[GitHub] [tvm] CR1SceNT opened a new issue, #12921: [Bug] All the measured programs in auto-scheduler report errors in Windows.

[GitBox <gi...@apache.org>]

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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