Feature Value Detection
Overview
Background
During model training, processors may encounter feature value detection anomalies, resulting in computational errors without reporting. Feature value detection anomalies may seriously affect model training.
Solution
The MindSpore framework version 2.4 provides a solution for feature value detection of Transformer structure models. Internally a feature value detection operator is inserted before the communication operator in the backward graph to monitor feature value and prevent anomaly from spreading to other cards.
For default feature value detection checkpoints, users can enable detection capability using the environment variable NPU_ASD_ENABLE=1, NPU_ASD_ENABLE=2 or NPU_ASD_ENABLE=3, and adjust the detection intensity by configuring the environment variables NPU_ASD_UPPER_THRESH and NPU_ASD_SIGMA_THRESH.
For information on configuring related environment variables, see Feature Switches and Configuration.
For an introduction to default feature value detection checkpoints, and design guidelines for custom feature value detection checkpoints, see Usage Recommendations and Detection Principles.
Usage Recommendations and Detection Principles
When processors encounter feature value detection anomalies, erroneous results are calculated. Due to the structure of Transformer models, these erroneous calculation results will propagate.
Based on experimental results, the following empirical conclusions are drawn:
Not all feature value detection anomalies necessarily affect model convergence and performance. In fact, most feature value detection anomalies do not have observable effects on the model. See reference.
Statistically, feature value detection anomalies during the backpropagation calculation process have a much greater impact than during the forward calculation process.
In parallel training scenarios, calculation error results will propagate due to parallel computation.
Setting too many checkpoints will affect model training performance.
Based on experiments on the sensitivity of calculation errors, the MindSpore framework defaults to selecting the
Normactivation value gradient in the backpropagation calculation process as the detection feature value, with performance loss less than 2% based on Llama 2 - 7B testing.
After enabling the detection switch (set NPU_ASD_ENABLE to 1, 2 or 3), during the backpropagation phase of training Transformer structure models, abnormality is determined by collecting the activation value gradients of the Norm layer through calling the detection operator inserted before the communication operator in the backward graph, and using an algorithm to determine if an anomaly exists. If an anomaly occurs, training is terminated, the NPU status on the device where the anomaly is detected is set to Warning, and a fault event is reported.
The reasons for feature value anomalies can be divided into two categories: hardware errors and software errors, which can be referred to in the Fault Handling section for further analysis.
Usage Restrictions
Currently, this feature only supports Atlas A2 training series products, detects abnormal feature value during the training process with Transformer model and bfloat16, float32 data type.
Feature Switches and Configuration
The environment variable NPU_ASD_ENABLE serves as a feature switch, export NPU_ASD_ENABLE=1, export NPU_ASD_ENABLE=2 or export NPU_ASD_ENABLE=3 to enable this feature; if this environment variable is not configured or export NPU_ASD_ENABLE=0, this feature is disabled.
The environment variable NPU_ASD_UPPER_THRESH controls the absolute numerical threshold of detection, in the format of integer pairs, where the first element controls the first-level threshold of absolute numerical values, and the second element controls the second-level threshold of absolute numerical values; reducing the threshold can detect smaller fluctuations in abnormal data, increase the detection rate, and increasing the threshold is the opposite. In the default case where this environment variable is not configured, NPU_ASD_UPPER_THRESH=1000000,10000.
The environment variable NPU_ASD_SIGMA_THRESH controls the relative numerical threshold of detection, in the same format as the above, where the first element controls the first-level threshold of numerical changes, and the second element controls the second-level threshold of numerical changes; by default, NPU_ASD_SIGMA_THRESH=100000,5000.
For details of above environment variables, see Environment Variables.
Use Cases
This document describes the usage methods and use cases of feature value detection.
Model and Dataset Preparation
To provide a complete experience, here we implement the usage case of feature value detection based on a simple network and a fake dataset.
The full python script (silent_check.py) is as below:
"""Silent Check Demo"""
import os
import numpy as np
import mindspore as ms
import mindspore.dataset as ds
from mindspore import nn, ops
from mindspore.communication import init
from mindspore.common.initializer import initializer
ms.set_context(mode=ms.GRAPH_MODE)
ms.set_context(max_device_memory="2GB")
ms.set_auto_parallel_context(parallel_mode=ms.ParallelMode.SEMI_AUTO_PARALLEL)
init()
ms.set_seed(1)
np.random.seed(1)
class Network(nn.Cell):
"""Network"""
def __init__(self):
super().__init__()
self.flatten = ops.Flatten()
self.fc1_weight = ms.Parameter(initializer("normal", [28*28, 512], ms.float32))
self.fc2_weight = ms.Parameter(initializer("normal", [512, 512], ms.float32))
self.fc3_weight = ms.Parameter(initializer("normal", [512, 10], ms.float32))
self.matmul1 = ops.MatMul()
self.relu1 = ops.ReLU()
self.matmul2 = ops.MatMul()
self.relu2 = ops.ReLU()
self.matmul3 = ops.MatMul()
# ====== begin ====== operator and parameter for injecting fault ==========================
self.eod_mask = ops.auto_generate.GenerateEodMaskV2()
self.cur_step = ms.Parameter(ms.Tensor(-1, ms.int64), requires_grad=False)
rank_id = os.environ['RANK_ID']
print(f'rank id of process {os.getpid()} is {rank_id}')
if rank_id == '2':
self.flip_mode = 'bitflip_designed' # bitflip, bitflip_designed, multiply, multiply_max
else:
self.flip_mode = 'multiply' # bitflip, bitflip_designed, multiply, multiply_max
# ====== *end* ====== operator and parameter for injecting fault ==========================
def construct(self, x):
x = self.flatten(x)
x = self.matmul1(x, self.fc1_weight)
# ====== begin ====== inject eod_mask =====================================================
ele_pos = ms.Tensor(0, ms.int64)
seed = ms.Tensor(0, ms.int64)
offset = ms.Tensor(0, ms.int64)
start = 0
steps = [5]
error_mode = 'cycle' # cycle, specific
multiply_factor = 1.0
bit_pos = 0
flip_probability = 0.0
# GenerateEodMaskV2()(input=<Tensor>, ele_pos=<Tensor>, cur_step=<Tensor>, seed=<Tensor>
# , offset=<Tensor>, start=<int>, steps=<int, list of int, tuple of int>, error_mode=<string>
# , flip_mode=<string>, multiply_factor=<float>, bit_pos=<int>, flip_probability=<float>)
self.cur_step = self.cur_step + 1
x = self.eod_mask(x, ele_pos, self.cur_step, seed, offset, start, steps, error_mode, self.flip_mode,
multiply_factor, bit_pos, flip_probability)
# ====== *end* ====== inject eod_mask =====================================================
x = self.relu1(x)
x = self.matmul2(x, self.fc2_weight)
x = self.relu2(x)
logits = self.matmul3(x, self.fc3_weight)
return logits
net = Network()
net.matmul1.shard(((1, 4), (4, 1)))
net.relu1.shard(((4, 1),))
net.matmul2.shard(((1, 4), (4, 1)))
net.relu2.shard(((4, 1),))
# fake dataset
def create_dataset(batch_size):
# """create dataset"""
# Random-accessible object as input source
class RandomAccessDataset:
def __init__(self):
self.dataset_size = 20
def __getitem__(self, index):
image_np = np.random.randn(batch_size, 1, 28, 28).astype(np.float32)
label_np = np.random.randint(low=0, high=10, size=batch_size, dtype=np.int32)
return ms.Tensor(image_np), ms.Tensor(label_np)
def __len__(self):
return self.dataset_size
loader = RandomAccessDataset()
return ds.GeneratorDataset(source=loader, column_names=["image", "label"])
data_set = create_dataset(32)
optimizer = nn.SGD(net.trainable_params(), 1e-2)
loss_fn = nn.CrossEntropyLoss()
def forward_fn(data, target):
"""forward propagation"""
logits = net(data)
loss = loss_fn(logits, target)
return loss, logits
grad_fn = ms.value_and_grad(forward_fn, None, net.trainable_params(), has_aux=True)
@ms.jit
def train_step(inputs, targets):
"""train_step"""
(loss_value, _), grads = grad_fn(inputs, targets)
optimizer(grads)
return loss_value
# training
for epoch in range(1):
i = 0
for image, label in data_set:
loss_output = train_step(image, label)
myrank_id = os.environ['RANK_ID']
if i % 10 == 0 and myrank_id == '0':
print("rank %s, epoch: %s, step: %s, loss is %s" % (myrank_id, epoch, i, loss_output))
i += 1
Running silent check script
This silent check demo uses 4 NPU cards, the start script (run_silent_check.sh) is as follows:
#!/bin/bash
# set cann log level to info
export ASCEND_GLOBAL_LOG_LEVEL=1
mpirun -n 4 --output-filename log_output --merge-stderr-to-stdout python silent_check.py
Different detection levels and running results
Execution result of setting NPU_ASD_ENABLE to 1
When NPU_ASD_ENABLE was set to 1, if error was detected, just print ERROR log, not stop training process.
Start command:
NPU_ASD_ENABLE=1 bash run_silent_check.sh
From the CANN log, by default the log path os ~/ascend/log/, the main ERROR logs are as follows, there are many ERROR logs. After error was detected, the training process was not stopped.
$ cd ~/ascend/log/debug/
$ grep -nr 'silent_check_v2.cc:1.*SilentCheck' device-*
device-0/device-109652_20240913141212740.log:2508:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:28.956.344 [silent_check_v2.cc:136][ComputeL1Error][tid:24528]SilentCheck get L1 Error, input message: val = [nan], pre_val = [0.100089], min_val = [0.089474], max_val = [0.130423], step = [6], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
device-0/device-109652_20240913141212740.log:2556:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:28.961.061 [silent_check_v2.cc:136][ComputeL1Error][tid:24523]SilentCheck get L1 Error, input message: val = [nan], pre_val = [0.026273], min_val = [0.026273], max_val = [0.026949], step = [6], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
device-0/device-109652_20240913141212740.log:2604:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:28.969.720 [silent_check_v2.cc:136][ComputeL1Error][tid:24524]SilentCheck get L1 Error, input message: val = [nan], pre_val = [0.005759], min_val = [0.005759], max_val = [0.005931], step = [6], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
device-0/device-109652_20240913141212740.log:2652:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:28.977.256 [silent_check_v2.cc:136][ComputeL1Error][tid:24525]SilentCheck get L1 Error, input message: val = [nan], pre_val = [0.004188], min_val = [0.004188], max_val = [0.004272], step = [6], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
device-0/device-109652_20240913141212740.log:2786:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:29.057.454 [silent_check_v2.cc:136][ComputeL1Error][tid:24526]SilentCheck get L1 Error, input message: val = [nan], pre_val = [nan], min_val = [0.089474], max_val = [0.130423], step = [7], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
device-0/device-109652_20240913141212740.log:2834:[ERROR] AICPU(24515,aicpu_scheduler):2024-09-13-14:12:29.062.242 [silent_check_v2.cc:136][ComputeL1Error][tid:24527]SilentCheck get L1 Error, input message: val = [nan], pre_val = [nan], min_val = [0.026273], max_val = [0.026949], step = [7], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [1].
Execution result of setting NPU_ASD_ENABLE to 2
When NPU_ASD_ENABLE was set to 2, if error was detected, print ERROR log and stop training process.
Start command:
NPU_ASD_ENABLE=2 bash run_silent_check.sh
From the CANN log, by default the log path os ~/ascend/log/, the main ERROR logs are as follows, there only one ERROR log. After error was detected, the training process was stopped.
$ cd ~/ascend/log/debug/
$ grep -nr 'silent_check_v2.cc:1.*SilentCheck' device-*
device-2/device-134035_20240913141623807.log:2204:[ERROR] AICPU(1685,aicpu_scheduler):2024-09-13-14:16:43.130.671 [silent_check_v2.cc:136][ComputeL1Error][tid:1695]SilentCheck get L1 Error, input message: val = [inf], pre_val = [0.105156], min_val = [0.089474], max_val = [0.130423], step = [5], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [2].
$
Execution result of setting NPU_ASD_ENABLE to 3
When NPU_ASD_ENABLE was set to 3, the action is similar to detection level 2, i.e. if error was detected, print ERROR log and stop training process. Besides an INFO log as also output for
non anomaly feature values (In oreder to see logs of level INFO, need to set export ASCEND_GLOBAL_LOG_LEVEL=0 to enable log level DEBUG or set export ASCEND_GLOBAL_LOG_LEVEL=1 to enable log level INFO).
Start command:
NPU_ASD_ENABLE=3 bash run_silent_check.sh
From the CANN log, by default the log path os ~/ascend/log/, the main ERROR logs are as follows, there are INFO logs except for ERORR log about feature value info.
$ cd ~/ascend/log/debug/
$ grep -nr 'silent_check_v2.cc:1.*SilentCheck' device-*
device-2/device-151329_20240913141834821.log:2056:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.048.647 [silent_check_v2.cc:124][SilentCheck][tid:2257]c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000], npu_asd_detect = [3]
device-2/device-151329_20240913141834821.log:2104:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.057.632 [silent_check_v2.cc:122][SilentCheck][tid:2258]val = [0.004220], pre_val = [0.004272], min_val = [0.004189], max_val = [0.004272], step = [4].
device-2/device-151329_20240913141834821.log:2105:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.057.642 [silent_check_v2.cc:124][SilentCheck][tid:2258]c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000], npu_asd_detect = [3]
device-2/device-151329_20240913141834821.log:2149:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.096.224 [silent_check_v2.cc:122][SilentCheck][tid:2257]val = [0.007812], pre_val = [0.007812], min_val = [0.007812], max_val = [0.007812], step = [5].
device-2/device-151329_20240913141834821.log:2150:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.096.234 [silent_check_v2.cc:124][SilentCheck][tid:2257]c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000], npu_asd_detect = [3]
device-2/device-151329_20240913141834821.log:2194:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.099.659 [silent_check_v2.cc:122][SilentCheck][tid:2258]val = [0.000000], pre_val = [0.000000], min_val = [0.000000], max_val = [0.000000], step = [5].
device-2/device-151329_20240913141834821.log:2195:[INFO] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.099.668 [silent_check_v2.cc:124][SilentCheck][tid:2258]c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000], npu_asd_detect = [3]
device-2/device-151329_20240913141834821.log:2243:[ERROR] AICPU(2249,aicpu_scheduler):2024-09-13-14:18:53.127.190 [silent_check_v2.cc:136][ComputeL1Error][tid:2259]SilentCheck get L1 Error, input message: val = [inf], pre_val = [0.105156], min_val = [0.089474], max_val = [0.130423], step = [5], c_min_steps = [100], c_thresh_l1 = [1000000.000000], c_coeff_l1 = [100000.000000], c_thresh_l2 = [10000.000000], c_coeff_l2 = [5000.000000] npu_asd_detect = [3].
Detection Results and Handling
Abnormal Detection Results
When no numerical anomalies are detected, the training task runs without impact.
When numerical anomalies are detected, the training task fails and alerts are reported. To locate the faulty device, do one of the following:
Search application logs for ERROR level error logs with the keyword "accuracy sensitivity feature abnormal";
Monitor the NPU health status: if Health Status displays Warning, Error Code displays 80818C00, and Error Information displays node type=SoC, sensor type=Check Sensor, event state=check fail;
Check the Ascend Device Plugin events, report error code 80818C00, event type is fault event, and the fault level is minor.
Fault Handling
Isolate the abnormal device, resume training with checkpoint recovery; meanwhile, on the abnormal device, use the Ascend-DMI tool to perform AICore ERROR stress diagnostics to detect whether there are faulty NPUs on the device. For details, see ToolBox User Guide in the "ascend-dmi tool usage > fault diagnosis" section.
If a faulty card is detected on the abnormal device, contact Huawei engineers for maintenance and replacement; if all NPUs on the abnormal device are normal, it is a software-related issue triggering feature value overflow, and it is recommended to check the processes and operators'es causes.