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"""Callbacks for Trainer class"""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, Dict, List
import evaluate
import numpy as np
import pandas as pd
import torch
import torch.distributed as dist
from datasets import load_dataset
from optimum.bettertransformer import BetterTransformer
from tqdm import tqdm
from transformers import (
TrainerCallback,
TrainerControl,
TrainerState,
TrainingArguments,
)
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
from axolotl.utils.bench import log_gpu_memory_usage
from axolotl.utils.distributed import (
barrier,
gather_scalar_from_all_ranks,
get_world_size,
is_main_process,
zero_first,
)
if TYPE_CHECKING:
from axolotl.utils.trainer import AxolotlTrainingArguments
LOG = logging.getLogger("axolotl.callbacks")
IGNORE_INDEX = -100
class SavePeftModelCallback(TrainerCallback): # pylint: disable=too-few-public-methods
"""Callback to save the PEFT adapter"""
def on_save(
self,
args: TrainingArguments,
state: TrainerState,
control: TrainerControl,
**kwargs,
):
checkpoint_folder = os.path.join(
args.output_dir,
f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
)
peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
kwargs["model"].save_pretrained(
peft_model_path, save_safetensors=args.save_safetensors
)
return control
class SaveBetterTransformerModelCallback(
TrainerCallback
): # pylint: disable=too-few-public-methods
"""Callback to save the BetterTransformer wrapped model"""
def on_step_end(
self,
args: TrainingArguments,
state: TrainerState,
control: TrainerControl,
**kwargs,
):
# Save
if (
args.save_strategy == IntervalStrategy.STEPS
and args.save_steps > 0
and state.global_step % args.save_steps == 0
):
control.should_save = True
if control.should_save:
checkpoint_folder = os.path.join(
args.output_dir,
f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
)
model = BetterTransformer.reverse(kwargs["model"])
model.save_pretrained(checkpoint_folder)
# FIXME - need to cleanup old checkpoints
# since we're saving here, we don't need the trainer loop to attempt to save too b/c
# the trainer will raise an exception since it can't save a BetterTransformer wrapped model
control.should_save = False
return control
class GPUStatsCallback(
TrainerCallback
): # pylint: disable=too-few-public-methods disable=unused-argument
"""Callback to track GPU utilization"""
def __init__(self, cfg):
self.cfg = cfg
self.logged = False
def on_step_end(
self,
args: TrainingArguments,
state: TrainerState,
control: TrainerControl,
**kwargs,
):
if not self.logged and state.global_step > 1:
log_gpu_memory_usage(LOG, "while training", self.cfg.device)
self.logged = True
return control
def bench_eval_callback_factory(trainer, tokenizer):
accuracy = evaluate.load("accuracy")
abcd_idx = [
tokenizer("A", add_special_tokens=False).input_ids[0],
tokenizer("B", add_special_tokens=False).input_ids[0],
tokenizer("C", add_special_tokens=False).input_ids[0],
tokenizer("D", add_special_tokens=False).input_ids[0],
tokenizer("E", add_special_tokens=False).input_ids[0],
tokenizer("F", add_special_tokens=False).input_ids[0],
tokenizer("G", add_special_tokens=False).input_ids[0],
]
bench_split = "eval"
def transform_bench_subject(example):
# Split on ':' and trim whitespace
parts = example["subject"].split(":")
first_part = (
parts[0].strip().lower().replace("-", "_")
) # Lowercase the first part
second_part = (
parts[1].strip().replace("-", "_") if len(parts) > 1 else "all"
) # Replace hyphens with underscores
# Return the transformed values
return {"name": first_part, "subject": second_part}
if trainer.args.bench_dataset == "mmlu-zs":
bench_dataset = load_dataset(
"openaccess-ai-collective/mmlu-evals",
data_files={
"eval": "zero_shot_mmlu_val.json",
"test": "zero_shot_mmlu_test.json",
},
)
# bench_dataset = bench_dataset.remove_columns("subject")
# MMLU Five-shot (Eval/Test only)
elif trainer.args.bench_dataset in ["mmlu", "mmlu-fs"]:
bench_dataset = load_dataset(
"openaccess-ai-collective/mmlu-evals",
data_files={
"eval": "five_shot_mmlu_val.json",
"test": "five_shot_mmlu_test.json",
},
)
# bench_dataset = bench_dataset.remove_columns('subject')
elif "/" in trainer.args.bench_dataset:
bench_ds = trainer.args.bench_dataset
bench_ds_name = "/".join(bench_ds.split("/", 2)[:2])
bench_ds_data_file = "/".join(bench_ds.split("/", 2)[2:])
bench_dataset = load_dataset(
bench_ds_name,
data_files={
"eval": bench_ds_data_file,
},
)
bench_dataset["eval"] = bench_dataset["eval"].map(transform_bench_subject)
else:
raise ValueError(
f"unhandled value `{trainer.args.bench_dataset}` for bench_dataset training args"
)
bench_dataset = bench_dataset[trainer.args.bench_split]
if trainer.args.max_bench_samples is not None:
bench_dataset = bench_dataset.select(range(trainer.args.max_bench_samples))
def tokenize_evals(example):
source = f"{tokenizer.bos_token}{example['input']}"
target = f"{example['output']}{tokenizer.eos_token}"
tokenized_source = tokenizer(
source,
max_length=2048,
truncation=True,
add_special_tokens=False,
)
tokenized_target = tokenizer(
target,
max_length=2048,
truncation=True,
add_special_tokens=False,
)
input_ids = tokenized_source["input_ids"] + tokenized_target["input_ids"]
labels = [IGNORE_INDEX] * len(tokenized_source["input_ids"]) + tokenized_target[
"input_ids"
]
return {
"input_ids": input_ids,
"labels": labels,
"subject": example["subject"],
}
with zero_first(is_main_process()):
bench_dataset = bench_dataset.map(tokenize_evals)
bench_dataset = bench_dataset.filter(lambda x: x["labels"][-2] in abcd_idx)
class BenchEvalCallback(TrainerCallback):
"""
TrainerCallback that runs the MMLU evals
"""
def on_evaluate(
self,
args: AxolotlTrainingArguments,
state: TrainerState, # pylint: disable=unused-argument
control: TrainerControl, # pylint: disable=unused-argument
metrics: Dict[str, float], # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
):
data_loader = trainer.get_bench_dataloader(
bench_dataset.remove_columns(["input", "subject", "output", "name"])
)
trainer.model.eval()
preds, refs = [], []
loss_bench = 0
for batch in tqdm(data_loader, total=len(data_loader)):
(loss, logits, labels) = trainer.prediction_step(
trainer.model,
batch,
prediction_loss_only=False,
)
# There are two tokens, the output, and eos token.
for i, logit in enumerate(logits):
label_non_zero_id = (batch["labels"][i] != IGNORE_INDEX).nonzero()[
0
][0]
logit_abcd = logit[label_non_zero_id - 1][abcd_idx]
preds.append(torch.argmax(logit_abcd).item())
labels = labels[labels != IGNORE_INDEX].view(-1, 2)[:, 0]
refs += [
abcd_idx.index(label) if label in abcd_idx else -1
for label in labels.tolist()
]
loss_bench += loss.item()
# Extract results by subject.
bench_name = bench_dataset["name"]
bench_names: dict = {s: {"refs": [], "preds": []} for s in set(bench_name)}
for s, p, r in zip(bench_name, preds, refs): # pylint: disable=invalid-name
bench_names[s]["preds"].append(p)
bench_names[s]["refs"].append(r)
barrier()
local_bench_names = bench_names
gathered_bench_names: List[Dict] = [{} for _ in range(get_world_size())]
# Gather results from all GPUs to GPU 0
loss_bench_ranks = gather_scalar_from_all_ranks(
lambda: loss_bench, get_world_size()
)
len_data_loader_ranks = gather_scalar_from_all_ranks(
lambda: len(data_loader), get_world_size()
)
if not is_main_process():
dist.gather_object(local_bench_names, dst=0)
else:
dist.gather_object(local_bench_names, gathered_bench_names, dst=0)
bench_loss = sum(loss_bench_ranks) / sum(len_data_loader_ranks)
results = {f"{bench_split}_bench_loss": bench_loss}
# Combine results from all GPUs
combined_bench_names: Dict[str, Dict[str, List]] = {}
for bench_name in gathered_bench_names:
for name, data in bench_name.items():
if name not in combined_bench_names:
combined_bench_names[name] = {"refs": [], "preds": []}
combined_bench_names[name]["refs"].extend(data["refs"])
combined_bench_names[name]["preds"].extend(data["preds"])
bench_scores = []
bench_refs = []
bench_preds = []
for (
bench_name
) in combined_bench_names: # pylint: disable=consider-using-dict-items
bench_score = accuracy.compute(
references=combined_bench_names[bench_name]["refs"],
predictions=combined_bench_names[bench_name]["preds"],
)["accuracy"]
bench_refs.extend(combined_bench_names[bench_name]["refs"])
bench_preds.extend(combined_bench_names[bench_name]["preds"])
if not pd.isna(bench_score):
results[
f"{bench_split}_bench_accuracy_{bench_name}"
] = bench_score
bench_scores.append(bench_score)
else:
results[f"{bench_split}_bench_accuracy_{bench_name}"] = 0.0
bench_scores.append(0.0)
results[f"{bench_split}_bench_average_accuracy"] = np.mean(bench_scores)
results[f"{bench_split}_bench_total_accuracy"] = accuracy.compute(
references=bench_refs, predictions=bench_preds
)["accuracy"]
trainer.log(results)
return BenchEvalCallback
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