Support for asynchronous execution in TensorFlow DLPack interface
I am new to OpenSource .Can u please assign me this issue
 @lwlsaysnuaa,
Could you please  elaborate about your Feature. Also, please specify the Use Cases for this feature, it helps to analyse the issue/feature in an effective way. Thank you! @tilakrayal,
### Use Case — dlpack_execute.py
tensorflow version: 2.9.0
```python
import tensorflow as tf
import tensorflow.experimental.dlpack as tfdlpack

# Define a pre-processing network that runs on the TensorFlow framework and
# consists of 3 matmuls in series.
def pre_process_network(input_tensor):
    w1 = tf.Variable(tf.random.normal([10000, 10000]))
    w2 = tf.Variable(tf.random.normal([10000, 10000]))
    w3 = tf.Variable(tf.random.normal([10000, 10000]))
    x = tf.matmul(input_tensor, w1)
    x = tf.matmul(x, w2)
    x = tf.matmul(x, w3)
    return x

# Define an intermediate processing network that will actually run on another
# inference framework. For the sake of demonstration, we use the TensorFlow
# framework to simulate the execution of other inference engines. The input
# and output of the model are both dlpack tensors, and the content is 3 matmuls
# in series.
def mid_process_network_sync(input_tensor):
    w1 = tf.Variable(tf.random.normal([10000, 100]))
    w2 = tf.Variable(tf.random.normal([100, 100]))
    w3 = tf.Variable(tf.random.normal([100, 10000]))
    input_tensor = tfdlpack.from_dlpack(input_tensor)
    x = tf.matmul(input_tensor, w1)
    x = tf.matmul(x, w2)
    x = tf.matmul(x, w3)
    x = tfdlpack.to_dlpack(x)
    return x

# Define an intermediate processing network that runs on the TensorFlow
# framework and consists of 3 matmuls in series.
def mid_process_network_async(input_tensor):
    w1 = tf.Variable(tf.random.normal([10000, 100]))
    w2 = tf.Variable(tf.random.normal([100, 100]))
    w3 = tf.Variable(tf.random.normal([100, 10000]))
    x = tf.matmul(input_tensor, w1)
    x = tf.matmul(x, w2)
    x = tf.matmul(x, w3)
    return x

# Define a post-processing network that runs on the TensorFlow framework and
# consists of 3 matmuls in series.
def post_process_network(input_tensor):
    w1 = tf.Variable(tf.random.normal([10000, 10000]))
    w2 = tf.Variable(tf.random.normal([10000, 10000]))
    w3 = tf.Variable(tf.random.normal([10000, 10000]))
    x = tf.matmul(input_tensor, w1)
    x = tf.matmul(x, w2)
    x = tf.matmul(x, w3)
    return x

# Main function: create input tensor and call pre-processing network to get
# output pre_output, call mid-process network (other inference engine) with
# pre_output passed to mid-process networkvia tfdlpack.to_dlpack to get output
# mid_output, call post-process network with output mid_output passed via
# tfdlpack.from_dlpack. This process is repeated 10 times through a loop,
# print the final result.
def main():
    # Optimal performance: all three networks run asynchronously on the
    # TensorFlow framework.
    input_tensor = tf.ones([1, 10000])
    for i in range(10):
        pre_output = pre_process_network(input_tensor)
        mid_output = mid_process_network_async(pre_output)
        post_output = post_process_network(mid_output)
        input_tensor = post_output
    print(post_output)

    # Poor performance: intermediate network runs on other inference engine
    # but there is synchronization due to dlpack before and after.
    input_tensor = tf.ones([1, 10000])
    for i in range(10):
        pre_output = pre_process_network(input_tensor)
        mid_output = tfdlpack.from_dlpack(
            mid_process_network_sync(tfdlpack.to_dlpack(pre_output)))
        post_output = post_process_network(mid_output)
        input_tensor = post_output
    print(post_output)

if __name__ == '__main__':
    main()
```

By running the following command, we can obtain the Nsight Systems file. Opening the file, we can see the result shown in the figure below. It can be seen that the dlpack_execute.py is mainly divided into two execution stages. In the first stage, the pre-, mid-, and post-networks are all executed asynchronously on the TensorFlow framework, and the gap time between kernels is almost zero. In the second stage, the mid-network uses DLPack to convert inputs and outputs, and the intermediate execution is on another inference engine. Since DLPack is executed synchronously, it introduces many synchronization operations, resulting in gap time between kernels, leading to a decrease in hardware utilization and an increase in end-to-end execution time. If DLPack supports asynchronous operations, this part of the execution logic would be similar to the first stage, using asynchronous methods throughout, which could improve hardware utilization.

`nsys profile -w true -t cuda,nvtx,cudnn,cublas -f true -x true -o dlpack_execute python dlpack_execute.py`

![image](https://github.com/tensorflow/tensorflow/assets/29362872/8da144e3-7cbf-4c7d-9e28-5dd0bd78c691)
 I would like to work on this issue can you please assign me this issue?
 @amishhaa , We don't assign any external contributors to the issue.
But, you're always welcome to contribute on any issue.
Feel free to create a PR and link this issue. Thanks!
I would like to inquire about the possibility of adding asynchronous execution support to the TensorFlow DLPack interface.
Currently, the `tensorflow.experimental.dlpack.to_dlpack` and `tensorflow.experimental.dlpack.from_dlpack` functions are synchronous operations, which can introduce synchronization overhead when working across frameworks.

If the TensorFlow DLPack interface supported asynchronous execution, it could help reduce the synchronization overhead and improve the overall execution efficiency when working with multiple frameworks. This would be a valuable addition to the TensorFlow ecosystem.

Thank you for your hard work and dedication to improving TensorFlow. I look forward to hearing your thoughts on this matter.

### Standalone code to reproduce the issue

```python
import tensorflow as tf
import tensorflow.experimental.dlpack as tfdlpack

# Pre-frame processing asynchronous
...

# Handle synchronization across frameworks
output = model(**({'input': tfdlpack.to_dlpack(input)}))

# Framework post-processing asynchronous
...
```


### Relevant log output

_No response_