No valid solution found using the tutorial example

Source Code:

import tvm

def gemm(A, B):
    k = tvm.reduce_axis((0, B.shape[0]))
    return tvm.compute((A.shape[0], B.shape[1]), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k))

def wrap_gemm(N, K, M):
    A = tvm.placeholder((N, K))
    B = tvm.placeholder((K, M))
    Output = gemm(A, B)
    return [Output.op], [A, B, Output]

from flextensor.task import register_task, Task
from flextensor.model import WalkerGroup
from flextensor.scheduler import schedule

if __name__ == '__main__':
  task = Task(
    "gemm",
    "gemm",
    wrap_gemm,
    (1024, 1024, 1024),
    "llvm",
    0)
  register_task(task)
  s, bufs, configs = schedule(
            task.key, # give the key of target task
            slevel=4,
            rlevel=3,
            op_trial=100,
            timeout=10,
            op_stop=30,
            method="searching",
            parallel=4,
            )

Output:

graph space size 1
op 0 space size: 43188288
[Warning] Directory lib is not empty, but reusing it
warm up [1584960963.652893] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584960977.754150] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584960991.611287] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961005.486223] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961019.563355] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961033.576784] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961047.659202] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961061.669718] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961075.572861] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961089.390276] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961103.236112] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961117.260888] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961131.503041] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961145.477987] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961159.549252] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961173.469501] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961187.413991] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961201.389768] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961215.701637] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
warm up [1584961230.179169] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
Warning: No valid schedule found in warm up process, please use more trials
Now automatically use more trials, increase 20
warm up [1584961244.064868] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
Warning: No valid schedule found in warm up process, please use more trials
Now automatically use more trials, increase 20
warm up [1584961257.944109] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
Warning: No valid schedule found in warm up process, please use more trials
Now automatically use more trials, increase 20
warm up [1584961271.798598] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
Warning: No valid schedule found in warm up process, please use more trials
Now automatically use more trials, increase 20
warm up [1584961285.662753] [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf ]
Warning: No valid schedule found in warm up process, please use more trials
...
...

[KnowingNothing]

This is a small bug and I have checked in a fix for this. BTW, if you want to try some advanced searching, please take a look at flextensor/optimize directory. For example, you can try optimize_conv2d.py with the following command:

python optimize_conv2d.py --shapes yolo --target cuda --trials 1000 --timeout 10 --parallel 8 --log tmp_log.txt

The meanings of different options are:

--shapes: input shapes for conv2d, pre-defined in `configs/conv2d_config.py`
--target: run on which hardware
--trials: number of trials in searching, recommend >= 1000
--timeout: timeout threshold for single point (in seconds)
--parallel: number of parallel runners
--log: dump final schedule config to file

[KnowingNothing]

Great. Thanks for your suggestion.