feat(imperative): add inplace add_update option in optimizer

GitOrigin-RevId: b8feb493218e4a8e62cf5d8f14b13cfa12461dc0

imperative/python/megengine/functional/inplace.py

+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from ..core._imperative_rt.core2 import apply
+from ..core.ops import builtin
+from ..core.ops.builtin import InplaceAdd
+
+
+def _inplace_add_(dest, delta, alpha, beta):
+    return dest._reset(apply(InplaceAdd(), dest, delta, alpha, beta)[0])

imperative/python/megengine/jit/tracing.py

@@ -502,6 +502,8 @@ class trace:
         # profile
         if self._profiling:
             self._profiler = GraphProfiler(graph)
+        if int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0")):
+            graph.options.var_sanity_check_first_run = False
 
     def _compile(self):
         graph = self._graph = G.Graph()
@@ -1073,7 +1075,7 @@ def apply_compiled_mode(op: OpDef, *args: RawTensor):
     return active_trace._apply_op(op, args)
 
 
-def apply_const_compiled_mode(value, dtype, device, is_const):
+def apply_const_compiled_mode(value, dtype, device, is_const, no_cache):
     if skip_tracing:
         args = [
             RawTensor(x._dev_tensor()) if x.__class__ is CompiledTensorProxy else x
@@ -1099,7 +1101,7 @@ def apply_with_tracing(op: OpDef, *args: RawTensor):
     return list(outputs)
 
 
-def apply_const_with_tracing(value, dtype, device, is_const):
+def apply_const_with_tracing(value, dtype, device, is_const, no_cache):
     if active_trace._symbolic:
         outputs = apply_const_symbolic_mode(value, dtype, device)
     else:

imperative/python/megengine/optimizer/adam.py

@@ -6,8 +6,10 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import os
 from typing import Iterable, Tuple, Union
 
+from ..functional.inplace import _inplace_add_
 from ..tensor import Parameter, tensor
 from .optimizer import Optimizer
 
@@ -58,15 +60,24 @@ class Adam(Optimizer):
         eps = param_group["eps"]
         beta0, beta1 = param_group["betas"]
 
+        def make_scalar(val):
+            return tensor([val])
+
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor
-        _lr = tensor([lr])
-        _weight_decay = tensor([weight_decay])
-        _eps = tensor([eps])
-        _beta0, _beta1 = tensor([beta0]), tensor([beta1])
 
-        c1 = tensor([1.0])
-        c05 = tensor([0.5])
+        _lr, _neg_lr = map(make_scalar, (lr, -lr))
+        _weight_decay = make_scalar(weight_decay)
+        _eps = make_scalar(eps)
+        _beta0, _beta1 = map(make_scalar, (beta0, beta1))
+
+        c1, c05 = map(make_scalar, (1.0, 0.5))
+
+        inplace_mode = int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0"))
+        if inplace_mode:
+            # reduce device sync
+            c1_sub_beta0, c1_sub_beta1 = map(make_scalar, (1 - beta0, 1 - beta1))
+
         for param in param_group["params"]:
 
             if param.grad is None:
@@ -77,18 +88,38 @@ class Adam(Optimizer):
                 grad += param * _weight_decay
 
             states = self._state[param]
-            step = states["step"]
+
+            step, exp_avg, exp_avg_sq = (
+                states["step"],
+                states["exp_avg"],
+                states["exp_avg_sq"],
+            )
+
+            if inplace_mode:
+                _inplace_add_(step, c1, alpha=c1, beta=c1)
+                _inplace_add_(exp_avg, grad, alpha=_beta0, beta=c1_sub_beta0)
+                _inplace_add_(
+                    exp_avg_sq, grad * grad, alpha=_beta1, beta=c1_sub_beta1,
+                )
+
+                delta = (exp_avg / (c1 - _beta0 ** step)) / (
+                    (exp_avg_sq / (c1 - _beta1 ** step)) ** c05 + _eps
+                )
+                _inplace_add_(param, delta, alpha=c1, beta=_neg_lr)
+                continue
+
+            # step = step + c1
             step += c1
-            exp_avg = states["exp_avg"]
-            exp_avg_sq = states["exp_avg_sq"]
-            exp_avg = _beta0 * exp_avg + grad * (c1 - _beta0)
-            exp_avg_sq = _beta1 * exp_avg_sq + (c1 - _beta1) * (grad * grad)
+
+            # exp_avg = _beta0 * exp_avg + grad * (c1 - _beta0)
+            exp_avg *= _beta0
+            exp_avg += grad * (c1 - _beta0)
+
+            # exp_avg_sq = _beta1 * exp_avg_sq + (c1 - _beta1) * (grad * grad)
+            exp_avg_sq *= _beta1
+            exp_avg_sq += (c1 - _beta1) * (grad * grad)
 
             delta = (exp_avg / (c1 - _beta0 ** step)) / (
                 (exp_avg_sq / (c1 - _beta1 ** step)) ** c05 + _eps
             )
             param -= _lr * delta
-
-            # not inplace change, need to update underlying tensor handler in state
-            states["exp_avg"]._reset(exp_avg)
-            states["exp_avg_sq"]._reset(exp_avg_sq)

imperative/python/megengine/optimizer/optimizer.py

@@ -96,6 +96,7 @@ class Optimizer(metaclass=ABCMeta):
                     "optimizer can only optimize Parameters, but one of the params is "
                     + str(type(param))
                 )
+            param._reset(Tensor(param.numpy(), no_cache=True))
 
         for name, default in self._defaults.items():
             if default is required and name not in param_group:
@@ -121,7 +122,7 @@ class Optimizer(metaclass=ABCMeta):
             initializer = np.zeros(param.shape, dtype=np.float32)
         state_dict = self._state.setdefault(param, {})
         assert state_name not in state_dict
-        state = Tensor(initializer)
+        state = Tensor(initializer, no_cache=True)
         state_dict[state_name] = state
 
     @abstractmethod

imperative/python/megengine/optimizer/sgd.py

@@ -6,8 +6,10 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import os
 from typing import Iterable, Union
 
+from ..functional.inplace import _inplace_add_
 from ..tensor import Parameter, tensor
 from .optimizer import Optimizer
 
@@ -54,10 +56,16 @@ class SGD(Optimizer):
 
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor
+
         _lr = tensor([lr])
         _weight_decay = tensor([weight_decay])
         _momentum = tensor([momentum])
 
+        inplace_mode = int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0"))
+        if inplace_mode:
+            _neg_lr = tensor([-lr])
+            c1 = tensor([1.0])
+
         for param in param_group["params"]:
             if param.grad is None:
                 continue
@@ -66,10 +74,21 @@ class SGD(Optimizer):
             if weight_decay != 0.0:
                 grad += param * _weight_decay
 
+            if inplace_mode:
+                if momentum:
+                    v = self._state[param]["momentum_buffer"]
+                    _inplace_add_(v, grad, alpha=_momentum, beta=c1)
+                    _inplace_add_(param, v, alpha=c1, beta=_neg_lr)
+                else:
+                    _inplace_add_(param, grad, alpha=c1, beta=_neg_lr)
+                continue
+
             if momentum:
                 v = self._state[param]["momentum_buffer"]
-                v = _momentum * v + grad
+                # v = v * _momentum + grad
+                v *= _momentum
+                v += grad
+
                 param -= _lr * v
-                self._state[param]["momentum_buffer"]._reset(v)
             else:
                 param -= _lr * grad

imperative/python/megengine/tensor.py

@@ -28,7 +28,7 @@ class Tensor(_Tensor, ArrayMethodMixin):
     dmap_callback = None
     q_dict = {"mode": None, "scale": None, "zero_point": None}
 
-    def __new__(cls, data, dtype=None, device=None, is_const=False):
+    def __new__(cls, data, dtype=None, device=None, is_const=False, no_cache=False):
         if device is None:
             cn = get_default_device()
         elif isinstance(device, str):
@@ -49,7 +49,7 @@ class Tensor(_Tensor, ArrayMethodMixin):
                 if 0 in data.strides:
                     data = data.squeeze().reshape(data.shape)
 
-            obj = _Tensor.__new__(cls, data, dtype, cn, is_const)
+            obj = _Tensor.__new__(cls, data, dtype, cn, is_const, no_cache)
         return obj
 
     @property

imperative/python/src/grad_override.cpp

@@ -38,9 +38,9 @@ std::shared_ptr<Tensor> broadcast_to(Tensor* x, Tensor* s) {
 std::shared_ptr<Tensor> make_tensor(CompNode cn, Tensor* shape, float v = 0) {
     HostTensorND scalar{cn, {{1}, dtype::Float32()}};
     scalar.ptr<float>()[0] = v;
-    interpreter::Interpreter::Handle handle = interpreter_for_py->put(scalar);
+    interpreter::Interpreter::Handle handle = interpreter_for_py->put(scalar, false);
     auto&& t = std::make_shared<Tensor>(handle);
-    auto&& res = broadcast_to(t.get(), shape);
+    auto res = broadcast_to(t.get(), shape);
     return res;
 }
 

imperative/python/src/tensor.cpp

@@ -231,13 +231,14 @@ TensorWrapper::TensorWrapper(PyObject* args, PyObject* kwargs) {
             }
         } else {
             py::detail::loader_life_support life_sup; // FIXME!!!required to cast DType
+            if (nargs != 4 && nargs != 5) {
+                throw py::type_error("expect 4 or 5 arguments");
+            }
             auto data = tup[0].cast<py::array>();
             DType dtype = tup[1].cast<DType>();
             CompNode cn = tup[2].cast<CompNode>();
             bool is_const = tup[3].cast<bool>();
-            if (nargs != 4) {
-                throw py::type_error("expect 3 arguments");
-            }
+            bool no_cache = nargs == 5 ? tup[4].cast<bool>() : false;
 
             // const op
             if (is_const && is_tracing) {
@@ -259,10 +260,10 @@ TensorWrapper::TensorWrapper(PyObject* args, PyObject* kwargs) {
             interpreter::Interpreter::Handle handle;
             constexpr auto size_threshhold = TensorShape::MAX_NDIM;
             if (data.size() > size_threshhold) {
-                handle = interpreter_for_py->put(npy::np2tensor(data.ptr(), npy::Meth::borrow(cn), dtype));
+                handle = interpreter_for_py->put(npy::np2tensor(data.ptr(), npy::Meth::borrow(cn), dtype), no_cache);
             } else {
                 HostTensorND ret(cn);
-                handle = interpreter_for_py->put(npy::np2tensor(data.ptr(), npy::Meth::copy_into(&ret), dtype));
+                handle = interpreter_for_py->put(npy::np2tensor(data.ptr(), npy::Meth::copy_into(&ret), dtype), no_cache);
             }
 
             m_tensor = std::make_shared<Tensor>(handle);

imperative/python/test/integration/test_sgd_momentum.py

@@ -6,6 +6,9 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import itertools
+import os
+
 import numpy as np
 
 import megengine
@@ -58,13 +61,16 @@ def test_sgd_momentum():
 
     np.testing.assert_almost_equal(loss.numpy(), 2.34 * (1.23 - 2.34), 5)
     np.testing.assert_almost_equal(
-        optim._state[net.a]["momentum_buffer"].numpy(), 0.9 * 2.34 + 2.34
+        optim._state[net.a]["momentum_buffer"].numpy(), 0.9 * 2.34 + 2.34, 5
     )
 
 
 def test_sgd_momentum_trace():
-
-    for symbolic in (True, False):
+    origin_inplace = os.getenv("MEGENGINE_INPLACE_UPDATE")
+    symbolic = (True, False)
+    inplace = (0, 1)
+    for symbolic, inplace in itertools.product(symbolic, inplace):
+        os.environ["MEGENGINE_INPLACE_UPDATE"] = str(inplace)
 
         @trace(symbolic=symbolic)
         def train_func(data, *, model=None, optim=None, gm=None):
@@ -101,5 +107,9 @@ def test_sgd_momentum_trace():
         train_func(data, model=net, optim=optim, gm=gm)
         np.testing.assert_almost_equal(loss.numpy(), 2.34 * (1.23 - 2.34), 5)
         np.testing.assert_almost_equal(
-            optim._state[net.a]["momentum_buffer"].numpy(), 0.9 * 2.34 + 2.34
+            optim._state[net.a]["momentum_buffer"].numpy(), 0.9 * 2.34 + 2.34, 5
         )
+    if origin_inplace:
+        os.environ["MEGENGINE_INPLACE_UPDATE"] = origin_inplace
+    else:
+        del os.environ["MEGENGINE_INPLACE_UPDATE"]

imperative/python/test/unit/test_tracing.py

@@ -325,7 +325,6 @@ def test_raise_on_trace():
 
     @trace
     def add_abc(a, b, c):
-        print("Hello")
         ps = a + b
         result = ps + c
         if step_count == bad_step:

imperative/src/impl/dnn_op_helper.h

@@ -11,6 +11,7 @@
 
 #include "megbrain/comp_node_env.h"
 #include "megbrain/comp_node.h"
+#include "megbrain/imperative/physical_tensor.h"
 
 using namespace megdnn;
 
@@ -29,10 +30,12 @@ struct DnnOprCaller {
     Workspace workspace;
     std::unique_ptr<Opr> op;
 
-    DnnOprCaller(CompNode cn): cn(cn) {
+    DnnOprCaller(CompNode cn): cn(cn), op(create_operator(cn)) {}
+
+    static std::unique_ptr<Opr> create_operator(CompNode cn) {
         auto&& handle = MegDNNHandle::get(
                                 CompNodeEnv::from_comp_node(cn)).handle();
-        op = handle->create_operator<Opr>();
+        return handle->create_operator<Opr>();
     }
 
     megdnn::Workspace create_workspace(TensorLayout layout) {
@@ -52,5 +55,36 @@ struct DnnOprCaller {
     }
 };
 
+template <size_t OSize>
+class MegDNNDynOutMallocImpl final: public megdnn::DynOutMallocPolicy {
+    using Output = std::array<TensorPtr, OSize>;
+
+    CompNode m_cn;
+    Output m_out;
+
+public:
+    MegDNNDynOutMallocImpl(CompNode cn): m_cn{cn} {}
+
+    megdnn::TensorND alloc_output(
+            size_t id, DType dtype, const TensorShape &shape,
+            void *user_data) override {
+        TensorLayout m_layout(shape, dtype);
+        m_out[id] = Tensor::make(m_layout, m_cn);
+        return m_out[id]->dev_tensor().as_megdnn();
+    }
+
+    void* alloc_workspace(size_t sz, void *user_data) override {
+        return m_cn.alloc_device(sz);
+    }
+
+    void free_workspace(void *ptr, void *user_data) override {
+        m_cn.free_device(ptr);
+    }
+
+    TensorPtr at(size_t id) {
+        return m_out[id];
+    }
+};
+
 } // namespace imperative
 } // namespace mgb

imperative/src/impl/interpreter_impl.cpp

@@ -28,13 +28,13 @@ Interpreter& Interpreter::inst() {
     return inst_;
 }
 
-void* ChannelImpl::put(const HostTensorND& value) {
+void* ChannelImpl::put(const HostTensorND& value, bool no_cache) {
     auto info = alloc();
     info->desc.layout = value.layout();
     info->desc.comp_node = value.comp_node();
     info->desc.value = value.proxy_to_default_cpu();
     m_valid_handle.insert(info);
-    m_worker.add_task(Put{info, value});
+    m_worker.add_task(Put{info, value, no_cache});
     return info;
 }
 
@@ -395,7 +395,8 @@ void ChannelImpl::process_one_task(Command& cmd) {
         using T = std::remove_reference_t<decltype(cmd)>;
         try {
             if constexpr (std::is_same_v<T, Put>) {
-                produce_tensor(cmd.dest, Tensor::make(cmd.value));
+                auto value = cmd.no_cache ? std::make_shared<Tensor>(cmd.value) : Tensor::make(cmd.value);
+                produce_tensor(cmd.dest, std::move(value));
             } else if constexpr (std::is_same_v<T, ApplyOp>) {
                 SmallVector<TensorPtr> tensor_inputs;
                 tensor_inputs.reserve(cmd.inputs.size());

imperative/src/impl/interpreter_impl.h

@@ -57,6 +57,7 @@ struct TensorInfo {
 struct Put {
     TensorInfo* dest;
     HostTensorND value;
+    bool no_cache = false;
 };
 struct ApplyOp {
     std::shared_ptr<OpDef> op;
@@ -92,7 +93,7 @@ struct ChannelImpl : Interpreter::Channel {
     ChannelImpl() : m_worker(this) {}
     ~ChannelImpl() override;
 
-    Handle put(const HostTensorND& value) override;
+    Handle put(const HostTensorND& value, bool no_cache) override;
     Handle put(const DeviceTensorND& value) override;
 
     void del(Handle) override;

imperative/src/impl/ops/cond_take.cpp

@@ -20,44 +20,6 @@ namespace mgb::imperative {
 
 namespace {
 
-class MegDNNDynOutMallocImpl final: public megdnn::DynOutMallocPolicy {
-    using Output = std::array<TensorPtr, 2>;
-
-    CompNode m_cn;
-    Output m_out;
-
-    public:
-        MegDNNDynOutMallocImpl(CompNode cn): m_cn{cn} {}
-
-        megdnn::TensorND alloc_output(
-                size_t id, DType dtype, const TensorShape &shape,
-                void *user_data) override;
-
-        void* alloc_workspace(size_t sz, void *user_data) override;
-        void free_workspace(void *ptr, void *user_data) override;
-        TensorPtr at(size_t id);
-};
-
-megdnn::TensorND MegDNNDynOutMallocImpl::alloc_output(
-        size_t id, DType dtype, const TensorShape &shape,
-        void * /*user_data*/) {
-    TensorLayout m_layout(shape, dtype);
-    m_out[id] = Tensor::make(m_layout, m_cn);
-    return m_out[id]->dev_tensor().as_megdnn();
-}
-
-void* MegDNNDynOutMallocImpl::alloc_workspace(size_t sz, void * /*user_data*/) {
-    return m_cn.alloc_device(sz);
-}
-
-void MegDNNDynOutMallocImpl::free_workspace(void *ptr, void * /*user_data*/) {
-    m_cn.free_device(ptr);
-}
-
-TensorPtr MegDNNDynOutMallocImpl::at(size_t id) {
-    return m_out[id];
-}
-
 cg::OperatorNodeBase* apply_on_var_node(
         const OpDef& def,
         const VarNodeArray& inputs) {
@@ -94,7 +56,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
                            dtype::Byte());
 
     auto dnn_workspace = dnn_op.create_workspace(m_layout);
-    MegDNNDynOutMallocImpl policy{inp->comp_node()};
+    MegDNNDynOutMallocImpl<2> policy{inp->comp_node()};
 
     dnn_op.op->exec(inp->dev_tensor().as_megdnn(),
                   msk->dev_tensor().as_megdnn(),

imperative/src/impl/ops/elemwise.cpp

@@ -11,8 +11,11 @@
 
 #include "megbrain/imperative/ops/autogen.h"
 #include "megbrain/opr/basic_arith.h"
+#include "megbrain/imperative/opr_utility.h"
+#include "megbrain/opr/utility.h"
 
 #include "../op_trait.h"
+#include "../dnn_op_helper.h"
 
 namespace mgb {
 namespace imperative {
@@ -84,12 +87,142 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
     return {Tensor::make(out)};
 }
 
+MGB_DEFINE_OPR_CLASS(ForceInplaceElemwise, cg::SingleCNOperatorNodeBaseT<opr::mixin::MegDNNOprHolder>) //{
+public:
+    struct Param{
+        using Mode = megdnn::Elemwise::Param::Mode;
+        Mode mode;
+        size_t inplace_index;
+    };
+    using Mode = Param::Mode;
+    ForceInplaceElemwise(const VarNodeArray& inputs, Param param,
+            OperatorNodeConfig config = {})
+    : Super(inputs[0]->owner_graph(), config, "device_add_update", inputs), m_param{param} {
+        for (auto* input: inputs) {
+            add_input({input});
+        }
+        add_output(None)->
+            set_fwd_in2out_writable_force(input(param.inplace_index)).
+            add_flag(VarNode::Flag::NO_MEM_RECLAIM);
+    }
+    static SymbolVar make(const VarNodeArray& inputs, Param param) {
+        return SymbolVar{inputs[0]}.insert_single_output_opr<ForceInplaceElemwise>(
+                inputs, param);
+    }
+    static cg::OperatorNodeBase* shallow_copy(
+        const serialization::OprShallowCopyContext &ctx,
+        const cg::OperatorNodeBase &opr_, const VarNodeArray &inputs,
+        const OperatorNodeConfig &config);
+protected:
+    NodeProp* do_make_node_prop() const override {
+        auto ret = Super::do_make_node_prop();
+        ret->add_flag(NodeProp::Flag::FORCE_UPDATE_INPUT_VAR);
+        return ret;
+    }
+    void create_megdnn_opr() override {
+        auto opr = DnnOprCaller<megdnn::Elemwise>::create_operator(comp_node());
+        opr->param().mode = m_param.mode;
+        set_megdnn_opr(std::move(opr));
+    }
+    void scn_do_execute() override {
+        auto to_dnnnd = [&](auto* var){ return var->dev_tensor().as_megdnn(); };
+        megdnn::TensorNDArray inputs_dnnnd;
+        for (auto* input: input()) {
+            inputs_dnnnd.push_back(to_dnnnd(input));
+        }
+        mgb_assert(input(m_param.inplace_index)->contain_flag(VarNode::Flag::NO_SYS_MEM_ALLOC),
+                "ForceInplaceElemwise cannot be applied in internal tensor");
+        auto* out_dest = output(0);
+        auto* opr = static_cast<megdnn::Elemwise*>(megdnn_opr());
+        opr->exec(std::move(inputs_dnnnd),
+                to_dnnnd(out_dest));
+    }
+    void init_output_static_infer_desc() override {
+        using namespace cg::static_infer;
+
+        owner_graph()->static_infer_manager().register_shape_infer(
+                output(0), ShapeInferDesc::make_identity(input(m_param.inplace_index)));
+    }
+private:
+    Param m_param;
+    void record_execute_deps(ExecDependencyArray& deps) override {
+        record_megdnn_opr(deps);
+    }
+};
+
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(ForceInplaceElemwise);
+
+cg::OperatorNodeBase* ForceInplaceElemwise::shallow_copy(
+        const serialization::OprShallowCopyContext &ctx,
+        const cg::OperatorNodeBase &opr_, const VarNodeArray &inputs,
+        const OperatorNodeConfig &config) {
+    auto &&opr = opr_.cast_final_safe<ForceInplaceElemwise>();
+    auto* graph = ctx.owner_graph(opr, inputs);
+    return graph->insert_opr(std::make_unique<ForceInplaceElemwise>(inputs, opr.m_param, config));
+}
+
+MGB_REG_OPR_SHALLOW_COPY(ForceInplaceElemwise, ForceInplaceElemwise::shallow_copy);
+
+cg::OperatorNodeBase* apply_inplace_add_on_var_node(
+        const OpDef& def,
+        const VarNodeArray& inputs) {
+    auto dest = inputs[0], delta = inputs[1],
+         alpha = inputs[2], beta = inputs[3];
+    auto mode = ForceInplaceElemwise::Param::Mode::FUSE_MUL_ADD4;
+    return ForceInplaceElemwise::make({alpha, dest, beta, delta}, {mode, 1}).node()->owner_opr();
+}
+
+SmallVector<TensorPtr> apply_inplace_add_on_physical_tensor(
+        const OpDef& def,
+        const SmallVector<TensorPtr>& inputs){
+    auto dest = inputs[0], delta = inputs[1],
+         alpha = inputs[2], beta = inputs[3];
+    auto tensor_to_scalar = [](const TensorPtr& tensor) -> float {
+        return *tensor->get_value().ptr<float>();
+    };
+    DnnOprCaller<megdnn::AddUpdate> caller{dest->comp_node()};
+    caller.op->param() = { tensor_to_scalar(alpha), tensor_to_scalar(beta) };
+    caller.op->exec(dest->dev_tensor().as_megdnn(), delta->dev_tensor().as_megdnn());
+    return { std::make_shared<Tensor>(dest->blob(), dest->offset(), dest->layout()) };
+}
+
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_inplace_add_output_attrs_fallible(
+        const OpDef& def,
+        const SmallVector<LogicalTensorDesc>& inputs) {
+    mgb_assert(inputs.size() == 4, "invalid input number for inplace_add");
+    CompNode cn;
+    for (auto&& input: inputs) {
+        if (!cn.valid()) {
+            cn = input.comp_node;
+        } else {
+            mgb_assert(input.comp_node == cn, "inputs should be in same comp_node");
+        }
+    }
+    auto dest = inputs[0], delta = inputs[1],
+         alpha = inputs[2], beta = inputs[3];
+    bool succeed = dest.layout.ndim != 0;
+    if (succeed) {
+        mgb_assert(delta.layout.ndim == 0 || dest.layout.eq_shape(delta.layout), "dest and delta must have same shape");
+        mgb_assert(alpha.layout.ndim == 0 || alpha.layout.eq_shape({1}), "alpha should be scalar");
+        mgb_assert(beta.layout.ndim == 0 || beta.layout.eq_shape({1}), "beta should be scalar");
+    }
+    mgb_assert(alpha.layout.dtype == dtype::Float32(), "alpha should be float32");
+    mgb_assert(beta.layout.dtype == dtype::Float32(), "beta should be float32");
+    return {{dest}, succeed};
+}
+
 OP_TRAIT_REG(Elemwise, Elemwise, opr::Elemwise)
     .make_from_op_node(make_from_op_node)
     .apply_on_var_node(apply_on_var_node)
     .infer_output_attrs_fallible(infer_output_attrs_fallible)
     .apply_on_physical_tensor(apply_on_physical_tensor)
     .fallback();
+
+OP_TRAIT_REG(InplaceAdd, InplaceAdd, opr::AddUpdate)
+    .apply_on_var_node(apply_inplace_add_on_var_node)
+    .apply_on_physical_tensor(apply_inplace_add_on_physical_tensor)
+    .infer_output_attrs_fallible(infer_inplace_add_output_attrs_fallible)
+    .fallback();
 } // anonymous namespace
 
 }  // namespace imperative

imperative/src/impl/proxy_graph_detail.cpp

@@ -32,14 +32,22 @@ SmallVector<Tensor*> to_raw_ptr_array(
     return ret;
 }
 
+SmallVector<LogicalTensorDesc>
+infer_output_attrs(const OpDef& def,
+        const SmallVector<TensorPtr>& inputs) {
+    auto&& graph = ProxyGraph::get_default_graph();
+    return graph->infer_output_attrs(def, to_raw_ptr_array(inputs));
+}
+} // anonymous namespace
+
 void exec(const OpDef& def,
-        const SmallVector<TensorPtr>& inputs_,
-        const SmallVector<TensorPtr>& outputs_) {
+        const SmallVector<TensorPtr>& inputs,
+        const SmallVector<TensorPtr>& outputs) {
     auto&& graph = ProxyGraph::get_default_graph();
-    auto inputs = to_raw_ptr_array(inputs_),
-         outputs = to_raw_ptr_array(outputs_);
+    auto raw_inputs = to_raw_ptr_array(inputs),
+         raw_outputs = to_raw_ptr_array(outputs);
     CompNode::UnorderedSet used_cns;
-    for (auto&& out: outputs) {
+    for (auto&& out: raw_outputs) {
         auto cn = out->comp_node();
         if (used_cns.insert(cn).second) {
             for (auto&& in: inputs) {
@@ -50,7 +58,7 @@ void exec(const OpDef& def,
             }
         }
     }
-    graph->invoke_op(def, inputs, outputs);
+    graph->invoke_op(def, raw_inputs, raw_outputs);
     for (auto&& cn: used_cns) {
         for (auto&& in: inputs) {
             if (in->comp_node() != cn) {
@@ -60,14 +68,6 @@ void exec(const OpDef& def,
     }
 }
 
-SmallVector<LogicalTensorDesc>
-infer_output_attrs(const OpDef& def,
-        const SmallVector<TensorPtr>& inputs) {
-    auto&& graph = ProxyGraph::get_default_graph();
-    return graph->infer_output_attrs(def, to_raw_ptr_array(inputs));
-}
-} // anonymous namespace
-
 SmallVector<TensorPtr>
 apply_on_physical_tensor(const OpDef& def,
         const SmallVector<TensorPtr>& inputs) {

imperative/src/include/megbrain/imperative/interpreter.h

@@ -21,7 +21,7 @@ struct Interpreter {
     struct Channel {
         virtual ~Channel() = default;
 
-        virtual Handle put(const HostTensorND& value) = 0;
+        virtual Handle put(const HostTensorND& value, bool no_cache) = 0;
         virtual Handle put(const DeviceTensorND& value) = 0;
 
         virtual void del(Handle) = 0;

imperative/src/include/megbrain/imperative/physical_tensor.h

@@ -101,6 +101,10 @@ public:
         return m_layout;
     }
 
+    size_t offset() const {
+        return m_offset;
+    }
+
     DeviceTensorND dev_tensor();
 
     static TensorPtr make_scalar(DTypeScalar value, CompNode cn);

imperative/src/include/megbrain/imperative/proxy_graph_detail.h

@@ -24,6 +24,10 @@ apply_on_physical_tensor(const OpDef& def,
 std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(const OpDef& def,
         const SmallVector<LogicalTensorDesc>& inputs);
 
+void exec(const OpDef& def,
+        const SmallVector<TensorPtr>& inputs,
+        const SmallVector<TensorPtr>& outputs);
+
 BackwardGraphResult
 make_backward_graph(const OpDef& def,
         const SmallVector<LogicalTensorDesc>& inputs,

src/core/include/megbrain/ir/ops.td

@@ -239,4 +239,6 @@ def ElemwiseMultiType: MgbHashableOp<"ElemwiseMultiType", [ElemwiseMultiTypePara
   );
 }
 
+def InplaceAdd: MgbHashableOp<"InplaceAdd", [EmptyParam]>;
+
 #endif // MGB_OPS

src/opr/impl/basic_arith.cpp

@@ -886,12 +886,9 @@ AddUpdate::AddUpdate(VarNode *dest, VarNode *delta,
     m_param{param}
 {
     auto dest_opr = dest->owner_opr();
-    mgb_throw_if(!(dest_opr->same_type<SharedDeviceTensor>() ||
-            dest_opr->same_type<VolatileSharedDeviceTensor>()),
+    mgb_throw_if(dest_opr->same_type<ImmutableTensor>(),
             GraphError,
-            "AddUpdate must be applied on SharedDeviceTensor; "
-            "got %s{%s} actually",
-            dest_opr->cname(), dest_opr->dyn_typeinfo()->name);
+            "AddUpdate cannot be applied on ImmutableTensor; ");
     add_input({dest, delta});
 
     /*

src/opr/impl/internal/identical_fwd.cpp

@@ -80,6 +80,22 @@ public:
 
 MGB_TYPEINFO_OBJ_IMPL(ForwardInputToOutput::MutableSrc);
 
+void ForwardInputToOutput::mixin_init_rt_force_dynamic_mem_alloc_imply_chain(
+        OperatorNodeBase &opr) {
+    VarNode *valid_out = nullptr;
+    for (auto i: opr.output()) {
+        if (!i->contain_flag(VarNode::Flag::VOLATILE_CONTENT)) {
+            mgb_assert(!valid_out);
+            valid_out = i;
+        }
+    }
+    mgb_assert(valid_out);
+
+    // There may be many inputs such as in opr::VirtualDep, but we only forward first one
+    opr.input(0)->add_rt_force_dynamic_mem_alloc_imply_chain(valid_out);
+    valid_out->add_rt_force_dynamic_mem_alloc_imply_chain(opr.input(0));
+}
+
 void ForwardInputToOutput::mixin_mem_plan_fwd_in2out_readonly(
         OperatorNodeBase& opr) {
     m_mem_fwd_success = opr.output(0)->set_fwd_in2out_readonly(

src/opr/include/megbrain/opr/internal/identical_fwd.h

@@ -67,6 +67,7 @@ class ForwardInputToOutput: public cg::OperatorNodeMixinBase {
 
         virtual void mixin_scn_do_execute(OperatorNodeBase &opr);
 
+        void mixin_init_rt_force_dynamic_mem_alloc_imply_chain(OperatorNodeBase &opr);
         void mixin_mem_plan_fwd_in2out_readonly(OperatorNodeBase &opr);
         void mixin_init_output_static_infer_desc(OperatorNodeBase &opr);
         virtual cg::static_infer::ValueInferDesc mixin_get_static_infer_desc(OperatorNodeBase &opr);
@@ -173,8 +174,7 @@ MGB_DEFINE_CLS_WITH_SUPER(ForwardInputToOutput,
     protected:
         using Super::Super;
         void init_rt_force_dynamic_mem_alloc_imply_chain() override {
-            mixin::init_rt_force_dynamic_mem_alloc_imply_chain_for_dyn_pass_i2o(
-                    *this);
+            this->mixin_init_rt_force_dynamic_mem_alloc_imply_chain(*this);
         }
 
         void mem_plan_fwd_in2out_readonly() override {