Reenable autotvm silencer; fix e2e_auto_tir.py; fix lint.

Co-authored-by: YuchenJin <[email protected]>

apps/relax_examples/e2e_auto_tir.py

@@ -191,18 +191,21 @@ def main():
     relax_mod = apply_opt_before_tuning(relay_mod, params, target=ARGS.target)
     assert isinstance(relax_mod, tvm.IRModule)
 
-    executable = ms.tune_relax(
+    db = ms.relax_integration.tune_relax(
         mod=relax_mod,
         target=ARGS.target,
-        config=ms.TuneConfig(
-            strategy="evolutionary",
-            num_trials_per_iter=64,
-            max_trials_per_task=ARGS.num_trials,
-            max_trials_global=ARGS.num_trials,
-        ),
+        params=params,
+        num_trials_per_iter=64,
+        max_trials_per_task=ARGS.num_trials,
+        max_trials_global=ARGS.num_trials,
         runner=get_runner(),
         work_dir=ARGS.work_dir,
-        num_threads=os.cpu_count(),
+    )
+    executable = ms.relax_integration.compile_relax(
+        db,
+        mod=relax_mod,
+        target=ARGS.target,
+        params=params,
     )
 
     for input_name, input_shape in input_info.items():

python/tvm/relax/testing/relay_translator.py

@@ -28,6 +28,7 @@
 from tvm.relay.backend.te_compiler import select_implementation
 from tvm.runtime import NDArray
 from tvm.target import Target
+from tvm.meta_schedule.relay_integration import _autotvm_silencer
 
 
 def from_relay(
@@ -196,7 +197,7 @@ def visit_func(node):
 
     # Since optimization passes and OpStrategy are highly context-dependent,
     # we match the exact same context with `extract_task_from_relay()` env
-    with tvm.transform.PassContext(
+    with _autotvm_silencer(), tvm.transform.PassContext(
         opt_level=opt_level,
         config=pass_config,
         disabled_pass=disabled_pass,

python/tvm/relax/transform/transform.py

@@ -24,9 +24,6 @@
 import numpy as np
 import tvm.ir
 from tvm.runtime import NDArray
-from tvm.target import Target
-from tvm.meta_schedule.database import PyDatabase
-
 from . import _ffi_api