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Merge branch 'main' of https://github.com/OpenAccess-AI-Collective/axolotl into cj_tokenizer_default_prompt_template

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This commit is contained in:
Chirag Jain
2024-08-27 02:35:58 +05:30
parent 8ee30f5954 17af1d7081
commit 4805f3ca0a
27 changed files with 1552 additions and 36 deletions
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1
.github/workflows/tests-nightly.yml vendored
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@@ -42,7 +42,6 @@ jobs:
sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
sed -i 's#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt
- name: Install dependencies
run: |

3
.mypy.ini
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@@ -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

20
README.md
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@@ -55,6 +55,7 @@ Features:
- [FSDP + QLoRA](#fsdp--qlora)
- [Weights \& Biases Logging](#weights--biases-logging)
- [Special Tokens](#special-tokens)
- [Liger Kernel](#liger-kernel)
- [Inference Playground](#inference-playground)
- [Merge LORA to base](#merge-lora-to-base)
- [Common Errors 🧰](#common-errors-)
@@ -530,6 +531,25 @@ tokens: # these are delimiters
When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
##### Liger Kernel
Liger Kernel: Efficient Triton Kernels for LLM Training
https://github.com/linkedin/Liger-Kernel
Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training.
It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel
composes well and is compatible with both FSDP and Deepspeed.
```yaml
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
```
### Inference Playground
Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.

1
cicd/Dockerfile.jinja
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@@ -28,7 +28,6 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
sed -i 's#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt; \
fi
RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \

76
examples/llama-3/fft-8b-liger-fsdp.yaml Normal file
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@@ -0,0 +1,76 @@
base_model: NousResearch/Meta-Llama-3.1-8B
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
strict: false
chat_template: llama3
datasets:
- path: mlabonne/FineTome-100k
type: chat_template
split: train[:20%]
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./outputs/out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: true
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_backward_prefetch: BACKWARD_PRE
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|eot_id|>

4
examples/llama-3/fft-8b.yaml
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@@ -1,6 +1,4 @@
base_model: NousResearch/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
base_model: NousResearch/Meta-Llama-3.1-8B
load_in_8bit: false
load_in_4bit: false

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)

12
src/axolotl/core/trainer_builder.py
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@@ -4,6 +4,7 @@ Builder for the training args and trainer
"""
import abc
import gc
import importlib
import importlib.util
import logging
@@ -15,11 +16,12 @@ from collections import defaultdict
from dataclasses import dataclass, field
from functools import wraps
from pathlib import Path
from typing import Dict, List, Literal, Optional, Type, Union
from typing import Any, Dict, List, Literal, Optional, Type, Union
import torch
import transformers
from datasets import Dataset
from torch import nn
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
from transformers import (
@@ -997,6 +999,14 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
res[key] = res[key][1:]
return res
def training_step(
self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
) -> torch.Tensor:
loss: torch.Tensor = super().training_step(model, inputs)
gc.collect()
torch.cuda.empty_cache()
return loss
class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
"""

58
src/axolotl/integrations/LICENSE.md Normal file
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@@ -0,0 +1,58 @@
### AXOLOTL COMMUNITY LICENSE AGREEMENT
This Axolotl Community License Agreement (“Agreement”) is entered into by and between Axolotl AI Corp. (“Axolotl”) and
any individual or entity (“Licensee”) who wishes to use the Software (as defined below) in accordance with the terms
and conditions set forth in this Agreement.
1. Definitions
1.1 “Licensee” refers to any individual or entity who has obtained a copy of the Software under this Agreement.
1.2 “Plugin Integration” means independent integration software modules which may or may not be offered by Axolotl,
which may be licensed separately by their respective authors and/or licensors.
1.3 “Software” refers to the specific sub-directory of the Axolotl, Inc. software located at
https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations and its subdirectories which
permits Plugin Integrations to integrate with the Axolotl service.
2. Grant of License
2.1 Axolotl hereby grants Licensee a worldwide, non-exclusive, royalty-free, license to use, copy, modify, merge,
publish, distribute, sublicense, and/or otherwise exploit the Software, subject to the following conditions:
- Licensee must comply with all the terms and conditions of this Agreement.
- Licensee must include the original copyright notice and disclaimer of warranty in all copies or substantial
portions of the Software.
2.2 Licensee may use the Software for any lawful purpose, except as restricted in Section 3.
3. Restrictions
3.1 Licensee shall not use the Software for any activity that constitutes a commercial activity of offering for
free or for sale any services, platform, or equivalent to third parties for the purposes of allowing such
third parties to fine-tune artificial intelligence models.
3.2 Licensee shall not:
- Use the Software for any illegal or unauthorized purpose.
- Reverse engineer, decompile, or disassemble the Software.
- Remove or modify any copyright, trademark, or other proprietary notices contained in the Software.
- Use the Software in a way that could damage, disable, overburden, or impair the functionality of the
Software or interfere with any third-party use of the Software.
3.3 Axolotl reserves the right to restrict certain Plugin Integrations for use with the Software. To the extent Licensee integrates a permitted, applicable Plugin Integration with the Software, Licensee shall comply with any additional terms and conditions imposed by the licensors of such Plugin Integration for use of such Plugin Integrations. Licensee shall contact Axolotl if it has questions about whether its use of the Software falls beyond the scope of this Agreement.
4. Intellectual Property Rights
4.1 Axolotl and its contributors retain all intellectual property rights in and to the Software. Licensee
acknowledges that this Agreement does not transfer any ownership rights or intellectual property rights to
Licensee.
5. Disclaimer of Warranty
5.1 THE SOFTWARE IS PROVIDED “AS IS,” WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF
CONTRACT, TORT, OR OTHERWISE, ARISING FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
6. Termination
6.1 Axolotl may terminate this Agreement at any time if Licensee fails to comply with any of the terms and
conditions set forth herein. Upon termination, Licensee shall cease all use of the Software and destroy any
copies in its possession.
7. Governing Law
7.1 This Agreement shall be governed by and construed in accordance with the laws of the State of California,
without regards to conflicts of laws provisions thereof.
8. Entire Agreement
8.1 This Agreement constitutes the entire agreement between Axolotl and Licensee with respect to the subject matter
hereof and supersedes all prior or contemporaneous understandings or agreements between the parties concerning
the Software, whether written or oral. Axolotl may update the terms of this Agreement from time to time, and
Licensee’s continued use of the Software after any such updates shall constitute acceptance of updated terms
on a go-forward basis. Axolotl will use commercially reasonable efforts to provide Licensee notice of any
material updates. By using the Software, Licensee acknowledges that it has read, understood, and agrees to be
bound by the terms and conditions of this Agreement.
This Agreement was last updated on August 23, 2024.

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 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
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"Contributor" shall mean Licensor and any individual or Legal Entity
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(except as stated in this section) patent license to make, have made,
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where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
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or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
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(a) You must give any other recipients of the Work or
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(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
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attribution notices from the Source form of the Work,
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(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
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pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
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the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
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Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
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negligent acts) or agreed to in writing, shall any Contributor be
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incidental, or consequential damages of any character arising as a
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Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
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has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
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of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
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APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
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104
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# 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

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src/axolotl/integrations/liger/args.py Normal file
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# 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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"""
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,
)

202
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Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
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copyright license to reproduce, prepare Derivative Works of,
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Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
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worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
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
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Unless required by applicable law or agreed to in writing, software
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See the License for the specific language governing permissions and
limitations under the License.

21
src/axolotl/integrations/spectrum/README.md Normal file
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## Spectrum: Targeted Training on Signal to Noise Ratio
by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar
This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).
### Overview
Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.
### Usage
```yaml
plugins:
- axolotl.integrations.spectrum.SpectrumPlugin
spectrum_top_fraction: 0.5
# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
```

102
src/axolotl/integrations/spectrum/__init__.py Normal file
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@@ -0,0 +1,102 @@
# 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.
"""
Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
"""
import json
import logging
import requests
from axolotl.integrations.base import BasePlugin
from .args import SpectrumArgs # pylint: disable=unused-import. # noqa: F401
def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
unfrozen_parameters = {}
for layer_name, info in snr_data.items():
layer_type = info["type"]
if layer_type not in unfrozen_parameters:
unfrozen_parameters[layer_type] = []
unfrozen_parameters[layer_type].append((layer_name, info["snr"]))
top_layers_by_type = {}
for layer_type, layers in unfrozen_parameters.items():
layers_sorted = sorted(layers, key=lambda x: x[1], reverse=True)
num_top_layers = int(len(layers) * top_fraction)
top_layers_by_type[layer_type] = [
layer[0] for layer in layers_sorted[:num_top_layers]
]
unfrozen_parameters = [
"^lm_head.weight$",
"^model.embed_tokens.weight$",
]
for layer_type, layer_names in top_layers_by_type.items():
for layer_name in layer_names:
unfrozen_parameters.append(layer_name)
return unfrozen_parameters
class SpectrumPlugin(BasePlugin):
"""
Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
"""
base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
base_path = "./model_snr_results/"
snr_file_template = "snr_results_{model_name_slug}.json"
def get_input_args(self):
return "axolotl.integrations.spectrum.SpectrumArgs"
def pre_model_load(self, cfg):
if cfg.get("spectrum_model_name"):
model_name = cfg["spectrum_model_name"]
else:
model_name = cfg["base_model"]
top_fraction = cfg.get("spectrum_top_fraction", 50)
model_slug = model_name.replace("/", "-").replace("_", "-")
snr_url = self.base_url + self.snr_file_template.format(
model_name_slug=model_slug
)
snr_path = self.base_path + self.snr_file_template.format(
model_name_slug=model_slug
)
# first check if the files exist locally and read the json
snr_data = None
try:
with open(snr_path, "r", encoding="utf-8") as fin:
snr_data = json.load(fin)
except FileNotFoundError:
pass
except Exception as exc: # pylint: disable=broad-exception-caught
logging.warning(f"Failed to read SNR data from {snr_path}: {exc}")
if not snr_data:
try:
snr_data = requests.get(snr_url, timeout=60).json()
except requests.exceptions.RequestException as exc:
logging.warning(f"Failed to fetch SNR data from {snr_url}: {exc}")
return
# also catch json parsing errors
except json.JSONDecodeError as exc:
logging.warning(f"Failed to parse SNR data from {snr_url}: {exc}")
return
unfrozen_parameters = _generate_unfrozen_params_yaml(
snr_data, top_fraction=top_fraction
)
cfg["unfrozen_parameters"] = unfrozen_parameters

29
src/axolotl/integrations/spectrum/args.py Normal file
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@@ -0,0 +1,29 @@
# 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 Spectrum input arguments.
"""
from typing import Optional
from pydantic import BaseModel
class SpectrumArgs(BaseModel):
"""
Input args for Spectrum.
"""
spectrum_top_fraction: Optional[float] = 0.5
spectrum_model_name: Optional[str] = None

12
src/axolotl/monkeypatch/llama_patch_multipack.py
View File

@@ -9,18 +9,18 @@ from axolotl.monkeypatch.utils import (
def hijack_llama_prepare_4d_mask():
import transformers.modeling_attn_mask_utils
import transformers.models.llama.modeling_llama
from transformers import modeling_attn_mask_utils
from transformers.models.llama import modeling_llama
transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = ( # pylint: disable=protected-access
modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = ( # pylint: disable=protected-access
patched_prepare_4d_causal_attention_mask_for_sdpa
)
transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = ( # pylint: disable=protected-access
modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = ( # pylint: disable=protected-access
patched_prepare_4d_causal_attention_mask_for_sdpa
)
transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask = ( # pylint: disable=protected-access
modeling_llama._prepare_4d_causal_attention_mask = ( # pylint: disable=protected-access
patched_prepare_4d_causal_attention_mask
)
transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask = ( # pylint: disable=protected-access
modeling_attn_mask_utils._prepare_4d_causal_attention_mask = ( # pylint: disable=protected-access
patched_prepare_4d_causal_attention_mask
)

6
src/axolotl/monkeypatch/unsloth_.py
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@@ -16,8 +16,7 @@ from transformers.models.llama.modeling_llama import (
LOG = get_logger("axolotl.monkeypatch.unsloth")
ORIGINAL_CEL_CODE = """ if labels is not None:
# Shift so that tokens < n predict n
ORIGINAL_CEL_CODE = """# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
@@ -29,8 +28,7 @@ ORIGINAL_CEL_CODE = """ if labels is not None:
loss = loss_fct(shift_logits, shift_labels)
"""
PATCHED_CEL_CODE = """ if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = fast_cross_entropy_loss(
logits = shift_logits,

3
src/axolotl/prompt_strategies/chat_template.py
View File

@@ -354,7 +354,8 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
),
"roles": ds_cfg.get("roles"),
"drop_system_message": ds_cfg.get("drop_system_message", False),
"max_length": cfg.sequence_len,
# we need to add one for detecting sequences with exceeding the `sequence_len` limit.
"max_length": cfg.sequence_len + 1,
}
strategy_params = {

9
src/axolotl/prompters.py
View File

@@ -352,9 +352,12 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
"Please help us by creating an Issue to add support for this conversation type."
)
role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
ROLE=from_role
)
if self._conversation.name in ["llama3"]:
role = from_role
else:
role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
ROLE=from_role
)
if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
if (

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
@@ -208,6 +211,15 @@ 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(

26
src/axolotl/utils/models.py
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@@ -311,10 +311,17 @@ 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
@@ -585,19 +592,12 @@ def load_model(
# sample packing uses custom FA2 patch
if cfg.flash_attention:
if not cfg.sample_packing:
if cfg.s2_attention:
pass
# most other models support flash attention, we can define exceptions as they come up
model_kwargs["attn_implementation"] = "flash_attention_2"
model_config._attn_implementation = ( # pylint: disable=protected-access
"flash_attention_2"
)
else:
model_kwargs["attn_implementation"] = "flash_attention_2"
model_config._attn_implementation = ( # pylint: disable=protected-access
"flash_attention_2"
)
if not cfg.sample_packing and cfg.s2_attention:
pass
model_kwargs["attn_implementation"] = "flash_attention_2"
model_config._attn_implementation = ( # pylint: disable=protected-access
"flash_attention_2"
)
elif cfg.sdp_attention:
model_kwargs["attn_implementation"] = "sdpa"
model_config._attn_implementation = "sdpa" # pylint: disable=protected-access

18
src/axolotl/utils/trainer.py
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@@ -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,