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4e046d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import TYPE_CHECKING, Optional
from transformers import DataCollatorForLanguageModeling
from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ...extras.misc import calculate_tps
from ...extras.packages import is_hyper_parallel_available, is_transformers_version_greater_than
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..sft.metric import ComputeAccuracy, ComputeSimilarity, eval_logit_processor
from ..trainer_utils import create_modelcard_and_push, create_ref_model
from .trainer import HyperParallelTrainer
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments, TrainerCallback
from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
def _prepare_hp_args(finetuning_args: "FinetuningArguments", model_args: "ModelArguments"):
r"""Load HyperParallel arguments and apply LlamaFactory-side overrides.
When activation optimization is enabled, skip native gradient checkpointing
so HP can install its own via ``setup_activation_optimization``.
"""
if not is_hyper_parallel_available():
raise ImportError("hyper_parallel is not installed. Please install it with `pip install hyper_parallel`.")
from hyper_parallel.integration.llamafactory import HyperParallelArguments # pylint: disable=C0415
hp_args = HyperParallelArguments.from_finetuning_args(finetuning_args)
if getattr(hp_args, "cp_size", None) != finetuning_args.hyper_parallel_cp_size:
setattr(hp_args, "cp_size", finetuning_args.hyper_parallel_cp_size)
if hp_args.activation_mode != "none":
model_args.disable_gradient_checkpointing = True
return hp_args
def run_pt(
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
callbacks: Optional[list["TrainerCallback"]] = None,
):
hp_args = _prepare_hp_args(finetuning_args, model_args)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
template = get_template_and_fix_tokenizer(tokenizer, data_args)
dataset_module = get_dataset(template, model_args, data_args, training_args, stage="pt", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
trainer = HyperParallelTrainer(
hp_args=hp_args,
model=model,
args=training_args,
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
**dataset_module,
**tokenizer_module,
)
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
if trainer.is_world_process_zero() and finetuning_args.plot_loss:
keys = ["loss"]
if isinstance(dataset_module.get("eval_dataset"), dict):
keys += [f"eval_{key}_loss" for key in dataset_module["eval_dataset"].keys()]
else:
keys += ["eval_loss"]
plot_loss(training_args.output_dir, keys=keys)
if training_args.do_eval:
metrics = trainer.evaluate(metric_key_prefix="eval")
if isinstance(dataset_module.get("eval_dataset"), dict):
for key in dataset_module["eval_dataset"].keys():
try:
perplexity = math.exp(metrics[f"eval_{key}_loss"])
except OverflowError:
perplexity = float("inf")
metrics[f"eval_{key}_perplexity"] = perplexity
else:
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["eval_perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)
def run_sft(
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
callbacks: Optional[list["TrainerCallback"]] = None,
):
hp_args = _prepare_hp_args(finetuning_args, model_args)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
template = get_template_and_fix_tokenizer(tokenizer, data_args)
dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
ref_model = None
if finetuning_args.use_asft_loss:
ref_model = create_ref_model(model_args, finetuning_args)
data_collator = SFTDataCollatorWith4DAttentionMask(
template=template,
model=model if not training_args.predict_with_generate else None,
pad_to_multiple_of=8 if training_args.do_train else None,
label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
block_diag_attn=model_args.block_diag_attn,
attn_implementation=getattr(model.config, "_attn_implementation", None),
compute_dtype=model_args.compute_dtype,
**tokenizer_module,
)
# Metric utils
metric_module = {}
if training_args.predict_with_generate:
metric_module["compute_metrics"] = ComputeSimilarity(tokenizer=tokenizer)
elif finetuning_args.compute_accuracy:
metric_module["compute_metrics"] = ComputeAccuracy()
metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
# Keyword arguments for `model.generate`
gen_kwargs = generating_args.to_dict(obey_generation_config=True)
if is_transformers_version_greater_than("4.58.0"):
extra_ids = getattr(tokenizer, "additional_special_tokens_ids", None)
if not isinstance(extra_ids, list):
extra_special_tokens = getattr(tokenizer, "_extra_special_tokens", [])
string_tokens = [str(t) for t in extra_special_tokens]
extra_ids = tokenizer.convert_tokens_to_ids(string_tokens)
all_eos_ids = [tokenizer.eos_token_id] + [i for i in extra_ids if i != -1]
gen_kwargs["eos_token_id"] = list(dict.fromkeys(all_eos_ids))
else:
gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
trainer = HyperParallelTrainer(
hp_args=hp_args,
model=model,
args=training_args,
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
gen_kwargs=gen_kwargs,
ref_model=ref_model,
**dataset_module,
**tokenizer_module,
**metric_module,
)
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_model()
if finetuning_args.include_effective_tokens_per_second:
train_result.metrics["effective_tokens_per_sec"] = calculate_tps(
dataset_module["train_dataset"], train_result.metrics, stage="sft"
)
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
if trainer.is_world_process_zero() and finetuning_args.plot_loss:
keys = ["loss"]
if isinstance(dataset_module.get("eval_dataset"), dict):
keys += sum(
[[f"eval_{key}_loss", f"eval_{key}_accuracy"] for key in dataset_module["eval_dataset"].keys()],
[],
)
else:
keys += ["eval_loss", "eval_accuracy"]
plot_loss(training_args.output_dir, keys=keys)
if training_args.predict_with_generate:
tokenizer.padding_side = "left"
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Predict
if training_args.do_predict:
logger.warning_rank0_once("Batch generation can be very slow. Consider using `scripts/vllm_infer.py` instead.")
predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
trainer.log_metrics("predict", predict_results.metrics)
trainer.save_metrics("predict", predict_results.metrics)
trainer.save_predictions(dataset_module["eval_dataset"], predict_results, generating_args.skip_special_tokens)
# Create model card
create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)
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