You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/11/10 16:30:52 UTC

[GitHub] [incubator-tvm] bernhardklein opened a new issue #6894: [autoTVM][BUG] bitserial dense arm_cpu

bernhardklein opened a new issue #6894:
URL: https://github.com/apache/incubator-tvm/issues/6894


   The **bitserial dense** operation for **ARM CPU's** is not **auto-tune**-able for larger matrix sizes. For small sizes N=64,125,256,512 it works, but for N=768,1024,2048 it crashes with a broadcast shape error. I reproduced this with TVM v0.7 branch and the current head 80ca598e271c1a93eadc26e31e5bef00b8d86899 of main branch.
   
   error message:
   ```count of tasks =  1
   list of tasks:
   task =  bitserial_dense.arm_cpu
   tasks =  [Task(func_name=bitserial_dense.arm_cpu, args=(('TENSOR', (768, 768), 'uint8'), ('TENSOR', (768, 768), 'uint8'), 1, 1, 'uint8', 'int16', 1), kwargs={}, workload=('bitserial_dense.arm_cpu', ('TENSOR', (768, 768), 'uint8'), ('TENSOR', (768, 768), 'uint8'), 1, 1, 'uint8', 'int16', 1))]
   Tuning...
   [Task  1/ 1]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/108) | 0.00 sTraceback (most recent call last):
     File "autotuning.py", line 239, in <module>
       main( sys.argv[1:] )
     File "autotuning.py", line 174, in main
       autotune_bunchOfTinyNets( device_config, botn, timestamp )
     File "autotuning.py", line 91, in autotune_bunchOfTinyNets
       timestamp )
     File "autotuning.py", line 65, in autotune
       autotuner.tune( device_config, ops, network )
     File "./autotuner.py", line 173, in tune
       tune_tasks(tasks, **tuning_opt)
     File "./autotuner.py", line 139, in tune_tasks
       autotvm.callback.log_to_file(tmp_log_file)])
     File "tvm/python/tvm/autotvm/tuner/xgboost_tuner.py", line 103, in tune
       super(XGBTuner, self).tune(*args, **kwargs)
     File "tvm/python/tvm/autotvm/tuner/tuner.py", line 169, in tune
       self.update(inputs, results)
     File "tvm/python/tvm/autotvm/tuner/model_based_tuner.py", line 270, in update
       self.cost_model.fit(self.xs, self.ys, self.plan_size)
     File "tvm/python/tvm/autotvm/tuner/xgboost_cost_model.py", line 184, in fit
       x_train = self._get_feature(xs)
     File "tvm/python/tvm/autotvm/tuner/xgboost_cost_model.py", line 348, in _get_feature
       ret[i, :] = t if t is not None else 0
   ValueError: could not broadcast input array from shape (1854) into shape (1278)```
   
   


----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org



[GitHub] [incubator-tvm] comaniac closed issue #6894: [autoTVM][BUG] bitserial dense arm_cpu

Posted by GitBox <gi...@apache.org>.
comaniac closed issue #6894:
URL: https://github.com/apache/incubator-tvm/issues/6894


   


----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org



[GitHub] [incubator-tvm] comaniac commented on issue #6894: [autoTVM][BUG] bitserial dense arm_cpu

Posted by GitBox <gi...@apache.org>.
comaniac commented on issue #6894:
URL: https://github.com/apache/incubator-tvm/issues/6894#issuecomment-724854267


   At the first glance it looks like the problem in XGBTuner, so trying RandomTuner might workaround it. Let's move the future discussions to https://discuss.tvm.apache.org/.
   


----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org