diff --git a/dnn/src/common/reduce_helper_device.h b/dnn/src/common/reduce_helper_device.h
index 3799bcfe..cc6c445b 100644
--- a/dnn/src/common/reduce_helper_device.h
+++ b/dnn/src/common/reduce_helper_device.h
@@ -206,8 +206,8 @@ struct CheckNonFiniteOp {
         return lhs | rhs;
     }
     MEGDNN_HOST MEGDNN_DEVICE CheckNonFiniteOp(
-            src_ctype** srcs, size_t* srcs_total_nr_elems, dst_ctype* dst,
-            size_t B, src_ctype scale)
+            src_ctype** srcs, size_t* srcs_total_nr_elems, dst_ctype* dst, size_t B,
+            src_ctype scale)
             : INIT(wtype(0)),
               srcs(srcs),
               srcs_total_nr_elems(srcs_total_nr_elems),
diff --git a/imperative/python/megengine/amp/convert_format.py b/imperative/python/megengine/amp/convert_format.py
index 4f2cdd5a..86d04ace 100644
--- a/imperative/python/megengine/amp/convert_format.py
+++ b/imperative/python/megengine/amp/convert_format.py
@@ -8,9 +8,9 @@
 from copy import deepcopy
 
 from .. import functional as F
+from ..core import _config
 from ..module import Module
 from ..tensor import Tensor
-from ..core import _config
 
 
 def _is_nchw_format(param: Tensor):
@@ -39,7 +39,7 @@ def convert_tensor_format(x: Tensor, inplace: bool = True):
         else:
             # use mge interface to maintain grad
             x = F.transpose(x, pattern)
-            x.format="nhwc"
+            x.format = "nhwc"
     return x
 
 
diff --git a/imperative/python/megengine/core/_config.py b/imperative/python/megengine/core/_config.py
index 8a16ec86..dd1f4b4b 100644
--- a/imperative/python/megengine/core/_config.py
+++ b/imperative/python/megengine/core/_config.py
@@ -134,7 +134,6 @@ def _compute_mode(mod, _compute_mode: str):
     __compute_mode = _compute_mode
 
 
-
 @property
 def _bn_format(mod):
     r"""Get or set batchnorm param layout format. The default option is None and will
diff --git a/imperative/python/megengine/core/tensor/utils.py b/imperative/python/megengine/core/tensor/utils.py
index 1626b610..ccf48eb0 100644
--- a/imperative/python/megengine/core/tensor/utils.py
+++ b/imperative/python/megengine/core/tensor/utils.py
@@ -5,6 +5,7 @@ from typing import Iterable, Union
 
 import numpy as np
 
+from .. import _config
 from .._imperative_rt import make_const
 from .._imperative_rt.core2 import (
     Const,
@@ -24,7 +25,6 @@ from .._imperative_rt.ops import SubgraphBuilder as _SubgraphBuilder
 from .._imperative_rt.ops import jit_supported
 from .._wrap import as_device
 from ..autodiff.grad import Function
-from .. import _config
 from ..ops import builtin
 from .amp import _get_amp_high_prec_dtype, _get_amp_low_prec_dtype
 from .dtype import is_dtype_equal, is_quantize
diff --git a/imperative/python/megengine/optimizer/sgd.py b/imperative/python/megengine/optimizer/sgd.py
index caeafb36..bfb734c9 100644
--- a/imperative/python/megengine/optimizer/sgd.py
+++ b/imperative/python/megengine/optimizer/sgd.py
@@ -2,10 +2,10 @@
 import os
 from typing import Iterable, Union
 
+from ..core import _config
 from ..functional.inplace import _inplace_add_
 from ..tensor import Parameter, tensor
 from .optimizer import Optimizer
-from ..core import _config
 
 
 class SGD(Optimizer):
diff --git a/imperative/python/src/tensor.cpp b/imperative/python/src/tensor.cpp
index 9a3c7ab3..8f8bf036 100644
--- a/imperative/python/src/tensor.cpp
+++ b/imperative/python/src/tensor.cpp
@@ -1024,9 +1024,9 @@ void init_tensor(py::module m) {
                 using namespace std::placeholders;
                 self.compiled = std::make_shared<CompiledTransformation>(
                         *self.trace_result, self.record_input_shapes);
-                    self.compiled->set_value_comparator(
-                            std::bind(&Trace::compare_value, this, _1, _2));
-                    self.options_visitor(py::cast(&self.compiled->options()));
+                self.compiled->set_value_comparator(
+                        std::bind(&Trace::compare_value, this, _1, _2));
+                self.options_visitor(py::cast(&self.compiled->options()));
                 try {
                     self.compiled->compile();
                 } catch (const std::exception& e) {
diff --git a/imperative/python/src/tensor_utils.cpp b/imperative/python/src/tensor_utils.cpp
index 0c42dc39..62cfd861 100644
--- a/imperative/python/src/tensor_utils.cpp
+++ b/imperative/python/src/tensor_utils.cpp
@@ -320,7 +320,8 @@ py::object _Const(py::handle value, py::handle dtype, py::handle device) {
         }
     }
     py::object device_obj = device2obj(device, true);
-    py::tuple tup = py::make_tuple(val, dtype, device_obj, true, false, py::none(), py::none());
+    py::tuple tup =
+            py::make_tuple(val, dtype, device_obj, true, false, py::none(), py::none());
     return TensorWrapper::make(py_tensor_type, tup.ptr(), nullptr);
 }
 
diff --git a/imperative/python/test/unit/amp/test_grad_scaler.py b/imperative/python/test/unit/amp/test_grad_scaler.py
index d7c33414..bad22f61 100644
--- a/imperative/python/test/unit/amp/test_grad_scaler.py
+++ b/imperative/python/test/unit/amp/test_grad_scaler.py
@@ -47,6 +47,10 @@ def test_grad_scaler(is_trace):
         return loss
 
     for data in [np.random.random((1, 2, 3, 4)), 1.0]:
-        for calc in [double_variables, single_variable, double_variables_with_same_grad]:
+        for calc in [
+            double_variables,
+            single_variable,
+            double_variables_with_same_grad,
+        ]:
             for idx in range(3):
                 f(idx, data, calc)
diff --git a/imperative/src/impl/interpreter/interpreter_impl.cpp b/imperative/src/impl/interpreter/interpreter_impl.cpp
index c466f4e4..528e4092 100644
--- a/imperative/src/impl/interpreter/interpreter_impl.cpp
+++ b/imperative/src/impl/interpreter/interpreter_impl.cpp
@@ -260,7 +260,6 @@ void ChannelImpl::dispatch_default_cpu(
     CompNode output_cn;
     {
         MGB_LOCK_GUARD(m_mutex);
-        //mgb_log_warn(">>> MGB_LOCK_GUARD dispatch_default_cpu");
         for (auto&& info : input_infos) {
             auto input_cn = info->desc.comp_node;
             if (!output_cn.valid()) {
@@ -278,7 +277,6 @@ void ChannelImpl::dispatch_default_cpu(
                 input_tensornds.emplace_back(info->h_value.proxy_to_default_cpu());
             }
         }
-        //mgb_log_warn("<<< MGB_LOCK_GUARD dispatch_default_cpu");
     }
 
     SmallVector<DeviceTensorND> output_tensornds;
@@ -532,9 +530,7 @@ void ChannelImpl::sync() {
 void ChannelImpl::sync_impl() {
     m_worker.wait_all_task_finish();
     MGB_LOCK_GUARD(m_mutex);
-    //mgb_log_warn(">>> MGB_LOCK_GUARD sync_impl");
     check_worker_exc_unsafe();
-    //mgb_log_warn("<<< MGB_LOCK_GUARD sync_impl");
 }
 
 void ChannelImpl::close() {
@@ -693,7 +689,6 @@ ChannelImpl::~ChannelImpl() {
 void ChannelImpl::produce_tensor(TensorInfo* dest, TensorPtr ptr) {
     auto& state = get_worker_state();
     MGB_LOCK_GUARD(m_mutex);
-    //mgb_log_warn(">>> MGB_LOCK_GUARD produce_tensor");
     m_dtr.update_used_time(dest);
     MGB_RECORD_EVENT(
             TensorProduceEvent, dest->id, ptr->layout(), ptr->comp_node(),
@@ -720,19 +715,16 @@ void ChannelImpl::produce_tensor(TensorInfo* dest, TensorPtr ptr) {
         m_dtr.insert_candidate(dest);
     }
     notify_tensor_unsafe(dest);
-    //mgb_log_warn("<<< MGB_LOCK_GUARD produce_tensor");
 }
 
 void ChannelImpl::release_tensor(TensorInfo* dest) {
     MGB_RECORD_EVENT(TensorReleaseEvent, dest->id);
     MGB_LOCK_GUARD(m_mutex);
-    //mgb_log_warn(">>> MGB_LOCK_GUARD release_tensor");
     dest->ptr.reset();
     auto& state = get_worker_state();
     if (dest->size_exceeds_thd(state.options.dtr_evictee_minimum_size)) {
         m_dtr.erase_candidate(dest);
     }
-    //mgb_log_warn("<<< MGB_LOCK_GUARD release_tensor");
 }
 
 void ChannelImpl::regenerate(TensorInfo* dest) {
@@ -1008,7 +1000,6 @@ bool ChannelImpl::check_available() {
 
 TensorPtr ChannelImpl::wait_tensor(TensorInfo* info, TensorProp prop) {
     std::unique_lock<decltype(m_mutex)> lock(m_mutex);
-    //mgb_log_warn(">>> MGB_LOCK_GUARD wait_tensor");
     mgb_assert(!m_waitee, "duplicate waitee");
     m_waitee = info;
     m_waitee_id = Profiler::next_id();
@@ -1019,7 +1010,6 @@ TensorPtr ChannelImpl::wait_tensor(TensorInfo* info, TensorProp prop) {
     if (require_host && !host_available()) {
         // avoid dead lock
         lock.unlock();
-        //mgb_log_warn("<<< MGB_LOCK_GUARD wait_tensor unlock");
         if (Profiler::is_profiling()) {
             m_worker.add_task(
                     {Profiler::next_id(), GetValue{info},
@@ -1031,21 +1021,18 @@ TensorPtr ChannelImpl::wait_tensor(TensorInfo* info, TensorProp prop) {
             });
         }
         lock.lock();
-        //mgb_log_warn(">>> MGB_LOCK_GUARD wait_tensor lock");
         wait_host = true;
     }
     m_cv.wait(lock, [&]() {
         check_worker_exc_unsafe();
         return require_host ? host_available() : static_cast<bool>(info->ptr);
     });
-    //mgb_log_warn("after cv wait");
     MGB_RECORD_EVENT(TensorWaitPropFinishEvent, info->id, m_waitee_id, prop);
     m_waitee = nullptr;
     if (wait_host) {
         auto err = info->ptr->comp_node().check_async_error();
         mgb_assert(!err, "%s", err->what());
     }
-    //mgb_log_warn("<<< MGB_LOCK_GUARD wait_tensor");
     return info->ptr;
 }
 
@@ -1053,7 +1040,6 @@ void ChannelImpl::notify_tensor_unsafe(TensorInfo* info) {
     if (info == m_waitee) {
         MGB_RECORD_EVENT(TensorNotifyPropEvent, info->id);
         m_cv.notify_all();
-        //mgb_log_warn("cv notify_all");
     }
 }
 
@@ -1116,7 +1102,6 @@ void ChannelImpl::process_one_task(Command& icmd) {
     using namespace ranges::views;
     auto& state = get_worker_state();
     auto& options = state.options;
-    //mgb_log_warn("process_one_task %s", to_string<Command>(icmd).c_str());
     // TODO: remove std::visit for support osx 10.12
     auto cmd_visitor = [&](const auto& cmd) {
         using T = std::decay_t<decltype(cmd)>;
@@ -1138,11 +1123,9 @@ void ChannelImpl::process_one_task(Command& icmd) {
             for (auto& i : cmd.inputs) {
                 if (mgb_unlikely(i->invalid)) {
                     MGB_LOCK_GUARD(m_mutex);
-                    //mgb_log_warn(">>> MGB_LOCK_GUARD ApplyOp");
                     for (auto& i : cmd.outputs) {
                         i->invalid = true;
                     }
-                    //mgb_log_warn("<<< MGB_LOCK_GUARD ApplyOp");
                     return;
                 }
             }
@@ -1227,10 +1210,8 @@ void ChannelImpl::process_one_task(Command& icmd) {
             }
             cmd.dest->ptr->fetch_value();
             MGB_LOCK_GUARD(m_mutex);
-            //mgb_log_warn(">>> MGB_LOCK_GUARD GetValue");
             notify_tensor_unsafe(cmd.dest);
             imperative_log_profile_end("GetValue");
-            //mgb_log_warn("<<< MGB_LOCK_GUARD GetValue");
         } else if constexpr (std::is_same_v<T, Drop>) {
             if (cmd.dest->invalid)
                 return;
@@ -1290,7 +1271,6 @@ void ChannelImpl::process_one_task(Command& icmd) {
                     cmd_visitor(cmd);
                 } catch (...) {
                     MGB_LOCK_GUARD(m_mutex);
-                    //mgb_log_warn(">>> MGB_LOCK_GUARD catch exception");
                     if constexpr (std::is_same_v<T, ApplyOp>) {
                         for (auto oup : cmd.outputs) {
                             oup->invalid = true;
@@ -1303,7 +1283,6 @@ void ChannelImpl::process_one_task(Command& icmd) {
                     if (m_waitee) {
                         notify_tensor_unsafe(m_waitee);
                     }
-                    //mgb_log_warn("<<< MGB_LOCK_GUARD catch exception");
                 }
             },
             icmd.data);
diff --git a/imperative/src/impl/transformations/format.cpp b/imperative/src/impl/transformations/format.cpp
index 5a4fa7c0..4fc94ad8 100644
--- a/imperative/src/impl/transformations/format.cpp
+++ b/imperative/src/impl/transformations/format.cpp
@@ -380,7 +380,8 @@ ValueRefList identity_rule_helper(
         const OpDef& op, const Span<ValueRef>& inputs, const FormatTransformation& t) {
     // mgb_assert(inputs.size() == 1);
     if (auto& src = inputs[0].as_ref(t.value_type())) {
-        return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)), src->format());
+        return t.wrap_outputs(
+                imperative::apply(op, t.unwrap_inputs(inputs)), src->format());
     } else {
         return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
     }
diff --git a/imperative/src/include/megbrain/imperative/transformations/format.h b/imperative/src/include/megbrain/imperative/transformations/format.h
index 95a18e1c..fcd572a1 100644
--- a/imperative/src/include/megbrain/imperative/transformations/format.h
+++ b/imperative/src/include/megbrain/imperative/transformations/format.h
@@ -49,7 +49,7 @@ public:
             const Operator& op, Span<ValueRef> inputs) override;
 
     ValueRef unwrap(ValueRef value) override {
-        //mgb_assert(!value.is(m_value_type));
+        // mgb_assert(!value.is(m_value_type));
         if (auto format_val = value.as_ref(m_value_type)) {
             return format_val->value();
         }
@@ -69,8 +69,7 @@ public:
     inline ValueRef wrap_output(
             const ValueRef& output, Format format = Format::Type::DEFAULT) const;
     inline ValueRefList wrap_outputs(
-            const ValueRefList& outputs,
-            Format format = Format::Type::DEFAULT) const;
+            const ValueRefList& outputs, Format format = Format::Type::DEFAULT) const;
 
     TypedValueRef<FormattedTensorValue> as(
             const FormattedTensorValue&, const Format::Type& target) const;