tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

gradient accumulation tests, embeddings w pad_token fix, smaller models (#2059)

Browse Source 
* add more test cases for gradient accumulation and fix zero3

* swap out for smaller model

* fix missing return

* fix missing pad_token in config

* support concurrency for multigpu testing

* cast empty deepspeed to empty string for zero3 check

* fix temp_dir as fixture so parametrize works properly

* fix test file for multigpu evals

* don't use default

* don't use default for fsdp_state_dict_type

* don't use llama tokenizer w smollm

* also automatically cancel multigpu for concurrency
This commit is contained in:
Wing Lian
2024-11-14 12:59:00 -05:00
committed by GitHub
parent f3a5d119af
commit 71d4030b79
8 changed files with 118 additions and 71 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
tests/e2e/conftest.py Normal file
View File

@@ -0,0 +1,16 @@
"""
shared pytest fixtures
"""
import shutil
import tempfile
import pytest
@pytest.fixture
def temp_dir():
# Create a temporary directory
_temp_dir = tempfile.mkdtemp()
yield _temp_dir
# Clean up the directory after the test
shutil.rmtree(_temp_dir)

12
tests/e2e/multigpu/test_eval.py
View File

@@ -3,28 +3,25 @@ E2E tests for multigpu eval
"""
import logging
import os
import unittest
from pathlib import Path
import yaml
from accelerate.test_utils import execute_subprocess_async
from transformers.testing_utils import get_torch_dist_unique_port
from axolotl.utils.dict import DictDefault
from ..utils import with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
os.environ["WANDB_DISABLED"] = "true"
AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
class TestMultiGPUEval(unittest.TestCase):
class TestMultiGPUEval:
"""
Test case for MultiGPU Eval Sample Packing
"""
@with_temp_dir
def test_eval_sample_packing(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
@@ -83,13 +80,14 @@ class TestMultiGPUEval(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_eval(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
@@ -148,6 +146,8 @@ class TestMultiGPUEval(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),

115
tests/e2e/multigpu/test_llama.py
View File

@@ -4,17 +4,17 @@ E2E tests for multigpu lora tinyllama
import logging
import os
import unittest
from pathlib import Path
import pytest
import yaml
from accelerate.test_utils import execute_subprocess_async
from huggingface_hub import snapshot_download
from transformers.testing_utils import get_torch_dist_unique_port
from axolotl.utils.dict import DictDefault
from ..utils import is_hopper, with_temp_dir
from ..utils import is_hopper
LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
os.environ["WANDB_DISABLED"] = "true"
@@ -28,18 +28,16 @@ def download_model():
snapshot_download("TinyLlama/TinyLlama_v1.1")
class TestMultiGPULlama(unittest.TestCase):
class TestMultiGPULlama:
"""
Test case for Llama models using LoRA
"""
@with_temp_dir
def test_lora_ddp(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"sequence_len": 2048,
"adapter": "lora",
"lora_r": 8,
@@ -48,9 +46,7 @@ class TestMultiGPULlama(unittest.TestCase):
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -81,19 +77,23 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_lora_ddp_packed(self, temp_dir):
@pytest.mark.parametrize(
"gradient_accumulation_steps",
[1, 4],
)
def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"sequence_len": 2048,
"sample_packing": True,
"eval_sample_packing": False,
@@ -105,9 +105,7 @@ class TestMultiGPULlama(unittest.TestCase):
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -118,7 +116,7 @@ class TestMultiGPULlama(unittest.TestCase):
"num_epochs": 1,
"max_steps": 15,
"micro_batch_size": 4,
"gradient_accumulation_steps": 4,
"gradient_accumulation_steps": gradient_accumulation_steps,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_8bit",
@@ -138,6 +136,8 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
@@ -145,7 +145,6 @@ class TestMultiGPULlama(unittest.TestCase):
)
@pytest.mark.skipif(is_hopper(), reason="h100 doesn't support 8-bit lora")
@with_temp_dir
def test_dpo_lora_ddp(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
@@ -210,13 +209,14 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_dpo_qlora_ddp(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
@@ -278,25 +278,27 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_fsdp(self, temp_dir):
@pytest.mark.parametrize(
"gradient_accumulation_steps",
[1, 4],
)
def test_fsdp(self, temp_dir, gradient_accumulation_steps):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"sequence_len": 2048,
"val_set_size": 0.05,
"val_set_size": 0.01,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -305,9 +307,9 @@ class TestMultiGPULlama(unittest.TestCase):
},
],
"num_epochs": 1,
"max_steps": 15,
"max_steps": 10,
"micro_batch_size": 4,
"gradient_accumulation_steps": 4,
"gradient_accumulation_steps": gradient_accumulation_steps,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
@@ -324,7 +326,7 @@ class TestMultiGPULlama(unittest.TestCase):
"fsdp_use_orig_params": False,
"fsdp_cpu_ram_efficient_loading": False,
"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
"fsdp_state_dict_type": "SHARDED_STATE_DICT",
"fsdp_state_dict_type": "FULL_STATE_DICT",
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
},
}
@@ -341,28 +343,29 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_fsdp_packed(self, temp_dir):
@pytest.mark.parametrize(
"fsdp_state_dict_type",
["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
)
def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"sample_packing": True,
"eval_sample_packing": False,
"pad_to_sequence_len": True,
"sequence_len": 2048,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -390,7 +393,7 @@ class TestMultiGPULlama(unittest.TestCase):
"fsdp_use_orig_params": False,
"fsdp_cpu_ram_efficient_loading": False,
"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
"fsdp_state_dict_type": "SHARDED_STATE_DICT",
"fsdp_state_dict_type": fsdp_state_dict_type,
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
},
}
@@ -407,13 +410,14 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_fsdp_qlora_prequant_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
@@ -483,28 +487,29 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_ds_zero3_packed(self, temp_dir):
@pytest.mark.parametrize(
"gradient_accumulation_steps",
[1, 4],
)
def test_ds_zero3_packed(self, temp_dir, gradient_accumulation_steps):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"sample_packing": True,
"eval_sample_packing": False,
"pad_to_sequence_len": True,
"sequence_len": 2048,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -515,7 +520,7 @@ class TestMultiGPULlama(unittest.TestCase):
"num_epochs": 1,
"max_steps": 15,
"micro_batch_size": 4,
"gradient_accumulation_steps": 4,
"gradient_accumulation_steps": gradient_accumulation_steps,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
@@ -536,19 +541,19 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_ds_zero3_qlora_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"base_model": "HuggingFaceTB/SmolLM-135M",
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 8,
@@ -561,9 +566,7 @@ class TestMultiGPULlama(unittest.TestCase):
"sequence_len": 2048,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -595,6 +598,8 @@ class TestMultiGPULlama(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),

19
tests/e2e/multigpu/test_qwen2.py
View File

@@ -4,31 +4,30 @@ E2E tests for multigpu qwen2
import logging
import os
import unittest
from pathlib import Path
import pytest
import yaml
from accelerate.test_utils import execute_subprocess_async
from transformers.testing_utils import get_torch_dist_unique_port
from axolotl.utils.dict import DictDefault
from ..utils import with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
os.environ["WANDB_DISABLED"] = "true"
class TestMultiGPUQwen2(unittest.TestCase):
class TestMultiGPUQwen2:
"""
Test case for Llama models using LoRA
"""
@with_temp_dir
def test_qlora_fsdp_dpo(self, temp_dir):
@pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
def test_qlora_fsdp_dpo(self, base_model, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "Qwen/Qwen2-1.5B",
"base_model": base_model,
"load_in_4bit": True,
"rl": "dpo",
"chat_template": "chatml",
@@ -47,9 +46,9 @@ class TestMultiGPUQwen2(unittest.TestCase):
},
],
"num_epochs": 1,
"max_steps": 15,
"max_steps": 5,
"warmup_steps": 20,
"micro_batch_size": 4,
"micro_batch_size": 2,
"gradient_accumulation_steps": 2,
"output_dir": temp_dir,
"learning_rate": 0.00001,
@@ -91,6 +90,8 @@ class TestMultiGPUQwen2(unittest.TestCase):
"launch",
"--num-processes",
"2",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),