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Code change to use flash attn 2 #202

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Code change to use flash attn 2 #202

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4e1f54b
Code change to use flash attn 2
yhyu13 Nov 22, 2023
f13f9c8
Use QLoRA along side flash attn2
yhyu13 Nov 23, 2023
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10 changes: 9 additions & 1 deletion toolbench/train/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -273,10 +273,18 @@ def train():
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
bnb_config = transformers.BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
device_map=device_map
device_map=device_map,
use_flash_attention_2=True,
quantization_config=bnb_config,
)
model.config.use_cache = False
trainer = Trainer(
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19 changes: 14 additions & 5 deletions toolbench/train/train_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
from dataclasses import dataclass, field
import logging
import pathlib
Expand All @@ -33,11 +34,11 @@
make_supervised_data_module,
)

from toolbench.train.llama_flash_attn_monkey_patch import (
replace_llama_attn_with_flash_attn,
)
# from toolbench.train.llama_flash_attn_monkey_patch import (
# replace_llama_attn_with_flash_attn,
# )
from toolbench.train.llama_condense_monkey_patch import replace_llama_with_condense
replace_llama_attn_with_flash_attn()
# replace_llama_attn_with_flash_attn()


@dataclass
Expand Down Expand Up @@ -107,10 +108,18 @@ def train():
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
bnb_config = transformers.BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
device_map=device_map
device_map=device_map,
use_flash_attention_2=True,
quantization_config=bnb_config,
)
lora_config = LoraConfig(
r=lora_args.lora_r,
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10 changes: 5 additions & 5 deletions toolbench/train/train_mem.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn.

# Need to call this before importing transformers.
from toolbench.train.llama_flash_attn_monkey_patch import (
replace_llama_attn_with_flash_attn,
)
# # Need to call this before importing transformers.
# from toolbench.train.llama_flash_attn_monkey_patch import (
# replace_llama_attn_with_flash_attn,
# )

replace_llama_attn_with_flash_attn()
# replace_llama_attn_with_flash_attn()

from toolbench.train.train import train

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