chore(imperative/amp): adapt dev

GitOrigin-RevId: 41eb0faadf34f4c31fb26b2de7850748666589c5

dnn/src/cuda/check_non_finite/kern.cu

@@ -10,8 +10,7 @@ namespace cuda {
 
 #define cb(_dtype)                                                                 \
     INST_REDUCE(                                                                   \
-            device_reduce::CheckNonFiniteOp<                                \
-                    _dtype COMMA dt_float32 COMMA dt_int32 COMMA dt_int32>, \
+            device_reduce::CheckNonFiniteOp<_dtype COMMA dt_int32 COMMA dt_int32>, \
             false);
 
 cb(dt_float32);

dnn/src/cuda/check_non_finite/opr_impl.cpp

@@ -14,7 +14,7 @@ using device_reduce::CheckNonFiniteOp;
 template <typename T>
 size_t CheckNonFiniteImpl::_get_workspace_in_bytes() {
     // Call the _get_workspace_in_bytes to reduce the loop fetch workspace bytes
-    typedef CheckNonFiniteOp<T, dt_float32, dt_int32, dt_int32> Op;
+    typedef CheckNonFiniteOp<T, dt_int32, dt_int32> Op;
     megdnn_assert(m_size > 0);
     WorkspaceBundle bundle(
             nullptr, {
@@ -59,7 +59,7 @@ void CheckNonFiniteImpl::_exec(
         _megdnn_in const TensorNDArray& srcs, _megdnn_tensor_out dst,
         _megdnn_workspace workspace) {
     check_exec(srcs, dst, workspace.size);
-    typedef CheckNonFiniteOp<T, dt_float32, dt_int32, dt_int32> Op;
+    typedef CheckNonFiniteOp<T, dt_int32, dt_int32> Op;
     auto stream = cuda_stream(this->handle());
     SmallVector<size_t> workspace_sizes{
             sizeof(T*) * m_size,

imperative/python/megengine/core/_config.py

@@ -247,4 +247,4 @@ def _override(
 
 
 def _get_actual_op_param(function_param, config_param):
-    return function_param if config_param is "default" else config_param
+    return function_param if config_param == "default" else config_param

imperative/python/megengine/optimizer/optimizer.py

@@ -97,7 +97,7 @@ class Optimizer(metaclass=ABCMeta):
                     "optimizer can only optimize Parameters, but one of the params is "
                     + str(type(param))
                 )
-            param._reset(Tensor(param, no_cache=True))
+            param._reset(Tensor(param.numpy(), no_cache=True, format=param.format))
 
         for name, default in self._defaults.items():
             if default is required and name not in param_group:

imperative/src/impl/transformations/format.cpp

@@ -581,9 +581,9 @@ ValueRefList FormatTransformation::apply_transformation(
                     (GenericFunction&)inputs[1].cast<FunctionValue>();
             // make param grads as FormattedTensor
             GenericFunction new_callback =
-                    [this, callback, format](Span<ValueRef> inputs_) -> ValueRefList {
+                    [&, callback, format](Span<ValueRef> inputs_) -> ValueRefList {
                 auto wrapped_inputs = SmallVector<ValueRef>{
-                        this->value_type().make(inputs_.item(), format)};
+                        m_value_type.make(inputs_.item(), format)};
                 auto ret = callback(wrapped_inputs);
                 return ret;
             };

imperative/src/include/megbrain/imperative/value.h

@@ -67,7 +67,6 @@ template <typename T>
 class Type : public IType {
 protected:
     Type(std::string name) : IType(std::move(name)) {}
-    Type(IType&& type) : IType(std::move(type)) {}
     // TODO: each type owns an allocator
 
 public:
@@ -105,7 +104,6 @@ template <typename T>
 class ObjectType : public Type<T> {
 public:
     ObjectType(std::string name) : Type<T>(name) {}
-    ObjectType(IType&& type) : Type<T>(std::move(type)) {}
 };
 
 /**