_metrics)
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for key, vals in train_metrics.items():
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tag = f"train_{key}"
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for i, val in enumerate(vals):
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summary_writer.scalar(tag, val, step - len(vals) + i + 1)
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for metric_name, value in eval_metrics.items():
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summary_writer.scalar(f"eval_{metric_name}", value, step)
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num_epochs = int(args.num_train_epochs)
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rng = jax.random.PRNGKey(args.seed)
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dropout_rngs = jax.random.split(rng, jax.local_device_count())
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train_batch_size = args.per_device_train_batch_size * jax.local_device_count()
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eval_batch_size = args.per_device_eval_batch_size * jax.local_device_count()
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learning_rate_fn = create_learning_rate_fn(
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len(train_dataset), train_batch_size, args.num_train_epochs, args.num_warmup_steps, args.learning_rate
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)
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state = create_train_state(
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model, learning_rate_fn, is_regression, num_labels=num_labels, weight_decay=args.weight_decay
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)
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# define step functions
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def train_step(
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state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
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) -> Tuple[train_state.TrainState, float]:
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"""Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
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dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
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targets = batch.pop("labels")
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def loss_fn(params):
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logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
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loss = state.loss_fn(logits, targets)
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return loss
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grad_fn = jax.value_and_grad(loss_fn)
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loss, grad = grad_fn(state.params)
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grad = jax.lax.pmean(grad, "batch")
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new_state = state.apply_gradients(grads=grad)
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metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
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return new_state, metrics, new_dropout_rng
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p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
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def eval_step(state, batch):
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logits = state.apply_fn(**batch, params=state.params, train=False)[0]
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return state.logits_fn(logits)
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p_eval_step = jax.pmap(eval_step, axis_name="batch")
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if args.task_name is not None:
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metric = load_metric("glue", args.task_name)
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else:
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metric = load_metric("accuracy")
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logger.info(f"===== Starting training ({num_epochs} epochs) =====")
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train_time = 0
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# make sure weights are replicated on each device
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state = replicate(state)
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for epoch in range(1, num_epochs + 1):
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logger.info(f"Epoch {epoch}")
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logger.info(" Training...")
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|
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train_start = time.time()
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train_metrics = []
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rng, input_rng = jax.random.split(rng)
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# train
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for batch in glue_train_data_collator(input_rng, train_dataset, train_batch_size):
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state, metrics, dropout_rngs = p_train_step(state, batch, dropout_rngs)
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train_metrics.append(metrics)
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train_time += time.time() - train_start
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logger.info(f" Done! Training metrics: {unreplicate(metrics)}")
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logger.info(" Evaluating...")
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|
|
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# evaluate
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for batch in glue_eval_data_collator(eval_dataset, eval_batch_size):
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labels = batch.pop("labels")
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predictions = p_eval_step(state, batch)
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metric.add_batch(predictions=chain(*predictions), references=chain(*labels))
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|
|
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# evaluate also on leftover examples (not divisible by batch_size)
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num_leftover_samples = len(eval_dataset) % eval_batch_size
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|
|
|
# make sure leftover batch is evaluated on one device
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|
if num_leftover_samples > 0 and jax.process_index() == 0:
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|
# take leftover samples
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|
batch = eval_dataset[-num_leftover_samples:]
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|
batch = {k: jnp.array(v) for k, v in batch.items()}
|
|
|
|
labels = batch.pop("labels")
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|
predictions = eval_step(unreplicate(state), batch)
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|
metric.add_batch(predictions=predictions, references=labels)
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|
|
|
eval_metric = metric.compute()
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|
logger.info(f" Done! Eval metrics: {eval_metric}")
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|
|
|
cur_step = epoch * (len(train_dataset) // train_batch_size)
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|
write_metric(train_metrics, eval_metric, train_time, cur_step)
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|
|
|
# save last checkpoint
|
|
if jax.process_index() == 0:
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|
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
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|
model.save_pretrained(args.output_dir, params=params)
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if __name__ == "__main__":
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|
main()
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