CR1SceNT opened a new issue, #12921:
URL: https://github.com/apache/tvm/issues/12921
I'm trying to use auto scheduler to tune a matmul_add function. The code
works fine in Linux, but in Windows, it seems all the programs errored in
measurement.
```
----------------------------------------------------------------------
------------------------------ [ Search ]
----------------------------------------------------------------------
Generate Sketches #s: 3
Sample Initial Population #s: 905 fail_ct: 829 Time elapsed: 1.21
GA Iter: 0 Max score: 0.9997 Min score: 0.8380 #Pop: 128
#M+: 0 #M-: 0
GA Iter: 4 Max score: 0.9999 Min score: 0.9859 #Pop: 128
#M+: 1372 #M-: 82
EvolutionarySearch #s: 128 Time elapsed: 5.39
----------------------------------------------------------------------
------------------------------ [ Measure ]
----------------------------------------------------------------------
Get 16 programs to measure:
................*E*E*E*E*E*E*E*E*E*E*E*E*E*E*E*E
Time elapsed for measurement: 14.04 s
----------------------------------------------------------------------
------------------------------ [ Done ]
----------------------------------------------------------------------
```
Code show as below:
```python
import tvm
from tvm import auto_scheduler, te
@auto_scheduler.register_workload # Note the auto_scheduler decorator
def matmul_add(N, L, M, dtype):
# A * B + C
A = te.placeholder((N, L), name="A", dtype=dtype)
B = te.placeholder((L, M), name="B", dtype=dtype)
C = te.placeholder((N, M), name="C", dtype=dtype)
k = te.reduce_axis((0, L), name="k")
matmul = te.compute(
(N, M),
lambda i, j: te.sum(A[i, k] * B[k, j], axis=k),
name="matmul",
attrs={"layout_free_placeholders": [B]}, # enable automatic layout
transform for tensor B
)
out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
return [A, B, C, out]
target = tvm.target.Target("llvm -mcpu=core-avx2")
N = L = M = 4
task = auto_scheduler.SearchTask(
func=matmul_add, args=(N, L, M, "float32"), target=target
)
print("Computational DAG:")
print(task.compute_dag)
log_file = "matmul_add.json"
print("init cost model")
cost_model = auto_scheduler.XGBModel()
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=16, # change this to 1000 to achieve the best
performance
runner=auto_scheduler.LocalRunner(repeat=10,
enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
verbose=1,
)
print("start tune")
task.tune(tune_option)
```
### Environment
OS: Windows 10. TVM version: 0.10.dev644+gfa5045bf6
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