Batch matmul imprecision
@hunse Could you please share all the dependencies to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/925ea8b334191ed3da1aa6b81d488275/61683.ipynb). 
Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. @sushreebarsa ,

What dependencies you need still? Seems your colab file executed fine. Why do you think some dependencies missing?

@hunse ,
It seems in colab environment `assert_allclose()` is success for given tolerance. Are you sure the attached code snippet is the one raising error for you? Could you please execute the attached code snippet in colab and confirm the behaviour with gist?

 I looked into this a bit more, and it seems to be a CUDA/driver/GPU issue. We have two machines that show the error, and a few other machines that don't, despite trying to make all CUDA/driver/etc. versions equivalent among all machines.

Furthermore, on the new machines/environments that show the issue, it also appears in earlier TF versions.

I'll close this, since it does not seem to be TF, but rather some lower-level issue. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61683">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61683">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13 / 2.14.0-dev20230706

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

```
$ mamba list | grep cu
cuda-nvcc                 12.2.128                      0    nvidia
cudatoolkit               11.8.0              h4ba93d1_12    conda-forge
nvidia-cublas-cu11        2022.4.8                 pypi_0    pypi
nvidia-cublas-cu117       11.10.1.25               pypi_0    pypi
nvidia-cuda-nvrtc-cu11    2022.4.8                 pypi_0    pypi
nvidia-cuda-nvrtc-cu117   11.7.50                  pypi_0    pypi
nvidia-cudnn-cu11         8.9.4.25                 pypi_0    pypi
```
### GPU model and memory

NVIDIA GeForce RTX 3060 - 12 GB
Driver Version: 520.61.05

### Current behavior?

I'm trying to do a batch matrix multiply (i.e. I've got a bunch of m x n matrices, all in one tensor, and I want to do a matrix multiply of each of them with some other matrix). However, I'm getting slightly different results than I get from Numpy (and previous TensorFlow versions, this script passed for me in 2.9). 

One interesting thing is that the value 25 (the second dimension of `x`) is significant. If I reduce this to 16 or below, it passes. Also, if I change the second dimension of `c` from 2 to 1, it also passes.

### Standalone code to reproduce the issue

```python
import numpy as np
import tensorflow as tf

print(f"file: {tf.__file__}")
print(f"git version: {tf.version.GIT_VERSION}")
print(f"version: {tf.__version__}")

rng = np.random.RandomState(0)
x = rng.uniform(-1, 1, size=(1, 25, 5)).astype(np.float32)
c = rng.uniform(-1, 1, size=(5, 2)).astype(np.float32)

y = x @ c

x2 = np.tile(x, (2, 1, 1))
y2 = x2 @ c

for y2i in y2:
    np.testing.assert_allclose(y2i, y.squeeze(0))

tols = dict(atol=1e-7, rtol=1e-5)

z = tf.matmul(x, c).numpy()
# z = tf.einsum("...tq,...qr->...tr", x, c)
np.testing.assert_allclose(z, y, **tols)

z2 = tf.matmul(x2, c).numpy()
# z2 = tf.einsum("...tq,...qr->...tr", x2, c)
np.testing.assert_allclose(z2, y2, **tols)
```


### Relevant log output

```shell
file: /home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/tensorflow/__init__.py
git version: v1.12.1-96406-gfa4d29bfef8
version: 2.14.0-dev20230706
Traceback (most recent call last):
  File "test_batch.py", line 26, in <module>
    np.testing.assert_allclose(z2, y2)
  File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 1592, in assert_allclose
    assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
  File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/contextlib.py", line 75, in inner
    return func(*args, **kwds)
  File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 862, in assert_array_compare
    raise AssertionError(msg)
AssertionError: 
Not equal to tolerance rtol=1e-07, atol=0

Mismatched elements: 100 / 100 (100%)
Max absolute difference: 0.00046098
Max relative difference: 0.01047752
 x: array([[[-0.187503,  0.005696],
        [-0.255552, -0.84404 ],
        [ 0.268836, -1.250793],...
 y: array([[[-0.187499,  0.005691],
        [-0.255404, -0.844322],
        [ 0.268935, -1.251033],...
```
