formatting

python/tvm/autotvm/task/topi_integration.py

@@ -229,7 +229,11 @@ def wrapper(outs, *args, **kwargs):
             """wrapper function for topi schedule"""
             workload = get_workload(outs)
             if workload is None:
-                raise RuntimeError(f"Cannot find workload for {task_name}. You may need to register a compute function for it with `@tvm.autotvm.register_topi_compute(\"{task_name}\")`")
+                raise RuntimeError(
+                    f"Cannot find workload for {task_name}. You may need to "
+                    "register a compute function for it with "
+                    f'`@tvm.autotvm.register_topi_compute("{task_name}")`'
+                )
             tgt = Target.current()
             cfg = DispatchContext.current.query(tgt, workload)
             return topi_schedule(cfg, outs, *args, **kwargs)

python/tvm/relay/op/nn/_nn.py

@@ -748,7 +748,7 @@ def compute_space_to_depth(attrs, inputs, out_dtype):
 
 # embed
 @reg.register_compute("nn.embed")
-def compute_embed_grad(attrs, inputs, out_type):
+def compute_embed(attrs, inputs, out_type):
     """Compute definition of embed"""
     return [topi.nn.embed(inputs[0], inputs[1])]
 

python/tvm/te/hybrid/calls.py

@@ -137,7 +137,7 @@ def _cast(func_id, args):
     return _expr.Cast(func_id, args[0])
 
 
-def cast(func_id, args):
+def cast(_, args):
     _internal_assert(args.__len__() == 2, "cast requires two arguments: dtype, value")
     return _expr.Cast(args[0], args[1])
 

python/tvm/topi/cuda/nn.py

@@ -18,10 +18,9 @@
 """scheduler functions for cuda backend"""
 from __future__ import absolute_import as _abs
 
+from tvm import te
 from .. import cpp
 
-from tvm import autotvm, te
-
 
 def schedule_lrn(outs):
     """Schedule for LRN
@@ -44,8 +43,8 @@ def loads_per_thread(dtype):
     """Number elements per load per thread"""
     s = regex.search("[0-9]+", dtype)
     assert s is not None
-    bytes = int(s.group()) // 8
-    return 16 // bytes
+    byts = int(s.group()) // 8
+    return 16 // byts
 
 
 def schedule_embed_grad(outs):
@@ -63,7 +62,8 @@ def schedule_embed_grad(outs):
         The computation schedule for the op.
     """
     s = te.create_schedule([outs[0].op])
-    vec_size = loads_per_thread(outs[0].dtype) # this should be autotuned, but we can't with hybrid script
+    # this should be autotuned, but we can't with hybrid script
+    vec_size = loads_per_thread(outs[0].dtype)
     warp_size = int(tvm.target.Target.current(allow_none=False).thread_warp_size)
     num_warps = 4
     out = s.outputs[0].output(0)
@@ -72,7 +72,7 @@ def schedule_embed_grad(outs):
     s[out].vectorize(ji)
     joo, joi = s[out].split(jo, factor=warp_size)
     s[out].bind(joi, te.thread_axis("threadIdx.x"))
-    jooo, jooi = s[out].split(joo, factor=num_warps)
+    _, jooi = s[out].split(joo, factor=num_warps)
     s[out].bind(jooi, te.thread_axis("threadIdx.y"))
     s[out].bind(i, te.thread_axis("blockIdx.x"))
 

python/tvm/topi/x86/nn.py

@@ -16,7 +16,7 @@
 # under the License.
 # pylint: disable=invalid-name,too-many-locals,unused-variable
 """x86 nn operators"""
-from tvm import autotvm, te
+from tvm import te
 
 
 def schedule_softmax(outs):

tests/python/topi/python/test_topi_embed.py

@@ -1,3 +1,19 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
 import tvm
 import tvm.testing
 import tvm.topi.testing