cxx122 opened a new issue, #15199:
URL: https://github.com/apache/tvm/issues/15199
The te programs before schedule optimizations didn't report any problems.
But after the schedule optimization, it will report the error message "`Check
failed: pb->value != 0 (0 vs. 0) : Divide by zero`"
### Expected behavior
There is no error message after the schedule optimization.
### Actual behavior
`Check failed: pb->value != 0 (0 vs. 0) : Divide by zero`
### Environment
Operating System: Ubuntu 18.04
TVM version: v0.10.dev0
### Steps to reproduce
```
import tvm
import json
import random
import numpy as np
from tvm import te
from tvm import tir
from tvm import testing
from tvm import auto_scheduler
from tvm.auto_scheduler.workload_registry import register_workload_tensors
POLICY_PARAMS = {
"eps_greedy": 0.05,
"retry_search_one_round_on_empty": 1,
"sample_init_min_population": 3,
"sample_init_use_measured_ratio": 0.2,
"evolutionary_search_population": 5,
"evolutionary_search_num_iters": 4,
"evolutionary_search_mutation_prob": 0.85,
"cpu_multi_level_tiling_structure": "SSRSRS",
"gpu_multi_level_tiling_structure": "SSSRRSRS",
# Notice: the default thread bind policy of GPU assumes the tiling
structure to have at
# least 3 spatial tiling levels in outermost
"max_innermost_split_factor": 64,
"max_vectorize_size": 16,
"disable_change_compute_location": 0,
}
def te_test():
placeholder_1 = te.placeholder([1, 64, 52, 52, 4], name='placeholder')
rv0 = te.reduce_axis([0, 2], name='rv0')
rv1 = te.reduce_axis([0, 2], name='rv1')
tensor = te.compute([1, 64, 26, 26, 4], lambda ax0, ax1, ax2, ax3, ax4 :
te.max(placeholder_1[ax0, ax1, te.floormod((ax2 + 2), rv0), (tir.Div((ax3*2),
rv1)), ax4], axis=[rv0, rv1]), name='tensor')
return [placeholder_1, tensor]
# Get dag and print it.
tensors = te_test()
dag = auto_scheduler.ComputeDAG(tensors)
dict = json.loads(tvm.ir.save_json(tensors))
with open("./saved_json.txt", "w") as file:
file.write(tvm.ir.save_json(tensors))
print(dag)
# Get inputs.
inputs = []
for tensor in dag.tensors:
shape = [x.value if 'value' in dir(x) and isinstance(x.value, int) else
1 for x in tensor.shape]
inputs.append((2 * np.random.uniform(size=shape)+1).astype(tensor.dtype))
# Get program with no schedule.
results = []
mod_list = []
pre_schedule_list = dag.apply_steps_from_state(dag.get_init_state())
pre_mod = tvm.lower(pre_schedule_list[0], pre_schedule_list[1],
simple_mode=True)
mod_list.append(pre_mod)
with tvm.transform.PassContext(opt_level=0):
mod_exec = tvm.build(pre_mod)
print(pre_mod)
new_inputs = [tvm.nd.array(x, tvm.cpu()) for x in inputs.copy()]
mod_exec(*new_inputs)
result = []
for x in new_inputs:
try:
result.append(x.numpy() if isinstance(
x, tvm.runtime.NDArray) else x)
except (ValueError, tvm.TVMError):
result.append(None)
results.append(result)
# Get program with schedule.
register_workload_tensors(dag.workload_key(), tensors)
task = auto_scheduler.SearchTask(workload_key=dag.workload_key(),
target=tvm.target.Target("llvm"))
policy = auto_scheduler.SketchPolicy(task, verbose=0, params=POLICY_PARAMS)
states = policy.sample_initial_population()
for state in states:
schedule_list = dag.apply_steps_from_state(state)
mod = tvm.lower(schedule_list[0], schedule_list[1], simple_mode=True)
mod_list.append(mod)
print(mod)
with tvm.transform.PassContext(opt_level=0):
mod_exec = tvm.build(mod)
new_inputs = [tvm.nd.array(x, tvm.cpu()) for x in inputs.copy()]
mod_exec(*new_inputs)
result = []
for x in new_inputs:
try:
result.append(x.numpy() if isinstance(
x, tvm.runtime.NDArray) else x)
except (ValueError, tvm.TVMError):
result.append(None)
results.append(result)
for i in range(1, len(results)):
result = results[i]
for compare_idex in [-1]:
try:
tvm.testing.assert_allclose(results[0][compare_idex],
result[compare_idex], rtol=1e-5)
except AssertionError as e:
print(e)
print(mod_list[i])
break
```
### Triage
* tune:auto_scheduler
* tir:schedule
* tir:transform
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