lsy643 commented on pull request #6076:
URL: https://github.com/apache/incubator-tvm/pull/6076#issuecomment-659854233
@zhiics @kevinthesun I am not trying to support heterogeneous execution in
this PR. What I want is to make a model with operators all on gpu and dynamic
input can compile and execute as expected. Such as this network:
```python
debug_graph = tf.Graph()
with debug_graph.as_default():
input_1 = tf.placeholder(dtype=tf.int32, shape=[None], name='input_1')
result = tf.nn.relu(input_1, name='result')
target = "cuda"
context = tvm.gpu()
```
After the compilation of the above network, the shape function for `relu` is
located on cpu but the output tensor for the shape function is located on gpu,
which causes the `Argument arg1.device_type has an unsatisfied constraint`
error. By creating a temporary cpu tensor for outputs of shape function and
then copying back to the tensor on gpu, the compilation and execution of the
above network can currently work.
Besides, is it reasonable to deal with shape functions separately, like
adding a `Opcode::InvokeShapeFunc` or something else? Since the shape function
seems to always locate on cpu.
I am highly looking forward to the heterogeneous compilation and execution
of the virtual machine, which will make my current work much easier. If there
is anything I can help, just let me know. Thanks a lot.
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