handle empty offset for quant state

.github/workflows/preview-docs.yml

@@ -4,12 +4,6 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
 
-    # Run the workflow only when one of these files changes
-    paths:
-      - '**/*.md'      # any Markdown file
-      - '**/*.qmd'     # any Quarto file
-      - '_quarto.yaml'
-
 permissions:
   checks: write
   contents: write

src/axolotl/cli/art.py

@@ -16,15 +16,8 @@ AXOLOTL_LOGO = """
     @@@@  @@@@@@@@@@@@@@@@
 """
 
-HAS_PRINTED_LOGO = False
-
 
 def print_axolotl_text_art():
     """Prints axolotl ASCII art."""
-
-    global HAS_PRINTED_LOGO  # pylint: disable=global-statement
-    if HAS_PRINTED_LOGO:
-        return
     if is_main_process():
-        HAS_PRINTED_LOGO = True
         print(AXOLOTL_LOGO)

src/axolotl/cli/evaluate.py

@@ -15,7 +15,7 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.evaluate import evaluate
-from axolotl.utils import patch_optimized_env
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
@@ -32,7 +32,7 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cli_args: CLI arguments.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    patch_optimized_env()
+    set_pytorch_cuda_alloc_conf()
 
     # pylint: disable=duplicate-code
     print_axolotl_text_art()

src/axolotl/cli/main.py

@@ -29,7 +29,7 @@ from axolotl.cli.utils import (
     filter_none_kwargs,
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
-from axolotl.utils import patch_optimized_env
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.schemas.config import AxolotlInputConfig
 
 
@@ -55,8 +55,6 @@ def preprocess(config: str, cloud: Optional[str] = None, **kwargs) -> None:
         kwargs: Additional keyword arguments which correspond to CLI args or `axolotl`
             config options.
     """
-    patch_optimized_env()
-
     if cloud:
         from axolotl.cli.cloud import do_cli_preprocess
 
@@ -102,7 +100,7 @@ def train(
             config options.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    patch_optimized_env()
+    set_pytorch_cuda_alloc_conf()
 
     if "use_ray" in kwargs and kwargs["use_ray"]:
         accelerate = False

src/axolotl/cli/train.py

@@ -18,7 +18,7 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import patch_optimized_env
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
@@ -36,7 +36,7 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         cli_args: Training-specific CLI arguments.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    patch_optimized_env()
+    set_pytorch_cuda_alloc_conf()
 
     print_axolotl_text_art()
     check_accelerate_default_config()

src/axolotl/common/datasets.py

@@ -48,7 +48,6 @@ def load_datasets(
     *,
     cfg: DictDefault,
     cli_args: PreprocessCliArgs | TrainerCliArgs | None = None,
-    debug: bool = False,
 ) -> TrainDatasetMeta:
     """
     Loads one or more training or evaluation datasets, calling
@@ -57,7 +56,6 @@ def load_datasets(
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Command-specific CLI arguments.
-        debug: Whether to print out tokenization of sample
 
     Returns:
         Dataclass with fields for training and evaluation datasets and the computed
@@ -79,25 +77,20 @@ def load_datasets(
         preprocess_iterable=preprocess_iterable,
     )
 
-    if (  # pylint: disable=too-many-boolean-expressions
+    if cli_args and (
-        cli_args
+        cli_args.debug
-        and (
+        or cfg.debug
-            cli_args.debug
+        or cli_args.debug_text_only
-            or cfg.debug
+        or int(cli_args.debug_num_examples) > 0
-            or cli_args.debug_text_only
+    ):
-            or int(cli_args.debug_num_examples) > 0
-        )
-    ) or debug:
         LOG.info("check_dataset_labels...")
 
-        num_examples = cli_args.debug_num_examples if cli_args else 1
+        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
-        text_only = cli_args.debug_text_only if cli_args else False
-        train_samples = sample_dataset(train_dataset, num_examples)
         check_dataset_labels(
             train_samples,
             tokenizer,
-            num_examples=num_examples,
+            num_examples=cli_args.debug_num_examples,
-            text_only=text_only,
+            text_only=cli_args.debug_text_only,
         )
 
         LOG.info("printing prompters...")

src/axolotl/core/trainer_builder.py

@@ -21,7 +21,6 @@ import importlib.util
 import inspect
 import logging
 import math
-import os
 import sys
 from abc import abstractmethod
 from pathlib import Path
@@ -73,7 +72,6 @@ from axolotl.utils.callbacks import (
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
-    colab_inference_post_train_callback,
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
@@ -295,10 +293,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             callbacks.append(lisa_callback_factory(trainer))
 
-        if any("COLAB_" in key for key in os.environ):
-            ColabCallback = colab_inference_post_train_callback(trainer)
-            callbacks.append(ColabCallback(self.cfg))
-
         callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
         return callbacks
 

src/axolotl/core/trainers/base.py

@@ -114,8 +114,6 @@ class AxolotlTrainer(
             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,
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
         )

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

@@ -177,8 +177,12 @@ class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
             # 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
 

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -72,7 +72,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from .monkeypatch.patch import (
+            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
                 cce_patch,
             )
 

src/axolotl/integrations/liger/__init__.py

@@ -151,30 +151,6 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
-        elif cfg.model_config_type == "qwen3":
-            from axolotl.integrations.liger.models.qwen3 import (
-                apply_liger_kernel_to_qwen3,
-            )
-
-            apply_liger_kernel_to_qwen3(
-                cross_entropy=cfg.liger_cross_entropy,
-                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
-                glu_activation=cfg.liger_glu_activation,
-                rms_norm=cfg.liger_rms_norm,
-                layer_norm=cfg.liger_layer_norm,
-            )
-        elif cfg.model_config_type == "qwen3_moe":
-            from axolotl.integrations.liger.models.qwen3_moe import (
-                apply_liger_kernel_to_qwen3_moe,
-            )
-
-            apply_liger_kernel_to_qwen3_moe(
-                cross_entropy=cfg.liger_cross_entropy,
-                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
-                glu_activation=cfg.liger_glu_activation,
-                rms_norm=cfg.liger_rms_norm,
-                layer_norm=cfg.liger_layer_norm,
-            )
         else:
             logging.warning(
                 f"Unsupported model config type: {cfg.model_config_type}. Liger not applied."

src/axolotl/integrations/liger/models/qwen3.py

@@ -1,160 +0,0 @@
-"""
-Liger FLCE for Qwen3. Based on transformers v4.51.3.
-"""
-
-import sys
-from typing import Optional, Tuple, Union
-
-import torch
-from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-
-def lce_forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Cache] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-    Args:
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-    """
-
-    # pylint: disable=duplicate-code
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-
-    logits = None
-    loss = None
-    # if in training mode, don't materialize logits
-    if self.training and (labels is not None):
-        loss = LigerForCausalLMLoss(
-            hidden_states=hidden_states,
-            lm_head_weight=self.lm_head.weight,
-            labels=labels,
-            hidden_size=self.config.hidden_size,
-            **kwargs,
-        )
-
-    else:  # if in inference mode materialize logits
-        slice_indices = (
-            slice(-logits_to_keep, None)
-            if isinstance(logits_to_keep, int)
-            else logits_to_keep
-        )
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def apply_liger_kernel_to_qwen3(
-    cross_entropy: bool = False,
-    fused_linear_cross_entropy: bool = False,
-    rms_norm: bool = False,
-    glu_activation: bool = False,
-    layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
-) -> None:
-    # pylint: disable=duplicate-code
-    """
-    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
-
-    Args:
-        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
-        fused_linear_cross_entropy (bool):
-            Whether to apply Liger's fused linear cross entropy loss. Default is False.
-            `cross_entropy` and `fused_linear_cross_entropy` cannot both be False.
-            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
-        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
-        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
-        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
-    """
-
-    import transformers.models.qwen3.modeling_qwen3  # noqa: F401  # pylint: disable=unused-import
-    from liger_kernel.transformers.functional import liger_cross_entropy
-    from liger_kernel.transformers.layer_norm import LigerLayerNorm
-    from liger_kernel.transformers.rms_norm import LigerRMSNorm
-    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
-
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
-
-    modeling_qwen3 = sys.modules["transformers.models.qwen3.modeling_qwen3"]
-
-    if rms_norm:
-        modeling_qwen3.Qwen3RMSNorm = LigerRMSNorm
-
-    if glu_activation:
-        modeling_qwen3.Qwen3MLP = LigerSwiGLUMLP
-
-    if layer_norm:
-        modeling_qwen3.nn.LayerNorm = LigerLayerNorm
-
-    if cross_entropy:
-        from transformers.loss.loss_utils import nn
-
-        nn.functional.cross_entropy = liger_cross_entropy
-
-    if fused_linear_cross_entropy:
-        modeling_qwen3.Qwen3ForCausalLM.forward = lce_forward

src/axolotl/integrations/liger/models/qwen3_moe.py

@@ -1,191 +0,0 @@
-"""
-Liger FLCE for Qwen3 MoE. Based on transformers v4.51.3.
-"""
-
-import sys
-from copy import deepcopy
-from typing import List, Optional, Union
-
-import torch
-from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
-from transformers.modeling_outputs import MoeCausalLMOutputWithPast
-from transformers.models.qwen3_moe.modeling_qwen3_moe import load_balancing_loss_func
-
-
-def lce_forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    output_router_logits: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs,
-) -> MoeCausalLMOutputWithPast:
-    r"""
-    Args:
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-    """
-
-    # pylint: disable=duplicate-code
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_router_logits = (
-        output_router_logits
-        if output_router_logits is not None
-        else self.config.output_router_logits
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        output_router_logits=output_router_logits,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-
-    logits = None
-    loss = None
-    # if in training mode, don't materialize logits
-    if self.training and (labels is not None):
-        loss = LigerForCausalLMLoss(
-            hidden_states=hidden_states,
-            lm_head_weight=self.lm_head.weight,
-            labels=labels,
-            hidden_size=self.config.hidden_size,
-            **kwargs,
-        )
-
-    else:  # if in inference mode materialize logits
-        slice_indices = (
-            slice(-logits_to_keep, None)
-            if isinstance(logits_to_keep, int)
-            else logits_to_keep
-        )
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    aux_loss = None
-    if output_router_logits:
-        aux_loss = load_balancing_loss_func(
-            outputs.router_logits,
-            self.num_experts,
-            self.num_experts_per_tok,
-            attention_mask,
-        )
-        if labels is not None:
-            loss += self.router_aux_loss_coef * aux_loss.to(
-                loss.device
-            )  # make sure to reside in the same device
-
-    return MoeCausalLMOutputWithPast(
-        loss=loss,
-        aux_loss=aux_loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def apply_liger_kernel_to_qwen3_moe(
-    cross_entropy: bool = False,
-    fused_linear_cross_entropy: bool = False,
-    rms_norm: bool = False,
-    glu_activation: bool = False,
-    layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
-) -> None:
-    # pylint: disable=duplicate-code
-    """
-    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
-
-    Args:
-        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
-        fused_linear_cross_entropy (bool):
-            Whether to apply Liger's fused linear cross entropy loss. Default is False.
-            `cross_entropy` and `fused_linear_cross_entropy` cannot both be False.
-            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
-        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
-        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
-        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
-    """
-
-    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401  # pylint: disable=unused-import
-    from liger_kernel.transformers.functional import liger_cross_entropy
-    from liger_kernel.transformers.layer_norm import LigerLayerNorm
-    from liger_kernel.transformers.rms_norm import LigerRMSNorm
-    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
-
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
-
-    modeling_qwen3_moe = sys.modules["transformers.models.qwen3_moe.modeling_qwen3_moe"]
-
-    if rms_norm:
-        modeling_qwen3_moe.Qwen3MoeRMSNorm = LigerRMSNorm
-
-    if glu_activation:
-
-        def _liger_swiglu_mlp_wrapper(config, intermediate_size=None, **kwargs):
-            "Accepts intermediate_size to pass to LigerSwiGLUMLP"
-            # clone config to avoid modifying the original
-            config = deepcopy(config)
-            if intermediate_size:
-                setattr(config, "intermediate_size", intermediate_size)
-            return LigerSwiGLUMLP(config, **kwargs)
-
-        modeling_qwen3_moe.Qwen3MoeMLP = _liger_swiglu_mlp_wrapper
-
-    if layer_norm:
-        modeling_qwen3_moe.nn.LayerNorm = LigerLayerNorm
-
-    if cross_entropy:
-        from transformers.loss.loss_utils import nn
-
-        nn.functional.cross_entropy = liger_cross_entropy
-
-    if fused_linear_cross_entropy:
-        modeling_qwen3_moe.Qwen3MoeForCausalLM.forward = lce_forward

src/axolotl/kernels/quantize.py

@@ -55,13 +55,16 @@ def dequantize(
     target_device = W.device
 
     # Extract quantization state
+    nested = False
     if not isinstance(quant_state, list):
         # New style quant_state class
         absmax = quant_state.absmax.to(target_device)
         shape = quant_state.shape
         dtype = quant_state.dtype
         blocksize = quant_state.blocksize
-        offset = quant_state.offset.to(target_device)
+        if quant_state.nested:
+            nested = True
+            offset = quant_state.offset.to(target_device)
         state2 = quant_state.state2
         absmax2 = state2.absmax.to(target_device)
         code2 = state2.code.to(target_device)
@@ -115,7 +118,8 @@ def dequantize(
             ctypes.c_int(n_elements_absmax),
         )
 
-    out_absmax += offset
+    if nested:
+        out_absmax += offset
 
     # Choose appropriate dequantization function
     fx = (

src/axolotl/monkeypatch/attention/__init__.py

@@ -1,19 +0,0 @@
-"""
-attention module for attention monkeypatches
-"""
-
-from transformers.integrations.flash_attention import flash_attention_forward
-
-
-def patch_xformers_attn_over_fa2():
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    from .xformers import xformers_attention_forward
-
-    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = xformers_attention_forward
-
-
-def unpatch_xformers_attn_over_fa2():
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = flash_attention_forward()

src/axolotl/monkeypatch/attention/xformers.py

@@ -1,160 +0,0 @@
-"""
-xformers attention implementation for packing
-"""
-
-from typing import Optional
-
-import torch
-import xformers
-import xformers.ops.fmha
-from transformers.modeling_flash_attention_utils import (
-    _upad_input,
-)
-
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-
-xformers_attention = xformers.ops.fmha.memory_efficient_attention
-
-
-def xformers_attention_forward(
-    module: torch.nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    dropout: float = 0.0,  # pylint: disable=unused-argument
-    scaling: Optional[float] = None,  # pylint: disable=unused-argument
-    sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
-    softcap: Optional[float] = None,  # pylint: disable=unused-argument
-    cu_seq_lens_q: Optional[torch.LongTensor] = None,
-    cu_seq_lens_k: Optional[torch.LongTensor] = None,
-    max_length_q: Optional[int] = None,
-    max_length_k: Optional[int] = None,  # pylint: disable=unused-argument
-    **kwargs,  # pylint: disable=unused-argument
-):
-    # Get dimensions
-    # query: [batch, heads, seq_len, hidden_dim]
-    batch_size = query.size(0)
-    query_length = query.shape[2]
-    key_length = key.shape[2]
-
-    # Default causal mask
-    attn_bias = xformers.ops.LowerTriangularMask()
-
-    # Check if we have sliding window attention
-    has_sliding_window = sliding_window is not None and sliding_window < query_length
-
-    # Transpose dimensions for xformers (Q: [b, h, s, d] -> [b, s, h, d])
-    query = query.transpose(1, 2)
-    key = key.transpose(1, 2)
-    value = value.transpose(1, 2)
-
-    # Get GQA parameters
-    num_attention_heads = module.config.num_attention_heads
-    num_key_value_heads = module.config.num_key_value_heads
-    head_dim = query.size(-1)
-    is_gqa = num_attention_heads != num_key_value_heads
-    n_groups = num_attention_heads // num_key_value_heads if is_gqa else 1
-
-    # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
-    # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
-    # Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
-    if position_ids is not None and (
-        max_length_q is not None
-        or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all())
-    ):
-        if cu_seq_lens_q is None or cu_seq_lens_k is None:
-            cu_seq_lens_q = get_cu_seqlens_from_pos_ids(position_ids)[0]
-            cu_seq_lens_q = cu_seq_lens_q.squeeze()
-            seq_lengths = cu_seq_lens_q[1:] - cu_seq_lens_q[:-1]
-            attn_bias = (
-                xformers.ops.fmha.attn_bias.BlockDiagonalCausalMask.from_seqlens(
-                    q_seqlen=seq_lengths.tolist(),
-                )
-            )
-        else:
-            query = query.reshape(-1, query.size(-2), query.size(-1))
-            key = key.reshape(-1, key.size(-2), key.size(-1))
-            value = value.reshape(-1, value.size(-2), value.size(-1))
-
-        # Handle GQA
-        if is_gqa:
-            key = key.repeat_interleave(n_groups, dim=2)
-            value = value.repeat_interleave(n_groups, dim=2)
-
-    elif attention_mask is not None:
-        query, key, value, _, cu_seq_lens, _ = _upad_input(
-            query, key, value, attention_mask, query_length
-        )
-        cu_seq_lens_q, cu_seq_lens_k = cu_seq_lens
-        seq_lengths = []
-        for i in range(len(cu_seq_lens_q) - 1):
-            seq_lengths.append(cu_seq_lens_q[i + 1] - cu_seq_lens_q[i])
-        attn_bias = xformers.ops.fmha.attn_bias.BlockDiagonalCausalMask.from_seqlens(
-            q_seqlen=seq_lengths,
-            kv_seqlen=seq_lengths,
-        )
-
-        # Handle GQA
-        if is_gqa:
-            key = key.repeat_interleave(n_groups, dim=2)
-            value = value.repeat_interleave(n_groups, dim=2)
-    else:
-        # Handle Group Query Attention (GQA) using view/expand approach from reference
-        key = key.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
-        value = value.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
-        key = key.expand(
-            batch_size, key_length, num_key_value_heads, n_groups, head_dim
-        )
-        value = value.expand(
-            batch_size, key_length, num_key_value_heads, n_groups, head_dim
-        )
-
-        if module.training:
-            key = key.reshape(batch_size, key_length, num_attention_heads, head_dim)
-            value = value.reshape(batch_size, key_length, num_attention_heads, head_dim)
-
-            if has_sliding_window:
-                query = query.view(
-                    1, batch_size * query_length, num_attention_heads, head_dim
-                )
-                key = key.view(
-                    1, batch_size * key_length, num_attention_heads, head_dim
-                )
-                value = value.view(
-                    1, batch_size * key_length, num_attention_heads, head_dim
-                )
-        else:
-            query = query.view(
-                batch_size, query_length, num_key_value_heads, n_groups, head_dim
-            )
-
-            # If we need a sliding window attention
-            if has_sliding_window:
-                query = query.view(
-                    1,
-                    batch_size * query_length,
-                    num_key_value_heads,
-                    n_groups,
-                    head_dim,
-                )
-                key = key.view(
-                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
-                )
-                value = value.view(
-                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
-                )
-
-    # Run the xformers attention
-    attn_output = xformers_attention(
-        query,
-        key,
-        value,
-        attn_bias=attn_bias,
-    )
-
-    attn_output = attn_output.view(
-        batch_size, -1, attn_output.size(-2), attn_output.size(-1)
-    )
-    return attn_output, None

src/axolotl/monkeypatch/loss/chunked.py

@@ -1,134 +0,0 @@
-"""
-chunked ce loss
-"""
-
-from typing import List, Optional
-
-import torch
-import torch.nn.functional as F
-
-
-# copied and modified from torchtune.modules.loss.CEWithChunkedOutputLoss
-class CEWithChunkedOutputLoss(torch.nn.Module):
-    """
-    Cross-entropy with chunked outputs that saves memory by only upcasting one chunk at a time.
-
-    For more details, please refer to: https://github.com/pytorch/torchtune/pull/1390
-    """
-
-    def __init__(self, num_output_chunks: int = 8, ignore_index: int = -100):
-        super().__init__()
-        self.num_output_chunks = num_output_chunks
-        self.ignore_index = ignore_index
-
-    def compute_cross_entropy(
-        self,
-        logits: torch.Tensor,
-        labels: torch.Tensor,
-        normalize: bool = True,  # pylint: disable=unused-argument
-    ) -> torch.Tensor:
-        """
-        Upcast logits to fp32 and compute cross entropy loss.
-        """
-        return F.cross_entropy(
-            logits.float(), labels, ignore_index=self.ignore_index, reduction="sum"
-        )
-
-    def forward(
-        self, logits: List[torch.Tensor], labels: torch.Tensor, reduction="sum"
-    ) -> torch.Tensor:
-        """
-        Args:
-            logits (List[torch.Tensor]): List of chunked logits of length
-                ``self.num_output_chunks``, where each chunk has shape
-                ``(batch_size, num_tokens / num_output_chunks, vocab_size)``.
-            labels (torch.Tensor): Ground truth labels of shape ``(batch_size, num_tokens)``.
-            reduction (str): The reduction to apply to the output.
-
-        Returns:
-            torch.Tensor: Cross entropy loss of shape (1,).
-        """
-
-        total_elements = (labels != self.ignore_index).sum()
-
-        # chunk and reshape labels (bsz, num_tokens, vocab) -> [(bsz*num_tokens/num_chunks, vocab)]
-        labels = [
-            target_chunk.reshape(-1)
-            for target_chunk in labels.chunk(self.num_output_chunks, dim=1)
-        ]
-        # reshape logits [(bsz, num_tokens/num_chunks, vocab)] -> [(bsz*num_tokens/num_chunks, vocab)]
-        logits = [
-            logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits
-        ]
-
-        # compute one chunk at a time
-        total_loss = 0.0
-        for logits_chunk, labels_chunk in zip(logits, labels):
-            total_loss += self.compute_cross_entropy(logits_chunk, labels_chunk)
-
-        if reduction == "sum":
-            return total_loss
-        return total_loss / total_elements
-
-
-def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
-    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
-    loss_fn_ce.compute_cross_entropy = torch.compile(
-        loss_fn_ce.compute_cross_entropy, backend="inductor"
-    )
-    return loss_fn_ce
-
-
-def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
-    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
-
-    def chunked_fix_cross_entropy(
-        source,
-        target,
-        num_items_in_batch: int = None,
-        ignore_index: int = -100,
-        **kwargs,
-    ):  # pylint: disable=unused-argument
-        reduction = "sum" if num_items_in_batch is not None else "mean"
-        logit_chunks = [  # pylint: disable=unnecessary-comprehension
-            chunk for chunk in source.chunk(loss_fn_ce.num_output_chunks, dim=1)
-        ]
-        loss = loss_fn_ce(logit_chunks, target, reduction=reduction)
-        if reduction == "sum":
-            loss = loss / num_items_in_batch
-        return loss
-
-    def for_causal_lm_chunked_loss(
-        logits,
-        labels,
-        vocab_size: int = None,  # pylint: disable=unused-argument
-        num_items_in_batch: Optional[int] = None,
-        ignore_index: int = -100,
-        shift_labels: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        # skip the upcast to float since we handle that in the chunking loss
-        if shift_labels is None:
-            # Shift so that tokens < n predict n
-            labels = F.pad(labels, (0, 1), value=ignore_index)
-            shift_labels = labels[..., 1:].contiguous()
-
-        # Skip Flattening the tokens
-        # Enable model parallelism
-        shift_labels = shift_labels.to(logits.device)
-        loss = chunked_fix_cross_entropy(
-            logits, shift_labels, num_items_in_batch, ignore_index, **kwargs
-        )
-        return loss
-
-    return for_causal_lm_chunked_loss
-
-
-def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
-    import transformers.loss.loss_utils
-
-    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
-    transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
-    transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
-        for_causal_lm_chunked_loss
-    )

src/axolotl/monkeypatch/multipack.py

@@ -18,8 +18,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "mixtral",
     "qwen2",
     "qwen2_moe",
-    "qwen3",
-    "qwen3_moe",
     "falcon",
     "phi",
     "phi3",

src/axolotl/monkeypatch/peft/utils.py

@@ -1,78 +0,0 @@
-"""
-Patch prepare_model_for_kbit_training to not upcast everything
-"""
-
-import inspect
-import logging
-
-import peft
-
-import axolotl
-from axolotl.monkeypatch.utils import detab_code
-
-LOG = logging.getLogger(__name__)
-
-ORIGINAL_PREPARE_CODE = """
-        for param in model.parameters():
-            if (
-                (param.dtype == torch.float16) or (param.dtype == torch.bfloat16)
-            ) and param.__class__.__name__ != "Params4bit":
-                param.data = param.data.to(torch.float32)
-"""
-
-PATCHED_PREPARE_CODE = """
-        for name, param in model.named_parameters():
-            if (
-                (param.dtype == torch.float16) or (param.dtype == torch.bfloat16)
-            ) and param.__class__.__name__ != "Params4bit" and "norm" in name:
-                param.data = param.data.to(torch.float32)
-"""
-
-
-def get_peft_prep_code() -> str:
-    prepare = inspect.getsource(peft.utils.other.prepare_model_for_kbit_training)
-    return prepare
-
-
-def check_peft_prep_code_is_patchable() -> bool:
-    prep_code = get_peft_prep_code()
-    prep_code, _ = detab_code(prep_code)
-    return ORIGINAL_PREPARE_CODE in prep_code
-
-
-def patch_peft_prep_code():
-    """
-    monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs
-    """
-
-    try:
-        prep_code = get_peft_prep_code()
-    except OSError:
-        return
-    peft.utils.other._original_create_accelerator_and_postprocess = (  # pylint: disable=protected-access
-        prep_code
-    )
-    prep_code, _ = detab_code(prep_code)
-    if ORIGINAL_PREPARE_CODE not in prep_code:
-        return
-
-    prep_code = prep_code.replace(ORIGINAL_PREPARE_CODE, PATCHED_PREPARE_CODE)
-    prep_code = prep_code.replace(
-        "def prepare_model_for_kbit_training(",
-        "def fixed_prepare_model_for_kbit_training(",
-        1,
-    )
-
-    items_to_import = []
-    for item in dir(peft.utils.other):
-        if item in prep_code:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from peft.utils.other import (" + ", ".join(x for x in items_to_import) + ")",
-        globals(),
-    )
-    exec(prep_code, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching prepare_model_for_kbit_training to allow for overrides")
-    peft.utils.other.prepare_model_for_kbit_training = fixed_prepare_model_for_kbit_training  # pylint: disable=protected-access  # pylint: disable=undefined-variable  # noqa: F821
-    axolotl.utils.models.prepare_model_for_kbit_training = fixed_prepare_model_for_kbit_training  # pylint: disable=protected-access  # pylint: disable=undefined-variable  # noqa: F821

src/axolotl/train.py

@@ -21,7 +21,6 @@ from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
@@ -517,8 +516,6 @@ def train(
     Returns:
         Tuple of (model, tokenizer) after training
     """
-    print_axolotl_text_art()
-
     # Setup model, tokenizer, (causal or RLHF) trainer, etc.
     (
         trainer,

src/axolotl/utils/__init__.py

@@ -43,12 +43,3 @@ def set_pytorch_cuda_alloc_conf():
             os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
                 "expandable_segments:True,roundup_power2_divisions:16"
             )
-
-
-def patch_optimized_env():
-    """
-    Patch environment variables to improve VRAM usage and increase download speed
-    """
-    if os.getenv("HF_HUB_ENABLE_HF_TRANSFER") is None:
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-    set_pytorch_cuda_alloc_conf()

src/axolotl/utils/callbacks/__init__.py

@@ -868,29 +868,3 @@ class GCCallback(TrainerCallback):
     ):
         torch.cuda.empty_cache()
         gc.collect()
-
-
-def colab_inference_post_train_callback(trainer: Trainer):
-    class ColabCallback(TrainerCallback):
-        """Callback to prep model for inference on Google Colab"""
-
-        def __init__(self, cfg):
-            self.gpu_name = torch.cuda.get_device_name(0)
-            self.cfg = cfg
-
-        def on_train_end(
-            self, args, state, control, **kwargs
-        ):  # pylint: disable=unused-argument
-            """
-            handle T4 gpu, we need to convert attention to eager for inference
-            """
-            if "Tesla T4" in self.gpu_name and self.cfg.xformers_attention:
-                trainer.model.eval()
-            trainer.model.config._attn_implementation = (  # pylint: disable=protected-access
-                "eager"
-            )
-            trainer.model.gradient_checkpointing_disable()
-            trainer.model.config.use_cache = True
-            trainer.model.eval()
-
-    return ColabCallback

src/axolotl/utils/config/__init__.py

@@ -59,7 +59,7 @@ def choose_device(cfg):
 
 def resolve_dtype(cfg):
     if (
-        not cfg.fp16 and cfg.bf16 == "auto" and not cfg.use_ray
+        cfg.bf16 == "auto" and not cfg.use_ray
     ):  # if we use ray we want to defer this check to the worker node
         if is_torch_bf16_gpu_available():
             LOG.debug("bf16 support detected, enabling for this configuration.")
@@ -70,9 +70,6 @@ def resolve_dtype(cfg):
             if cfg.fp16 is None and not cfg.float16:
                 cfg.fp16 = True
 
-    if cfg.fp16 and cfg.bf16 == "auto":
-        cfg.bf16 = False
-
     if cfg.device == "mps":
         cfg.load_in_8bit = False
         cfg.tf32 = False

src/axolotl/utils/models.py

@@ -556,30 +556,11 @@ class ModelLoader:
         self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
-        if self.cfg.xformers_attention and self.cfg.sample_packing:
-            from axolotl.monkeypatch.attention import patch_xformers_attn_over_fa2
-
-            patch_xformers_attn_over_fa2()
-            self.cfg.flash_attention = True
-
-        if self.cfg.chunked_cross_entropy:
-            from axolotl.monkeypatch.loss.chunked import patch_chunked_ce_loss_fn
-
-            if self.cfg.chunked_cross_entropy_num_chunks:
-                patch_chunked_ce_loss_fn(self.cfg.chunked_cross_entropy_num_chunks)
-            else:
-                patch_chunked_ce_loss_fn()
-
         if self.cfg.fsdp_config and str(self.cfg.fsdp_config.fsdp_version) == "2":
             from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp_utils
 
             patch_accelerate_fsdp_utils()
 
-        if self.cfg.adapter:
-            from axolotl.monkeypatch.peft.utils import patch_peft_prep_code
-
-            patch_peft_prep_code()
-
         if self.cfg.flex_attention:
             from axolotl.monkeypatch.attention.flex_attn import (
                 patch_flex_make_mask,
@@ -1199,7 +1180,7 @@ class ModelLoader:
                 ],
             )
 
-    def prepare_model(self, qlora_fsdp: bool) -> None:
+    def prepare_model(self, qlora_fsdp) -> None:
         skip_prepare_model_for_kbit_training = False
         if self.cfg.model_config_type == "qwen" and self.cfg.adapter == "lora":
             # Qwen doesn't play nicely with LoRA if this is enabled
@@ -1328,7 +1309,7 @@ class ModelLoader:
 
         # make sure these are fp32 per Ramesh et al. (2021)
         embedding_modules = get_linear_embedding_layers(self.cfg.model_config_type)
-        if self.cfg.fsdp:
+        if not self.cfg.fsdp:
             # FSDP doesn't like mixed Float and BFloat16
             self.convert_embedding_modules_dtype(
                 embedding_modules,

src/axolotl/utils/samplers/multipack.py

@@ -1,13 +1,10 @@
+# pylint: skip-file
 """
-Multipack Batch Sampler - An efficient batch sampler for packing variable-length sequences
+Multipack Batch Sampler
-into fixed-capacity batches to optimize memory usage and training throughput.
 """
-
 import logging
 import math
-from concurrent.futures import ProcessPoolExecutor
+from typing import Any, Iterable, List, Union
-from multiprocessing import cpu_count
-from typing import Iterable, List, Union
 
 import numba
 import numpy as np
@@ -16,39 +13,26 @@ from torch.utils.data import BatchSampler, Sampler, SequentialSampler
 from axolotl.utils.distributed import reduce_and_broadcast
 
 LOG = logging.getLogger(__name__)
+
 LOG.setLevel(logging.INFO)
 
 
 @numba.njit
-def ffd_check(sequence_lengths: np.ndarray, bin_capacity: int, num_bins: int):
+def ffd_check(a: np.ndarray, c: int, n: int):
-    """
+    # First-fit-decreasing bin packing
-    First-fit-decreasing bin packing algorithm check
+    # Check if a[] could fit in n bins with capacity c
+    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
 
-    Checks if sequences with the given lengths could fit in the specified number of bins
+    a = np.sort(a)[::-1]
-
+    bins = np.full((n,), c, dtype=a.dtype)
-    Args:
+    for size in a:
-        sequence_lengths: Array of sequence lengths
-        bin_capacity: Maximum capacity of each bin
-        num_bins: Number of bins available
-
-    Returns:
-        True if all sequences can be packed, False otherwise
-    """
-    # Sort sequence lengths in descending order for optimal packing
-    sequence_lengths = np.sort(sequence_lengths)[::-1]
-    # Initialize all bins with full capacity
-    bins = np.full((num_bins,), bin_capacity, dtype=sequence_lengths.dtype)
-
-    # Try to place each sequence in the first bin it fits
-    for size in sequence_lengths:
         not_found = True
-        for idx in range(num_bins):
+        for idx in range(n):
             if bins[idx] >= size:
                 bins[idx] -= size
                 not_found = False
                 break
 
-        # If no bin could fit this sequence, packing failed
         if not_found:
             return False
 
@@ -56,380 +40,240 @@ def ffd_check(sequence_lengths: np.ndarray, bin_capacity: int, num_bins: int):
 
 
 @numba.njit
-def pack_group(
+def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    sequence_lengths: np.ndarray,
+    # First-fit-decreasing bin packing (with result return)
-    group_offset: int,
-    bin_capacity: int,
-    max_bins: int,
-    bin_size: int,
-    safe_mode: bool = True,
-):
-    """
-    Pack a group of sequences into bins using First-Fit Decreasing algorithm
 
-    Args:
+    indices = np.argsort(a)[::-1]
-        sequence_lengths: Array of sequence lengths
+    a = a[indices]
-        group_offset: Offset to apply to indices when returning results
-        bin_capacity: Maximum capacity of each bin
-        max_bins: Maximum number of bins to use
-        bin_size: Maximum number of sequences per bin
-        safe_mode: If True, use a more conservative packing approach
 
-    Returns:
+    bins: List[Any] = []
-        List of bins, where each bin contains indices of sequences assigned to it
+    bins_result: List[Any] = []
-    """
+    for a_id, size in enumerate(a):
-    # Get sorting indices and sort lengths in descending order
+        add_new = True
-    indices = np.argsort(sequence_lengths)[::-1]
+        for idx in range(len(bins)):
-    sorted_lengths = sequence_lengths[indices]
+            if bins[idx] >= size:
-
+                bins[idx] -= size
-    bins_remaining_space: list = []  # Tracks remaining capacity in each bin
+                bins_result[idx].append(indices[a_id] + start_index)
-    bins_assigned_sequences: list = []  # Tracks sequence indices assigned to each bin
+                add_new = False
-
-    for seq_id, size in enumerate(sorted_lengths):
-        global_idx = indices[seq_id] + group_offset
-
-        # Try to place sequence in existing bins
-        add_new_bin = True
-        for bin_idx, _ in enumerate(bins_remaining_space):
-            if (
-                bins_remaining_space[bin_idx] >= size
-                and len(bins_assigned_sequences[bin_idx]) < bin_size
-            ):
-                bins_remaining_space[bin_idx] -= size
-                bins_assigned_sequences[bin_idx].append(global_idx)
-                add_new_bin = False
                 break
 
-        # Create a new bin if needed and if we haven't reached the limit
+        if add_new:
-        if add_new_bin:
+            bins.append(c - size)
-            if len(bins_remaining_space) >= max_bins and safe_mode:
+            bins_result.append([indices[a_id] + start_index])
-                # In safe mode, skip items that would exceed max_bins
-                continue
-            bins_remaining_space.append(bin_capacity - size)
-            bins_assigned_sequences.append([global_idx])
 
-            # Safety check to avoid infinite bins
+    return bins_result
-            if len(bins_remaining_space) > len(sequence_lengths):
-                break
-
-    return bins_assigned_sequences
-
-
-# Define a standalone function for multiprocessing
-def _process_group(args):
-    group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode = args
-    return pack_group(
-        group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode
-    )
-
-
-def pack_parallel(
-    sequence_lengths: np.ndarray,
-    bin_capacity: int,
-    group_size: int,
-    bin_size: int,
-    num_processes: int | None = None,
-    safe_mode: bool = True,
-):
-    """
-    Pack sequences into bins using parallel processing
-
-    Args:
-        sequence_lengths: Array of sequence lengths
-        bin_capacity: Maximum capacity of each bin as total number of tokens
-        group_size: Number of sequences to process in each group
-        bin_size: Maximum number of bins to use
-        num_processes: Number of parallel processes to use
-        safe_mode: If True, use a more conservative packing approach
-
-    Returns:
-        List of bins, where each bin contains indices of sequences assigned to it
-    """
-    num_items = len(sequence_lengths)
-    if num_processes is None:
-        num_processes = max(1, min(num_items // group_size, cpu_count()))
-
-    # Create tasks for parallel processing
-    tasks = []
-    for i in range(0, num_items, group_size):
-        group_lengths = sequence_lengths[i : i + group_size]
-        max_bins = len(group_lengths)  # Allow as many bins as items in the group
-        tasks.append((group_lengths, i, bin_capacity, max_bins, bin_size, safe_mode))
-
-    # Process groups in parallel
-    all_bins = []
-    with ProcessPoolExecutor(max_workers=num_processes) as executor:
-        for group_bins in executor.map(_process_group, tasks):
-            all_bins.extend(group_bins)
-
-    return all_bins
 
 
 @numba.njit
-def allocate_sequentially(
+def allocate(
-    sequence_lengths: np.ndarray, rank: int, bin_capacity: int, num_ranks: int
+    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
 ):
+    # Dynamic batch allocator, similar to Multifit
+    # https://en.wikipedia.org/wiki/Multifit_algorithm
+    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
+
+    s = 0
+    start_index = 0
+    result = []
+
+    while True:
+        # binary search [l, r)
+        left = 1
+        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
+
+        while right - left > 1:
+            mid = (left + right) // 2
+            if ffd_check(lengths[start_index : start_index + mid], c, n):
+                left = mid
+            else:
+                right = mid
+
+        # use length l
+        batch = ffd_with_result(
+            lengths[start_index : start_index + left], c, start_index
+        )
+        assert len(batch) <= n
+        if len(batch) < n:
+            break
+
+        start_index += left
+        s = lengths_cumsum[start_index - 1]
+
+        # add local rank
+        result.append(batch[rank])
+
+    return result, s, len(result) * c * n
+
+
+@numba.njit
+def allocate_sequentially(lengths: np.ndarray, rank: int, c: int, n: int):
     """
     Sequential allocator that preserves example order
 
     Parameters:
-        sequence_lengths: The lengths of all examples
+    - lengths: The lengths of all examples
-        rank: The current rank (for distributed training)
+    - rank: The current rank (for distributed training)
-        bin_capacity: The capacity of each bin (maximum sequence length)
+    - c: The capacity of each bin (maximum sequence length)
-        num_ranks: Number of ranks (processes/GPUs)
+    - n: Number of ranks
 
     Returns:
-        rank_batches: List of batches for the current rank
+    - result: List of batches for the current rank
-        total_tokens_used: Number of actual example tokens
+    - total_used: Number of actual example tokens
-        total_token_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
+    - total_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
     """
-    rank_batches = []
+    result = []
-    total_tokens_used = 0
+    total_used = 0
 
     # First, do sequential packing into bins
     all_bins = []
-    current_bin = []
+    current_bin = [0 for i in range(0)]  # numba hint
-    remaining_capacity = bin_capacity
+    remaining_capacity = c
 
-    # Process each sequence in order
+    for idx, size in enumerate(lengths):
-    for idx, size in enumerate(sequence_lengths):
         if size <= remaining_capacity:
             # Example fits in current bin
             current_bin.append(idx)
             remaining_capacity -= size
-            total_tokens_used += size
+            total_used += size
         else:
             # Example doesn't fit, start a new bin
             if current_bin:  # Add non-empty bin to all_bins
                 all_bins.append(current_bin)
             current_bin = [idx]
-            remaining_capacity = bin_capacity - size
+            remaining_capacity = c - size
-            total_tokens_used += size
+            total_used += size
 
     # Add the last bin if not empty
     if current_bin:
         all_bins.append(current_bin)
 
-    # Assign bins to ranks - each rank gets every num_ranks-th bin
+    # Assign bins to ranks - each rank gets every n-th bin
-    for bin_idx in range(rank, len(all_bins), num_ranks):
+    for bin_idx in range(rank, len(all_bins), n):
-        rank_batches.append(all_bins[bin_idx])
+        result.append(all_bins[bin_idx])
 
-    return rank_batches, total_tokens_used, len(all_bins) * bin_capacity
+    return result, total_used, len(all_bins) * c
 
 
 class MultipackBatchSampler(BatchSampler):
-    """
+    """Batch sampler class for multipack"""
-    Batch sampler class for efficient packing of variable-length sequences
-
-    This sampler packs sequences into fixed-capacity bins (batches) to maximize
-    GPU memory utilization and training throughput by reducing padding.
-
-    It supports both parallel packing (using FFD algorithm) and
-    sequential packing (preserving original sequence order).
-    """
 
     def __init__(
         self,
         sampler: Union[Sampler[int], Iterable[int]],
-        batch_size: int,  # Number of bins per batch
+        batch_size: int,
-        batch_max_len: int,  # Maximum sequence length (bin capacity)
+        batch_max_len: int,
-        lengths: np.ndarray,  # Sequence lengths
+        lengths: np.ndarray,
-        packing_efficiency_estimate: float = 1.0,  # Initial efficiency estimate
+        packing_efficiency_estimate: float = 1.0,
-        drop_last: bool = False,  # Whether to drop incomplete batches
+        drop_last: bool = False,
-        num_count_samples: int = 16,  # Number of samples to estimate batch count
+        num_count_samples: int = 16,
-        sequential: bool = False,  # Whether to use sequential packing
+        sequential: bool = False,
-        group_size: int = 100_000,  # Size of groups for parallel packing
+        **kwargs,
-        bin_size: int = 200,  # The max number of samples that can be packed in a single bin
-        num_processes: int | None = None,  # Number of processes for parallel packing
-        safe_mode: bool = True,  # Conservative packing to prevent training instability
-        **kwargs,  # pylint: disable=unused-argument
     ):
         super().__init__(sampler, batch_size, drop_last)
         self.batch_size = batch_size
         self.batch_max_len = batch_max_len
-        self.lengths = np.array(lengths, dtype=np.int32)
+        self.lengths: np.ndarray = lengths
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
         self.sequential = sequential
-        self.group_size = group_size
-        self.bin_size = bin_size
-        self.num_processes = num_processes
-        self.safe_mode = safe_mode
 
         assert isinstance(self.lengths, np.ndarray)
 
         self.epoch = 0
 
-        # Efficiency statistics tracking
+        # statistics
-        self.total_tokens_used = 0
+        self.eff_total_used = 0
-        self.total_token_slots = 0
+        self.eff_total_slots = 0
 
-        # The number of times to calculate batches to determine minimum packed dataset length
+        # The number of times to calculate the batches to determine the minimum packed dataset length for the local rank
         self.num_count_samples = num_count_samples
-        # Minimum packed dataset length across all ranks (determined by gather/broadcast)
+        # the minimum packed dataset length across all ranks determined by a gather/broadcast
         self.len_across_ranks = None
 
-        # Cache for batches
-        self._batches = None
-
         if self.sequential and not isinstance(sampler, SequentialSampler):
-            LOG.warning(
+            LOG.warn(
                 "using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?"
             )
 
     def set_epoch(self, epoch: int):
-        """Set the epoch number, used for reproducible shuffling across epochs"""
         self.epoch = epoch
-        self._batches = None  # Invalidate batch cache
 
     def generate_batches(self, set_stats=False):
-        """
+        indices = [idx for idx in self.sampler]
-        Generate packed batches for training
 
-        Args:
-            set_stats: Whether to update efficiency statistics
-
-        Returns:
-            List of batches, where each batch contains multiple bins,
-            and each bin contains multiple sequence indices
-        """
-        if self._batches is not None:
-            return self._batches
-
-        # Get indices from the sampler
-        indices = [  # pylint: disable=unnecessary-comprehension
-            idx for idx in self.sampler
-        ]
-
-        # Get lengths of the selected sequences
         lengths = self.lengths[indices]
+        lengths_cumsum = np.cumsum(lengths)
 
-        # Pack sequences into bins using either sequential or parallel packing
         if self.sequential:
-            bins, total_used, total_slots = allocate_sequentially(
+            batches, total_used, total_slots = allocate_sequentially(
-                lengths,
+                lengths=lengths,
                 rank=0,
-                bin_capacity=self.batch_max_len,
+                c=self.batch_max_len,
-                num_ranks=1,
+                n=1,
             )
         else:
-            # Use parallel packing
+            batches, total_used, total_slots = allocate(
-            all_bins = pack_parallel(
+                lengths=lengths,
-                lengths,
+                lengths_cumsum=lengths_cumsum,
-                bin_capacity=self.batch_max_len,
+                rank=0,
-                group_size=self.group_size,
+                c=self.batch_max_len,
-                bin_size=self.bin_size,
+                n=1,
-                num_processes=self.num_processes,
-                safe_mode=self.safe_mode,
             )
 
-            # Map bin indices back to original indices
-            bins = [
-                [indices[b_idx] for b_idx in bin_indices] for bin_indices in all_bins
-            ]
-
-            # Calculate efficiency statistics
-            total_used = lengths.sum()
-            total_slots = len(all_bins) * self.batch_max_len
-
-        # Group bins into batches (each batch contains batch_size bins)
         batches = [
-            bins[i : i + self.batch_size] for i in range(0, len(bins), self.batch_size)
+            [
+                [indices[b_idx] for b_idx in batch]
+                for batch in batches[i : i + self.batch_size]
+            ]
+            for i in range(0, len(batches), self.batch_size)
         ]
 
-        # Drop last batch if requested and it's incomplete
+        # statistics
-        if self.drop_last and len(batches[-1]) < self.batch_size:
-            batches = batches[:-1]
-            # Adjust total_slots if we dropped a batch
-            if not self.sequential:
-                total_slots -= (self.batch_size - len(batches[-1])) * self.batch_max_len
-
-        # Update statistics if requested
         if set_stats:
-            self.total_tokens_used += total_used
+            self.eff_total_used += total_used
-            self.total_token_slots += total_slots
+            self.eff_total_slots += total_slots
 
-        self._batches = batches
         return batches
 
     def __iter__(self):
-        """
-        Return an iterator over batches
-
-        The batches are truncated to match the minimum number of batches across all ranks
-        to ensure distributed training balance
-        """
         batches = self.generate_batches(set_stats=True)
         if self.len_across_ranks:
-            # Truncate batches to ensure all ranks have the same number of batches
+            # make sure the batches we iterate over is truncated to the same min length across all ranks
             batches = batches[: self.len_across_ranks]
         return iter(batches)
 
+    def num_batches(self):
+        batches = self.generate_batches(set_stats=True)
+        return len(batches)
+
     def efficiency(self):
-        """
+        return self.eff_total_used / self.eff_total_slots
-        Calculate the packing efficiency (ratio of tokens used to total token slots)
-        Higher is better - 1.0 would mean perfect packing with no wasted space
-        """
-        if self.total_token_slots == 0:
-            self.generate_batches(set_stats=True)
-        if self.total_token_slots == 0:
-            return 0.0
-        # Return a Python float instead of potentially a numpy float
-        return float(self.total_tokens_used / self.total_token_slots)
 
     def gather_efficiency(self):
-        """
-        Gather and synchronize packing efficiency estimates across all distributed ranks
-        Returns a conservative efficiency estimate based on the measurements
-        """
-
         def calc_sample_packing_eff_est(estimates: List[float]):
             LOG.debug(f"sample_packing_eff_est across ranks: {repr(estimates)}")
-            # Use 99.7% of max observed efficiency as a safe estimate
+            return math.floor(0.997 * max(estimates))
-            max_eff = max(float(eff) for eff in estimates)
-            return math.floor(0.997 * max_eff)
 
-        # Gather efficiency from all ranks and apply the calculation function
         sample_packing_actual_eff_all = reduce_and_broadcast(
-            lambda: float(self.efficiency()),  # pylint: disable=unnecessary-lambda
+            lambda: self.efficiency(),  # pylint: disable=unnecessary-lambda
             calc_sample_packing_eff_est,
         )
-
-        # Quantize to 0.5% intervals for stability
         sample_packing_eff_est = (
             math.ceil(sample_packing_actual_eff_all * 200.0) / 200.0
         )
         return sample_packing_eff_est
 
     def gather_len_batches(self, num):
-        """
-        Gather and synchronize batch counts across all distributed ranks
-        Returns the minimum number of batches available on any rank
-        """
-
         def calc_min_len(estimates: list[(int, float)]):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(min(estimates))
 
-        # Find minimum batch count across ranks to ensure balance
         min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
         return min_len_batches
 
     def __len__(self):
-        """
+        if not self.len_across_ranks:
-        Return the total number of batches that will be yielded by this sampler
+            len_batches = min(
-
+                [self.num_batches() for _ in range(self.num_count_samples)]
-        This is calculated as the minimum number of batches available on any rank
-        to ensure balanced distributed training
-        """
-        if self._batches is None:
-            self._batches = self.generate_batches(set_stats=True)
-
-        if self.len_across_ranks is None:
-            # Sample multiple times to get stable estimate
-            len_batches = min(  # pylint: disable=consider-using-generator
-                [len(self._batches) for _ in range(self.num_count_samples)]
             )
-            # Gather minimum across all ranks
             self.len_across_ranks = self.gather_len_batches(len_batches)
-
         return self.len_across_ranks

src/axolotl/utils/schemas/config.py

@@ -242,9 +242,6 @@ class AxolotlInputConfig(
     unsloth_rms_norm: bool | None = None
     unsloth_rope: bool | None = None
 
-    chunked_cross_entropy: bool | None = None
-    chunked_cross_entropy_num_chunks: int | None = None
-
     lora_mlp_kernel: bool | None = None
     lora_qkv_kernel: bool | None = None
     lora_o_kernel: bool | None = None
@@ -438,6 +435,16 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_sample_packing_w_xformers(cls, data):
+        if data.get("sample_packing") and data.get("xformers_attention"):
+            raise ValueError(
+                "sample_packing not compatible with xformers_attention. Use flash_attention"
+            )
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     # pylint: disable=duplicate-code
@@ -505,17 +512,10 @@ class AxolotlInputConfig(
     @model_validator(mode="before")
     @classmethod
     def hint_sample_packing_padding(cls, data):
-        if data.get("sample_packing"):
+        if data.get("sample_packing") and not data.get("pad_to_sequence_len"):
-            pad_to_sequence_len = data.get("pad_to_sequence_len")
+            LOG.warning(
-            if pad_to_sequence_len is False:
+                "`pad_to_sequence_len: true` is recommended when using sample_packing"
-                LOG.warning(
+            )
-                    "`pad_to_sequence_len: true` is recommended when using sample_packing"
-                )
-            elif pad_to_sequence_len is None:
-                LOG.info(
-                    "Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing"
-                )
-                data["pad_to_sequence_len"] = True
         return data
 
     @model_validator(mode="before")

tests/patched/test_validation.py

@@ -648,7 +648,7 @@ class TestValidation(BaseValidation):
             DictDefault(
                 {
                     "sample_packing": True,
-                    "pad_to_sequence_len": False,
+                    "pad_to_sequence_len": None,
                     "flash_attention": True,
                 }
             )
@@ -662,26 +662,6 @@ class TestValidation(BaseValidation):
                 for record in self._caplog.records
             )
 
-    def test_packing_autoset(self, minimal_cfg):
-        cfg = (
-            DictDefault(
-                {
-                    "sample_packing": True,
-                    "pad_to_sequence_len": None,
-                    "flash_attention": True,
-                }
-            )
-            | minimal_cfg
-        )
-        with self._caplog.at_level(logging.INFO):
-            cfg = validate_config(cfg)
-            assert any(
-                "Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing"
-                in record.message
-                for record in self._caplog.records
-            )
-            assert cfg.pad_to_sequence_len is True
-
     def test_merge_lora_no_bf16_fail(self, minimal_cfg):
         """
         This is assumed to be run on a CPU machine, so bf16 is not supported.

tests/test_chunked_xentropy.py

@@ -1,40 +0,0 @@
-"""
-test suite for chunked cross entropy
-"""
-
-import pytest
-import torch
-from torch import nn
-
-from axolotl.monkeypatch.loss.chunked import get_causal_lm_loss
-
-
-@pytest.fixture
-def chunked_fixtures():
-    model_dim = 512
-    vocab_size = 1024 * 256
-    seq_len = 2048
-    batch_size = 1
-
-    lm_head = nn.Linear(model_dim, vocab_size)
-    hidden_state = torch.randn(batch_size, seq_len, model_dim)
-    labels = torch.randint(low=0, high=vocab_size, size=(batch_size, seq_len))
-    return lm_head, hidden_state, labels, vocab_size
-
-
-def test_chunked_forward(chunked_fixtures):  # pylint: disable=redefined-outer-name
-    lm_head, hidden_state, labels, vocab_size = chunked_fixtures
-    lm_loss = get_causal_lm_loss()
-
-    logits = lm_head(hidden_state)
-
-    chunked_lm_loss = lm_loss(logits, labels)
-
-    logits_flattened = logits.view(-1, vocab_size)
-    labels_flattened = labels.view(-1)
-
-    loss = nn.functional.cross_entropy(
-        logits_flattened.float(), labels_flattened, reduction="mean"
-    )
-
-    assert torch.allclose(chunked_lm_loss, loss, atol=1e-2, rtol=1e-2)