Compare commits
15 Commits
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c06b8f0243 |
6
.github/workflows/main.yml
vendored
6
.github/workflows/main.yml
vendored
@@ -10,7 +10,7 @@ on:
|
||||
|
||||
jobs:
|
||||
build-axolotl:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
@@ -77,7 +77,7 @@ jobs:
|
||||
|
||||
build-axolotl-cloud:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
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||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
@@ -140,7 +140,7 @@ jobs:
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||||
|
||||
build-axolotl-cloud-no-tmux:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
|
||||
2
.github/workflows/multi-gpu-e2e.yml
vendored
2
.github/workflows/multi-gpu-e2e.yml
vendored
@@ -15,7 +15,7 @@ concurrency:
|
||||
|
||||
jobs:
|
||||
test-axolotl-multigpu:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
|
||||
4
.github/workflows/nightlies.yml
vendored
4
.github/workflows/nightlies.yml
vendored
@@ -7,7 +7,7 @@ on:
|
||||
|
||||
jobs:
|
||||
build-axolotl:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
@@ -71,7 +71,7 @@ jobs:
|
||||
|
||||
build-axolotl-cloud:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
|
||||
6
.github/workflows/pypi.yml
vendored
6
.github/workflows/pypi.yml
vendored
@@ -10,6 +10,8 @@ jobs:
|
||||
setup_release:
|
||||
name: Create Release
|
||||
runs-on: ubuntu-latest
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||||
permissions:
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||||
contents: write
|
||||
steps:
|
||||
- name: Get the tag version
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||||
id: extract_branch
|
||||
@@ -56,9 +58,9 @@ jobs:
|
||||
run: |
|
||||
sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
|
||||
|
||||
- name: Build a binary wheel
|
||||
- name: Build a source dist
|
||||
run: |
|
||||
python setup.py sdist bdist_wheel
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||||
python setup.py sdist
|
||||
|
||||
- name: Publish package distributions to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
|
||||
50
.github/workflows/tests.yml
vendored
50
.github/workflows/tests.yml
vendored
@@ -71,18 +71,62 @@ jobs:
|
||||
|
||||
- name: Run tests
|
||||
run: |
|
||||
pytest --ignore=tests/e2e/ tests/
|
||||
pytest -n8 --ignore=tests/e2e/ tests/
|
||||
|
||||
- name: cleanup pip cache
|
||||
run: |
|
||||
find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
|
||||
|
||||
pytest-sdist:
|
||||
name: PyTest from Source Dist
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python_version: ["3.11"]
|
||||
pytorch_version: ["2.4.1", "2.5.1"]
|
||||
timeout-minutes: 20
|
||||
|
||||
steps:
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python_version }}
|
||||
cache: 'pip' # caching pip dependencies
|
||||
|
||||
- name: upgrade pip
|
||||
run: |
|
||||
pip3 install --upgrade pip
|
||||
pip3 install --upgrade packaging setuptools wheel
|
||||
|
||||
- name: Install PyTorch
|
||||
run: |
|
||||
pip3 install torch==${{ matrix.pytorch_version }}
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip3 show torch
|
||||
python3 setup.py sdist
|
||||
pip3 install dist/axolotl*.tar.gz
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
|
||||
- name: Run tests
|
||||
run: |
|
||||
pytest -n8 --ignore=tests/e2e/ tests/
|
||||
|
||||
- name: cleanup pip cache
|
||||
run: |
|
||||
find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
|
||||
|
||||
docker-e2e-tests-1st:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 90
|
||||
needs: [pre-commit, pytest]
|
||||
needs: [pre-commit, pytest, pytest-sdist]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
|
||||
4
MANIFEST.in
Normal file
4
MANIFEST.in
Normal file
@@ -0,0 +1,4 @@
|
||||
include requirements.txt
|
||||
include README.md
|
||||
include LICENSE
|
||||
recursive-include axolotl *.py
|
||||
23
README.md
23
README.md
@@ -1,8 +1,21 @@
|
||||
# Axolotl
|
||||
<p align="center">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="image/axolotl_logo_digital_white.svg">
|
||||
<source media="(prefers-color-scheme: light)" srcset="image/axolotl_logo_digital_black.svg">
|
||||
<img alt="Axolotl" src="image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
|
||||
</picture>
|
||||
</p>
|
||||
|
||||
|
||||
|
||||
|
||||
<p align="center">
|
||||
<img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
|
||||
<img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
|
||||
<a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
|
||||
<img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
|
||||
</p>
|
||||
<p align="center">
|
||||
<img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
|
||||
<img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
|
||||
</p>
|
||||
|
||||
Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
|
||||
|
||||
@@ -75,7 +88,7 @@ Features:
|
||||
<td>
|
||||
|
||||
<div align="center">
|
||||
<img src="image/axolotl.png" alt="axolotl" width="160">
|
||||
<img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
|
||||
<div>
|
||||
<p>
|
||||
<b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
pytest -n4 --ignore=tests/e2e/ /workspace/axolotl/tests/
|
||||
pytest -n8 --ignore=tests/e2e/ /workspace/axolotl/tests/
|
||||
pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
|
||||
pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
|
||||
|
||||
BIN
image/axolotl-badge-web-legacy.png
Normal file
BIN
image/axolotl-badge-web-legacy.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 11 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 11 KiB After Width: | Height: | Size: 24 KiB |
19
image/axolotl_logo_digital_black.svg
Normal file
19
image/axolotl_logo_digital_black.svg
Normal file
@@ -0,0 +1,19 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 1113 283.5">
|
||||
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||||
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|
||||
|
After Width: | Height: | Size: 3.2 KiB |
11
image/axolotl_logo_digital_white.svg
Normal file
11
image/axolotl_logo_digital_white.svg
Normal file
@@ -0,0 +1,11 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 1113 283.5">
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--extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
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||||
addict
|
||||
fire
|
||||
@@ -31,7 +31,7 @@ art
|
||||
gradio==3.50.2
|
||||
tensorboard
|
||||
python-dotenv==1.0.1
|
||||
autoawq>=0.2.5
|
||||
autoawq==0.2.7.post2
|
||||
triton>=2.3.0
|
||||
liger-kernel==0.4.1
|
||||
|
||||
|
||||
6
setup.py
6
setup.py
@@ -96,11 +96,11 @@ install_requires, dependency_links = parse_requirements()
|
||||
|
||||
setup(
|
||||
name="axolotl",
|
||||
version="0.5.0",
|
||||
version="0.5.1",
|
||||
description="LLM Trainer",
|
||||
long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
|
||||
package_dir={"": "src"},
|
||||
packages=find_packages(),
|
||||
packages=find_packages("src"),
|
||||
install_requires=install_requires,
|
||||
dependency_links=dependency_links,
|
||||
extras_require={
|
||||
@@ -108,7 +108,7 @@ setup(
|
||||
"flash-attn==2.7.0.post2",
|
||||
],
|
||||
"deepspeed": [
|
||||
"deepspeed==0.14.4",
|
||||
"deepspeed==0.15.4",
|
||||
"deepspeed-kernels",
|
||||
],
|
||||
"mamba-ssm": [
|
||||
|
||||
@@ -10,7 +10,6 @@ MOE_ARCH_BLOCK = {
|
||||
"JetMoeMoE",
|
||||
],
|
||||
"mixtral": "MixtralSparseMoeBlock",
|
||||
"phimoe": "PhiMoESparseMoeBlock",
|
||||
"qwen2_moe": "Qwen2MoeSparseMoeBlock",
|
||||
"deepseek_v2": "DeepseekV2MoE",
|
||||
}
|
||||
|
||||
0
src/axolotl/monkeypatch/__init__.py
Normal file
0
src/axolotl/monkeypatch/__init__.py
Normal file
0
src/axolotl/monkeypatch/attention/__init__.py
Normal file
0
src/axolotl/monkeypatch/attention/__init__.py
Normal file
@@ -20,7 +20,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
|
||||
"falcon",
|
||||
"phi",
|
||||
"phi3",
|
||||
"phimoe",
|
||||
"gemma",
|
||||
"gemma2",
|
||||
"gemmoe",
|
||||
|
||||
@@ -1,83 +0,0 @@
|
||||
"""
|
||||
fix for FSDP gradient accumulation
|
||||
see https://github.com/huggingface/transformers/pull/34645
|
||||
"""
|
||||
import inspect
|
||||
|
||||
from accelerate.logging import get_logger
|
||||
from transformers.trainer import Trainer
|
||||
|
||||
from axolotl.monkeypatch.unsloth_ import detab_code
|
||||
|
||||
LOG = get_logger("axolotl.monkeypatch.trainer_fsdp_grad_accumulation")
|
||||
|
||||
ORIGINAL_CONTEXT_CODE = """
|
||||
context = (
|
||||
functools.partial(self.accelerator.no_sync, model=model)
|
||||
if i == len(batch_samples) - 1
|
||||
else contextlib.nullcontext
|
||||
)
|
||||
"""
|
||||
|
||||
PATCHED_CONTEXT_CODE = """
|
||||
context = (
|
||||
functools.partial(self.accelerator.no_sync, model=model)
|
||||
if i != len(batch_samples) - 1
|
||||
else contextlib.nullcontext
|
||||
)
|
||||
"""
|
||||
|
||||
|
||||
def get_training_loop_code() -> str:
|
||||
training_loop = inspect.getsource(
|
||||
Trainer._inner_training_loop # pylint: disable=protected-access
|
||||
)
|
||||
return training_loop
|
||||
|
||||
|
||||
def check_training_loop_is_patchable() -> bool:
|
||||
train_loop = get_training_loop_code()
|
||||
train_loop, _ = detab_code(train_loop)
|
||||
return ORIGINAL_CONTEXT_CODE in train_loop
|
||||
|
||||
|
||||
def patch_training_loop_for_fsdp_grad_accum():
|
||||
"""
|
||||
monkeypatch for fixing the training loop for FSDP gradient accumulation
|
||||
"""
|
||||
|
||||
train_loop = get_training_loop_code()
|
||||
Trainer._original_inner_training_loop = ( # pylint: disable=protected-access
|
||||
train_loop
|
||||
)
|
||||
train_loop, _ = detab_code(train_loop)
|
||||
assert (
|
||||
ORIGINAL_CONTEXT_CODE in train_loop
|
||||
), "Original _inner_training_loop code not found"
|
||||
|
||||
train_loop = train_loop.replace(ORIGINAL_CONTEXT_CODE, PATCHED_CONTEXT_CODE)
|
||||
train_loop = train_loop.replace(
|
||||
"def _inner_training_loop(",
|
||||
"def _fixed_inner_training_loop(",
|
||||
1,
|
||||
)
|
||||
|
||||
# load imports necessary
|
||||
import transformers.trainer
|
||||
|
||||
items_to_import = []
|
||||
for item in dir(transformers.trainer):
|
||||
if item in train_loop:
|
||||
items_to_import.append(item)
|
||||
|
||||
exec( # pylint: disable=exec-used # nosec B102
|
||||
"from transformers.trainer import ("
|
||||
+ ", ".join(x for x in items_to_import)
|
||||
+ ")",
|
||||
globals(),
|
||||
)
|
||||
exec(train_loop, globals()) # pylint: disable=exec-used # nosec B102
|
||||
LOG.info("patching _inner_training_loop", main_process_only=True)
|
||||
Trainer._inner_training_loop = ( # pylint: disable=protected-access
|
||||
_fixed_inner_training_loop # pylint: disable=undefined-variable # noqa: F821
|
||||
)
|
||||
@@ -250,8 +250,10 @@ class KTODataset(BaseModel):
|
||||
class LoftQConfig(BaseModel):
|
||||
"""LoftQ configuration subset"""
|
||||
|
||||
loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
|
||||
# loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
|
||||
loftq_bits: int = Field(
|
||||
default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
|
||||
)
|
||||
# loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
|
||||
|
||||
|
||||
class PeftConfig(BaseModel):
|
||||
@@ -294,8 +296,8 @@ class LoraConfig(BaseModel):
|
||||
|
||||
qlora_sharded_model_loading: Optional[bool] = Field(
|
||||
default=False,
|
||||
metadata={
|
||||
"help": "load qlora model in sharded format for FSDP using answer.ai technique."
|
||||
json_schema_extra={
|
||||
"description": "load qlora model in sharded format for FSDP using answer.ai technique."
|
||||
},
|
||||
)
|
||||
lora_on_cpu: Optional[bool] = None
|
||||
@@ -304,13 +306,15 @@ class LoraConfig(BaseModel):
|
||||
|
||||
loraplus_lr_ratio: Optional[float] = Field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
|
||||
json_schema_extra={
|
||||
"description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
|
||||
},
|
||||
)
|
||||
loraplus_lr_embedding: Optional[float] = Field(
|
||||
default=1e-6,
|
||||
metadata={"help": "loraplus learning rate for lora embedding layers."},
|
||||
json_schema_extra={
|
||||
"description": "loraplus learning rate for lora embedding layers."
|
||||
},
|
||||
)
|
||||
|
||||
merge_lora: Optional[bool] = None
|
||||
@@ -380,10 +384,10 @@ class ModelInputConfig(BaseModel):
|
||||
tokenizer_use_fast: Optional[bool] = None
|
||||
tokenizer_legacy: Optional[bool] = None
|
||||
tokenizer_type: Optional[str] = Field(
|
||||
default=None, metadata={"help": "transformers tokenizer class"}
|
||||
default=None, json_schema_extra={"description": "transformers tokenizer class"}
|
||||
)
|
||||
processor_type: Optional[str] = Field(
|
||||
default=None, metadata={"help": "transformers processor class"}
|
||||
default=None, json_schema_extra={"description": "transformers processor class"}
|
||||
)
|
||||
trust_remote_code: Optional[bool] = None
|
||||
|
||||
@@ -405,18 +409,18 @@ class HyperparametersConfig(BaseModel):
|
||||
gradient_accumulation_steps: Optional[int] = Field(default=1)
|
||||
micro_batch_size: Optional[int] = Field(
|
||||
default=1,
|
||||
metadata={"help": "per gpu micro batch size for training"},
|
||||
json_schema_extra={"description": "per gpu micro batch size for training"},
|
||||
)
|
||||
batch_size: Optional[int] = Field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "Total batch size, we do not recommended setting this manually"
|
||||
json_schema_extra={
|
||||
"description": "Total batch size, we do not recommended setting this manually"
|
||||
},
|
||||
)
|
||||
eval_batch_size: Optional[int] = Field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
|
||||
json_schema_extra={
|
||||
"description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
|
||||
},
|
||||
)
|
||||
|
||||
@@ -441,12 +445,13 @@ class HyperparametersConfig(BaseModel):
|
||||
]
|
||||
] = OptimizerNames.ADAMW_HF.value
|
||||
optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
|
||||
default=None, metadata={"help": "Optional arguments to supply to optimizer."}
|
||||
default=None,
|
||||
json_schema_extra={"description": "Optional arguments to supply to optimizer."},
|
||||
)
|
||||
optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The target modules to optimize, i.e. the module names that you would like to train."
|
||||
json_schema_extra={
|
||||
"description": "The target modules to optimize, i.e. the module names that you would like to train."
|
||||
},
|
||||
)
|
||||
torchdistx_path: Optional[str] = None
|
||||
@@ -506,15 +511,15 @@ class LISAConfig(BaseModel):
|
||||
|
||||
lisa_n_layers: Optional[int] = Field(
|
||||
default=None,
|
||||
metadata={"help": "the number of activate layers in LISA"},
|
||||
json_schema_extra={"description": "the number of activate layers in LISA"},
|
||||
)
|
||||
lisa_step_interval: Optional[int] = Field(
|
||||
default=None,
|
||||
metadata={"help": "how often to switch layers in LISA"},
|
||||
json_schema_extra={"description": "how often to switch layers in LISA"},
|
||||
)
|
||||
lisa_layers_attribute: Optional[str] = Field(
|
||||
default="model.layers",
|
||||
metadata={"help": "path under the model to access the layers"},
|
||||
json_schema_extra={"description": "path under the model to access the layers"},
|
||||
)
|
||||
|
||||
|
||||
@@ -613,7 +618,8 @@ class AxolotlInputConfig(
|
||||
pretraining_dataset: Optional[ # type: ignore
|
||||
conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
|
||||
] = Field(
|
||||
default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
|
||||
default=None,
|
||||
json_schema_extra={"description": "streaming dataset to use for pretraining"},
|
||||
)
|
||||
dataset_processes: Optional[int] = Field(default=os.cpu_count())
|
||||
dataset_keep_in_memory: Optional[bool] = None
|
||||
@@ -673,7 +679,8 @@ class AxolotlInputConfig(
|
||||
sequence_len: int = Field(default=512)
|
||||
min_sample_len: Optional[int] = None
|
||||
max_prompt_len: int = Field(
|
||||
default=512, metadata={"help": "maximum prompt length for RL training"}
|
||||
default=512,
|
||||
json_schema_extra={"description": "maximum prompt length for RL training"},
|
||||
)
|
||||
sample_packing: Optional[bool] = None
|
||||
sample_packing_group_size: Optional[int] = 100_000
|
||||
@@ -692,8 +699,8 @@ class AxolotlInputConfig(
|
||||
pretrain_multipack_buffer_size: Optional[int] = 10_000
|
||||
pretrain_multipack_attn: Optional[bool] = Field(
|
||||
default=True,
|
||||
metadata={
|
||||
"help": "whether to prevent cross attention for packed sequences during pretraining",
|
||||
json_schema_extra={
|
||||
"description": "whether to prevent cross attention for packed sequences during pretraining",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@@ -64,15 +64,57 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer):
|
||||
tokenizer = load_tokenizer(cfg)
|
||||
ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
|
||||
|
||||
if isinstance(data_set, DatasetDict):
|
||||
data_set = data_set["train"]
|
||||
|
||||
data_set = data_set.map(
|
||||
ds_transform_fn,
|
||||
desc="Mapping RL Dataset",
|
||||
)
|
||||
if isinstance(data_set, DatasetDict):
|
||||
data_set = data_set["train"]
|
||||
|
||||
return data_set
|
||||
|
||||
|
||||
def drop_long_rl_seq(
|
||||
sample, rl, tokenizer, sequence_len # pylint: disable=invalid-name
|
||||
):
|
||||
if rl in ("dpo", "ipo", "orpo", "simpo"):
|
||||
if not (
|
||||
sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
|
||||
):
|
||||
raise ValueError(
|
||||
"Prompt, chosen and rejected keys are required for DPO/ORPO datasets"
|
||||
)
|
||||
|
||||
prompt = sample["prompt"]
|
||||
chosen = sample["chosen"]
|
||||
rejected = sample["rejected"]
|
||||
|
||||
len_prompt = len(tokenizer(prompt, add_special_tokens=False)["input_ids"])
|
||||
len_chosen = len(tokenizer(chosen, add_special_tokens=False)["input_ids"])
|
||||
len_rejected = len(tokenizer(rejected, add_special_tokens=False)["input_ids"])
|
||||
|
||||
return (len_prompt + len_chosen) <= sequence_len and (
|
||||
len_prompt + len_rejected
|
||||
) <= sequence_len
|
||||
|
||||
if rl == "kto":
|
||||
if not (sample.get("prompt") and sample.get("completion")):
|
||||
raise ValueError("Prompt and completion keys are required for KTO datasets")
|
||||
|
||||
prompt = sample["prompt"]
|
||||
completion = sample["completion"]
|
||||
|
||||
len_prompt = len(tokenizer(prompt, add_special_tokens=False)["input_ids"])
|
||||
len_completion = len(
|
||||
tokenizer(completion, add_special_tokens=False)["input_ids"]
|
||||
)
|
||||
|
||||
return (len_prompt + len_completion) <= sequence_len
|
||||
|
||||
raise ValueError("Unknown RL type")
|
||||
|
||||
|
||||
def load_prepare_dpo_datasets(cfg):
|
||||
def load_split(dataset_cfgs, _cfg):
|
||||
split_datasets: List[Any] = []
|
||||
@@ -94,7 +136,7 @@ def load_prepare_dpo_datasets(cfg):
|
||||
)
|
||||
split_datasets.insert(i, ds)
|
||||
|
||||
tokenizer = None
|
||||
tokenizer = load_tokenizer(cfg)
|
||||
|
||||
for i, data_set in enumerate(split_datasets):
|
||||
_type = dataset_cfgs[i]["type"]
|
||||
@@ -121,7 +163,28 @@ def load_prepare_dpo_datasets(cfg):
|
||||
# "prompt", "chosen" and "rejected" already preprocessed
|
||||
split_datasets[i] = data_set
|
||||
|
||||
return concatenate_datasets(split_datasets)
|
||||
drop_long = partial(
|
||||
drop_long_rl_seq,
|
||||
rl=_cfg.rl,
|
||||
tokenizer=tokenizer,
|
||||
sequence_len=cfg.sequence_len,
|
||||
)
|
||||
|
||||
prior_len = len(split_datasets[i])
|
||||
split_datasets[i] = split_datasets[i].filter(
|
||||
drop_long,
|
||||
num_proc=cfg.dataset_processes,
|
||||
load_from_cache_file=not cfg.is_preprocess,
|
||||
desc="Dropping Long Sequences",
|
||||
)
|
||||
dropped = prior_len - len(split_datasets[i])
|
||||
if dropped:
|
||||
LOG.warning(f"Dropped {dropped} long samples from dataset index {i}")
|
||||
|
||||
combined_datasets = concatenate_datasets(split_datasets)
|
||||
combined_datasets = combined_datasets.shuffle(seed=cfg.seed)
|
||||
|
||||
return combined_datasets
|
||||
|
||||
with zero_first(is_main_process()):
|
||||
train_is_preprocessed = False
|
||||
|
||||
@@ -66,28 +66,47 @@ def process_tokens_for_rl_debug(tokens, color, tokenizer, text_only):
|
||||
|
||||
|
||||
def check_rl_example_labels(example, tokenizer, text_only=False):
|
||||
field_prompt, field_chosen, field_rejected = "prompt", "chosen", "rejected"
|
||||
field_prompt, field_chosen, field_rejected, field_completion = (
|
||||
"prompt",
|
||||
"chosen",
|
||||
"rejected",
|
||||
"completion",
|
||||
)
|
||||
|
||||
input_tokens = example[field_prompt]
|
||||
labels_chosen, labels_rejected = example[field_chosen], example[field_rejected]
|
||||
|
||||
labels_chosen = example.get(field_chosen)
|
||||
labels_rejected = example.get(field_rejected)
|
||||
labels_completion = example.get(field_completion)
|
||||
|
||||
# Create a delimiter based on text_only flag
|
||||
delimiter = "" if text_only else " "
|
||||
|
||||
# Process and color each type of token
|
||||
colored_tokens = process_tokens_for_rl_debug(
|
||||
input_tokens, "yellow", tokenizer, text_only
|
||||
)
|
||||
colored_chosens = process_tokens_for_rl_debug(
|
||||
labels_chosen, "green", tokenizer, text_only
|
||||
)
|
||||
colored_rejecteds = process_tokens_for_rl_debug(
|
||||
labels_rejected, "red", tokenizer, text_only
|
||||
)
|
||||
|
||||
# Create a delimiter based on text_only flag
|
||||
delimiter = "" if text_only else " "
|
||||
# Process tokens
|
||||
if labels_completion is None:
|
||||
colored_chosens = process_tokens_for_rl_debug(
|
||||
labels_chosen, "green", tokenizer, text_only
|
||||
)
|
||||
colored_rejecteds = process_tokens_for_rl_debug(
|
||||
labels_rejected, "red", tokenizer, text_only
|
||||
)
|
||||
else:
|
||||
colored_completion = process_tokens_for_rl_debug(
|
||||
labels_completion, "green", tokenizer, text_only
|
||||
)
|
||||
|
||||
# Logging information
|
||||
LOG.info(f"INPUT PROMPT: {delimiter.join(colored_tokens)}\n\n")
|
||||
LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
|
||||
LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
|
||||
|
||||
if labels_completion is None:
|
||||
LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
|
||||
LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
|
||||
else:
|
||||
LOG.info(f"COMPLETION RESPONSE: {delimiter.join(colored_completion)}\n\n\n")
|
||||
|
||||
return delimiter.join(colored_tokens)
|
||||
|
||||
@@ -16,9 +16,6 @@ from torch.utils.data import DataLoader, RandomSampler
|
||||
from transformers.utils import is_torch_bf16_gpu_available
|
||||
|
||||
from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
|
||||
from axolotl.monkeypatch.trainer_fsdp_grad_accum import (
|
||||
patch_training_loop_for_fsdp_grad_accum,
|
||||
)
|
||||
from axolotl.utils.distributed import reduce_and_broadcast
|
||||
from axolotl.utils.environment import check_cuda_p2p_ib_support
|
||||
from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
|
||||
@@ -206,37 +203,59 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
|
||||
if eval_dataset and "token_type_ids" in eval_dataset.column_names:
|
||||
eval_dataset = eval_dataset.remove_columns("token_type_ids")
|
||||
|
||||
prior_len = len(train_dataset)
|
||||
train_dataset = train_dataset.filter(
|
||||
drop_long,
|
||||
num_proc=cfg.dataset_processes,
|
||||
load_from_cache_file=not cfg.is_preprocess,
|
||||
desc="Dropping Long Sequences",
|
||||
)
|
||||
dropped = prior_len - len(train_dataset)
|
||||
if dropped:
|
||||
LOG.warning(f"Dropped {dropped} long samples from train dataset")
|
||||
|
||||
if eval_dataset:
|
||||
prior_len = len(eval_dataset)
|
||||
eval_dataset = eval_dataset.filter(
|
||||
drop_long,
|
||||
num_proc=cfg.dataset_processes,
|
||||
load_from_cache_file=not cfg.is_preprocess,
|
||||
desc="Dropping Long Sequences",
|
||||
)
|
||||
dropped = prior_len - len(eval_dataset)
|
||||
if dropped:
|
||||
LOG.warning(f"Dropped {dropped} long samples from eval dataset")
|
||||
|
||||
# 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
|
||||
|
||||
prior_len = len(train_dataset)
|
||||
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",
|
||||
)
|
||||
dropped = prior_len - len(train_dataset)
|
||||
if dropped:
|
||||
LOG.warning(
|
||||
f"Dropped {dropped} samples with no trainable tokens from train dataset"
|
||||
)
|
||||
|
||||
if eval_dataset:
|
||||
prior_len = len(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",
|
||||
)
|
||||
dropped = prior_len - len(eval_dataset)
|
||||
if dropped:
|
||||
LOG.warning(
|
||||
f"Dropped {dropped} samples with no trainable tokens from eval dataset"
|
||||
)
|
||||
|
||||
if cfg.group_by_length:
|
||||
train_dataset = train_dataset.map(
|
||||
@@ -496,12 +515,7 @@ def prepare_opinionated_env(cfg):
|
||||
def setup_trainer(
|
||||
cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
|
||||
):
|
||||
if cfg.fsdp:
|
||||
try:
|
||||
patch_training_loop_for_fsdp_grad_accum()
|
||||
except AssertionError:
|
||||
pass
|
||||
if cfg.rl in ["dpo", "ipo", "orpo", "kto", "simpo"]:
|
||||
if cfg.rl in ("dpo", "ipo", "orpo", "kto", "simpo"):
|
||||
trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
|
||||
trainer_builder.model_ref = model[1]
|
||||
trainer_builder.peft_config = model[2]
|
||||
|
||||
@@ -5,6 +5,25 @@ import shutil
|
||||
import tempfile
|
||||
|
||||
import pytest
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def download_smollm2_135m_model():
|
||||
# download the model
|
||||
snapshot_download("HuggingFaceTB/SmolLM2-135M")
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def download_tatsu_lab_alpaca_dataset():
|
||||
# download the model
|
||||
snapshot_download("tatsu-lab/alpaca", repo_type="dataset")
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def download_mhenrichsen_alpaca_2k_dataset():
|
||||
# download the model
|
||||
snapshot_download("mhenrichsen/alpaca_2k_test", repo_type="dataset")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
||||
@@ -25,7 +25,7 @@ AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def download_model():
|
||||
# download the model
|
||||
snapshot_download("TinyLlama/TinyLlama_v1.1")
|
||||
snapshot_download("HuggingFaceTB/SmolLM2-135M")
|
||||
|
||||
|
||||
class TestMultiGPULlama:
|
||||
@@ -37,7 +37,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 2048,
|
||||
"adapter": "lora",
|
||||
"lora_r": 8,
|
||||
@@ -93,7 +93,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 2048,
|
||||
"sample_packing": True,
|
||||
"eval_sample_packing": False,
|
||||
@@ -149,8 +149,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "TinyLlama/TinyLlama_v1.1",
|
||||
"tokenizer_type": "LlamaTokenizer",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 2048,
|
||||
"sample_packing": False,
|
||||
"eval_sample_packing": False,
|
||||
@@ -163,12 +162,10 @@ class TestMultiGPULlama:
|
||||
"lora_target_linear": True,
|
||||
"val_set_size": 0.05,
|
||||
"special_tokens": {
|
||||
"unk_token": "<unk>",
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"pad_token": "<|endoftext|>",
|
||||
},
|
||||
"rl": "dpo",
|
||||
"chat_template": "llama3",
|
||||
"chat_template": "chatml",
|
||||
"datasets": [
|
||||
{
|
||||
"path": "fozziethebeat/alpaca_messages_2k_dpo_test",
|
||||
@@ -221,7 +218,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 2048,
|
||||
"sample_packing": False,
|
||||
"eval_sample_packing": False,
|
||||
@@ -294,7 +291,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 2048,
|
||||
"val_set_size": 0.01,
|
||||
"special_tokens": {
|
||||
@@ -359,7 +356,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sample_packing": True,
|
||||
"pad_to_sequence_len": True,
|
||||
"sequence_len": 2048,
|
||||
@@ -422,8 +419,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
|
||||
"tokenizer_type": "AutoTokenizer",
|
||||
"base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
|
||||
"adapter": "qlora",
|
||||
"mean_resizing_embeddings": True,
|
||||
"load_in_4bit": True,
|
||||
@@ -431,17 +427,17 @@ class TestMultiGPULlama:
|
||||
"lora_alpha": 16,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_target_linear": True,
|
||||
"lora_modules_to_save": [
|
||||
"embed_tokens",
|
||||
"lm_head",
|
||||
],
|
||||
# "lora_modules_to_save": [
|
||||
# "embed_tokens",
|
||||
# "lm_head",
|
||||
# ],
|
||||
"sample_packing": True,
|
||||
"eval_sample_packing": False,
|
||||
"pad_to_sequence_len": True,
|
||||
"sequence_len": 2048,
|
||||
"val_set_size": 0.05,
|
||||
"special_tokens": {
|
||||
"pad_token": "</s>",
|
||||
"pad_token": "<|endoftext|>",
|
||||
},
|
||||
"datasets": [
|
||||
{
|
||||
@@ -503,7 +499,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sample_packing": True,
|
||||
"pad_to_sequence_len": True,
|
||||
"sequence_len": 2048,
|
||||
@@ -553,7 +549,7 @@ class TestMultiGPULlama:
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"load_in_4bit": True,
|
||||
"adapter": "qlora",
|
||||
"lora_r": 8,
|
||||
|
||||
@@ -66,6 +66,8 @@ class TestFAXentropyLlama(unittest.TestCase):
|
||||
},
|
||||
],
|
||||
"num_epochs": 1,
|
||||
"max_steps": 10,
|
||||
"save_steps": 10,
|
||||
"micro_batch_size": 8,
|
||||
"gradient_accumulation_steps": 1,
|
||||
"output_dir": temp_dir,
|
||||
|
||||
@@ -56,6 +56,8 @@ class TestLoraLlama(unittest.TestCase):
|
||||
},
|
||||
],
|
||||
"num_epochs": 2,
|
||||
"max_steps": 20,
|
||||
"save_steps": 10,
|
||||
"micro_batch_size": 8,
|
||||
"gradient_accumulation_steps": 1,
|
||||
"output_dir": temp_dir,
|
||||
@@ -109,6 +111,7 @@ class TestLoraLlama(unittest.TestCase):
|
||||
},
|
||||
],
|
||||
"num_epochs": 2,
|
||||
"max_steps": 20,
|
||||
"save_steps": 0.5,
|
||||
"micro_batch_size": 8,
|
||||
"gradient_accumulation_steps": 1,
|
||||
|
||||
@@ -1,15 +0,0 @@
|
||||
"""Test module for checking whether the integration of Unsloth with Hugging Face Transformers is working as expected."""
|
||||
import unittest
|
||||
|
||||
from axolotl.monkeypatch.trainer_fsdp_grad_accum import check_training_loop_is_patchable
|
||||
|
||||
|
||||
class TestTrainerFSDPIntegration(unittest.TestCase):
|
||||
"""Unsloth monkeypatch integration tests."""
|
||||
|
||||
def test_train_loop_patchable(self):
|
||||
# ensures the current version of transformers has loss code that matches our patching code
|
||||
self.assertTrue(
|
||||
check_training_loop_is_patchable(),
|
||||
"HF transformers _inner_training_loop has changed and isn't patchable",
|
||||
)
|
||||
@@ -113,7 +113,7 @@ class TestCustomOptimizers(unittest.TestCase):
|
||||
def test_fft_schedule_free_adamw(self, temp_dir):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 1024,
|
||||
"val_set_size": 0.1,
|
||||
"special_tokens": {
|
||||
|
||||
@@ -31,7 +31,7 @@ class TestPackedLlama(unittest.TestCase):
|
||||
# pylint: disable=duplicate-code
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "HuggingFaceTB/SmolLM-135M",
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"sequence_len": 1024,
|
||||
"sample_packing": True,
|
||||
"flash_attention": True,
|
||||
|
||||
@@ -32,16 +32,19 @@ class TestCosineConstantLr(unittest.TestCase):
|
||||
def test_schedulers(self):
|
||||
self.assertEqual(self.lr_scheduler.get_last_lr()[0], 0)
|
||||
for _ in range(self.warmup_steps):
|
||||
self.optimizer.step()
|
||||
self.lr_scheduler.step()
|
||||
self.assertEqual(self.lr_scheduler.get_last_lr()[0], self._lr)
|
||||
constant_step = int(self.train_steps * self.constant_lr_ratio)
|
||||
remaining_step = self.train_steps - constant_step
|
||||
for _ in range(constant_step):
|
||||
self.optimizer.step()
|
||||
self.lr_scheduler.step()
|
||||
self.assertEqual(
|
||||
self.lr_scheduler.get_last_lr()[0], self._lr * self.min_lr_ratio
|
||||
)
|
||||
for _ in range(remaining_step):
|
||||
self.optimizer.step()
|
||||
self.lr_scheduler.step()
|
||||
self.assertEqual(
|
||||
self.lr_scheduler.get_last_lr()[0], self._lr * self.min_lr_ratio
|
||||
|
||||
Reference in New Issue
Block a user