问题类型

错误

来源

来源

tensorflow 版本

2.10.0

自定义代码

是的

操作系统平台和分发

Ubuntu 20.04.4语言版本

移动的设备

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Python版本

3.8.10

Bazel版本

5.1.1

GCC/编译器版本

9.4.0

CUDA/cuDNN版本

11.2

GPU型号和内存

RTX 3090 2* 24克

当前行为？

The results of `LRNGrad` operators are inconsistent between CPU and GPU.

I've used two calls `tf.raw_ops.LRNGrad` and `nn.lrn_grad`,
and also changed the order of calls for different devices, 
still inconsistent results.

重现问题的独立代码

import tensorflow as tf
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops

# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/lrn_op.cc

input_grads = random_ops.random_uniform(
        shape=[1, 1, 1, 3],
        minval=-10000,
        maxval=10000,
        dtype=tf.float32,
        seed=2022)
input_img   = random_ops.random_uniform(
        shape=[1, 1, 1, 3],
        minval=-10000,
        maxval=10000,
        dtype=tf.float32,
        seed=2022)
output_img  = random_ops.random_uniform(
        shape=[1, 1, 1, 3],
        minval=-10000,
        maxval=10000,
        dtype=tf.float32,
        seed=2022)

with tf.device('/GPU:0'):
    out = tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_img, output_image=output_img)
    #out = nn.lrn_grad(input_grads=input_grads, input_image=input_img, output_image=output_img)
    print(out)

with tf.device('/CPU:0'):
    out = tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_img, output_image=output_img)
    #out = nn.lrn_grad(input_grads=input_grads, input_image=input_img, output_image=output_img)
    print(out)

相关日志输出

# python LRNgrad-test.py

2022-07-21 12:54:57.023583: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-07-21 12:54:57.132843: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2022-07-21 12:54:57.161994: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-07-21 12:55:00.101305: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-07-21 12:55:01.526010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22298 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:9b:00.0, compute capability: 8.6
2022-07-21 12:55:01.527383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22298 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:c8:00.0, compute capability: 8.6
2022-07-21 12:55:02.763201: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100

tf.Tensor([[[[-0.29212222  0.97755533 -0.28474247]]]], shape=(1, 1, 1, 3), dtype=float32)

tf.Tensor([[[[2362.0498 1360.1172 2242.2402]]]], shape=(1, 1, 1, 3), dtype=float32)