refactor trainer to prevent circular dependencies later

fix loader default

docs/rlhf.qmd

@@ -29,7 +29,7 @@ datasets:
     type: chatml.intel
   - path: argilla/ultrafeedback-binarized-preferences
     split: train
-    type: chatml.argilla
+    type: chatml
 ```
 
 #### IPO

src/axolotl/core/trainers/base.py

@@ -0,0 +1,933 @@
+"""
+module for customized trainers
+"""
+
+from __future__ import annotations
+
+# pylint: disable=too-many-lines
+import gc
+import logging
+import os
+from collections import defaultdict
+from functools import wraps
+from typing import Any, Dict, Literal, Optional, Union
+
+import torch
+import transformers
+from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
+from torch.optim.lr_scheduler import OneCycleLR
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
+from transformers import Trainer
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
+from transformers.utils import is_sagemaker_mp_enabled
+from trl import CPOTrainer, DPOTrainer, KTOTrainer, ORPOTrainer, RewardTrainer
+from trl.trainer.utils import pad_to_length
+
+from axolotl.monkeypatch.relora import ReLoRAScheduler
+from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
+from axolotl.utils.schedulers import (
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+    get_cosine_schedule_with_warmup_decay_constant,
+)
+
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+LOG = logging.getLogger("axolotl.core.trainer_builder")
+
+
+def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
+    if isinstance(tag_names, str):
+        tag_names = [tag_names]
+
+    if kwargs is not None:
+        if "tags" not in kwargs:
+            kwargs["tags"] = tag_names
+        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+            kwargs["tags"].extend(tag_names)
+        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+            tag_names.append(kwargs["tags"])
+            kwargs["tags"] = tag_names
+
+    return kwargs
+
+
+def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
+    if isinstance(dataset_tags, str):
+        dataset_tags = [dataset_tags]
+
+    if (dataset_tags is not None) and (kwargs is not None):
+        if "dataset_tags" not in kwargs:
+            kwargs["dataset_tags"] = dataset_tags
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
+            kwargs["dataset_tags"].extend(dataset_tags)
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
+            dataset_tags.append(kwargs["dataset_tags"])
+            kwargs["dataset_tags"] = dataset_tags
+
+    return kwargs
+
+
+class SchedulerMixin(Trainer):
+    """
+    Mixin class for scheduler setup in CausalTrainer.
+    """
+
+    args = None  # type: AxolotlTrainingArguments
+
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+    ):
+        """
+        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        passed as an argument.
+
+        Args:
+            num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
+        """
+        use_cosine_quadratic = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.lr_quadratic_warmup is True
+        )
+
+        use_cosine_min_lr = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.cosine_min_lr_ratio is not None
+        )
+
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if self.args.alternate_lr_scheduler_type == "one_cycle":
+                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
+                pct_start = num_warmup_steps / num_training_steps
+                extra_lr_kwargs = {}
+                if "pct_start" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["pct_start"] = pct_start
+                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["anneal_strategy"] = "cos"
+
+                self.lr_scheduler = OneCycleLR(
+                    optimizer,
+                    max_lr=self.args.learning_rate,
+                    total_steps=num_training_steps,
+                    **extra_lr_kwargs,
+                    **self.args.lr_scheduler_kwargs,
+                )
+            elif use_cosine_quadratic:
+                if use_cosine_min_lr:
+                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
+
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                )
+            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
+                )
+            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                )
+            else:
+                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+        else:
+            if use_cosine_quadratic:
+                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
+
+            if use_cosine_min_lr:
+                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
+
+        return self.lr_scheduler
+
+
+class AxolotlTrainer(SchedulerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
+
+    args = None  # type: AxolotlTrainingArguments
+    tag_names = ["axolotl"]
+
+    def __init__(
+        self,
+        *_args,
+        bench_data_collator=None,
+        eval_data_collator=None,
+        dataset_tags=None,
+        **kwargs,
+    ):
+        self.bench_data_collator = bench_data_collator
+        self.eval_data_collator = eval_data_collator
+        self.dataset_tags = dataset_tags
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        if self.args.orpo_alpha:
+            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+
+    def _wrap_model(self, model, training=True, dataloader=None):
+        if self.args.torch_compile:
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
+            model = torch.compile(
+                model,
+                backend=self.args.torch_compile_backend,
+                mode=self.args.torch_compile_mode,
+            )
+        return super()._wrap_model(model, training=training, dataloader=dataloader)
+
+    def create_optimizer(self):
+        if (
+            self.args.loraplus_lr_ratio is None
+            and self.args.embedding_lr_scale is None
+            and self.args.embedding_lr is None
+            and self.args.alternate_optimizer
+            not in [
+            "optimi_adamw",
+            "ao_adamw_8bit",
+            "ao_adamw_4bit",
+            "ao_adamw_fp8",
+            "adopt_adamw",
+        ]
+        ):
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+            decay_parameters = self.get_decay_parameter_names(opt_model)
+            params = {
+                "to_weight_decay": {},  # LayerNorm and bias
+                "embeddings": {},  # lm_head, embed_tokens,
+                "no_weight_decay": {},
+            }
+
+            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+                self.args,
+                opt_model,
+            )
+
+            for name, param in opt_model.named_parameters():
+                if not param.requires_grad:
+                    continue
+                if name.endswith("modules_to_save.default.weight") or any(
+                    embed_name in name for embed_name in ["embed_tokens", "lm_head"]
+                ):
+                    params["embeddings"][name] = param
+                elif name in decay_parameters:
+                    params["to_weight_decay"][name] = param
+                else:
+                    params["no_weight_decay"][name] = param
+            optimizer_grouped_parameters = []
+            if params["to_weight_decay"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["to_weight_decay"].values()),
+                        "weight_decay": self.args.weight_decay,
+                        "lr": optimizer_kwargs["lr"],
+                    }
+                )
+            if params["embeddings"]:
+                lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+                if self.args.embedding_lr_scale:
+                    lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+                elif self.args.embedding_lr:
+                    lr = self.args.embedding_lr  # pylint: disable=invalid-name
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["embeddings"].values()),
+                        "weight_decay": 0.0,
+                        "lr": lr,
+                    }
+                )
+            if params["no_weight_decay"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["no_weight_decay"].values()),
+                        "weight_decay": 0.0,
+                        "lr": optimizer_kwargs["lr"],
+                    }
+                )
+
+            if self.args.loraplus_lr_ratio is not None:
+                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+                loraplus_lr_embedding = getattr(
+                    self.args, "loraplus_lr_embedding", 1e-6
+                )
+                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                    opt_model,
+                    optimizer_cls,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
+                )
+            elif (
+                self.args.embedding_lr_scale is not None
+                or self.args.embedding_lr is not None
+            ):
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW(
+                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
+                    )
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_4bit":
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    ADOPT(
+                        optimizer_grouped_parameters,
+                        decouple=True,
+                        **optimizer_kwargs,
+                    )
+                )
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and not self.args.pretraining:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_train_batch_size
+                batch_max_len = self.args.max_seq_length
+            else:
+                batch_size = 1
+                train_batch_size = (
+                    self.state.train_batch_size or self.args.per_device_train_batch_size
+                )
+                batch_max_len = train_batch_size * self.args.max_seq_length
+
+            if self.args.curriculum_sampling:
+                sampler = SequentialSampler(self.train_dataset)
+            else:
+                sampler = RandomSampler(self.train_dataset)
+
+            return MultipackBatchSampler(
+                sampler,
+                lengths=get_dataset_lengths(self.train_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
+            )
+        if self.args.curriculum_sampling:
+            return SequentialSampler(self.train_dataset)
+        return super()._get_train_sampler()
+
+    def _get_eval_sampler(
+        self, eval_dataset: Dataset
+    ) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_eval_batch_size
+                batch_max_len = self.args.max_seq_length
+            else:
+                batch_size = 1
+                batch_max_len = (
+                    self.args.per_device_eval_batch_size * self.args.max_seq_length
+                )
+            return MultipackBatchSampler(
+                SequentialSampler(eval_dataset),
+                lengths=get_dataset_lengths(self.eval_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
+            )
+        return super()._get_eval_sampler(eval_dataset)
+
+    def get_train_dataloader(self) -> DataLoader:
+        if self.args.sample_packing and not self.args.pretraining:
+            train_dataset = self.train_dataset
+            if "length" in train_dataset.features.keys():
+                train_dataset = train_dataset.remove_columns(["length"])
+            data_collator = self.data_collator
+            dataloader_params = {
+                "batch_size": self._train_batch_size,
+                "collate_fn": data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+            }
+            if self.args.dataloader_prefetch_factor:
+                dataloader_params[
+                    "prefetch_factor"
+                ] = self.args.dataloader_prefetch_factor
+
+            sampler = self._get_train_sampler()
+            if isinstance(sampler, BatchSampler):
+                dataloader_params["batch_sampler"] = sampler
+                del dataloader_params["batch_size"]
+            else:
+                dataloader_params["sampler"] = sampler
+                dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["worker_init_fn"] = seed_worker
+
+            self.accelerator.even_batches = False
+            return self.accelerator.prepare_data_loader(
+                DataLoader(train_dataset, **dataloader_params)
+            )
+        return super().get_train_dataloader()
+
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        if self.args.sample_packing and self.args.eval_sample_packing is False:
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.eval_data_collator
+            )
+            if eval_dataset:
+                eval_dataset = eval_dataset.remove_columns(["length"])
+            dataloader = super().get_eval_dataloader(eval_dataset)
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.train_data_collator
+            )
+            return dataloader
+
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            eval_dataset = (
+                eval_dataset if eval_dataset is not None else self.eval_dataset
+            )
+
+            eval_sampler = self._get_eval_sampler(eval_dataset)
+            eval_dataset = eval_dataset.remove_columns(["length"])
+            data_collator = self.data_collator
+            dataloader_params = {
+                "batch_size": self.args.eval_batch_size,
+                "collate_fn": data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+            }
+            if self.args.dataloader_prefetch_factor:
+                dataloader_params[
+                    "prefetch_factor"
+                ] = self.args.dataloader_prefetch_factor
+
+            if isinstance(eval_sampler, BatchSampler):
+                dataloader_params["batch_sampler"] = eval_sampler
+                del dataloader_params["batch_size"]
+            else:
+                dataloader_params["sampler"] = eval_sampler
+                dataloader_params["drop_last"] = self.args.dataloader_drop_last
+
+            self.accelerator.even_batches = False
+            return self.accelerator.prepare_data_loader(
+                DataLoader(eval_dataset, **dataloader_params)
+            )
+
+        return super().get_eval_dataloader(eval_dataset)
+
+    def _get_bench_sampler(
+        self, bench_dataset: Dataset
+    ) -> Optional[torch.utils.data.Sampler]:
+        if self.args.world_size <= 1:
+            return SequentialSampler(bench_dataset)
+        return None
+
+    def get_bench_dataloader(
+        self,
+        bench_dataset: Dataset,
+    ) -> DataLoader:
+        dataloader_params = {
+            "batch_size": self.args.eval_batch_size,
+            "collate_fn": self.bench_data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+        }
+        if self.args.dataloader_prefetch_factor:
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        if not isinstance(bench_dataset, torch.utils.data.IterableDataset):
+            dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
+            dataloader_params["drop_last"] = self.args.dataloader_drop_last
+
+        return DataLoader(bench_dataset, **dataloader_params)
+        # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
+
+    def compute_loss(
+        self, model, inputs, return_outputs=False, num_items_in_batch=None
+    ):
+        # use one's weighted cross entropy loss calc
+        # if self.args.sample_packing:
+        #     labels = inputs.pop("labels")
+        #     outputs = model(**inputs)
+        #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
+        #     return (loss, outputs) if return_outputs else loss
+        if self.args.orpo_alpha:
+            return self.orpo_compute_loss(
+                model,
+                inputs,
+                return_outputs=return_outputs,
+                num_items_in_batch=num_items_in_batch,
+            )
+        return super().compute_loss(
+            model,
+            inputs,
+            return_outputs=return_outputs,
+            num_items_in_batch=num_items_in_batch,
+        )
+
+    @staticmethod
+    def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
+        concatenated_batch = {}
+
+        max_length = max(
+            inputs["input_ids"].shape[1], inputs["rejected_input_ids"].shape[1]
+        )
+        # Concatenate positive and negative inputs
+        concatenated_batch["input_ids"] = pad_to_length(
+            inputs["input_ids"], max_length, pad_token
+        )
+        concatenated_batch["rejected_input_ids"] = pad_to_length(
+            inputs["rejected_input_ids"], max_length, pad_token
+        )
+        concatenated_batch["labels"] = pad_to_length(
+            inputs["labels"], max_length, label_pad_token
+        )
+        concatenated_batch["rejected_labels"] = pad_to_length(
+            inputs["rejected_labels"], max_length, label_pad_token
+        )
+        concatenated_batch["attention_mask"] = pad_to_length(
+            inputs["attention_mask"], max_length, 0
+        )
+        concatenated_batch["rejected_attention_mask"] = pad_to_length(
+            inputs["rejected_attention_mask"], max_length, 0
+        )
+        concatenated_batch["prompt_attention_mask"] = pad_to_length(
+            inputs["prompt_attention_mask"], max_length, 0
+        ).to(device=device)
+
+        input_ids = torch.cat(
+            [concatenated_batch["input_ids"], concatenated_batch["rejected_input_ids"]],
+            dim=0,
+        ).to(device=device)
+        attention_mask = torch.cat(
+            [
+                concatenated_batch["attention_mask"],
+                concatenated_batch["rejected_attention_mask"],
+            ],
+            dim=0,
+        ).to(device=device)
+        labels = torch.cat(
+            [concatenated_batch["labels"], concatenated_batch["rejected_labels"]], dim=0
+        ).to(device=device)
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": attention_mask,
+            "prompt_attention_mask": concatenated_batch["prompt_attention_mask"],
+        }
+
+    def orpo_compute_custom_loss(self, logits, labels):
+        logits = logits.contiguous()
+        loss = 0.0
+
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+
+            # Flatten the tokens
+            loss = self.loss_fct(shift_logits.transpose(2, 1), shift_labels).mean(
+                dim=-1
+            )
+
+        return loss
+
+    def orpo_compute_logps(
+        self, prompt_attention_mask, chosen_inputs, chosen_attention_mask, logits
+    ):
+        # Get the shape of chosen_attention_mask[:, :-1]
+        chosen_shape = chosen_attention_mask[:, :-1].shape
+
+        # Calculate the padding size
+        pad_length = chosen_shape[1] - (prompt_attention_mask.shape[1] - 1)
+
+        # Pad prompt_attention_mask with zeros to match the desired shape
+        prompt_attention_mask_padded = torch.nn.functional.pad(
+            prompt_attention_mask[:, 1:], (0, pad_length), mode="constant", value=0
+        )
+
+        # Perform the subtraction operation
+        mask = chosen_attention_mask[:, :-1] > prompt_attention_mask_padded
+
+        per_token_logps = torch.gather(
+            logits[:, :-1, :].log_softmax(-1),
+            dim=2,
+            index=(mask * chosen_inputs[:, 1:]).unsqueeze(2),
+        ).squeeze(2)
+        return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
+
+    def orpo_compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
+        concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
+            inputs,
+            label_pad_token=-100,
+            pad_token=self.tokenizer.pad_token_id,
+            device=self.accelerator.device,
+        )
+
+        # Perform a single forward pass
+        outputs = model(
+            **{
+                "input_ids": concat_inputs["input_ids"],
+                "attention_mask": concat_inputs["attention_mask"],
+                "labels": concat_inputs["labels"],
+            },
+            output_hidden_states=True,
+        )
+
+        # Split the outputs for positive and negative examples
+        outputs_pos, outputs_neg = outputs.logits.chunk(2)
+
+        # Calculate NLL loss
+        pos_loss = self.orpo_compute_custom_loss(
+            logits=outputs_pos, labels=concat_inputs["input_ids"].chunk(2)[0]
+        )
+
+        # Calculate Log Probability
+        pos_prob = self.orpo_compute_logps(
+            prompt_attention_mask=concat_inputs["prompt_attention_mask"],
+            chosen_inputs=concat_inputs["input_ids"].chunk(2)[0],
+            chosen_attention_mask=concat_inputs["attention_mask"].chunk(2)[0],
+            logits=outputs_pos,
+        )
+        neg_prob = self.orpo_compute_logps(
+            prompt_attention_mask=concat_inputs["prompt_attention_mask"],
+            chosen_inputs=concat_inputs["input_ids"].chunk(2)[1],
+            chosen_attention_mask=concat_inputs["attention_mask"].chunk(2)[1],
+            logits=outputs_neg,
+        )
+
+        # Calculate log odds
+        log_odds = (pos_prob - neg_prob) - (
+            torch.log(1 - torch.exp(pos_prob)) - torch.log(1 - torch.exp(neg_prob))
+        )
+        sig_ratio = torch.nn.functional.sigmoid(log_odds)
+        ratio = torch.log(sig_ratio)
+
+        # Calculate the Final Loss
+        loss = torch.mean(pos_loss - self.args.orpo_alpha * ratio).to(
+            dtype=torch.bfloat16
+        )
+
+        metrics = {}
+        metrics["chosen_geometric_mean"] = torch.mean(pos_prob).cpu().item()
+        metrics["rejected_geometric_mean"] = torch.mean(neg_prob).cpu().item()
+        metrics["log_odds_ratio"] = torch.mean(ratio).cpu().item()
+        metrics["log_odds"] = torch.mean(log_odds).cpu().item()
+        self.store_metrics(metrics, train_eval="train")
+
+        return (loss, outputs_pos) if return_outputs else loss
+
+    @wraps(Trainer.push_to_hub)
+    def push_to_hub(self, *args, **kwargs) -> str:
+        """
+        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
+        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
+        """
+        kwargs = _sanitize_kwargs_for_ds_tagging(
+            dataset_tags=self.dataset_tags, kwargs=kwargs
+        )
+        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+
+        return super().push_to_hub(*args, **kwargs)
+
+    @wraps(Trainer.create_accelerator_and_postprocess)
+    def create_accelerator_and_postprocess(self):
+        res = super().create_accelerator_and_postprocess()
+
+        if self.is_fsdp_enabled:
+            if (
+                "limit_all_gathers" in self.args.fsdp_config
+                and self.args.fsdp_config["limit_all_gathers"]
+            ):
+                self.accelerator.state.fsdp_plugin.limit_all_gathers = True
+
+        return res
+
+    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
+        """
+        Log `logs` on the various objects watching training, including stored metrics.
+
+        Args:
+            logs (`Dict[str, float]`):
+                The values to log.
+            start_time (`Optional[float]`):
+                The start of training.
+        """
+        # logs either has 'loss' or 'eval_loss'
+        train_eval = "train" if "loss" in logs else "eval"
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[key] = torch.tensor(metrics).mean().item()
+        del self._stored_metrics[train_eval]
+
+        return super().log(logs, start_time)
+
+    def store_metrics(
+        self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
+    ) -> None:
+        for key, value in metrics.items():
+            self._stored_metrics[train_eval][key].append(value)
+
+    def _save_checkpoint(self, model, trial, **kwargs):
+        # make sure the checkpoint dir exists, since trainer is flakey
+        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
+        run_dir = self._get_output_dir(trial=trial)
+        output_dir = os.path.join(run_dir, checkpoint_folder)
+        os.makedirs(output_dir, exist_ok=True)
+        return super()._save_checkpoint(model, trial, **kwargs)
+
+
+class AxolotlMambaTrainer(AxolotlTrainer):
+    """
+    Mamba specific trainer to handle loss calculation
+    """
+
+    tag_names = ["axolotl", "mamba"]
+
+    def compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
+        input_ids = inputs.pop("input_ids")
+        lm_logits = model(input_ids).logits
+
+        labels = input_ids.to(lm_logits.device)
+        shift_logits = lm_logits[:, :-1, :].contiguous()
+        labels = labels[:, 1:].contiguous()
+
+        loss_fct = torch.nn.CrossEntropyLoss()
+        lm_loss = loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
+        )
+
+        return lm_loss
+
+
+class ReLoRATrainer(AxolotlTrainer):
+    """
+    Trainer subclass that uses the OneCycleLR scheduler
+    """
+
+    tag_names = ["axolotl", "relora"]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lr_scheduler = None
+
+    def create_scheduler(
+        self,
+        num_training_steps: int,
+        optimizer: Optional[torch.optim.Optimizer] = None,
+    ):
+        optimizer = self.optimizer if optimizer is None else optimizer
+        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+
+        if self.args.relora_steps:
+            warmup_steps = (
+                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
+            )
+            anneal_steps = (
+                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
+            )
+            self.lr_scheduler = ReLoRAScheduler(
+                optimizer,
+                lr_scheduler,
+                self.args.relora_steps,
+                anneal_steps,
+                warmup_steps,
+            )
+        else:
+            self.lr_scheduler = lr_scheduler
+
+        return self.lr_scheduler
+
+
+class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
+    """
+    Extend the base DPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "dpo"]
+
+    def __init__(self, *args, dataset_tags=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dataset_tags = dataset_tags
+        self.optimizer = None
+
+    def create_optimizer(self):
+        if self.args.loraplus_lr_ratio is None:
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+                self.args,
+                opt_model,
+            )
+
+            loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+            if loraplus_lr_ratio:
+                print("Using lora+")
+            loraplus_lr_embedding = getattr(self.args, "loraplus_lr_embedding", None)
+            self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                opt_model,
+                optimizer_cls,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
+            )
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
+    @wraps(DPOTrainer.push_to_hub)
+    def push_to_hub(self, *args, **kwargs) -> str:
+        """
+        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
+        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
+        """
+        kwargs = _sanitize_kwargs_for_ds_tagging(
+            dataset_tags=self.dataset_tags, kwargs=kwargs
+        )
+        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+
+        return super().push_to_hub(*args, **kwargs)
+
+    @staticmethod
+    def tokenize_row(
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
+    ) -> Dict:
+        res = DPOTrainer.tokenize_row(
+            features,
+            processing_class,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
+        )
+        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
+        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
+            for key in res.keys():
+                res[key] = res[key][1:]
+
+        if processing_class.bos_token and processing_class.bos_token_id is not None:
+            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
+            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
+                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
+                res["chosen_labels"] = res["chosen_labels"][1:]
+                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
+            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
+                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
+                res["rejected_labels"] = res["rejected_labels"][1:]
+                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
+
+        return res
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
+    ) -> torch.Tensor:
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return loss
+
+
+class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
+    """
+    Extend the base ORPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "orpo"]
+
+
+class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+
+class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
+    """
+    Extend the base CPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "cpo"]
+
+
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]

src/axolotl/core/training_args.py

@@ -0,0 +1,220 @@
+"""
+extra axolotl specific training args
+"""
+from dataclasses import dataclass, field
+from typing import Optional
+
+from transformers import TrainingArguments
+from trl import CPOConfig, DPOConfig, KTOConfig, ORPOConfig, RewardConfig
+
+
+@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."},
+    )
+    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."},
+    )
+    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_optimizer: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "workaround to pass an alternate optimizer to the HF trainer"
+        },
+    )
+    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"},
+    )
+
+
+@dataclass
+class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
+    """
+    Training arguments for Causal trainer
+
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
+    so it can't be used as a mixin.
+    """
+
+
+@dataclass
+class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
+    """
+    DPO config for DPO training
+    """
+
+
+@dataclass
+class AxolotlORPOConfig(AxolotlTrainingMixins, ORPOConfig):
+    """
+    ORPO config for ORPO training
+    """
+
+
+@dataclass
+class AxolotlKTOConfig(AxolotlTrainingMixins, KTOConfig):
+    """
+    KTO config for KTO training
+    """
+
+
+@dataclass
+class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
+    """
+    CPO config for CPO training
+    """
+
+    simpo_gamma: Optional[float] = field(
+        default=None,
+        metadata={"help": "simpo gamma parameter"},
+    )
+
+
+@dataclass
+class AxolotlRewardConfig(AxolotlTrainingMixins, RewardConfig):
+    """
+    Reward config for Reward training
+    """

src/axolotl/prompt_strategies/base.py

@@ -10,6 +10,8 @@ LOG = logging.getLogger("axolotl")
 
 def load(strategy, cfg, module_base=None, **kwargs):
     try:
+        if len(strategy.split(".")) == 1:
+            strategy = strategy + ".default"
         load_fn = strategy.split(".")[-1]
         strategy = ".".join(strategy.split(".")[:-1])
         mod = importlib.import_module(f".{strategy}", module_base)

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -3,22 +3,41 @@ DPO strategies for chatml
 """
 
 
-def argilla(
+def default(
     cfg,
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
+        if "prompt" in sample.keys():
+            prompt_key = "prompt"
+        elif "input" in sample.keys():
+            prompt_key = "input"
+        elif "question" in sample.keys():
+            prompt_key = "question"
+        else:
+            prompt_key = "instruction"
+
+        if "chosen" in sample.keys():
+            chosen_key = "chosen"
+        else:
+            chosen_key = "chosen_response"
+
+        if "rejected" in sample.keys():
+            rejected_key = "rejected"
+        else:
+            rejected_key = "rejected_response"
+
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+                f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
+            ] = f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample[chosen_key]}<|im_end|>"
+        sample["rejected"] = f"{sample[rejected_key]}<|im_end|>"
         return sample
 
     return transform_fn

src/axolotl/prompt_strategies/dpo/llama3.py

@@ -3,22 +3,42 @@ DPO strategies for llama-3 chat template
 """
 
 
-def argilla(
+def default(
     cfg,
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
+        # pylint: disable=duplicate-code
+        if "prompt" in sample.keys():
+            prompt_key = "prompt"
+        elif "input" in sample.keys():
+            prompt_key = "input"
+        elif "question" in sample.keys():
+            prompt_key = "question"
+        else:
+            prompt_key = "instruction"
+
+        if "chosen" in sample.keys():
+            chosen_key = "chosen"
+        else:
+            chosen_key = "chosen_response"
+
+        if "rejected" in sample.keys():
+            rejected_key = "rejected"
+        else:
+            rejected_key = "rejected_response"
+
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|start_header_id|>system<|end_header_id|>\n\n{sample['system']}<|eot_id|>"
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-        sample["chosen"] = f"{sample['chosen_response']}<|eot_id|>"
-        sample["rejected"] = f"{sample['rejected_response']}<|eot_id|>"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["chosen"] = f"{sample[chosen_key]}<|eot_id|>"
+        sample["rejected"] = f"{sample[rejected_key]}<|eot_id|>"
         return sample
 
     return transform_fn