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keep gate in fp32 for loras #1105

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merged 5 commits into from
Jan 12, 2024
Merged

keep gate in fp32 for loras #1105

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05f4555
keep gate in fp32 for loras
winglian Jan 12, 2024
ff2a221
add e2e check for lora w/o flash attention for mixtral to check gate
winglian Jan 12, 2024
0e73c29
add checks for gate in fp32 for mixtral, add typehints to train outputs
winglian Jan 12, 2024
76ff8b0
mixtral doesn't support basic lora :facepalm:
winglian Jan 12, 2024
8db0248
ensure we're using fp16/bf16 for 16bit and qlora is always going to b…
winglian Jan 12, 2024
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6 changes: 4 additions & 2 deletions src/axolotl/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,14 +5,16 @@
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
from typing import Optional, Tuple, Union

import torch
import transformers.modelcard
from accelerate.logging import get_logger
from datasets import Dataset
from optimum.bettertransformer import BetterTransformer
from peft import PeftModel
from pkg_resources import get_distribution # type: ignore
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.deepspeed import is_deepspeed_zero3_enabled

from axolotl.common.cli import TrainerCliArgs
Expand Down Expand Up @@ -43,7 +45,7 @@ class TrainDatasetMeta:

def train(
*, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
):
) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
# load the tokenizer first
LOG.debug(
f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
Expand Down
2 changes: 1 addition & 1 deletion src/axolotl/utils/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -590,7 +590,7 @@ def load_model(
# make sure these are fp32 per Ramesh et al. (2021)
embedding_modules = get_linear_embedding_layers(cfg.model_config_type)
for name, module in model.named_modules():
if "norm" in name:
if any(m in name for m in ["norm", "gate"]):
module.to(torch.float32)
if model_config.model_type == "btlm":
# don't upcast lm_head for btlm
Expand Down
191 changes: 186 additions & 5 deletions tests/e2e/test_mixtral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
import unittest
from pathlib import Path

import torch
from transformers.utils import is_torch_bf16_gpu_available

from axolotl.cli import load_datasets
Expand All @@ -27,7 +28,7 @@ class TestMixtral(unittest.TestCase):
"""

@with_temp_dir
def test_qlora(self, temp_dir):
def test_qlora_w_fa2(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
Expand All @@ -37,10 +38,18 @@ def test_qlora(self, temp_dir):
"sequence_len": 1024,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 32,
"lora_r": 4,
"lora_alpha": 8,
"lora_dropout": 0.1,
"lora_target_linear": True,
"lora_target_modules": [
"o_proj",
"w3",
"k_proj",
"v_proj",
"w1",
"q_proj",
"w2",
],
"val_set_size": 0.1,
"special_tokens": {},
"datasets": [
Expand All @@ -65,7 +74,179 @@ def test_qlora(self, temp_dir):
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
== torch.uint8
)
assert (Path(temp_dir) / "adapter_model.bin").exists()

@with_temp_dir
def test_qlora_wo_fa2(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "hf-internal-testing/Mixtral-tiny",
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
"flash_attention": False,
"sequence_len": 1024,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 4,
"lora_alpha": 8,
"lora_dropout": 0.1,
"lora_target_modules": [
"o_proj",
"w3",
"k_proj",
"v_proj",
"w1",
"q_proj",
"w2",
],
"val_set_size": 0.1,
"special_tokens": {},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
== torch.uint8
)
assert (Path(temp_dir) / "adapter_model.bin").exists()

@with_temp_dir
def test_16bit_lora_w_fa2(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "hf-internal-testing/Mixtral-tiny",
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
"flash_attention": True,
"sequence_len": 1024,
"adapter": "lora",
"lora_r": 4,
"lora_alpha": 8,
"lora_dropout": 0.1,
"lora_target_modules": [
"o_proj",
"w3",
"k_proj",
"v_proj",
"w1",
"q_proj",
"w2",
],
"val_set_size": 0.1,
"special_tokens": {},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
}
)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
else:
cfg.fp16 = True
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
== torch.float32
)
assert (Path(temp_dir) / "adapter_model.bin").exists()

@with_temp_dir
def test_16bit_lora_wo_fa2(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "hf-internal-testing/Mixtral-tiny",
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
"flash_attention": False,
"sequence_len": 1024,
"adapter": "lora",
"lora_r": 4,
"lora_alpha": 8,
"lora_dropout": 0.1,
"lora_target_modules": [
"o_proj",
"w3",
"k_proj",
"v_proj",
"w1",
"q_proj",
"w2",
],
"val_set_size": 0.1,
"special_tokens": {},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
}
)
normalize_config(cfg)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
else:
cfg.fp16 = True
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
== torch.float32
)
assert (Path(temp_dir) / "adapter_model.bin").exists()

@with_temp_dir
Expand Down