perf(opdef/reshape): specialize Reshape

GitOrigin-RevId: 26d0e151ca89058782c554026907fd1ad3ec7340

imperative/src/impl/ops/broadcast.cpp

@@ -152,9 +152,43 @@ std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
     return {{{TensorLayout(out_shape, src.layout.dtype), src.comp_node}}, true};
 }
 
+std::tuple<SmallVector<MemoryDesc>, SmallVector<MemoryDesc>> infer_output_mem_desc(
+        const OpDef& def,
+        const SmallVector<TensorPtr>& inputs,
+        const SmallVector<MemoryDesc>& inputs_mems) {
+    auto&& op_def = def.cast_final_safe<Reshape>();
+    size_t nr_inp = inputs.size();
+    mgb_assert(nr_inp == 2, "Reshape expects 2 inputs; got %lu actually", nr_inp);
+    auto&& src = inputs[0];
+    auto&& tshp_nd = inputs[1];
+    auto slayout = src->layout();
+
+    TensorShape tshp;
+    cg::copy_tensor_value_to_shape(tshp, tshp_nd->get_value().proxy_to_default_cpu());
+    if (op_def.axis != opr::Reshape::Param::INVALID_AXIS) {
+        mgb_assert(tshp[op_def.axis] == -1);
+        tshp[op_def.axis] = 1;
+        tshp[op_def.axis] = src->layout().total_nr_elems() / tshp.total_nr_elems();
+    }
+    TensorLayout tlayout = slayout.reshape(tshp);
+    // memory forward
+    return {{{tlayout, 0, src->comp_node(), StorageIdentifier::make(&inputs_mems[0])}}, {}};
+}
+
+void execute(
+        const OpDef& def,
+        SmallVector<TensorPtr> inputs,
+        SmallVector<TensorPtr> outputs,
+        SmallVector<TensorPtr> workspace) {
+    mgb_assert(inputs[0]->offset() == outputs[0]->offset());
+    mgb_assert(inputs[0]->blob() == outputs[0]->blob());
+}
+
 OP_TRAIT_REG(Reshape, Reshape)
     .apply_on_var_node(apply_on_var_node)
     .infer_output_attrs_fallible(infer_output_attrs_fallible)
+    .infer_output_mem_desc(infer_output_mem_desc)
+    .execute(execute)
     .fallback();
 } // reshape