fix(mge): fix sublinear memory in jit.trace

GitOrigin-RevId: 190a330a8ce2016d24faed0246cc0c294c755946

imperative/python/megengine/jit/tracing.py

@@ -210,6 +210,7 @@ class trace:
                 info.external = True
                 info.device = x.device
                 info.dtype = x.dtype
+                info.shape = x.shape
                 if self._capture_as_const:
                     info.bound_data = x
 
@@ -338,7 +339,7 @@ class trace:
             for h in itertools.chain(self._arg_bindings, self._kwarg_bindings.values()):
                 info = self._tinfo[h]
                 opnode = info.data_setter = G.InputNode(
-                    device=info.device, dtype=info.dtype, graph=graph
+                    device=info.device, dtype=info.dtype, shape=info.shape, graph=graph
                 )
                 need_reset_nodes.append(opnode)
                 info.varnode = opnode.outputs[0]
@@ -355,7 +356,11 @@ class trace:
                         info.varnode = graph.make_const(info.bound_data._dev_tensor())
                     else:
                         opnode = info.data_setter = G.InputNode(
-                            *links, device=info.device, dtype=info.dtype, graph=graph
+                            *links,
+                            device=info.device,
+                            dtype=info.dtype,
+                            shape=info.shape,
+                            graph=graph,
                         )
                         need_reset_nodes.append(opnode)
                         info.varnode, *links = opnode.outputs

imperative/src/impl/mgb_core_impl

+../../../src/core/impl
\ No newline at end of file

imperative/src/impl/opr_utility.cpp

@@ -10,6 +10,7 @@
  */
 
 #include "megbrain/imperative/opr_utility.h"
+#include "./mgb_core_impl/graph/cg_impl.h"
 
 // FIXME; setup_config_cn is copied from src/opr/impl/utility.cpp
 namespace {
@@ -64,14 +65,18 @@ SymbolVarArray InputCallback::make(cg::ComputingGraph& graph,
 
 void InputCallback::init_output_static_infer_desc() {
     if (m_output_shape.ndim) {
+        // Write this shape to static infer manager. The effect is
+        // that infer_shape_fallible() will return a non-empty shape
+        // while get_infer_type() remains NO_DESC. Most places check
+        // infer type before relying on inferred shape so things
+        // won't break. Memory optimizer however, deliberately omits
+        // infer type check so it will be able to use this shape for hint.
         using namespace cg::static_infer;
-        auto &&mgr = owner_graph()->static_infer_manager();
-        auto infer_shape = [this](TensorShape &dest, const InpVal &) {
-            dest = m_output_shape;
-            return true;
-        };
-        mgr.register_shape_infer(output(0),
-                {SourceType::CONSTANT, {}, infer_shape});
+        auto* var = output(0);
+        var->shape(m_output_shape);
+        auto&& mgr = cg::ComputingGraphImpl::downcast(owner_graph())->static_infer_manager_impl();
+        auto* handle = mgr.get_tag_handler_for_shape(var);
+        handle->sync_from_var();
     }
 }
 
@@ -86,9 +91,6 @@ cg::OperatorNodeBase::NodeProp* InputCallback::do_make_node_prop() const {
 
 void InputCallback::scn_do_execute() {
     auto dev_tensor = m_callback();
-    if (m_output_shape.ndim) {
-        mgb_assert(dev_tensor.shape().eq_shape(m_output_shape));
-    }
     output(0)->reset_dev_tensor_from_tensor(dev_tensor);
 }
 

src/core/impl/graph/memory_optimizer.cpp

@@ -99,7 +99,9 @@ MemoryOptimizerHelper::split_into_cn2oprseq(const OprNodeArray& oprseq,
 
         auto&& infer_mgr = m_owner_graph->static_infer_manager();
         for (auto j : i->output()) {
-            if (!j->contain_flag(BAD_VAR_FLAG) && is_static_var_shape(j)) {
+            if (!j->contain_flag(BAD_VAR_FLAG)) {
+                // omit infer type check
+                // inferred shape will be used as-is
                 if (auto shape = infer_mgr.infer_shape_fallible(j)) {
                     have_static_shape_out = true;
                     m_var_memsize[j] = j->dtype().size(shape->total_nr_elems());