p12475398
/
LLaMA-Factory-Mirror
forked from p04798526/LLaMA-Factory-Mirror
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add tests

This commit is contained in:
hiyouga 2024-06-15 19:51:20 +08:00
parent 572d8bbfdd
commit 1b834f50be
8 changed files with 166 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Makefile
View File

@ -11,4 +11,4 @@ style:
ruff format $(check_dirs)
test:
pytest tests/
CUDA_VISIBLE_DEVICES= pytest tests/

9
src/llamafactory/extras/misc.py
View File

@ -22,6 +22,7 @@ from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList, PreTr
from transformers.utils import (
SAFE_WEIGHTS_NAME,
WEIGHTS_NAME,
is_safetensors_available,
is_torch_bf16_gpu_available,
is_torch_cuda_available,
is_torch_mps_available,
@ -34,6 +35,11 @@ from .constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from .logging import get_logger
if is_safetensors_available():
from safetensors import safe_open
from safetensors.torch import save_file
_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
try:
_is_bf16_available = is_torch_bf16_gpu_available()
@ -128,9 +134,6 @@ def fix_valuehead_checkpoint(
return
if safe_serialization:
from safetensors import safe_open
from safetensors.torch import save_file
path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}

2
tests/data/test_supervised.py
View File

@ -41,7 +41,7 @@ TRAIN_ARGS = {
}
@pytest.mark.parametrize("num_samples", [10])
@pytest.mark.parametrize("num_samples", [16])
def test_supervised(num_samples: int):
model_args, data_args, training_args, _, _ = get_train_args(TRAIN_ARGS)
tokenizer_module = load_tokenizer(model_args)

74
tests/model/model_utils/test_checkpointing.py Normal file
View File

@ -0,0 +1,74 @@
# 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

30
tests/model/test_base.py
View File

@ -13,16 +13,21 @@
# limitations under the License.
import os
from typing import Dict
import torch
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_model, load_tokenizer
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA,
"template": "llama3",
@ -38,9 +43,32 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
def test_base():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
ref_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
ref_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
)
compare_model(model, ref_model)
def test_valuehead():
AutoModelForCausalLMWithValueHead.post_init = post_init # patch for CPU test
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(
tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False, add_valuehead=True
)
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(
TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
)
compare_model(model, ref_model)

3
tests/model/test_freeze.py
View File

@ -49,6 +49,7 @@ def test_freeze_train_all_modules():
model_args, _, _, finetuning_args, _ = get_train_args({"freeze_trainable_layers": 1, **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 name.startswith("model.layers.1."):
assert param.requires_grad is True
@ -64,6 +65,7 @@ def test_freeze_train_extra_modules():
)
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 name.startswith("model.layers.1.") or any(module in name for module in ["embed_tokens", "lm_head"]):
assert param.requires_grad is True
@ -77,6 +79,7 @@ def test_freeze_inference():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
for param in model.parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16

2
tests/model/test_full.py
View File

@ -49,6 +49,7 @@ def test_full_train():
model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
for param in model.parameters():
assert param.requires_grad is True
assert param.dtype == torch.float32
@ -58,6 +59,7 @@ def test_full_inference():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
for param in model.parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16

58
tests/model/test_lora.py
View File

@ -18,7 +18,9 @@ from typing import Sequence
import torch
from peft import LoraModel, PeftModel
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args, get_train_args
from llamafactory.model import load_model, load_tokenizer
@ -27,6 +29,8 @@ TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_ADAPTER = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")
TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA,
"stage": "sft",
@ -67,10 +71,29 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
def test_lora_train_qv_modules():
model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "q_proj,v_proj", **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
linear_modules = set()
for name, param in model.named_parameters():
if any(module in name for module in ["lora_A", "lora_B"]):
linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
assert param.requires_grad is True
assert param.dtype == torch.float32
else:
assert param.requires_grad is False
assert param.dtype == torch.float16
assert linear_modules == {"q_proj", "v_proj"}
def test_lora_train_all_modules():
model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "all", **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
linear_modules = set()
for name, param in model.named_parameters():
if any(module in name for module in ["lora_A", "lora_B"]):
@ -90,6 +113,7 @@ def test_lora_train_extra_modules():
)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
extra_modules = set()
for name, param in model.named_parameters():
if any(module in name for module in ["lora_A", "lora_B"]):
@ -113,7 +137,9 @@ def test_lora_train_old_adapters():
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
param.data = param.data.to(torch.float32)
@ -128,7 +154,9 @@ def test_lora_train_new_adapters():
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
param.data = param.data.to(torch.float32)
@ -138,17 +166,31 @@ def test_lora_train_new_adapters():
)
def test_lora_train_valuehead():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(
tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True, add_valuehead=True
)
ref_model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
)
state_dict = model.state_dict()
ref_state_dict = ref_model.state_dict()
assert torch.allclose(state_dict["v_head.summary.weight"], ref_state_dict["v_head.summary.weight"])
assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])
def test_lora_inference():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER)
ref_model = ref_model.merge_and_unload()
compare_model(model, ref_model)
for name, param in model.named_parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16
assert "lora" not in name