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support for dynamic plugin training args mixins and symmetric kl

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This commit is contained in:
Wing Lian
2025-05-27 17:49:33 -04:00
parent 24b96b1c4f
commit 770bb0605a
16 changed files with 600 additions and 383 deletions
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23
src/axolotl/core/builders/causal.py
View File

@@ -149,6 +149,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
return AxolotlTrainer
def build(self, total_num_steps):
from axolotl.core.training_args import (
AxolotlPRMConfig,
AxolotlRewardConfig,
AxolotlTrainingArguments,
)
training_arguments_kwargs, trainer_kwargs = self._set_base_training_args(
total_num_steps
)
@@ -317,12 +323,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
training_arguments_kwargs["image_resize_algorithm"] = (
self.cfg.image_resize_algorithm
)
if self.cfg.kd_ce_alpha is not None:
training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
if self.cfg.kd_alpha is not None:
training_arguments_kwargs["kd_alpha"] = self.cfg.kd_alpha
if self.cfg.kd_temperature is not None:
training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
if self.cfg.plugins:
plugin_manager = PluginManager.get_instance()
plugin_training_args = plugin_manager.get_training_args(self.cfg)
if plugin_training_args:
training_arguments_kwargs.update(plugin_training_args)
if self.cfg.reward_model:
training_args_cls = AxolotlRewardConfig
@@ -403,7 +409,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
return trainer
def build_collator(
self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
self,
training_args, # type: "AxolotlTrainingArguments"
is_eval=False,
**kwargs,
):
if training_args.pretraining:
if (

18
src/axolotl/core/builders/rl.py
View File

@@ -12,11 +12,6 @@ from axolotl.core.trainers import (
from axolotl.core.trainers.dpo import DPOStrategy
from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
from axolotl.core.trainers.grpo import GRPOStrategy
from axolotl.core.training_args import (
AxolotlCPOConfig,
AxolotlKTOConfig,
AxolotlORPOConfig,
)
from axolotl.integrations.base import PluginManager
from axolotl.loaders.utils import ensure_dtype
from axolotl.utils.logging import get_logger
@@ -79,6 +74,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
"""
Returns training_args and trainer_kwargs
"""
from axolotl.core.training_args import (
AxolotlCPOConfig,
AxolotlKTOConfig,
AxolotlORPOConfig,
)
training_args_kwargs, trainer_kwargs = self._set_base_training_args(
total_num_steps=total_num_steps
)
@@ -165,6 +166,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
if blocklist_key in training_args_kwargs:
del training_args_kwargs[blocklist_key]
if self.cfg.plugins:
plugin_manager = PluginManager.get_instance()
plugin_training_args = plugin_manager.get_training_args(self.cfg)
if plugin_training_args:
training_args_kwargs.update(plugin_training_args)
training_args = training_args_cls( # pylint: disable=unexpected-keyword-arg
logging_first_step=True,
**training_args_kwargs,

219
src/axolotl/core/training_args.py
View File

@@ -2,224 +2,17 @@
extra axolotl specific training args
"""
from dataclasses import dataclass, field
from typing import Optional
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional, Type
from PIL.Image import Resampling
from transformers import TrainingArguments
from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
from axolotl.integrations.config import merge_training_args
@dataclass
class AxolotlTrainingMixins:
"""
Mixin class for the Axolotl training args.
"""
# pylint: disable=duplicate-code
model_type: Optional[str] = field(
default=None, metadata={"help": "HF model configuration model_type."}
)
lr_quadratic_warmup: bool = field(
default=False,
metadata={"help": "Use quadratic warmup for cosine scheduling."},
)
pretraining: bool = field(
default=False,
metadata={
"help": "Indicates to trainer whether we are doing continued pretraining."
},
)
sample_packing: bool = field(
default=False,
metadata={"help": "Use sample packing for efficient training."},
)
sample_packing_sequentially: bool = field(
default=False,
metadata={
"help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
},
)
multipack_real_batches: bool = field(
default=False,
metadata={"help": "Use real batches for efficient training."},
)
eval_sample_packing: Optional[bool] = field(
default=None,
metadata={"help": "Use sample packing for efficient evals."},
)
sample_packing_efficiency: float = field(
default=1.0,
metadata={"help": "Sample packing efficiency for calculating batch length."},
)
sample_packing_bin_size: int = field(
default=200,
metadata={
"help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
},
)
sample_packing_group_size: int = field(
default=100000,
metadata={
"help": "The number of samples to group together for packing. Increase for better packing."
},
)
max_seq_length: int = field(
default=2048,
metadata={"help": "The maximum sequence length the model can handle"},
)
relora_steps: Optional[int] = field(
default=None,
metadata={"help": "how often to reset for ReLoRA"},
)
relora_warmup_steps: Optional[int] = field(
default=None,
metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
)
relora_anneal_steps: Optional[int] = field(
default=None,
metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
)
relora_prune_ratio: Optional[float] = field(
default=0.9,
metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
)
bench_split: Optional[str] = field(
default="eval", metadata={"help": "The benchmark split to run on"}
)
bench_dataset: Optional[str] = field(
default="pharaouk/dharma-1/dharma_1_mini.json",
metadata={
"help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
},
)
do_bench_eval: Optional[bool] = field(
default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
)
do_causal_lm_eval: Optional[bool] = field(
default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
)
max_bench_samples: Optional[int] = field(
default=None,
metadata={
"help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
},
)
bench_source_max_len: int = field(
default=2048, metadata={"help": "Maximum source sequence length for bench."}
)
dataloader_prefetch_factor: Optional[int] = field(
default=None,
metadata={"help": "prefetch_factor argument to the dataloader"},
)
cosine_min_lr_ratio: Optional[float] = field(
default=None,
metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
)
cosine_constant_lr_ratio: Optional[float] = field(
default=None,
metadata={
"help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
},
)
loraplus_lr_ratio: Optional[float] = field(
default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
)
loraplus_lr_embedding: Optional[float] = field(
default=1e-6,
metadata={"help": "loraplus learning rate for lora embedding layers."},
)
embedding_lr_scale: Optional[float] = field(
default=None,
metadata={"help": "Scale the learning rate for the embedding layers."},
)
lr_groups: Optional[list[dict]] = field(
default=None,
metadata={"help": "Specify learning rate groups for with different LRs."},
)
embedding_lr: Optional[float] = field(
default=None,
metadata={"help": "absolute learning rate for the embedding layers."},
)
qlora: bool = field(
default=False,
metadata={"help": "whether this is a qlora training"},
)
orpo_alpha: Optional[float] = field(
default=None,
)
lisa_n_layers: Optional[int] = field(
default=None,
metadata={"help": "the number of activate layers in LISA"},
)
lisa_step_interval: Optional[int] = field(
default=None,
metadata={"help": "how often to switch layers in LISA"},
)
lisa_layers_attribute: Optional[str] = field(
default=None,
metadata={"help": "path under the model to access the layers"},
)
curriculum_sampling: Optional[bool] = field(
default=None,
metadata={"help": "whether to use sequential sampling for curriculum learning"},
)
alternate_lr_scheduler_type: Optional[str] = field(
default=None,
metadata={
"help": "workaround to pass an alternate lr scheduler to the HF trainer"
},
)
chat_template: Optional[str] = field(
default=None,
metadata={"help": "Chat template converting chat messages to text"},
)
kd_ce_alpha: Optional[float] = field(
default=None,
metadata={
"help": "The alpha scaling parameter for SFT cross entropy loss when using KD"
},
)
kd_alpha: Optional[float] = field(
default=1.0,
metadata={"help": "The alpha scaling parameter for KD loss"},
)
kd_temperature: Optional[float] = field(
default=1.0,
metadata={
"help": "the temperature parameter for KL divergence loss when using KD"
},
)
adam_beta3: Optional[float] = field(
default=None,
metadata={
"help": "The beta3 hyperparameter used in some optimizers such as CAME"
},
)
adam_epsilon2: Optional[float] = field(
default=None,
metadata={
"help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
},
)
# multi-modal section
image_size: int | tuple[int, int] | None = field(
default=None,
metadata={"help": "The size of the image to resize to"},
)
image_resize_algorithm: Resampling | None = field(
default=None,
metadata={"help": "The algorithm to use for image resizing"},
)
# end of multi-modal section
AxolotlTrainingMixins: Type = merge_training_args()
@dataclass

222
src/axolotl/core/training_args_base.py Normal file
View File

@@ -0,0 +1,222 @@
"""
Base Axolotl Training Mixins shared across various trainer configs
"""
from dataclasses import dataclass, field
from typing import Optional
from PIL.Image import Resampling
from axolotl.utils.schemas.enums import RingAttnFunc
@dataclass
class AxolotlTrainingMixins:
"""
Mixin class for the Axolotl training args.
"""
# pylint: disable=duplicate-code
model_type: Optional[str] = field(
default=None, metadata={"help": "HF model configuration model_type."}
)
lr_quadratic_warmup: bool = field(
default=False,
metadata={"help": "Use quadratic warmup for cosine scheduling."},
)
pretraining: bool = field(
default=False,
metadata={
"help": "Indicates to trainer whether we are doing continued pretraining."
},
)
sample_packing: bool = field(
default=False,
metadata={"help": "Use sample packing for efficient training."},
)
sample_packing_sequentially: bool = field(
default=False,
metadata={
"help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
},
)
multipack_real_batches: bool = field(
default=False,
metadata={"help": "Use real batches for efficient training."},
)
eval_sample_packing: Optional[bool] = field(
default=None,
metadata={"help": "Use sample packing for efficient evals."},
)
sample_packing_efficiency: float = field(
default=1.0,
metadata={"help": "Sample packing efficiency for calculating batch length."},
)
sample_packing_bin_size: int = field(
default=200,
metadata={
"help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
},
)
sample_packing_group_size: int = field(
default=100000,
metadata={
"help": "The number of samples to group together for packing. Increase for better packing."
},
)
max_seq_length: int = field(
default=2048,
metadata={"help": "The maximum sequence length the model can handle"},
)
relora_steps: Optional[int] = field(
default=None,
metadata={"help": "how often to reset for ReLoRA"},
)
relora_warmup_steps: Optional[int] = field(
default=None,
metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
)
relora_anneal_steps: Optional[int] = field(
default=None,
metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
)
relora_prune_ratio: Optional[float] = field(
default=0.9,
metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
)
bench_split: Optional[str] = field(
default="eval", metadata={"help": "The benchmark split to run on"}
)
bench_dataset: Optional[str] = field(
default="pharaouk/dharma-1/dharma_1_mini.json",
metadata={
"help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
},
)
do_bench_eval: Optional[bool] = field(
default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
)
do_causal_lm_eval: Optional[bool] = field(
default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
)
max_bench_samples: Optional[int] = field(
default=None,
metadata={
"help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
},
)
bench_source_max_len: int = field(
default=2048, metadata={"help": "Maximum source sequence length for bench."}
)
dataloader_prefetch_factor: Optional[int] = field(
default=None,
metadata={"help": "prefetch_factor argument to the dataloader"},
)
cosine_min_lr_ratio: Optional[float] = field(
default=None,
metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
)
cosine_constant_lr_ratio: Optional[float] = field(
default=None,
metadata={
"help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
},
)
loraplus_lr_ratio: Optional[float] = field(
default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
)
loraplus_lr_embedding: Optional[float] = field(
default=1e-6,
metadata={"help": "loraplus learning rate for lora embedding layers."},
)
embedding_lr_scale: Optional[float] = field(
default=None,
metadata={"help": "Scale the learning rate for the embedding layers."},
)
lr_groups: Optional[list[dict]] = field(
default=None,
metadata={"help": "Specify learning rate groups for with different LRs."},
)
embedding_lr: Optional[float] = field(
default=None,
metadata={"help": "absolute learning rate for the embedding layers."},
)
qlora: bool = field(
default=False,
metadata={"help": "whether this is a qlora training"},
)
orpo_alpha: Optional[float] = field(
default=None,
)
lisa_n_layers: Optional[int] = field(
default=None,
metadata={"help": "the number of activate layers in LISA"},
)
lisa_step_interval: Optional[int] = field(
default=None,
metadata={"help": "how often to switch layers in LISA"},
)
lisa_layers_attribute: Optional[str] = field(
default=None,
metadata={"help": "path under the model to access the layers"},
)
curriculum_sampling: Optional[bool] = field(
default=None,
metadata={"help": "whether to use sequential sampling for curriculum learning"},
)
alternate_lr_scheduler_type: Optional[str] = field(
default=None,
metadata={
"help": "workaround to pass an alternate lr scheduler to the HF trainer"
},
)
chat_template: Optional[str] = field(
default=None,
metadata={"help": "Chat template converting chat messages to text"},
)
# kd_ce_alpha: Optional[float] = field(
# default=None,
# metadata={
# "help": "The alpha scaling parameter for SFT cross entropy loss when using KD"
# },
# )
#
# kd_alpha: Optional[float] = field(
# default=1.0,
# metadata={"help": "The alpha scaling parameter for KD loss"},
# )
#
# kd_temperature: Optional[float] = field(
# default=1.0,
# metadata={
# "help": "the temperature parameter for KL divergence loss when using KD"
# },
# )
adam_beta3: Optional[float] = field(
default=None,
metadata={
"help": "The beta3 hyperparameter used in some optimizers such as CAME"
},
)
adam_epsilon2: Optional[float] = field(
default=None,
metadata={
"help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
},
)
# multi-modal section
image_size: int | tuple[int, int] | None = field(
default=None,
metadata={"help": "The size of the image to resize to"},
)
image_resize_algorithm: Resampling | None = field(
default=None,
metadata={"help": "The algorithm to use for image resizing"},
)
# end of multi-modal section

154
src/axolotl/integrations/base.py
View File

@@ -22,6 +22,8 @@ from __future__ import annotations
import collections
import importlib
import logging
import traceback
from typing import TYPE_CHECKING, Callable, OrderedDict, Union
from peft import PeftModel
@@ -83,6 +85,11 @@ class BasePlugin:
def get_input_args(self) -> str | None:
"""Returns a pydantic model for the plugin's input arguments."""
def get_training_args_mixin(self) -> str | None:
"""
Returns a dataclass model for the plugin's training arguments.
"""
def load_datasets(
self, cfg: DictDefault, preprocess: bool = False
) -> Union["TrainDatasetMeta", None]:
@@ -158,6 +165,32 @@ class BasePlugin:
trainer: The trainer object for training.
"""
def get_training_args(self, cfg: DictDefault): # pylint: disable=unused-argument):
"""
Returns custom training arguments to set on TrainingArgs.
Args:
cfg: The global axolotl configuration.
Returns:
object: dict containing the training arguments.
"""
def get_collator_cls_and_kwargs(
self, cfg: DictDefault, is_eval: bool=False
): # pylint: disable=unused-argument):
"""
Returns a custom class for the collator.
Args:
cfg: The global axolotl configuration.
is_eval: Whether this is an eval split.
Returns:
class: The class for the collator.
"""
# pylint: disable=unused-argument
def create_optimizer(self, cfg: DictDefault, trainer: Trainer) -> Optimizer | None:
"""Creates and returns an optimizer for training.
@@ -167,84 +200,7 @@ class BasePlugin:
trainer: The trainer object for training.
Returns:
<<<<<<< HEAD
The created optimizer.
=======
None
"""
def pre_lora_load(self, cfg, model): # pylint: disable=unused-argument
"""
Performs actions before LoRA weights are loaded.
Args:
cfg (dict): The configuration for the plugin.
model (object): The loaded model.
Returns:
None
"""
def post_lora_load(self, cfg, model): # pylint: disable=unused-argument
"""
Performs actions after LoRA weights are loaded.
Args:
cfg (dict): The configuration for the plugin.
model (object): The loaded model.
Returns:
None
"""
def get_trainer_cls(self, cfg): # pylint: disable=unused-argument):
"""
Returns a custom class for the trainer.
Args:
cfg (dict): The global axolotl configuration.
Returns:
class: The class for the trainer.
"""
def get_collator_cls_and_kwargs(
self, cfg, is_eval=False
): # pylint: disable=unused-argument):
"""
Returns a custom class for the collator.
Args:
cfg (dict): The global axolotl configuration.
is_eval (bool): Whether this is an eval split.
Returns:
class: The class for the collator.
"""
def post_trainer_create(self, cfg, trainer): # pylint: disable=unused-argument
"""
Performs actions after the trainer is created.
Args:
cfg (dict): The configuration for the plugin.
trainer (object): The trainer object for training.
Returns:
None
"""
def create_optimizer(self, cfg, trainer): # pylint: disable=unused-argument
"""
Creates and returns an optimizer for training.
Args:
cfg (dict): The configuration for the plugin.
trainer (object): The trainer object for training.
Returns:
object: The created optimizer.
>>>>>>> f8df1563d (collator cls for plugins)
"""
# pylint: disable=unused-argument
@@ -355,7 +311,7 @@ def load_plugin(plugin_name: str) -> BasePlugin:
return plugin
class PluginManager:
class PluginManager: # pylint: disable=too-many-public-methods
"""The `PluginManager` class is responsible for loading and managing plugins. It
should be a singleton so it can be accessed from anywhere in the codebase.
@@ -414,8 +370,11 @@ class PluginManager:
plugin = load_plugin(plugin_name)
self.plugins[plugin_name] = plugin
LOG.info(f"Plugin loaded successfully: {plugin_name}")
except ImportError:
except ImportError as exc:
LOG.error(f"Failed to load plugin: {plugin_name}")
# print stacktrace
traceback.print_exc()
print(f"Error: {exc}")
def get_input_args(self) -> list[str]:
"""Returns a list of Pydantic classes for all registered plugins' input arguments.'
@@ -430,6 +389,21 @@ class PluginManager:
input_args.append(input_args_from_plugin)
return input_args
def get_training_args_mixin(self):
"""
Returns a list of dataclasses for all registered plugins' training args mixins'
Returns:
list[str]: A list of dataclsses
"""
training_args = []
for plugin in self.plugins.values():
training_args_from_plugin = plugin.get_training_args_mixin()
print(f"Training args from plugin: {plugin.__class__.__name__}")
if training_args_from_plugin is not None:
training_args.append(training_args_from_plugin)
return training_args
def load_datasets(
self, cfg: DictDefault, preprocess: bool = False
) -> Union["TrainDatasetMeta", None]:
@@ -519,6 +493,24 @@ class PluginManager:
return trainer_cls
return None
def get_training_args(self, cfg):
"""
Calls the get_training_args method of all registered plugins and returns the combined training arguments.
Parameters:
cfg (dict): The configuration for the plugins.
Returns:
object: The training arguments
"""
training_args_kwargs = {}
for plugin in self.plugins.values():
training_args = plugin.get_training_args(cfg)
if training_args is not None:
training_args_kwargs.update(training_args)
return training_args_kwargs
def get_collator_cls_and_kwargs(self, cfg, is_eval=False):
"""
Calls the get_collator_cls_and_kwargs method of all registered plugins and returns the first non-None collator class.
@@ -531,9 +523,7 @@ class PluginManager:
object: The collator class, or None if none was found.
"""
for plugin in self.plugins.values():
collator = plugin.get_collator_cls_and_kwargs(
cfg, is_eval=is_eval
)
collator = plugin.get_collator_cls_and_kwargs(cfg, is_eval=is_eval)
if collator is not None:
collator_cls, collator_kwargs = collator
return collator_cls, collator_kwargs

42
src/axolotl/integrations/config.py
View File

@@ -16,7 +16,7 @@ Module to handle merging the plugins' input arguments with the base configuratio
This was moved here to prevent circular imports.
"""
from typing import Any, Dict, List
from typing import Any, Dict, List, Type
from axolotl.utils.schemas.config import (
AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
@@ -61,3 +61,43 @@ def merge_input_args():
]
return AxolotlConfigWCapabilities, AxolotlInputConfig
return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase
def merge_training_args() -> Type:
"""
Merges training arguments from registered plugins with the base TrainingArguments.
This function retrieves the training arguments from registered plugins using the PluginManager.
It then dynamically creates new classes, AxolotlTrainingMixins,
that inherit from the base configurations and include the training arguments from the plugins.
Returns:
tuple: A tuple containing the newly created classes, AxolotlTrainingMixins.
"""
# pylint: disable=duplicate-code
from axolotl.core.training_args_base import (
AxolotlTrainingMixins as AxolotlTrainingMixinsBase,
)
from axolotl.integrations.base import PluginManager
plugin_manager = PluginManager.get_instance()
training_args_mixins: List[str] = plugin_manager.get_training_args_mixin()
mixin_classes = []
dynamic_input = ""
for plugin_args in training_args_mixins:
plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
mixin_classes.append(plugin_cls)
if dynamic_input:
dynamic_input += f"class AxolotlTrainingMixins(AxolotlTrainingMixinsBase, {', '.join(mixin_classes)}):\n pass\n"
namespace: Dict[Any, Any] = {}
local_vars = {"AxolotlTrainingMixinsBase": AxolotlTrainingMixinsBase}
exec( # pylint: disable=exec-used # nosec B102
dynamic_input, {**globals(), **local_vars}, namespace
)
AxolotlTrainingMixins = namespace[ # pylint: disable=invalid-name
"AxolotlTrainingMixins"
]
return AxolotlTrainingMixins
return AxolotlTrainingMixinsBase

37
src/axolotl/integrations/kd/__init__.py
View File

@@ -15,7 +15,12 @@
"""
Plugin init to add KD support to Axolotl.
"""
from typing import Any
from transformers import Trainer
from axolotl.integrations.base import BasePlugin
from axolotl.integrations.kd.callbacks import KDTemperatureSchedulerCallback
from .args import KDArgs # pylint: disable=unused-import. # noqa: F401
@@ -28,6 +33,9 @@ class KDPlugin(BasePlugin):
def get_input_args(self):
return "axolotl.integrations.kd.KDArgs"
def get_training_args_mixin(self):
return "axolotl.integrations.kd.args.KDTrainingArgsMixin"
def get_trainer_cls(self, cfg):
if cfg.kd_trainer:
from .trainer import AxolotlKDTrainer
@@ -35,6 +43,14 @@ class KDPlugin(BasePlugin):
return AxolotlKDTrainer
return None
def get_training_args(self, cfg):
return {
"kd_ce_alpha": cfg.kd_ce_alpha,
"kd_alpha": cfg.kd_alpha,
"kd_temperature": cfg.kd_temperature,
"kd_beta": cfg.kd_beta,
}
def get_collator_cls_and_kwargs(self, cfg, is_eval=False):
if not cfg.kd_trainer:
return None, None
@@ -66,3 +82,24 @@ class KDPlugin(BasePlugin):
from .kernels.models import apply_kernel
apply_kernel(cfg.model_config_type)
def add_callbacks_post_trainer(self, cfg: Any, trainer: Trainer) -> list:
"""
Adds temp scheduler callback to the Trainer instance.
Args:
cfg (Any): Configuration object containing the sparse recipe.
trainer (Trainer): Huggingface Trainer instance.
Returns:
list: List containing the configured callback instances.
"""
if cfg.kd_temperature_min is not None and cfg.kd_online_server_base_url:
callback = KDTemperatureSchedulerCallback(
cfg.kd_temperature,
cfg.kd_temperature_min,
trainer,
)
return [callback]
return []

33
src/axolotl/integrations/kd/args.py
View File

@@ -15,14 +15,19 @@
"""
Plugin args for KD support.
"""
from dataclasses import dataclass
from enum import Enum
from pydantic import BaseModel
from pydantic import BaseModel, Field
class InferenceServerType(str, Enum):
vllm = "vllm"
sglang = "sglang"
"""
Online inferences server types to handle different request args
"""
vllm = "vllm" # pylint: disable=invalid-name
sglang = "sglang" # pylint: disable=invalid-name
class KDArgs(BaseModel):
@@ -36,9 +41,29 @@ class KDArgs(BaseModel):
)
kd_alpha: float | None = None # loss coefficient for KD loss
kd_temperature: float | None = None # temperature for sampling during KD
kd_beta: float | None = None # beta coefficient for ratio of fwd and reverse KL
# TODO online kd
kd_online_server_base_url: str | None = None
kd_online_topk: int | None = None
kd_online_server: InferenceServerType | None = "vllm"
kd_online_server: InferenceServerType | None = Field(
default_factory=lambda: InferenceServerType.vllm
)
kd_online_timeout: int | None = 120
kd_temperature_min: float | None = (
None # kd temperature scheduling during online kd
)
@dataclass
class KDTrainingArgsMixin:
"""
Additional args for KD training.
"""
kd_ce_alpha: float | None = (
None # loss coefficient for cross-entropy loss during KD
)
kd_alpha: float | None = None # loss coefficient for KD loss
kd_temperature: float | None = None # temperature for sampling during KD
kd_beta: float | None = None # beta coefficient for ratio of fwd and reverse KL

36
src/axolotl/integrations/kd/callbacks.py Normal file
View File

@@ -0,0 +1,36 @@
"""
Transformers trainer callbacks to schedule the KD temperature during training
"""
import math
from transformers.trainer_callback import CallbackHandler
class KDTemperatureSchedulerCallback(CallbackHandler):
"""
KD temperature scheduler callback for the trainer.
"""
def __init__(self, temperature_start, temperature_min, trainer):
self.temperature_start = temperature_start
self.temperature_min = temperature_min
self.temperature = temperature_start
self.trainer = trainer
def on_step_end(
self, args, state, control, **kwargs
): # pylint: disable=unused-argument
# cosine decay temperature over the max steps
progress = state.global_step / state.max_steps
# Cosine decay factor: 0.5 * (1 + cos(pi * progress))
# This factor goes from 1 (at progress=0) to 0 (at progress=1)
decay_factor = 0.5 * (1.0 + math.cos(math.pi * progress))
self.temperature = self.temperature_start - (
(self.temperature_start - self.temperature_min) * decay_factor
)
if hasattr(self.trainer.data_collator, "kd_temperature"):
self.trainer.data_collator.kd_temperature = self.temperature

140
src/axolotl/integrations/kd/collator_online_teacher.py
View File

@@ -2,12 +2,14 @@
Packed data loader for online teacher training supporting vllm and sglang.
"""
import hashlib
import hmac
import logging
from typing import Any, Dict, List, Optional
import pandas as pd
import requests
import torch
from orjson import orjson
from axolotl.integrations.kd.collator import KDBatchSamplerDataCollatorForSeq2Seq
from axolotl.utils.data.utils import retry_on_request_exceptions
@@ -15,6 +17,31 @@ from axolotl.utils.data.utils import retry_on_request_exceptions
LOG = logging.getLogger(__name__)
def hmac_sha_from_int_list(int_list, key, hash_func=hashlib.sha256):
"""
Create HMAC-SHA hash from a list of integers
Args:
int_list: List of integers
key: Secret key (string or bytes)
hash_func: Hash function (default: sha256)
Returns:
HMAC digest as hex string
"""
# Convert key to bytes if it's a string
if isinstance(key, str):
key = key.encode("utf-8")
# Convert list of ints to bytes
# Method 1: Convert each int to bytes and concatenate
data = b"".join(i.to_bytes(4, byteorder="big") for i in int_list)
# Create HMAC
h = hmac.new(key, data, hash_func)
return h.hexdigest()
class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
"""
Collator for online teacher training.
@@ -30,6 +57,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
kd_temperature: Optional[float] = 1.0,
kd_online_server: Optional[str] = "vllm",
kd_online_timeout: Optional[int] = 120,
kd_cache_dir: Optional[str] = None,
**kwargs: Any,
):
super().__init__(*args, **kwargs)
@@ -49,6 +77,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
self.kd_online_server = kd_online_server
self.http_session = requests.Session()
self.kd_online_timeout = kd_online_timeout
self.kd_cache_dir = kd_cache_dir
def _normalize_logprobs(self, raw_logprobs: List[float]) -> List[float]:
"""
@@ -109,7 +138,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
return final_logprobs_tensor.tolist()
except Exception as e:
except Exception as e: # pylint: disable=broad-exception-caught
LOG.error(
f"Error during online logprob scaling: {e}. Returning raw logprobs.",
exc_info=True,
@@ -142,11 +171,9 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
}
# Initialize with empty lists, so if API call fails, these are returned.
ret_logprobs_data = {
"target_token_ids": [],
"target_logprobs": [],
"target_mask": [],
}
ret_data_target_token_ids: List[List[List[int]]] = []
ret_data_target_logprobs: List[List[List[float]]] = []
ret_data_target_mask: List[List[List[int]]] = []
try:
response = self.http_session.post(
@@ -162,7 +189,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
f"items, got {type(api_data)} with length {len(api_data) if isinstance(api_data, list) else 'N/A'}."
)
# Return empty data; items processed later will get default empty KD fields
return ret_logprobs_data
return {
"target_token_ids": ret_data_target_token_ids,
"target_logprobs": ret_data_target_logprobs,
"target_mask": ret_data_target_mask,
}
for sequence_data, seq_input_ids, seq_labels in zip(
api_data, batch_input_ids, labels
@@ -185,7 +216,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
# basic check that the logprob data len matches the input len, so no need to handle padding
assert len(seq_input_ids) == len(input_top_logprobs)
for i, input_id, label in zip(
for i, _, label in zip(
range(len(seq_input_ids)), seq_input_ids, seq_labels
):
if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
@@ -254,9 +285,9 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
current_target_token_ids.append([0] * self.kd_online_topk)
current_target_mask.append([0] * self.kd_online_topk)
ret_logprobs_data["target_token_ids"].append(current_target_token_ids)
ret_logprobs_data["target_logprobs"].append(current_target_logprobs)
ret_logprobs_data["target_mask"].append(current_target_mask)
ret_data_target_token_ids.append(current_target_token_ids)
ret_data_target_logprobs.append(current_target_logprobs)
ret_data_target_mask.append(current_target_mask)
except requests.exceptions.RequestException as e:
LOG.error(f"Error fetching logprobs from online teacher: {e}")
@@ -269,7 +300,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
)
raise e
return ret_logprobs_data
return {
"target_token_ids": ret_data_target_token_ids,
"target_logprobs": ret_data_target_logprobs,
"target_mask": ret_data_target_mask,
}
@retry_on_request_exceptions(max_retries=10, delay=5)
def fetch_online_logprobs_vllm(
@@ -296,18 +331,19 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
}
# Initialize with empty lists, so if API call fails, these are returned.
ret_logprobs_data = {
"target_token_ids": [],
"target_logprobs": [],
"target_mask": [],
}
ret_data_target_token_ids: List[List[List[int]]] = []
ret_data_target_logprobs: List[List[List[float]]] = []
ret_data_target_mask: List[List[List[int]]] = []
try:
response = self.http_session.post(
api_endpoint, json=payload, timeout=self.kd_online_timeout
api_endpoint,
json=payload,
timeout=self.kd_online_timeout,
# json_decoder=orjson.loads,
)
response.raise_for_status()
api_data: dict = response.json()
api_data: dict = orjson.loads(response.content)
choices: list[dict] = api_data["choices"]
# Ensure api_data is a list, and its length matches batch_input_ids
@@ -317,7 +353,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
f"items, got {type(api_data)} with length {len(api_data) if isinstance(api_data, list) else 'N/A'}."
)
# Return empty data; items processed later will get default empty KD fields
return ret_logprobs_data
return {
"target_token_ids": ret_data_target_token_ids,
"target_logprobs": ret_data_target_logprobs,
"target_mask": ret_data_target_mask,
}
for sequence_data, seq_input_ids, seq_labels in zip(
choices, batch_input_ids, labels
@@ -330,29 +370,10 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
current_target_mask = []
# Ensure input_top_logprobs is a list
input_top_logprobs: Optional[list[None | list[tuple]]] = (
input_top_logprobs: Optional[list[None | dict[str, dict]]] = (
sequence_data.pop("prompt_logprobs", [])
)
"""
vllm api data for prompt logprobs looks like:
"prompt_logprobs": [
null, # first token is always null
{ # second token logprobs
"8948": { # token ID
"logprob": -2.3841830625315197e-06,
"rank": 1,
"decoded_token": "system"
},
"1849": { # token ID
"logprob": -13.187501907348633,
"rank": 2,
"decoded_token": "Ġsystem"
},
... # rest of the top-k tokens/logprobs
},
... # more tokens
}
"""
if not isinstance(input_top_logprobs, list):
LOG.warning(
f"Received non-list input_top_logprobs: {input_top_logprobs}. Skipping sequence."
@@ -369,11 +390,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
# this is always the case for the first token.
# there is never logprob data for the first token since that's a true input
continue
elif (
if (
i < len(input_top_logprobs)
and input_top_logprobs[i] is not None
):
pos_top_logprobs_data: dict[str, dict] = input_top_logprobs[i]
pos_top_logprobs_data: dict[str, dict] = input_top_logprobs[i] # type: ignore[assignment]
# Ensure pos_top_logprobs_data is a list of lists as expected
if not (
isinstance(pos_top_logprobs_data, dict)
@@ -396,9 +417,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
continue
# pos_top_logprobs: list of logprobs, pos_token_ids: list of token_ids
pos_token_ids = pos_top_logprobs_data.keys()
pos_token_ids_str = list(pos_top_logprobs_data.keys())
pos_logprobs_dict = pos_top_logprobs_data.values()
pos_token_ids = [int(token_id) for token_id in pos_token_ids]
pos_token_ids = [
int(token_id) for token_id in pos_token_ids_str
]
pos_logprobs_raw = [
float(logprob.get("logprob", -float("inf")))
for logprob in pos_logprobs_dict
@@ -446,17 +469,18 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
current_target_token_ids.append(list(range(self.kd_online_topk)))
current_target_mask.append([0] * self.kd_online_topk)
ret_logprobs_data["target_token_ids"].append(current_target_token_ids)
ret_logprobs_data["target_logprobs"].append(current_target_logprobs)
ret_logprobs_data["target_mask"].append(current_target_mask)
ret_data_target_token_ids.append(current_target_token_ids)
ret_data_target_logprobs.append(current_target_logprobs)
ret_data_target_mask.append(current_target_mask)
# TODO save and load targets to disk for caching for next epoch
# generate a hash over seq_input_ids and convert it to an int
# hash_input_ids: int = hash(tuple(seq_input_ids))
# with open(f"/tmp/target_logprobs_{hash_input_ids}.parquet", "wb") as f:
# pd.DataFrame(current_target_logprobs).to_parquet(f, index=False)
# with open(f"/tmp/target_token_ids_{hash_input_ids}.parquet", "wb") as f:
# pd.DataFrame(current_target_token_ids).to_parquet(f, index=False)
# generate a hmac SHA256 hash over the list seq_input_ids and convert it to an int
# if self.kd_cache_dir:
# hash_input_ids = hmac_sha_from_int_list(
# seq_input_ids, f"{self.kd_online_server_base_url}:{self.kd_online_topk}"
# )
# with open(f"{self.kd_cache_dir}/{hash_input_ids}.parquet", "wb") as f:
# pd.DataFrame(ret_logprobs_data).to_parquet(f, index=False)
except requests.exceptions.RequestException as e:
LOG.error(f"Error fetching logprobs from online teacher: {e}")
@@ -469,7 +493,11 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
)
raise e
return ret_logprobs_data
return {
"target_token_ids": ret_data_target_token_ids,
"target_logprobs": ret_data_target_logprobs,
"target_mask": ret_data_target_mask,
}
def __call__(
self, features: List[List[Dict[str, Any]]], return_tensors: Optional[str] = None

40
src/axolotl/integrations/kd/kernels/liger.py
View File

@@ -20,6 +20,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
target_token_ids_chunk: torch.Tensor, # [chunk_size, top_k]
target_logprobs_chunk: torch.Tensor, # [chunk_size, top_k], already temp-scaled and normalized logprobs
target_mask_chunk: torch.Tensor, # [chunk_size, top_k]
beta: float = 0.0,
) -> torch.Tensor:
"""
Compute Top-K KL divergence loss for a chunk.
@@ -28,6 +29,10 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
target_token_ids_chunk: Top-k teacher token IDs. Shape: (N, K).
target_logprobs_chunk: Top-k teacher log probabilities (temp-scaled, normalized). Shape: (N, K).
target_mask_chunk: Mask for valid top-k tokens. Shape: (N, K).
beta: Controls the type of KL divergence.
0.0 for Forward KL (P_teacher || P_student).
1.0 for Reverse KL (P_student || P_teacher).
0.5 for Symmetric KL (average of Forward and Reverse).
Returns:
Sum of KL divergence losses for the chunk.
"""
@@ -59,6 +64,10 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
# Teacher probabilities P(y|x_teacher) from logprobs
# target_logprobs_valid are already normalized (log(softmax(teacher_logits/T)))
teacher_probs_valid = target_logprobs_valid.exp()
# Student probabilities P_student from log P_student
student_probs_topk_valid = student_logprobs_topk_valid.exp()
kd_loss_per_token = torch.zeros_like(target_logprobs_valid)
# KL divergence: sum(P_teacher * (log P_teacher - log P_student))
# = sum(P_teacher * log P_teacher) - sum(P_teacher * log P_student)
@@ -66,9 +75,17 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
# or as sum(P_teacher * (log_softmax_teacher - log_softmax_student))
# Here, target_logprobs_valid are log_softmax_teacher.
# student_logprobs_topk_valid are log_softmax_student (for the selected K indices).
kd_loss_per_token = teacher_probs_valid * (
target_logprobs_valid - student_logprobs_topk_valid
)
if beta < 1.0: # Contribution from Forward KL
fwd_kl_per_token = teacher_probs_valid * (
target_logprobs_valid - student_logprobs_topk_valid
)
kd_loss_per_token += (1.0 - beta) * fwd_kl_per_token
if beta > 0.0: # Contribution from Reverse KL
rev_kl_per_token = student_probs_topk_valid * (
student_logprobs_topk_valid - target_logprobs_valid
)
kd_loss_per_token += beta * rev_kl_per_token
kd_loss = kd_loss_per_token.sum()
return kd_loss
@@ -91,6 +108,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
weight_soft_loss: float = 0.5,
compute_ce_loss: bool = True,
temperature: float = 1.0,
beta: float = 0.0,
):
# Compute student logits for the chunk from hidden states and LM head
# student_input_chunk: [chunk_size, hidden_dim]
@@ -125,10 +143,9 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
target_token_ids_chunk,
target_logprobs_chunk,
target_mask_chunk,
beta=beta,
)
loss = weight_hard_loss * ce_loss + weight_soft_loss * soft_loss
return soft_loss, ce_loss
@classmethod
@@ -146,6 +163,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
weight_soft_loss: float = 0.5,
ignore_index: int = -100,
temperature: float = 1.0,
beta: float = 0.0,
compiled: bool = False,
chunk_size: int = 1024,
compute_ce_loss: bool = True,
@@ -192,6 +210,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
weight_soft_loss=weight_soft_loss,
compute_ce_loss=compute_ce_loss,
temperature=temperature,
beta=beta,
)
def accumulate_chunk_grads(
@@ -288,13 +307,13 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
ctx.save_for_backward(grad_inputs_combined, grad_weight_acc, grad_bias_acc)
# For matching None returns in backward for non-tensor/non-grad_requiring inputs
ctx.hyperparams_count = 7 # Corresponds to number of hyperparams after main tensors in fwd signature
ctx.hyperparams_count = 8 # Corresponds to number of hyperparams after main tensors in fwd signature
ctx.bias_was_none = student_lm_head_bias is None
ctx.orig_dims = (B, N, D, K)
# since this is packed, there is simply a single batch, so batchmean reduciton of kl-div is simply the accumulatedsum
# we still need to scale the kd_loss by the temp
kd_loss_acc = kd_loss_acc * (temperature ** 2)
# since this is packed, there is simply a single batch, so batchmean reduciton of kl-div is simply the accumulated sum
# we still need to scale the kd_loss by the temp^2
kd_loss_acc = kd_loss_acc * (temperature**2)
final_loss = weight_soft_loss * kd_loss_acc + weight_hard_loss * ce_loss_acc
return final_loss
@@ -373,6 +392,7 @@ class LigerFusedLinearKLTopKLogprobLoss(torch.nn.Module):
weight_hard_loss: float = 0.5,
weight_soft_loss: float = 0.5,
temperature: float = 1.0, # This is the kd_temperature
beta: float = 1.0,
ignore_index: int = -100,
compiled: bool = True,
chunk_size: int = 1024,
@@ -387,6 +407,7 @@ class LigerFusedLinearKLTopKLogprobLoss(torch.nn.Module):
self.weight_hard_loss = weight_hard_loss
self.weight_soft_loss = weight_soft_loss
self.temperature = temperature
self.beta = beta
self.ignore_index = ignore_index
self.compiled = compiled
self.chunk_size = chunk_size
@@ -424,6 +445,7 @@ class LigerFusedLinearKLTopKLogprobLoss(torch.nn.Module):
self.weight_soft_loss,
self.ignore_index,
self.temperature,
self.beta,
self.compiled,
self.chunk_size,
self.compute_ce_loss,

4
src/axolotl/integrations/kd/kernels/models.py
View File

@@ -75,8 +75,8 @@ def kldiv_forward_llama_like(
target_mask,
true_labels=labels,
)
num_items_in_batch = kwargs.pop("num_items_in_batch", None)
if num_items_in_batch is not None:
num_items_in_batch = kwargs.pop("num_items_in_batch", -1)
if num_items_in_batch is not None and num_items_in_batch > 0:
loss = loss / num_items_in_batch
return CausalLMOutputWithPast(

1
src/axolotl/integrations/kd/trainer.py
View File

@@ -33,6 +33,7 @@ class AxolotlKDTrainer(AxolotlTrainer):
self.args.kd_ce_alpha, # hard label loss
self.args.kd_alpha, # kd loss
self.args.kd_temperature,
self.args.kd_beta,
compute_ce_loss=bool(self.args.kd_ce_alpha),
)

9
src/axolotl/train.py
View File

@@ -1,10 +1,13 @@
"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
from __future__ import annotations
import importlib
import inspect
import os
import signal
import sys
import typing
import weakref
from contextlib import ExitStack
from pathlib import Path
@@ -25,7 +28,6 @@ from axolotl.common.datasets import TrainDatasetMeta
from axolotl.contribs.lgpl import ( # pylint: disable = no-name-in-module
fix_untrained_tokens,
)
from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
from axolotl.integrations.base import PluginManager
from axolotl.loaders import (
ModelLoader,
@@ -45,6 +47,9 @@ try:
except ImportError:
BetterTransformer = None
if typing.TYPE_CHECKING:
from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
LOG = get_logger(__name__)
@@ -472,7 +477,7 @@ def handle_untrained_tokens_fix(
def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[
HFRLTrainerBuilder | HFCausalTrainerBuilder,
"HFRLTrainerBuilder" | "HFCausalTrainerBuilder",
PeftModel | PreTrainedModel,
PreTrainedTokenizer,
PeftConfig | None,

2
src/axolotl/utils/chat_templates.py
View File

File diff suppressed because one or more lines are too long

3
src/axolotl/utils/trainer.py
View File

@@ -16,7 +16,6 @@ from datasets import IterableDataset, disable_caching, enable_caching
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers.utils import is_torch_bf16_gpu_available
from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
from axolotl.monkeypatch.trainer_eval_guard import patch_evaluation_loop_for_fsdp2
from axolotl.utils.distributed import reduce_and_broadcast
from axolotl.utils.environment import check_cuda_p2p_ib_support
@@ -629,6 +628,8 @@ def setup_trainer(
A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
on the provided parameters.
"""
from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
if (
cfg.torch_compile
and cfg.fsdp_config