refactor(imperative): fix broadcast,reshape,reduce

GitOrigin-RevId: ee3dc1487d

imperative/src/impl/ops/broadcast.cpp

@@ -81,10 +81,33 @@ std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
     return {{{TensorLayout(out_shape, src.layout.dtype), src.comp_node}}, true};
 }
 
+SmallVector<TensorPtr> apply_on_physical_tensor(
+        const OpDef& def, const SmallVector<TensorPtr>& inputs) {
+    auto& input = inputs[0];
+    TensorShape target_shape;
+    cg::copy_tensor_value_to_shape(
+            target_shape, inputs[1]->get_value().proxy_to_default_cpu());
+    TensorPtr output = Tensor::make(
+            TensorLayout(target_shape, input->dtype()), input->comp_node());
+    if (output->layout().is_empty()) {
+        return {output};
+    }
+    if (input->shape().eq_shape(output->shape())) {
+        mgb_assert(input->layout().eq_layout(output->layout()));
+        output->dev_tensor().copy_from_fixlayout(input->dev_tensor());
+    } else {
+        TensorLayout input_layout = input->layout().broadcast(output->shape());
+        output->dev_tensor().copy_from_fixlayout(
+                input->dev_tensor().sub(SubTensorSpec::make_from_layout(input_layout)));
+    }
+    return {output};
+}
+
 OP_TRAIT_REG(Broadcast, Broadcast, opr::Broadcast)
         .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();
 }  // namespace broadcast
 
@@ -147,9 +170,31 @@ std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
     return {{{TensorLayout(out_shape, src.layout.dtype), src.comp_node}}, true};
 }
 
+SmallVector<TensorPtr> apply_on_physical_tensor(
+        const OpDef& def, const SmallVector<TensorPtr>& inputs) {
+    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 {Tensor::make(src->blob(), 0, tlayout)};
+}
+
 OP_TRAIT_REG(Reshape, Reshape)
         .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();
 }  // namespace reshape
 

imperative/src/impl/ops/reduce.cpp

@@ -50,9 +50,18 @@ bool memory_forward_success(const OpDef& def, SmallVector<TensorPtr> inputs) {
     return false;
 }
 
+SmallVector<TensorPtr> apply_on_physical_tensor(
+        const OpDef& def, const SmallVector<TensorPtr>& inputs) {
+    if (memory_forward_success(def, inputs)) {
+        return {Tensor::make(inputs[0]->blob(), 0, inputs[0]->layout())};
+    }
+    return proxy_graph_detail::apply_on_physical_tensor(def, inputs);
+}
+
 OP_TRAIT_REG(Reduce, Reduce, opr::Reduce)
         .make_from_op_node(make_from_op_node)
         .apply_on_var_node(apply_on_var_node)
+        .apply_on_physical_tensor(apply_on_physical_tensor)
         .fallback();
 }  // namespace reduce
 }  // namespace