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# Copyright 2024 the LlamaFactory team.
#
# 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.
import os
import torch
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_train_args
from llamafactory.model import load_model, load_tokenizer
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"lora_target": "all",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"overwrite_cache": True,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
def test_checkpointing_enable():
model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": False, **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
assert getattr(module, "gradient_checkpointing") is True
def test_checkpointing_disable():
model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": True, **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
assert getattr(module, "gradient_checkpointing") is False
def test_upcast_layernorm():
model_args, _, _, finetuning_args, _ = get_train_args({"upcast_layernorm": True, **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
for name, param in model.named_parameters():
if param.ndim == 1 and "norm" in name:
assert param.dtype == torch.float32
def test_upcast_lmhead_output():
model_args, _, _, finetuning_args, _ = get_train_args({"upcast_lmhead_output": True, **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
inputs = torch.randn((1, 16), dtype=torch.float16, device=get_current_device())
outputs: "torch.Tensor" = model.lm_head(inputs)
assert outputs.dtype == torch.float32
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