paddle.amp.debugging. enable_tensor_checker ( checker_config ) [source]

The enable_tensor_checker(checker_config) function enables model-level accuracy checking and is used in combination with disables_tensor_checker() to achieve model-level precision checking by checking the output Tensors of all operators within the specified range.


checker_config (TensorCheckerConfig) – Checker_config is to collect the configuration for checking NaN and Inf values in the tensors of a module or operator.


If disable_tensor_checker() is called before backward(), the gradient operator will not be checked. If disable_tensor_checker() is called before optimizer.step(), the optimizer and other weight update related operators will not be checked.


import paddle

checker_config = paddle.amp.debugging.TensorCheckerConfig(enable=True, debug_mode=paddle.amp.debugging.DebugMode.CHECK_NAN_INF)

x = paddle.to_tensor([1, 0, 3], place=paddle.CPUPlace(), dtype='float32', stop_gradient=False)
y = paddle.to_tensor([0.2, 0, 0.5], place=paddle.CPUPlace(), dtype='float32')
res = paddle.pow(x, y)
paddle.autograd.backward(res, retain_graph=True)
#[PRECISION] [ERROR] in [device=cpu, op=elementwise_pow_grad, tensor=, dtype=fp32], numel=3, num_nan=1, num_inf=0, num_zero=0, max=2.886751e-01, min=2.000000e-01, mean=-nan

# when DebugMode.CHECK_NAN_INF_AND_ABORT and stack_height_limit = 1
# Traceback (most recent call last):
#   File "tp.py", line 8, in <module>
#     res = paddle.pow(x, y)
#   File "/usr/local/lib/python3.8/dist-packages/paddle/tensor/math.py", line 447, in pow
#     return _C_ops.elementwise_pow(x, y)