some refactoring

* Extend MultiPackBatchSampler test to include shorter sequence length and drop long sequences filter

* Fix get_dataset_lengths for datasets that were previously filtered (e.g., with drop_long_seq_in_dataset)

* Update src/axolotl/utils/samplers/utils.py

Fix get_dataset_lengths for datasets that do not have position_ids or length attributes

Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>

---------

Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>

docs/lora_optims.qmd

@@ -12,6 +12,7 @@ to leverage operator fusion and tensor re-use in order to improve speed and redu
 memory usage during the forward and backward passes of these calculations.
 
 We currently support several common model architectures, including (but not limited to):
+
 - `llama`
 - `mistral`
 - `qwen2`

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

@@ -9,6 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
+from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -30,31 +31,21 @@ class GRPOStrategy:
 
     @classmethod
     def set_training_args_kwargs(cls, cfg):
-        grpo_args_kwargs = {}
-        if cfg.trl and cfg.trl.use_vllm:
-            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
-            if cfg.trl and cfg.trl.vllm_device:
-                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
-            else:
-                grpo_args_kwargs["vllm_device"] = "auto"
-            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs[
-                    "vllm_gpu_memory_utilization"
-                ] = cfg.trl.vllm_gpu_memory_utilization
-            if cfg.trl and cfg.trl.vllm_max_model_len:
-                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
-        if cfg.trl and cfg.trl.num_generations:
-            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
-        if cfg.trl and cfg.trl.sync_ref_model:
-            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
-            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
-                grpo_args_kwargs[
-                    "ref_model_mixup_alpha"
-                ] = cfg.trl.ref_model_mixup_alpha
-            if cfg.trl and cfg.trl.ref_model_sync_steps:
-                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
-        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
-        grpo_args_kwargs["log_completions"] = cfg.trl.log_completions
+        training_kwargs = [
+            "use_vllm",
+            "vllm_device",
+            "vllm_gpu_memory_utilization",
+            "vllm_max_model_len",
+            "vllm_dtype",
+            "use_liger_loss",
+            "num_generations",
+            "log_completions",
+            "sync_ref_model",
+            "ref_model_mixup_alpha",
+            "ref_model_sync_steps",
+            "max_completion_length",
+        ]
+        grpo_args_kwargs = {k: cfg.trl[k] for k in training_kwargs if cfg.trl[k]}
         return grpo_args_kwargs
 
     @classmethod
@@ -71,9 +62,7 @@ class GRPOStrategy:
     def set_trainer_kwargs(cls, cfg):
         trainer_kwargs = {}
         if cfg.trl and cfg.trl.reward_processing_classes:
-            trainer_kwargs[
-                "reward_processing_classes"
-            ] = cfg.trl.reward_processing_classes
+            trainer_kwargs["reward_processing_classes"] = cfg.trl.reward_processing_classes
         return trainer_kwargs
 
     @classmethod

src/axolotl/core/trainers/grpo/args.py

@@ -13,3 +13,4 @@ class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
     """
     Axolotl GRPO Config for GRPO training
     """
+    use_liger_loss: bool = False

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

@@ -1,13 +1,24 @@
 """
 Axolotl GRPO trainer
 """
+
+from contextlib import contextmanager, nullcontext
 from accelerate.utils import is_peft_model
 from accelerate.utils.other import is_compiled_module
+import torch
 from transformers import PreTrainedModel
 from trl import GRPOConfig, GRPOTrainer
 from trl.models import unwrap_model_for_generation
 
 from axolotl.core.trainers.base import SchedulerMixin
+from transformers.utils import is_liger_kernel_available
+
+if is_liger_kernel_available():
+    from liger_kernel.chunked_loss.grpo_loss import LigerFusedLinearGRPOLoss
+
+from trl.data_utils import apply_chat_template, is_conversational, maybe_apply_chat_template
+from accelerate.utils import broadcast_object_list, gather_object
+from trl.trainer.utils import pad
 
 
 # mypy: ignore-errors
@@ -20,7 +31,20 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-
+        
+        self.use_liger_loss = kwargs["args"].use_liger_loss
+        if self.use_liger_loss:
+            if not is_liger_kernel_available():
+                raise ValueError(
+                    "You set `use_liger_loss=True` but the liger kernel is not available. "
+                    "Please install liger-kernel first: `pip install liger-kernel`"
+                )
+            self.grpo_loss_fn = LigerFusedLinearGRPOLoss(
+                beta=self.beta,
+                compiled=is_compiled_module(self.model),
+                use_ref_model=True,
+                num_generations=self.args.num_generations,
+            )
         # pylint: disable=access-member-before-definition
         # Enable gradient checkpointing if requested
         if kwargs["args"].gradient_checkpointing:
@@ -29,9 +53,7 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
                 self.model.config.use_cache = False
 
             # Enable gradient checkpointing on the base model for PEFT
-            if is_peft_model(self.model) and hasattr(
-                self.model.base_model, "gradient_checkpointing_enable"
-            ):
+            if is_peft_model(self.model) and hasattr(self.model.base_model, "gradient_checkpointing_enable"):
                 self.model.base_model.gradient_checkpointing_enable()
             # Enable gradient checkpointing for non-PEFT models
             elif hasattr(self.model, "gradient_checkpointing_enable"):
@@ -39,15 +61,12 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             self.model = self._enable_gradient_checkpointing(self.model, kwargs["args"])
         # pylint: enable=access-member-before-definition
 
-    def _enable_gradient_checkpointing(
-        self, model: PreTrainedModel, args: GRPOConfig
-    ) -> PreTrainedModel:
+    def _enable_gradient_checkpointing(self, model: PreTrainedModel, args: GRPOConfig) -> PreTrainedModel:
         """Enables gradient checkpointing for the model."""
         # pylint: disable=unused-argument,redefined-builtin
         gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
         use_reentrant = (
-            "use_reentrant" not in gradient_checkpointing_kwargs
-            or gradient_checkpointing_kwargs["use_reentrant"]
+            "use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"]
         )
 
         if use_reentrant:
@@ -58,9 +77,7 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
                 def make_inputs_require_grad(module, input, output):
                     output.requires_grad_(True)
 
-                model.get_input_embeddings().register_forward_hook(
-                    make_inputs_require_grad
-                )
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
 
         return model
         # pylint: enable=unused-argument,redefined-builtin
@@ -72,26 +89,18 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             gather_deepspeed3_params=self.args.ds3_gather_for_generation,
         ) as unwrapped_model:
             if is_compiled_module(unwrapped_model):
-                unwrapped_model = (
-                    unwrapped_model._orig_mod  # pylint: disable=protected-access
-                )
+                unwrapped_model = unwrapped_model._orig_mod  # pylint: disable=protected-access
             if is_peft_model(unwrapped_model):
                 unwrapped_model.merge_adapter()
                 state_dict = unwrapped_model.state_dict()
                 unwrapped_model.unmerge_adapter()
                 # Remove base_model and base_layer prefixes
                 state_dict = {
-                    k.removeprefix("base_model.model.")
-                    .removeprefix("base_model.model.")
-                    .replace(".base_layer", ""): v
+                    k.removeprefix("base_model.model.").removeprefix("base_model.model.").replace(".base_layer", ""): v
                     for k, v in state_dict.items()
                 }
                 # Remove values with adapter prefix (example: "_lora")
-                state_dict = {
-                    k: v
-                    for k, v in state_dict.items()
-                    if unwrapped_model.prefix not in k
-                }
+                state_dict = {k: v for k, v in state_dict.items() if unwrapped_model.prefix not in k}
                 # When module to save, remove its prefix and discard the original module
                 state_dict = {
                     k.replace("modules_to_save.default.", ""): v
@@ -101,7 +110,217 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             else:
                 state_dict = unwrapped_model.state_dict()
         if self.accelerator.is_main_process:
-            llm_model = (
-                self.llm.llm_engine.model_executor.driver_worker.model_runner.model
-            )
+            llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
             llm_model.load_weights(state_dict.items())
+
+    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
+        if self.use_liger_loss:
+            if return_outputs:
+                raise ValueError("The GRPOTrainer does not support returning outputs")
+
+            device = self.accelerator.device
+            prompts = [x["prompt"] for x in inputs]
+            prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
+            prompt_inputs = self.processing_class(
+                prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False
+            )
+            prompt_inputs = super()._prepare_inputs(prompt_inputs)
+
+            if self.max_prompt_length is not None:
+                prompt_inputs["input_ids"] = prompt_inputs["input_ids"][:, -self.max_prompt_length :]
+                prompt_inputs["attention_mask"] = prompt_inputs["attention_mask"][:, -self.max_prompt_length :]
+
+            # Generate completions using either vLLM or regular generation
+            if self.args.use_vllm:
+                # First, have main process load weights if needed
+                if self.state.global_step != self._last_loaded_step:
+                    with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+                        state_dict = unwrapped_model.state_dict()
+                    if self.accelerator.is_main_process:
+                        llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
+                        llm_model.load_weights(state_dict.items())
+                    self._last_loaded_step = self.state.global_step
+
+                # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
+                all_prompts_text = gather_object(prompts_text)
+                if self.accelerator.is_main_process:
+                    outputs = self.llm.generate(all_prompts_text, sampling_params=self.sampling_params, use_tqdm=False)
+                    completion_ids = [out.token_ids for completions in outputs for out in completions.outputs]
+                else:
+                    completion_ids = [None] * len(all_prompts_text) * self.num_generations
+
+                # Broadcast the completions from the main process to all processes, ensuring each process receives its
+                # corresponding slice.
+                completion_ids = broadcast_object_list(completion_ids, from_process=0)
+                process_slice = slice(
+                    self.accelerator.process_index * len(prompts) * self.num_generations,
+                    (self.accelerator.process_index + 1) * len(prompts) * self.num_generations,
+                )
+                completion_ids = completion_ids[process_slice]
+
+                # Pad the completions, and concatenate them with the prompts
+                completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
+                completion_ids = pad(completion_ids, padding_value=self.processing_class.pad_token_id)
+                prompt_inputs_repeated = torch.repeat_interleave(
+                    prompt_inputs["input_ids"], self.num_generations, dim=0
+                )
+                prompt_completion_ids = torch.cat([prompt_inputs_repeated, completion_ids], dim=1)
+            else:
+                # Regular generation path
+                with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+                    prompt_completion_ids = unwrapped_model.generate(
+                        **prompt_inputs, generation_config=self.generation_config
+                    )
+
+            prompt_length = prompt_inputs["input_ids"].size(1)
+            completion_ids = prompt_completion_ids[:, prompt_length:]
+
+            # Get the per-token log probabilities for the completions for the model and the reference model
+            def get_per_token_logps(model, input_ids, num_logits_to_keep):
+                # We add 1 to `num_logits_to_keep` because the last logits of the sequence is later excluded
+                outputs = model(input_ids, num_logits_to_keep=num_logits_to_keep + 1)
+                hidden_states = outputs.last_hidden_state[:, :-1]
+                logits = outputs.logits  # (B, L, V)
+                logits = logits[
+                    :, :-1, :
+                ]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+
+                # Compute the log probabilities for the input tokens. Use a loop to reduce memory peak.
+                per_token_logps = []
+                for logits_row, input_ids_row in zip(logits, input_ids[:, -num_logits_to_keep:]):
+                    log_probs = logits_row.log_softmax(dim=-1)
+                    token_log_prob = torch.gather(log_probs, dim=1, index=input_ids_row.unsqueeze(1)).squeeze(1)
+                    per_token_logps.append(token_log_prob)
+                return torch.stack(per_token_logps), hidden_states
+
+            num_logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
+            per_token_logps, hidden_states = get_per_token_logps(model, prompt_completion_ids, num_logits_to_keep)
+
+            with torch.inference_mode():
+                if self.ref_model is not None:
+                    ref_per_token_logps, ref_hidden_states = get_per_token_logps(
+                        self.ref_model, prompt_completion_ids, num_logits_to_keep
+                    )
+                else:
+                    with self.accelerator.unwrap_model(model).disable_adapter():
+                        ref_per_token_logps, ref_hidden_states = get_per_token_logps(
+                            model, prompt_completion_ids, num_logits_to_keep
+                        )
+
+            # done in liger
+            # Compute the KL divergence between the model and the reference model
+            # per_token_kl = (
+            #     torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
+            # )
+
+            # Mask everything after the first EOS token
+            is_eos = completion_ids == self.processing_class.eos_token_id
+            eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device)
+            eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
+            sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1)
+            completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
+
+            # Decode the generated completions
+            completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
+            if is_conversational(inputs[0]):
+                completions = [[{"role": "assistant", "content": completion}] for completion in completions]
+
+            # Compute the rewards
+            prompts = [prompt for prompt in prompts for _ in range(self.num_generations)]
+
+            rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)
+            for i, (reward_func, reward_processing_class) in enumerate(
+                zip(self.reward_funcs, self.reward_processing_classes)
+            ):
+                if isinstance(reward_func, PreTrainedModel):
+                    if is_conversational(inputs[0]):
+                        messages = [{"messages": p + c} for p, c in zip(prompts, completions)]
+                        texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages]
+                    else:
+                        texts = [p + c for p, c in zip(prompts, completions)]
+                    reward_inputs = reward_processing_class(
+                        texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
+                    )
+                    reward_inputs = super()._prepare_inputs(reward_inputs)
+                    with torch.inference_mode():
+                        rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0]  # Shape (B*G,)
+                else:
+                    # Repeat all input columns (but "prompt" and "completion") to match the number of generations
+                    reward_kwargs = {key: [] for key in inputs[0].keys() if key not in ["prompt", "completion"]}
+                    for key in reward_kwargs:
+                        for example in inputs:
+                            # Repeat each value in the column for `num_generations` times
+                            reward_kwargs[key].extend([example[key]] * self.num_generations)
+                    output_reward_func = reward_func(prompts=prompts, completions=completions, **reward_kwargs)
+                    rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)
+
+            # Sum the rewards from all reward functions
+            rewards = rewards_per_func.sum(dim=1)
+
+            # done in liger
+            # # Compute grouped-wise rewards
+            # mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1)
+            # std_grouped_rewards = rewards.view(-1, self.num_generations).std(dim=1)
+
+            # done in liger
+            # # Normalize the rewards to compute the advantages
+            # mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(self.num_generations, dim=0)
+            # std_grouped_rewards = std_grouped_rewards.repeat_interleave(self.num_generations, dim=0)
+            # advantages = (rewards - mean_grouped_rewards) / (std_grouped_rewards + 1e-4)
+
+            # done in liger
+            # x - x.detach() allows for preserving gradients from x
+            # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
+            # per_token_loss = -(per_token_loss - self.beta * per_token_kl)
+            # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
+
+            # Log the metrics
+            completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
+            self._metrics["completion_length"].append(completion_length)
+
+            reward_per_func = self.accelerator.gather_for_metrics(rewards_per_func).mean(0)
+            for i, reward_func in enumerate(self.reward_funcs):
+                if isinstance(reward_func, PreTrainedModel):
+                    reward_func_name = reward_func.config._name_or_path.split("/")[-1]
+                else:
+                    reward_func_name = reward_func.__name__
+                self._metrics[f"rewards/{reward_func_name}"].append(reward_per_func[i].item())
+
+            self._metrics["reward"].append(self.accelerator.gather_for_metrics(rewards).mean().item())
+
+            lm_head = model.get_output_embeddings()
+
+            if self.ref_model is not None:
+                ref_lm_head = self.ref_model.get_output_embeddings()
+            else:
+                with self.null_ref_context():
+                    ref_lm_head = model.get_output_embeddings()
+            ref_weight = ref_lm_head.weight
+            ref_bias = ref_lm_head.bias if hasattr(ref_lm_head, "bias") else None
+
+            loss, metrics = self.grpo_loss_fn(
+                lm_head,
+                hidden_states,  # this is the hidden states from the model
+                completion_mask,
+                rewards,
+                bias=lm_head.bias if hasattr(lm_head, "bias") else None,
+                ref_input=ref_hidden_states,  # this is the hidden states from the ref model
+                ref_weight=ref_weight,
+                ref_bias=ref_bias,
+            )
+        else:
+            super().compute_loss(model, inputs, return_outputs, num_items_in_batch)
+
+    @contextmanager
+    def null_ref_context(self):
+        """Context manager for handling null reference model (that is, peft adapter manipulation)."""
+        with (
+            self.accelerator.unwrap_model(self.model).disable_adapter()
+            if self.is_peft_model and not self.ref_adapter_name
+            else nullcontext()
+        ):
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.ref_adapter_name)
+            yield
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.model_adapter_name or "default")

src/axolotl/monkeypatch/lora_kernels.py

@@ -4,12 +4,13 @@ import importlib
 import inspect
 import logging
 import types
+from typing import Type
 
 import torch
 from accelerate.logging import get_logger
 from peft import PeftModelForCausalLM
 from torch import nn
-from transformers.modeling_utils import PreTrainedModel
+from transformers import AutoConfig
 
 from axolotl.kernels.lora import (
     apply_lora_mlp_geglu,
@@ -95,90 +96,108 @@ def original_apply_o(self: nn.Module, hidden_states: torch.Tensor) -> torch.Tens
     return attn_output
 
 
-# pylint: disable=protected-access
-def patch_self_attn_lora(model: PreTrainedModel):
+def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
     """
-    Patches the attention classes in a transformer model with optimized LoRA implementations.
+    Get the appropriate attention class by inspecting the model config.
+    Uses dynamic import to support any model architecture that follows
+    the standard transformers naming convention.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        The appropriate attention class for the model.
+
+    Raises:
+        ValueError: If `base_model` not specified or attention class cannot be imported
+        ImportError: If the model module or attention class doesn't exist
+    """
+    if "base_model" not in cfg:
+        raise ValueError("base_model must be specified in config")
+
+    # Get model config without loading the model
+    model_config = AutoConfig.from_pretrained(cfg["base_model"])
+    model_type = model_config.model_type
+
+    # Special case for model_type = "qwen2"
+    if model_type == "qwen2":
+        from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention
+
+        return Qwen2Attention
+
+    try:
+        # Dynamically import the module and attention class
+        module_path = f"transformers.models.{model_type}.modeling_{model_type}"
+        module = __import__(
+            module_path, fromlist=[f"{model_type.capitalize()}Attention"]
+        )
+        attention_cls = getattr(module, f"{model_type.capitalize()}Attention")
+
+        return attention_cls
+    except (ImportError, AttributeError) as e:
+        raise ValueError(
+            f"Could not import attention class for model_type: {model_type}. "
+            f"Error: {str(e)}"
+        ) from e
+
+
+# pylint: disable=protected-access
+def patch_self_attn_lora(cfg: DictDefault):
+    """
+    Given an `axolotl` config, this method patches the inferred attention class forward
+    pass with optimized LoRA implementations.
 
     It modifies the attention class to use optimized QKV and output projections. The
     original implementation is preserved and can be restored if needed.
 
     Args:
-        model: A HuggingFace transformers model.
+        cfg: Dictionary mapping `axolotl` config keys to values.
 
     Raises:
         AssertionError: If the required code blocks are not found in the attention
             implementation.
     """
-    # Find all attention modules in the model
-    attention_modules = [
-        module
-        for module in model.modules()
-        if "attention" in module.__class__.__name__.lower()
-        and hasattr(module, "forward")
-    ]
+    attention_cls = get_attention_cls_from_config(cfg)
 
-    if not attention_modules:
-        LOG.warning("No attention modules found in model")
+    # Check if already patched
+    if hasattr(attention_cls, "_original_forward"):
+        LOG.info(f"{attention_cls.__name__} already patched")
         return
 
-    attention_classes = {type(module) for module in attention_modules}
-    LOG.info(f"Found attention classes: {[cls.__name__ for cls in attention_classes]}")
+    self_attn_forward = inspect.getsource(attention_cls.forward)
+    attention_cls._original_forward = self_attn_forward
+    self_attn_forward, _ = detab_code(self_attn_forward)
 
-    for attention_cls in attention_classes:
-        # Skip if already patched
-        if hasattr(attention_cls, "_original_forward"):
-            LOG.info(f"{attention_cls.__name__} already patched")
-            continue
+    assert ORIGINAL_QKV_CODE in self_attn_forward, "Original QKV code not found"
+    assert ORIGINAL_O_CODE in self_attn_forward, "Original O code not found"
 
-        # Get and store original forward implementation
-        self_attn_forward = inspect.getsource(attention_cls.forward)
-        attention_cls._original_forward = self_attn_forward
+    self_attn_forward = self_attn_forward.replace(ORIGINAL_QKV_CODE, PATCHED_QKV_CODE)
+    self_attn_forward = self_attn_forward.replace(ORIGINAL_O_CODE, PATCHED_O_CODE)
+    self_attn_forward = self_attn_forward.replace(
+        "def forward(",
+        "def axolotl_attn_forward(",
+        1,
+    )
 
-        # Remove indentation
-        self_attn_forward, _ = detab_code(self_attn_forward)
+    # Load necessary imports
+    module_name = attention_cls.__module__
+    module = importlib.import_module(module_name)
 
-        # Verify required code blocks exist
-        assert (
-            ORIGINAL_QKV_CODE in self_attn_forward
-        ), f"Original QKV code not found in {attention_cls.__name__}"
-        assert (
-            ORIGINAL_O_CODE in self_attn_forward
-        ), f"Original O code not found in {attention_cls.__name__}"
+    items_to_import = []
+    for item in dir(module):
+        if item in self_attn_forward:
+            items_to_import.append(item)
 
-        # Replace code blocks
-        self_attn_forward = self_attn_forward.replace(
-            ORIGINAL_QKV_CODE, PATCHED_QKV_CODE
-        )
-        self_attn_forward = self_attn_forward.replace(ORIGINAL_O_CODE, PATCHED_O_CODE)
-        self_attn_forward = self_attn_forward.replace(
-            "def forward(",
-            "def axolotl_attn_forward(",
-            1,
-        )
+    exec(  # pylint: disable=exec-used  # nosec B102
+        f"from {module_name} import ({', '.join(items_to_import)})",
+        globals(),
+    )
+    exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
 
-        # Import necessary symbols from the attention module
-        module_name = attention_cls.__module__
-        module = importlib.import_module(module_name)
-
-        items_to_import = []
-        for item in dir(module):
-            if item in self_attn_forward:
-                items_to_import.append(item)
-
-        if items_to_import:
-            exec(  # pylint: disable=exec-used  # nosec B102
-                f"from {module_name} import ({', '.join(items_to_import)})",
-                globals(),
-            )
-
-        # Execute the new implementation
-        exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
-
-        LOG.info(f"Patched attention class with LoRA optims: {attention_cls.__name__}")
-        attention_cls.forward = (
-            axolotl_attn_forward  # pylint: disable=undefined-variable  # noqa: F821
-        )
+    LOG.info(f"Patched attention class with LoRA optims: {attention_cls.__name__}")
+    attention_cls.forward = (
+        axolotl_attn_forward  # pylint: disable=undefined-variable  # noqa: F821
+    )
 
 
 def apply_lora_kernel_patches(

src/axolotl/utils/config/models/input/v0_4_1/trl.py

@@ -33,3 +33,4 @@ class TRLConfig(BaseModel):
     sync_ref_model: Optional[bool] = False
     ref_model_mixup_alpha: Optional[float] = 0.9
     ref_model_sync_steps: Optional[int] = 64
+    use_liger_loss: Optional[bool] = False

src/axolotl/utils/models.py

@@ -439,6 +439,11 @@ class ModelLoader:
 
             patch_mistral_cross_entropy()
 
+        if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
+            from axolotl.monkeypatch.lora_kernels import patch_self_attn_lora
+
+            patch_self_attn_lora(self.cfg)
+
     def patch_attention(self) -> None:
         if hasattr(self.model_config, "model_type"):
             if self.model_config.model_type == "mllama" and self.cfg.flash_attention:
@@ -1023,12 +1028,6 @@ class ModelLoader:
             integrate_rope_embeddings()
 
     def apply_lora_patch(self) -> None:
-        """Applies patching relevant to LoRA Triton kernels if enabled."""
-        if self.cfg.lora_qkv_kernel or self.cfg.lora_o_kernel:
-            from axolotl.monkeypatch.lora_kernels import patch_self_attn_lora
-
-            patch_self_attn_lora(self.model)
-
         if (
             self.cfg.lora_mlp_kernel
             or self.cfg.lora_qkv_kernel
@@ -1182,7 +1181,6 @@ class ModelLoader:
         if self.cfg.adapter is not None:
             log_gpu_memory_usage(LOG, "after adapters", self.model.device)
 
-        # TODO: Deprecate this.
         self.apply_unsloth_lora_patch()
         self.apply_lora_patch()
 
@@ -1203,7 +1201,9 @@ def load_model(
     reference_model: bool = False,
     **kwargs,  # pylint: disable=unused-argument
 ) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
-    """Load a model for a given configuration and tokenizer."""
+    """
+    Load a model for a given configuration and tokenizer.
+    """
     loader = ModelLoader(
         cfg,
         tokenizer,

src/axolotl/utils/samplers/utils.py

@@ -5,12 +5,12 @@ import numpy as np
 
 
 def get_dataset_lengths(dataset):
-    if "length" in dataset.data.column_names:
-        lengths = np.array(dataset.data.column("length"))
-    elif "position_ids" in dataset.data.column_names:
-        position_ids = dataset.data.column("position_ids")
+    if "length" in dataset.column_names:
+        lengths = np.array(dataset["length"])
+    elif "position_ids" in dataset.column_names:
+        position_ids = dataset["position_ids"]
         lengths = np.array([x[-1] + 1 for x in position_ids])
     else:
-        input_ids = dataset.data.column("input_ids")
-        lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
+        input_ids = dataset["input_ids"]
+        lengths = np.array([len(seq) for seq in input_ids])
     return lengths

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -9,14 +9,16 @@ from transformers import AutoModelForCausalLM, LlamaForCausalLM
 from transformers.models.llama.configuration_llama import LlamaConfig
 from transformers.models.llama.modeling_llama import LlamaAttention
 
-from axolotl.cli.utils import load_model_and_tokenizer
 from axolotl.kernels.lora import (
     apply_lora_mlp_geglu,
     apply_lora_mlp_swiglu,
     apply_lora_o,
     apply_lora_qkv,
 )
-from axolotl.monkeypatch.lora_kernels import apply_lora_kernel_patches
+from axolotl.monkeypatch.lora_kernels import (
+    apply_lora_kernel_patches,
+    patch_self_attn_lora,
+)
 from axolotl.utils.dict import DictDefault
 
 MODEL_CONFIGS = [
@@ -63,45 +65,15 @@ def small_llama_model():
     return LlamaForCausalLM(LlamaConfig(**config))
 
 
-# pylint: disable=duplicate-code
-@pytest.fixture
-def minimal_cfg():
-    "Config of real HuggingFace Hub model"
-    cfg = DictDefault(
-        {
-            "base_model": "HuggingFaceTB/SmolLM2-135M",
-            "tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
-            "learning_rate": 0.000001,
-            "datasets": [
-                {
-                    "path": "mhenrichsen/alpaca_2k_test",
-                    "type": "alpaca",
-                }
-            ],
-            "micro_batch_size": 1,
-            "gradient_accumulation_steps": 1,
-            "adapter": "lora",
-            "lora_r": 8,
-            "lora_alpha": 16,
-            "lora_dropout": 0.0,
-            "lora_target_linear": True,
-            "sequence_len": 1024,
-            "lora_mlp_kernel": True,
-            "lora_qkv_kernel": True,
-            "lora_o_kernel": True,
-        }
-    )
-
-    return cfg
-
-
-def test_attention_patching_integration(minimal_cfg):
+def test_attention_patching_integration():
     """Test attention patching in integration context."""
+    cfg = {"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
+
     # Store the original implementation
     original_forward = getattr(LlamaAttention, "forward")
 
-    # Load model
-    _, _ = load_model_and_tokenizer(cfg=minimal_cfg)
+    # Apply patch
+    patch_self_attn_lora(cfg)
 
     # Get the new forward method
     patched_forward = LlamaAttention.forward
@@ -404,10 +376,38 @@ def test_model_architecture(model_config):
 
 
 # pylint: disable=duplicate-code
-def test_kernel_training_integration(minimal_cfg):
+def test_kernel_training_integration():
     """Test model loading with kernel patches enabled."""
+    from axolotl.cli.utils import load_model_and_tokenizer
+
+    # Create minimal config
+    cfg = DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
+            "learning_rate": 0.000001,
+            "datasets": [
+                {
+                    "path": "mhenrichsen/alpaca_2k_test",
+                    "type": "alpaca",
+                }
+            ],
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+            "adapter": "lora",
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.0,
+            "lora_target_linear": True,
+            "sequence_len": 1024,
+            "lora_mlp_kernel": True,
+            "lora_qkv_kernel": True,
+            "lora_o_kernel": True,
+        }
+    )
+
     # Load model
-    model, _ = load_model_and_tokenizer(cfg=minimal_cfg)
+    model, _ = load_model_and_tokenizer(cfg=cfg)
 
     # Verify correct activation function
     layer = model.model.model.layers[0]

tests/prompt_strategies/conftest.py

@@ -125,6 +125,12 @@ def fixture_llama3_tokenizer():
     return tokenizer
 
 
+@pytest.fixture(name="smollm2_tokenizer", scope="session", autouse=True)
+def fixture_smollm2_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
+    return tokenizer
+
+
 @pytest.fixture(name="mistralv03_tokenizer", scope="session", autouse=True)
 def fixture_mistralv03_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained(

tests/prompt_strategies/test_dpo_chatml.py

@@ -0,0 +1,61 @@
+"""
+Tests for loading DPO preference datasets with chatml formatting
+"""
+import unittest
+
+import pytest
+
+from axolotl.prompt_strategies.dpo import load as load_dpo
+from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="minimal_dpo_cfg")
+def fixture_cfg():
+    return DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
+            "rl": "dpo",
+            "learning_rate": 0.000001,
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
+            "sequence_len": 2048,
+        }
+    )
+
+
+class TestDPOChatml:
+    """
+    Test loading DPO preference datasets with chatml formatting
+    """
+
+    def test_default(self, minimal_dpo_cfg):
+        cfg = DictDefault(
+            {
+                "datasets": [
+                    {
+                        "path": "argilla/distilabel-intel-orca-dpo-pairs",
+                        "type": "chatml",
+                        "split": "train[:1%]",
+                    }
+                ]
+            }
+            | minimal_dpo_cfg
+        )
+
+        # test that dpo.load works
+        load_dpo("chatml", cfg)
+        # now actually load the datasets with the strategy
+        train_ds, _ = load_prepare_preference_datasets(cfg)
+        assert train_ds[0]["prompt"].startswith("<|im_start|>")
+        assert train_ds[0]["prompt"].endswith("<|im_start|>assistant\n")
+        assert "chosen" in train_ds[0]
+        assert "rejected" in train_ds[0]
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_packed_batch_sampler.py

@@ -7,6 +7,7 @@ from transformers import AutoTokenizer
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies.completion import load
 from axolotl.utils.collators import V2BatchSamplerDataCollatorForSeq2Seq
+from axolotl.utils.data.utils import drop_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -18,11 +19,6 @@ def fixture_tokenizer():
     return tokenizer
 
 
-@pytest.fixture(name="max_seq_length")
-def fixture_max_seq_length():
-    return 4096
-
-
 class TestBatchedSamplerPacking:
     """
     Test class for packing streaming dataset sequences
@@ -37,6 +33,7 @@ class TestBatchedSamplerPacking:
             (2, 2),
         ],
     )
+    @pytest.mark.parametrize("max_seq_length", [4096, 512])
     def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length):
         import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 
@@ -62,6 +59,9 @@ class TestBatchedSamplerPacking:
             dataset,
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
+
+        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg)
+
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(
             sampler=RandomSampler(train_dataset),
@@ -90,7 +90,7 @@ class TestBatchedSamplerPacking:
                 batch_idxs.extend(pack)
 
         for batch in loader:
-            assert len(batch["input_ids"]) <= batch_size * max_seq_length
+            assert batch["input_ids"].numel() <= batch_size * max_seq_length
             assert batch["input_ids"].shape[1] == max_seq_length
 
         original_idxs = set(range(len(train_dataset)))