upgrade transformers==5.3.0 trl==0.29.0 kernels (#3459)

* upgrade transformers==5.3.0 trl==0.29.0 kernels

* use latest deepspeed fixes

* use corect image for cleanup

* fix test outputs for tokenizer fixes upstream

* fix import:

* keep trl at 0.28.0

* handle updated API

* use latest trl since 0.28.0 doesn't work with latest transformers

* use trl experimental for pad to length

* monkeypatch trl with ORPOTrainer so liger doesn't croak

* upgrade accelerate

* more fixes

* move patch for orpotrainer

* load the imports later

* remove use_logits_to_keep

* fix loss_type arg as a list

* fetch hf cache from s3

* just manually download the missing model for now

* lint for pre-commit update

* a few more missing models on disk

* fix: loss_type internally now list

* fix: remove deprecated code and raise deprecate

* fix: remove unneeded blocklist

* fix: remove reliance on transformers api to find package available

* chore: refactor shim for less sideeffect

* fix: silent trl experimental warning

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

src/axolotl/cli/__init__.py

@@ -6,5 +6,6 @@ from axolotl.logging_config import configure_logging
 
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+os.environ.setdefault("TRL_EXPERIMENTAL_SILENCE", "1")
 
 configure_logging()

src/axolotl/core/builders/rl.py

@@ -120,11 +120,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.use_wandb:
             training_args_kwargs["run_name"] = self.cfg.wandb_name
 
-        if self.cfg.max_prompt_len:
-            training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
-        else:
-            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
-
         training_args_cls = None
         blocklist_args_kwargs = []
         if self.cfg.rl is RLType.SIMPO:

src/axolotl/core/trainers/base.py

@@ -26,7 +26,7 @@ from transformers import PreTrainedModel, Trainer
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
 from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
-from trl.trainer.utils import pad_to_length
+from trl.experimental.utils import pad_to_length
 from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (

src/axolotl/core/trainers/dpo/__init__.py

@@ -25,17 +25,13 @@ class DPOStrategy:
         # Label smoothing is not compatible with IPO
         if cfg.rl is RLType.DPO and cfg.dpo_label_smoothing:
             training_args_kwargs["label_smoothing"] = cfg.dpo_label_smoothing
-        training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
-        training_args_kwargs["generate_during_eval"] = cfg.dpo_generate_during_eval
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
         if cfg.dpo_padding_free is not None:
             training_args_kwargs["padding_free"] = cfg.dpo_padding_free
         if cfg.dpo_norm_loss is not None:
             training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
-        if cfg.dpo_use_logits_to_keep is not None:
-            training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
         if cfg.dpo_use_liger_kernel is not None:
             training_args_kwargs["use_liger_kernel"] = cfg.dpo_use_liger_kernel
         return training_args_kwargs

src/axolotl/core/trainers/dpo/trainer.py

@@ -103,10 +103,10 @@ class AxolotlDPOTrainer(
     ) -> dict[str, torch.Tensor]:
         if self.args.dpo_norm_loss:
             # fmt: off
-            loss_type: str = self.loss_type  # type: ignore[has-type]
+            loss_type: list[str] = self.loss_type  # type: ignore[has-type]
             # fmt: on
             # concatenated_forward handles avg token logprob for ipo case already
-            self.loss_type = "ipo"
+            self.loss_type = ["ipo"]
             res = super().concatenated_forward(model, batch, is_ref_model=is_ref_model)
             self.loss_type = loss_type
             return res

src/axolotl/core/trainers/mixins/optimizer.py

@@ -104,7 +104,7 @@ class OptimizerMixin(Trainer):
 
         return optimizer_grouped_parameters
 
-    def create_optimizer(self):
+    def create_optimizer(self, model=None):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.embedding_lr_scale is None
@@ -112,9 +112,9 @@ class OptimizerMixin(Trainer):
             and self.args.lr_groups is None
             and self.optimizer_cls_and_kwargs is None
         ):
-            return super().create_optimizer()
+            return super().create_optimizer(model=model)
 
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        opt_model = self.model if model is None else model
 
         if (
             not self.optimizer

src/axolotl/integrations/liger/plugin.py

@@ -8,9 +8,6 @@ import sys
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils.logging import get_logger
 
-from .models.base import patch_lce_forward
-from .utils import patch_with_compile_disable
-
 LOG = get_logger(__name__)
 
 
@@ -23,10 +20,18 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
+        # shim: liger-kernel 0.7.0 imports ORPOTrainer from old trl path
+        import trl.trainer
+        from trl.experimental.orpo import ORPOTrainer
+
+        trl.trainer.ORPOTrainer = ORPOTrainer
+
         if cfg.torch_compile:
             # torch compile will unnecessarily attempt to optimize the triton kernel unless explicitly disabled
             import liger_kernel.ops.fused_linear_cross_entropy
 
+            from .utils import patch_with_compile_disable
+
             patch_with_compile_disable(
                 liger_kernel.ops.fused_linear_cross_entropy,
                 "fused_linear_cross_entropy_forward",
@@ -35,6 +40,7 @@ class LigerPlugin(BasePlugin):
                 liger_kernel.ops.fused_linear_cross_entropy,
                 "fused_linear_cross_entropy_backward",
             )
+
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
         from liger_kernel.transformers.layer_norm import LigerLayerNorm
@@ -192,6 +198,8 @@ class LigerPlugin(BasePlugin):
             )
         elif cfg.liger_fused_linear_cross_entropy:
             try:
+                from .models.base import patch_lce_forward
+
                 patch_lce_forward(cfg.model_config_type)
                 LOG.warning_once(
                     f"Applied ONLY liger_fused_linear_cross_entropy genericpatches for model type: {cfg.model_config_type}"

src/axolotl/loaders/model.py

@@ -674,8 +674,8 @@ class ModelLoader:
                 del self.model_kwargs["device_map"]
 
             transformers.modeling_utils.is_deepspeed_zero3_enabled = lambda: True
-            transformers.integrations.deepspeed.is_deepspeed_zero3_enabled = (
-                lambda: True
+            transformers.integrations.deepspeed.is_deepspeed_zero3_enabled = lambda: (
+                True
             )
 
         return hf_ds_cfg

src/axolotl/loaders/tokenizer.py

@@ -201,7 +201,7 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
         tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
 
     if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
-        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        tokenizer.add_special_tokens({"pad_token": "[PAD]"})  # nosec B105
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
     # Mistral's official FA implementation requires left padding

src/axolotl/utils/data/shared.py

@@ -189,7 +189,7 @@ def _get_remote_filesystem(
         try:
             import gcsfs
 
-            storage_options = {"token": None}  # type: ignore
+            storage_options = {"token": None}  # type: ignore  # nosec B105
             return gcsfs.GCSFileSystem(**storage_options), storage_options
         except ImportError as exc:
             raise ImportError(

src/axolotl/utils/schemas/config.py

@@ -173,7 +173,6 @@ class AxolotlInputConfig(
             "description": "Whether to perform weighting in DPO trainer"
         },
     )
-    dpo_use_logits_to_keep: bool | None = None
     dpo_label_smoothing: float | None = None
     dpo_norm_loss: bool | None = None
 
@@ -183,7 +182,6 @@ class AxolotlInputConfig(
     )
 
     dpo_padding_free: bool | None = None
-    dpo_generate_during_eval: bool | None = None
 
     datasets: (
         Annotated[

src/axolotl/utils/schemas/deprecated.py

@@ -19,6 +19,8 @@ class DeprecatedParameters(BaseModel):
     evaluation_strategy: str | None = None
     eval_table_size: int | None = None
     eval_max_new_tokens: int | None = None
+    dpo_use_logits_to_keep: bool | None = None
+    dpo_generate_during_eval: bool | None = None
 
     @field_validator("max_packed_sequence_len")
     @classmethod
@@ -78,6 +80,26 @@ class DeprecatedParameters(BaseModel):
             )
         return eval_max_new_tokens
 
+    @field_validator("dpo_use_logits_to_keep")
+    @classmethod
+    def validate_dpo_use_logits_to_keep(cls, dpo_use_logits_to_keep):
+        if dpo_use_logits_to_keep is not None:
+            raise DeprecationWarning(
+                "`dpo_use_logits_to_keep` is no longer supported, "
+                "it has been removed in TRL >= 0.29.0"
+            )
+        return dpo_use_logits_to_keep
+
+    @field_validator("dpo_generate_during_eval")
+    @classmethod
+    def validate_dpo_generate_during_eval(cls, dpo_generate_during_eval):
+        if dpo_generate_during_eval is not None:
+            raise DeprecationWarning(
+                "`dpo_generate_during_eval` is no longer supported, "
+                "it has been removed in TRL >= 0.29.0"
+            )
+        return dpo_generate_during_eval
+
 
 class RemappedParameters(BaseModel):
     """Parameters that have been remapped to other names"""