Paddle functional.softmax结果与 torch 无法对齐。并且结果不合理。

hivapdat  于 5个月前  发布在  其他
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bug描述 Describe the Bug

利用 paddle.nn.functional.softmax 对一个几乎对称的 logits 取softmax操作,在输入logits 对角线值一样的情况下, 输出 对角线上的值不一致,复现代码如下

import paddle as p
from paddle.nn import functional as PF

import torch
from torch.nn import functional as TF

print(f'torch-versoin: {torch.version.git_version}')
print(f'paddle-version: {p.__git_commit__}')
print()

seqlen=8

x = t.arange(seqlen).long()
y = t.arange(seqlen).long()
x = x % 8
y = (y + 1) % 8
x = TF.one_hot(x, num_classes=8).float()
y = TF.one_hot(y, num_classes=8).float()
x = 2 * x + y
x = x.cuda()

torch_res = TF.softmax(x, -1).cpu().numpy()


seqlen=8

x = p.arange(seqlen).cast('int64')
y = p.arange(seqlen).cast('int64')
x = x % 8
y = (y + 1) % 8
x = PF.one_hot(x, num_classes=8)
y = PF.one_hot(y, num_classes=8)
x = 2 * x + y

paddle_res = PF.softmax(x, -1).numpy()

print(f'torch-vs-paddle diff: {paddle_res-torch_res}')
paddle_diag = paddle_res[range(8), range(8)]
torch_diag = torch_res[range(8), range(8)]
print()
print(f'paddle-对角线:{paddle_diag}')
print(f'torch-对角线:{torch_diag}')

运行结果

torch-versoin: 49444c3e546bf240bed24a101e747422d1f8a0ee
paddle-version: fd48f88b46d66c536ee3da0a373380746b2d1f05

torch-vs-paddle diff: [[-5.9604645e-08  0.0000000e+00 -7.4505806e-09 -7.4505806e-09
  -7.4505806e-09 -7.4505806e-09 -7.4505806e-09 -7.4505806e-09]
 [-7.4505806e-09 -5.9604645e-08  0.0000000e+00 -7.4505806e-09
  -7.4505806e-09 -7.4505806e-09 -7.4505806e-09 -7.4505806e-09]
 [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  1.4901161e-08
   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00]
 [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00
   1.4901161e-08  0.0000000e+00  0.0000000e+00  0.0000000e+00]
 [-7.4505806e-09 -7.4505806e-09 -7.4505806e-09 -7.4505806e-09
  -5.9604645e-08  0.0000000e+00 -7.4505806e-09 -7.4505806e-09]
 [-7.4505806e-09 -7.4505806e-09 -7.4505806e-09 -7.4505806e-09
  -7.4505806e-09 -5.9604645e-08  0.0000000e+00 -7.4505806e-09]
 [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00
   0.0000000e+00  0.0000000e+00  0.0000000e+00  1.4901161e-08]
 [ 1.4901161e-08  0.0000000e+00  0.0000000e+00  0.0000000e+00
   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00]]

paddle-对角线:[0.45873845 0.45873845 0.4587385  0.4587385  0.45873845 0.45873845
 0.4587385  0.4587385 ]
torch-对角线:[0.4587385 0.4587385 0.4587385 0.4587385 0.4587385 0.4587385 0.4587385
 0.4587385]

可见,在输入主对角线 值全为2.0的情况下,paddle softmax结果的对角线值并不一致(有的是 0.45873845 , 有的是 0.4587385

其他补充信息 Additional Supplementary Information

No response

pw9qyyiw

pw9qyyiw1#

def mysoftmax(x):
    exp = paddle.exp(x - paddle.max(x, -1, keepdim=True))
    exp_sum = paddle.sum(exp, -1, keepdim=True)
    return exp / exp_sum

用上面这个自己写的softmax替代,精度能完整对齐。

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