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Liger Kernel integration (#1861)

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* 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
This commit is contained in:
Wing Lian
2024-08-23 12:21:51 -04:00
committed by GitHub
parent e8ff5d5738
commit 1f686c576c
12 changed files with 1010 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
.mypy.ini
View File

@@ -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

2
requirements.txt
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@@ -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

6
src/axolotl/cli/__init__.py
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@@ -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)

383
src/axolotl/integrations/base.py Normal file
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@@ -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

65
src/axolotl/integrations/config.py Normal file
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@@ -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

202
src/axolotl/integrations/liger/LICENSE Normal file
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@@ -0,0 +1,202 @@
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104
src/axolotl/integrations/liger/__init__.py Normal file
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@@ -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

32
src/axolotl/integrations/liger/args.py Normal file
View File

@@ -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

173
src/axolotl/integrations/liger/models/jamba.py Normal file
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@@ -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,
)

18
src/axolotl/utils/config/__init__.py
View File

@@ -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,
AxolotlInputConfig,
AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
)
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(

7
src/axolotl/utils/models.py
View File

@@ -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

18
src/axolotl/utils/trainer.py
View File

@@ -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,