[GitHub] [tvm] qzylalala opened a new issue, #15206: [Bug] [Auto Scheduler] Cannot find any valid schedule

["qzylalala (via GitHub)" <gi...@apache.org>]

qzylalala opened a new issue, #15206:
URL: https://github.com/apache/tvm/issues/15206

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   ### Expected behavior
   
   I'm trying auto scheduling step by step, following this official doc https://tvm.apache.org/docs/tutorial/autotvm_matmul_x86.html#sphx-glr-download-tutorial-autotvm-matmul-x86-py
   
   It should work as showed in this tutorial.
   
   ### Actual behavior
   
   However, I got an error when it arrived at this line of code.
   ```python3
   # Run auto-tuning (search)
   task.tune(tune_option)
   # Apply the best schedule
   sch, args = task.apply_best(log_file)  # Got an error here
   ```
   I find others have the same problem, like this https://discuss.tvm.apache.org/t/solved-auto-scheduler-cannot-find-a-valid-schedule/11745/8.
   
   Unluckly, it not works for me.
   
   
   ### Environment
   
   OS: Ubuntu1804
   TVM version: newest (commit 683dfb0)
   Python: Anaconda python3.8.3
   LLVM: llvm-17
   
   ### Steps to reproduce
   
   ```python
   import numpy as np
   import tvm
   from tvm import te, auto_scheduler
   
   @auto_scheduler.register_workload  # Note the auto_scheduler decorator
   def matmul_add(N, L, M, dtype):
       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")
   N = L = M = 1024
   task = tvm.auto_scheduler.SearchTask(func=matmul_add, args=(N, L, M, "float32"), target=target)
   
   # Inspect the computational graph
   print("Computational DAG:")
   print(task.compute_dag)
   
   
   log_file = "matmul.json"
   tune_option = auto_scheduler.TuningOptions(
       num_measure_trials=10,
       measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
       verbose=2,
   )
   
   ################################################################################
   # Run the search
   # --------------
   # Now we get all inputs ready. Pretty simple, isn't it?  We can kick off the
   # search and let the auto-scheduler do its magic.  After some measurement
   # trials, we can load the best schedule from the log file and apply it.
   
   # Run auto-tuning (search)
   task.tune(tune_option)
   # Apply the best schedule
   sch, args = task.apply_best(log_file)
   
   print("Lowered TIR:")
   print(tvm.lower(sch, args, simple_mode=True))
   ```
   
   ### Triage
   
   Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below).
   
   * needs-triage
   


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Re: [I] [Bug] [Auto Scheduler] Cannot find any valid schedule [tvm]

["tqchen (via GitHub)" <gi...@apache.org>]

tqchen closed issue #15206: [Bug] [Auto Scheduler] Cannot find any valid schedule
URL: https://github.com/apache/tvm/issues/15206


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Re: [I] [Bug] [Auto Scheduler] Cannot find any valid schedule [tvm]

["vfdff (via GitHub)" <gi...@apache.org>]

vfdff commented on issue #15206:
URL: https://github.com/apache/tvm/issues/15206#issuecomment-2689665475

   do you fix the issue after  **pip install cloudpickle** ?


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