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update test loss #329

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update test loss #329

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36 changes: 20 additions & 16 deletions tests/test_training/test_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@
from internlm.utils.gputest import empty_cache_and_diag
from internlm.utils.megatron_timers import megatron_timer as timer

CONFIG_FILE_PATH = os.getenv("CONFIG_FILE_PATH", "./configs/7B_sft.py")
INTERNLM1_CKPT_PATH = os.path.join(os.environ["share_path"], "quailty_assurance/test_loss/model_ckpt")
CONFIG_FILE_PATH = os.getenv("CONFIG_FILE_PATH", "./configs/7B_internlm2.py")
INTERNLM2_CKPT_PATH = os.path.join(os.environ["share_path"], "quailty_assurance/test_loss_pri/model_ckpt")
TOTAL_STEPS = 10
LOSS_SPIKE_LIMIT = 1.5
LOSS_DEVIATION_LIMIT = 0.02
Expand Down Expand Up @@ -59,32 +59,34 @@ def train(
enable_sp: bool = False,
save_ckpt: bool = False,
load_ckpt: bool = False,
model_type: str = "INTERNLM",
optimizer_ver: str = "v1",
zero_bubble: bool = False,
model_type: str = None,
):
# initialize distributed environment
config = Config.from_file(CONFIG_FILE_PATH)

# init setting
config.data.total_steps = TOTAL_STEPS
config.data.total_steps = 50000
config.data.fixed_random_dataset_seqlen = False
config.lr_scheduler.total_steps = TOTAL_STEPS
config.model_type = model_type
config.lr_scheduler.total_steps = config.data.total_steps
config.ckpt.load_ckpt_folder = None
config.ckpt.load_ckpt_info = None
config.ckpt.auto_resume = False
total_steps = config.data.total_steps
total_steps = TOTAL_STEPS
skip_batches = config.data.skip_batches
label_smoothing = config.loss.label_smoothing

if not model_type:
model_type = config.model_type

if optimizer_ver == "v2":
config.hybrid_zero_optimizer.use_split_tensor_optim = True
config.all_gather_size = 512 * 1024 * 1024

# update ckpt config
if model_type == "INTERNLM" and tp_mode != "isp" and interleaved is False:
config.ckpt.load_ckpt_info = dict(path=INTERNLM1_CKPT_PATH, content=("model",), ckpt_type="internlm_test")
if model_type == "INTERNLM2_PUBLIC" and tp_mode != "isp" and interleaved is False:
config.ckpt.load_ckpt_info = dict(path=INTERNLM2_CKPT_PATH, content=("model",), ckpt_type="internlm2")

if save_ckpt:
config.ckpt.enable_save_ckpt = True
Expand All @@ -97,6 +99,7 @@ def train(

# update parallel config
config.parallel.tensor = dict(size=tp_size, mode=tp_mode)
config.parallel.zero1 = dict(size=-1)
if zero_bubble:
config.hybrid_zero_optimizer.overlap_sync_grad = False
config.parallel.pipeline = dict(size=pp_size, zero_bubble=True)
Expand All @@ -109,16 +112,17 @@ def train(

if "use_packed_dataset" not in config.data:
config.data.use_packed_dataset = True
if tp_mode == "isp" and internlm_accelerator.get_accelerator_backend() in [
AcceleratorType.NPU,
AcceleratorType.DIPU,
]:
config.data.use_packed_dataset = False
# if tp_mode == "isp" and internlm_accelerator.get_accelerator_backend() in [
# AcceleratorType.NPU,
# AcceleratorType.DIPU,
# ]:
# config.data.use_packed_dataset = False

if internlm_accelerator.get_accelerator_backend() == AcceleratorType.GPU:
launcher = "slurm"
else:
launcher = "torch"
config.model.checkpoint = True

initialize_distributed_env(config=config, launcher=launcher)
assert hasattr(gpc, "config") and gpc.config is not None
Expand Down Expand Up @@ -211,7 +215,7 @@ def train(

train_iter = iter(train_dl)

if model_type == "INTERNLM":
if model_type == "INTERNLM2_PUBLIC":
data_path = os.path.join(os.environ["share_path"], "quailty_assurance/test_loss/data_batch_4DP")
data_batch = torch.load(f"{data_path}/{gpc.get_local_rank(ParallelMode.DATA)}_data_batch.pt")

Expand All @@ -220,7 +224,7 @@ def train(
empty_cache_and_diag(batch_count, interval=gpc.config.data.empty_cache_and_diag_interval)
timer("one-batch").start()

if model_type == "INTERNLM":
if model_type == "INTERNLM2_PUBLIC":
if batch_count >= 10:
batch = data_batch[batch_count - 10]
else:
Expand Down
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