Liger Kernel integration (#1861)

* add initial plugin support w Liger kernel patches

* integrate the input args classes

* fix liger plugin and dynamic configuration class

* drop untrainable samples and refactor config plugins integration

* fix incorrect inputs and circular imports

* fix bool comparison

* fix for dropping untraibable tokens

* fix licensing so liger integration is Apache 2.0

* add jamba support

* pylint ignore

.mypy.ini

@@ -11,6 +11,9 @@ ignore_errors = True
 [mypy-axolotl.models.mixtral.*]
 ignore_errors = True
 
+[mypy-axolotl.integrations.liger.models.*]
+ignore_errors = True
+
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

requirements.txt

@@ -33,6 +33,8 @@ gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
+triton>=2.3.0
+liger-kernel
 
 mamba-ssm==1.2.0.post1
 

src/axolotl/cli/__init__.py

@@ -27,6 +27,7 @@ from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.import_utils import _is_package_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils.config import (
@@ -365,6 +366,11 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     cfg.axolotl_config_path = config
 
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        for plugin_name in cfg["plugins"]:
+            plugin_manager.register(plugin_name)
+
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)

src/axolotl/integrations/base.py

@@ -0,0 +1,383 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+Base class for all plugins.
+
+A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.
+Plugins can be used to integrate third-party models, modify the training process, or add new features.
+
+To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
+"""
+import importlib
+import logging
+from typing import List
+
+
+class BasePlugin:
+    """
+    Base class for all plugins. Defines the interface for plugin methods.
+
+    Attributes:
+    None
+
+    Methods:
+    register(cfg): Registers the plugin with the given configuration.
+    pre_model_load(cfg): Performs actions before the model is loaded.
+    post_model_load(cfg, model): Performs actions after the model is loaded.
+    pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
+    post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
+    create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
+    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
+    add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BasePlugin.
+        """
+
+    def register(self, cfg):
+        """
+        Registers the plugin with the given configuration.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def get_input_args(self):
+        """
+        Returns a pydantic model for the plugin's input arguments.
+        """
+
+    def pre_model_load(self, cfg):
+        """
+        Performs actions before the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def post_model_load(self, cfg, model):
+        """
+        Performs actions after the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Performs actions before LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_lora_load(self, cfg, model):
+        """
+        Performs actions after LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Creates and returns an optimizer for training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer.
+        """
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Creates and returns a learning rate scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler.
+        """
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Adds callbacks to the trainer before training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Adds callbacks to the trainer after training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+
+def load_plugin(plugin_name: str) -> BasePlugin:
+    """
+    Loads a plugin based on the given plugin name.
+
+    The plugin name should be in the format "module_name.class_name".
+    This function splits the plugin name into module and class, imports the module,
+    retrieves the class from the module, and creates an instance of the class.
+
+    Parameters:
+    plugin_name (str): The name of the plugin to be loaded. The name should be in the format "module_name.class_name".
+
+    Returns:
+    BasePlugin: An instance of the loaded plugin.
+
+    Raises:
+    ImportError: If the plugin module cannot be imported.
+    """
+    # split the plugin name into module and class
+    module_name, class_name = plugin_name.rsplit(".", 1)
+
+    # import the module
+    module = importlib.import_module(module_name)
+    # instantiate the class
+    plugin_class = getattr(module, class_name)
+    # create an instance of the class
+    plugin = plugin_class()
+
+    return plugin
+
+
+class PluginManager:
+    """
+    The PluginManager class is responsible for loading and managing plugins.
+    It should be a singleton so it can be accessed from anywhere in the codebase.
+
+    Attributes:
+    plugins (List[BasePlugin]): A list of loaded plugins.
+
+    Methods:
+    get_instance(): Static method to get the singleton instance of PluginManager.
+    register(plugin_name: str): Registers a new plugin by its name.
+    pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
+    """
+
+    plugins: List[BasePlugin] = []
+
+    _instance = None
+
+    def __new__(cls):
+        """
+        Creates a new instance of PluginManager if it doesn't exist yet.
+        """
+        if cls._instance is None:
+            cls._instance = super(PluginManager, cls).__new__(cls)
+            cls._instance.plugins: List[BasePlugin] = []
+        return cls._instance
+
+    @staticmethod
+    def get_instance() -> "PluginManager":
+        """
+        Returns the singleton instance of PluginManager.
+        If the instance doesn't exist, it creates a new one.
+        """
+        if PluginManager._instance is None:
+            PluginManager()
+        return PluginManager._instance  # type: ignore
+
+    def register(self, plugin_name: str):
+        """
+        Registers a new plugin by its name.
+
+        Parameters:
+        plugin_name (str): The name of the plugin to be registered.
+
+        Returns:
+        None
+
+        Raises:
+        ImportError: If the plugin module cannot be imported.
+        """
+        try:
+            plugin = load_plugin(plugin_name)
+            self.plugins.append(plugin)
+        except ImportError:
+            logging.error(f"Failed to load plugin: {plugin_name}")
+
+    def get_input_args(self):
+        """
+        Returns a list of Pydantic classes for all registered plugins' input arguments.'
+
+        Returns:
+        list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
+        """
+        input_args = []
+        for plugin in self.plugins:
+            input_args_from_plugin = plugin.get_input_args()
+            if input_args_from_plugin is not None:
+                input_args.append(input_args_from_plugin)
+        return input_args
+
+    def pre_model_load(self, cfg):
+        """
+        Calls the pre_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_model_load(cfg)
+
+    def post_model_load(self, cfg, model):
+        """
+        Calls the post_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_model_load(cfg, model)
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Calls the pre_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_lora_load(cfg, model)
+
+    def post_lora_load(self, cfg, model):
+        """
+        Calls the post_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_lora_load(cfg, model)
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer, or None if none was found.
+        """
+        for plugin in self.plugins:
+            optimizer = plugin.create_optimizer(cfg, trainer)
+            if optimizer is not None:
+                return optimizer
+        return None
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler, or None if none was found.
+        """
+        for plugin in self.plugins:
+            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            if scheduler is not None:
+                return scheduler
+        return None
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Calls the add_callbacks_pre_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
+        return callbacks
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Calls the add_callbacks_post_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
+        return callbacks

src/axolotl/integrations/config.py

@@ -0,0 +1,65 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+module to handle merging the plugins' input arguments with the base configurations.
+
+this was moved here to prevent circular imports
+"""
+
+from typing import Any, Dict, List
+
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
+
+
+def merge_input_args():
+    """
+    Merges input arguments from registered plugins with the base configurations.
+
+    This function retrieves the input arguments from registered plugins using the PluginManager.
+    It then dynamically creates new classes, AxolotlConfigWCapabilities and AxolotlInputConfig,
+    that inherit from the base configurations and include the input arguments from the plugins.
+
+    Returns:
+    tuple: A tuple containing the newly created classes, AxolotlConfigWCapabilities and AxolotlInputConfig.
+    """
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    input_args: List[str] = plugin_manager.get_input_args()
+    plugin_classes = []
+    dynamic_input = ""
+    for plugin_args in input_args:
+        plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
+        dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
+        plugin_classes.append(plugin_cls)
+    if dynamic_input:
+        dynamic_input += f"class AxolotlConfigWCapabilities(AxolotlConfigWCapabilitiesBase, {', '.join(plugin_classes)}):\n    pass\n"
+        dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
+
+        namespace: Dict[Any, Any] = {}
+        exec(  # pylint: disable=exec-used  # nosec B102
+            dynamic_input, globals(), namespace
+        )
+        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
+            "AxolotlInputConfig"
+        ]
+        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
+            "AxolotlConfigWCapabilities"
+        ]
+        return AxolotlConfigWCapabilities, AxolotlInputConfig
+    return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase

src/axolotl/integrations/liger/LICENSE

@@ -0,0 +1,202 @@
+
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+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

src/axolotl/integrations/liger/__init__.py

@@ -0,0 +1,104 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for the Plugin for LIGER integraton with Axolotl.
+
+Liger Kernel is the collection of Triton-native kernels for LLM Training.
+It is designed to be performant, correct, and light-weight.
+"""
+import logging
+
+from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
+from liger_kernel.transformers.model.llama import lce_forward
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LigerPlugin(BasePlugin):
+    """
+    Plugin for LIGER integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.liger.LigerArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.model_config_type == "llama":
+            from transformers.models.llama import modeling_llama
+
+            if cfg.liger_rope:
+                modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_llama.LlamaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_llama.LlamaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss
+            elif cfg.liger_fused_linear_cross_entropy:
+                modeling_llama.LlamaForCausalLM.forward = lce_forward
+
+        elif cfg.model_config_type == "mistral":
+            from transformers.models.mistral import modeling_mistral
+
+            if cfg.liger_rope:
+                modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_mistral.MistralRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mistral.MistralMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                logging.warning(
+                    "Fused linear cross entropy is not supported for Mistral."
+                )
+
+        elif cfg.model_config_type == "gemma":
+            from transformers.models.gemma import modeling_gemma
+
+            if cfg.liger_rope:
+                modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma.GemmaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_gemma.GemmaMLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                logging.warning(
+                    "Fused linear cross entropy is not supported for Gemma."
+                )
+
+        elif cfg.model_config_type == "jamba":
+            from transformers.models.jamba import modeling_jamba
+
+            from .models.jamba import lce_forward as jamba_lce_forward
+
+            if cfg.liger_rope:
+                modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_jamba.JambaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_jamba.JambaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward

src/axolotl/integrations/liger/args.py

@@ -0,0 +1,32 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for handling LIGER input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class LigerArgs(BaseModel):
+    """
+    Input args for LIGER.
+    """
+
+    liger_rope: Optional[bool] = None
+    liger_rms_norm: Optional[bool] = None
+    liger_swiglu: Optional[bool] = None
+    liger_cross_entropy: Optional[bool] = None
+    liger_fused_linear_cross_entropy: Optional[bool] = None

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

@@ -0,0 +1,173 @@
+"""
+Jamba model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.jamba.modeling_jamba import (
+    _CONFIG_FOR_DOC,
+    JAMBA_INPUTS_DOCSTRING,
+    HybridMambaAttentionDynamicCache,
+    load_balancing_loss_func,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[HybridMambaAttentionDynamicCache] = 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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: Optional[Union[int, None]] = None,
+) -> Union[Tuple, 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]`.
+
+        num_logits_to_keep (`int` or `None`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
+            `input_ids`. 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.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+    >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    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
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # 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,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        if num_logits_to_keep is None:
+            logits = self.lm_head(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
+        logits = logits.float()
+
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits if return_dict else outputs[-1],
+            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
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        if output_router_logits:
+            output = (aux_loss,) + output
+        return (loss,) + output if loss is not None else output
+
+    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,
+        router_logits=outputs.router_logits,
+    )

src/axolotl/utils/config/__init__.py

@@ -8,11 +8,14 @@ from typing import Optional
 import torch
 from transformers.utils import is_torch_bf16_gpu_available
 
+from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.config.models.input.v0_4_1 import SUPPORTED_METRICS
 from axolotl.utils.config.models.input.v0_4_1 import (
-    SUPPORTED_METRICS,
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
-    AxolotlConfigWCapabilities,
+)
-    AxolotlInputConfig,
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
 )
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
@@ -207,6 +210,15 @@ def normalize_cfg_datasets(cfg):
 
 
 def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
+    AxolotlConfigWCapabilities = AxolotlConfigWCapabilitiesBase
+    AxolotlInputConfig = AxolotlInputConfigBase
+
+    if cfg.plugins:
+        (
+            AxolotlConfigWCapabilities,  # pylint: disable=invalid-name
+            AxolotlInputConfig,  # pylint: disable=invalid-name
+        ) = merge_input_args()
+
     if capabilities:
         return DictDefault(
             dict(

src/axolotl/utils/models.py

@@ -308,10 +308,17 @@ def load_model(
     """
     Load a model for a given configuration and tokenizer.
     """
+
     base_model = cfg.base_model
     model_type = cfg.type_of_model
     model_config = load_model_config(cfg)
 
+    # load any patches from plugins
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    plugin_manager.pre_model_load(cfg)
+
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
 

src/axolotl/utils/trainer.py

@@ -217,6 +217,24 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             desc="Dropping Long Sequences",
         )
 
+    # drop samples with where the number of elements with labels not equal to -100 is zero
+    def drop_no_trainable_tokens(sample):
+        return np.sum(np.array(sample["labels"]) != -100) > 0
+
+    train_dataset = train_dataset.filter(
+        drop_no_trainable_tokens,
+        num_proc=cfg.dataset_processes,
+        load_from_cache_file=not cfg.is_preprocess,
+        desc="Drop Samples with Zero Trainable Tokens",
+    )
+    if eval_dataset:
+        eval_dataset = eval_dataset.filter(
+            drop_no_trainable_tokens,
+            num_proc=cfg.dataset_processes,
+            load_from_cache_file=not cfg.is_preprocess,
+            desc="Drop Samples with Zero Trainable Tokens",
+        )
+
     if cfg.group_by_length:
         train_dataset = train_dataset.map(
             add_length,