Currently, I try to learn and understand TVM. I have a mxnet model and I want
to do AutoTVM, so I try to use `autotvm.task.extract_from_program`. The code is
similar to this
```
target = tvm.target.cuda()
tasks = autotvm.task.extract_from_program(
mod["main"], target=target, target_host=None, params=param
)
```
I try to print the tasks and get this
```
Task(func_name=dense_nopack.x86, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_nopack.x86', ('TENSOR', (106, 64), 'float32'), ('TENSOR', (64,
64), 'float32'), None, 'float32'))
Task(func_name=dense_pack.x86, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_pack.x86', ('TENSOR', (106, 64), 'float32'), ('TENSOR', (64,
64), 'float32'), None, 'float32'))
Task(func_name=batch_matmul.x86, args=(('TENSOR', (8, 106, 8), 'float32'),
('TENSOR', (8, 106, 8), 'float32')), kwargs={}, workload=('batch_matmul.x86',
('TENSOR', (8, 106, 8), 'float32'), ('TENSOR', (8, 106, 8), 'float32')))
Task(func_name=batch_matmul.x86, args=(('TENSOR', (8, 106, 106), 'float32'),
('TENSOR', (8, 8, 106), 'float32')), kwargs={}, workload=('batch_matmul.x86',
('TENSOR', (8, 106, 106), 'float32'), ('TENSOR', (8, 8, 106), 'float32')))
Task(func_name=dense_nopack.x86, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_nopack.x86', ('TENSOR', (36, 64), 'float32'), ('TENSOR', (64,
64), 'float32'), None, 'float32'))
Task(func_name=dense_pack.x86, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_pack.x86', ('TENSOR', (36, 64), 'float32'), ('TENSOR', (64,
64), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 512), 'float32'),
('TENSOR', (2, 512), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 512), 'float32'),
('TENSOR', (2, 512), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 512), 'float32'),
('TENSOR', (2, 512), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 512), 'float32'),
('TENSOR', (2, 512), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 512), 'float32'),
('TENSOR', (512, 512), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 512), 'float32'),
('TENSOR', (512, 512), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 512), 'float32'),
('TENSOR', (512, 512), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 512), 'float32'),
('TENSOR', (512, 512), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (512, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (512, 64), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (512, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (512, 64), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (106, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 106, 36), 'float32'),
('TENSOR', (8, 8, 36), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 106, 36), 'float32'), ('TENSOR', (8, 8, 36), 'float32')))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 106, 8), 'float32'),
('TENSOR', (8, 36, 8), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 106, 8), 'float32'), ('TENSOR', (8, 36, 8), 'float32')))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 106, 106), 'float32'),
('TENSOR', (8, 8, 106), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 106, 106), 'float32'), ('TENSOR', (8, 8, 106), 'float32')))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 106, 8), 'float32'),
('TENSOR', (8, 106, 8), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 106, 8), 'float32'), ('TENSOR', (8, 106, 8), 'float32')))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (36, 64), 'float32'), ('TENSOR',
(64, 64), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (64, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (36, 64), 'float32'), ('TENSOR',
(64, 64), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (36, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (36, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (36, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (36, 128), 'float32'),
('TENSOR', (64, 128), 'float32'), None, 'float32'))
Task(func_name=dense_small_batch.cuda, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_small_batch.cuda', ('TENSOR', (36, 64), 'float32'), ('TENSOR',
(128, 64), 'float32'), None, 'float32'))
Task(func_name=dense_large_batch.cuda, args=(('TENSOR', (36, 64), 'float32'),
('TENSOR', (128, 64), 'float32'), None, 'float32'), kwargs={},
workload=('dense_large_batch.cuda', ('TENSOR', (36, 64), 'float32'), ('TENSOR',
(128, 64), 'float32'), None, 'float32'))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 36, 36), 'float32'),
('TENSOR', (8, 8, 36), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 36, 36), 'float32'), ('TENSOR', (8, 8, 36), 'float32')))
Task(func_name=batch_matmul.cuda, args=(('TENSOR', (8, 36, 8), 'float32'),
('TENSOR', (8, 36, 8), 'float32')), kwargs={}, workload=('batch_matmul.cuda',
('TENSOR', (8, 36, 8), 'float32'), ('TENSOR', (8, 36, 8), 'float32')))
...
```
My question is that why I get the `x86` tasks even if I specify the target to
cuda?
Another thing is that when I try to do autoTVM with these task, I get these
warning and GFLOPs always show 0 in first `x86` tasks.
```
Variable `placeholder` is directly accessed by host memory (it is not contained
in a thread environment or in the function arguments.
```
Thus, my work around method is to skip tune x86 tasks. Maybe, I do something
wrong here. Is anyone have some clue about this warning?
Thank you.
---
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Topic](https://discuss.tvm.apache.org/t/get-x86-task-from-autotvm-task-extract-from-program/11557/1)
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