perf(autograd): copy inputs before capture in backward_graph_grad_rule

GitOrigin-RevId: 8b9c067b2d4ad52d8a0ba876d8fb5e2e688f291b

imperative/python/src/grad.cpp

@@ -315,14 +315,25 @@ public:
 };
 
 apply_result_t backward_graph_grad_rule(ApplyContext& ctx, GradFnHelper& ret_grad_fn) {
-    auto outputs = apply(ctx);
+    // copy inputs first, or trace will make InputNodes for each usage
+    SmallVector<std::shared_ptr<Tensor>> inputs_copy;
+    SmallVector<Tensor*> inputs_copy_weak;
+    for (size_t i = 0; i < ctx.nargs; ++i) {
+        inputs_copy.push_back(python::apply(FastpathCopy::make(), ctx.args[i]->shared_from_this())[0]);
+        inputs_copy_weak.push_back(inputs_copy.back().get());
+        inputs_copy.back()->m_grad_info = ctx.args[i]->m_grad_info;
+    }
+    ApplyContext ctx_dup = ctx;
+    ctx_dup.args = inputs_copy_weak.data();
+
+    auto outputs = apply(ctx_dup);
 
-    auto backward_graph = make_backward_graph(ctx, outputs);
+    auto backward_graph = make_backward_graph(ctx_dup, outputs);
     if (!backward_graph) {
         return outputs;
     }
 
-    ret_grad_fn.emplace<BackwardGraphWithClosure>(std::move(backward_graph), ctx, outputs);
+    ret_grad_fn.emplace<BackwardGraphWithClosure>(std::move(backward_graph), ctx_dup, outputs);
 
     return outputs;
 }