feat(mgb/opr): let reduce support empty IO

GitOrigin-RevId: 88b37123a8

imperative/python/test/unit/functional/test_math.py

@@ -13,7 +13,7 @@ import pytest
 from utils import opr_test
 
 import megengine.functional as F
-from megengine import tensor
+from megengine import jit, tensor
 
 
 def common_test_reduce(opr, ref_opr):
@@ -204,3 +204,28 @@ def test_topk(descending, sorted, inp1d, kth_only):
         if not sorted:
             values = np_sort(values)
         np.testing.assert_equal(values, np_sort(data)[..., :k])
+
+
+@pytest.mark.parametrize("is_trace", [True, False])
+def test_reduce_on_empty_tensor(is_trace):
+    dtypes = [np.float32, np.int32, np.bool]
+    inputs = [
+        (np.random.random((0,)), None),
+        (np.random.random((3, 0, 2)), 1),
+        (np.random.random((10, 10, 0, 10)), 0),
+    ]
+
+    def run_test(fn, ref_fn, input, dtype, axis=None, symbolic=False):
+        if is_trace:
+            fn = jit.trace(symbolic=symbolic)(fn)
+        for i in range(3):
+            out = fn(tensor(input, dtype=dtype), axis=axis).numpy()
+            out_ref = ref_fn(input.astype(dtype), axis=axis)
+            np.testing.assert_equal(out, out_ref)
+
+    for dtype in dtypes:
+        for inp, axis in inputs:
+            run_test(F.sum, np.sum, inp, dtype, axis, True)
+            run_test(F.sum, np.sum, inp, dtype, axis, False)
+            run_test(F.prod, np.prod, inp, dtype, axis, True)
+            run_test(F.prod, np.prod, inp, dtype, axis, False)

imperative/src/impl/proxy_graph.cpp

@@ -84,6 +84,11 @@ public:
         auto&& dev_tensor = tensor.dev_tensor();
         var->m_comp_node = dev_tensor.comp_node();
         var->m_shape = dev_tensor.shape();
+        if (dev_tensor.empty()) {
+            auto layout = dev_tensor.layout();
+            layout.init_contiguous_stride();
+            dev_tensor.reset(dev_tensor.storage(), layout);
+        }
         var->m_dev_tensor = dev_tensor;
         var->m_mem_plan.reset_from_owner_var().chunk()
                 .mem_alloc_status.set_from_owner_var();

src/core/test/graph/misc.cpp

@@ -1364,7 +1364,7 @@ TEST(TestGraph, EmptyShapeCheck) {
     using Param = opr::CondTake::Param;
     auto x = opr::Host2DeviceCopy::make(*graph, host_x),
          y = opr::CondTake::make(x, x, {Param::Mode::GT})[0],
-         z = opr::reduce_sum(y, y.make_scalar(1));
+         z = opr::reduce_max(y, y.make_scalar(1));
     HostTensorND host_z;
     auto func = graph->compile({make_callback_copy(z, host_z)});
     func->execute();
@@ -1377,7 +1377,7 @@ TEST(TestGraph, EmptyShapeCheck) {
                     func->execute();
                 } catch (const MegBrainError& exc) {
                     std::string msg{exc.what()};
-                    ASSERT_TRUE(msg.find("empty output var") !=
+                    ASSERT_TRUE(msg.find("empty input is not allowed") !=
                                 std::string::npos)
                             << "bad message " << msg;
                     throw;
@@ -2413,8 +2413,6 @@ TEST(TestMemReuse, ResetEmptyDevTensor) {
              y = opr::Reduce::make(x, {opr::Reduce::Mode::MAX, 0});
         HostTensorND host_y;
         auto func = g->compile({make_callback_copy(y, host_y)});
-        auto &&recv = x.node()->owner_graph()->var_receiver_in_current_comp_seq(x.node());
-        ASSERT_TRUE(!recv.is_empty_allowed());
         if (inp_shp.is_empty()) {
             ASSERT_ANY_THROW(func->execute().wait());
         } else {

src/opr/impl/basic_arith.cpp

@@ -1072,6 +1072,7 @@ class Reduce::KernScheduler {
             m_apply_side_effect;
         std::unique_ptr<megdnn::Elemwise> m_elemwise_trans_opr;
         std::unique_ptr<megdnn::TypeCvt> m_typecvt_opr;
+        std::unique_ptr<megdnn::Fill> m_fill_opr;
         DeviceTensorND m_side_affect_wkspc;
 };
 
@@ -1338,6 +1339,47 @@ void Reduce::KernScheduler::execute(
     }
 
     mgb_assert(!m_kern_param.empty());
+
+    // empty input
+    if (input.shape_valid() && input.empty()) {
+        auto mode = m_kern_param[0].kparam.mode;
+        if (!m_fill_opr) {
+            m_fill_opr = intl::get_megdnn_handle(dest.comp_node())->
+                create_operator<megdnn::Fill>();
+        }
+        std::string err_msg;
+        switch (mode) {
+            case Reduce::Mode::SUM:
+                if (!dest.empty()) {
+                    m_fill_opr->param() = 0;
+                    m_fill_opr->exec(dest.as_megdnn(), {});
+                }
+                break;
+            case Reduce::Mode::PRODUCT:
+                if (!dest.empty()) {
+                    m_fill_opr->param() = 1;
+                    m_fill_opr->exec(dest.as_megdnn(), {});
+                }
+                break;
+            case Reduce::Mode::MEAN:
+                err_msg = "mean"; break;
+            case Reduce::Mode::MIN:
+                err_msg = "min"; break;
+            case Reduce::Mode::MAX:
+                err_msg = "max"; break;
+            case Reduce::Mode::SUM_SQR:
+                err_msg = "sum_sqr"; break;
+            default:
+                mgb_throw(MegBrainError, "bad reduce mode");
+        }
+        if (!err_msg.empty()) {
+            mgb_throw(
+                MegBrainError,
+                "empty input is not allowed for reduce mode: %s",
+                err_msg.c_str());
+        }
+        return;
+    }
     mgb_assert(input.layout().is_contiguous() &&
             input.raw_ptr() == m_kern_param[0].input.raw_ptr &&
             dest.raw_ptr() == m_kern_param.back().output.raw_ptr);
@@ -1425,7 +1467,9 @@ Reduce::Reduce(VarNode *inp, VarNode *target_shape, const Param &param,
             mgb_throw(GraphError, "invalid param data_type: %d",
                       int(param.data_type));
     }
-    add_output(None)->dtype(out_dtype);
+    add_output(None)
+        ->add_flag(VarNode::Flag::ALLOW_EMPTY_SHAPE)
+        .dtype(out_dtype);
     cg::add_workspace_output(this);
 
     add_equivalence_component<PODHash<Param>>(&m_param);
@@ -1703,6 +1747,13 @@ void Reduce::perform(
     ksched.execute(opr.get(), *input_contig, dest);
 }
 
+Reduce::NodeProp* Reduce::do_make_node_prop() const {
+    auto ret = Super::do_make_node_prop();
+    ret->add_dep_type_existing_var(input(0),
+                                   NodeProp::DepType::VALUE_ALLOW_EMPTY);
+    return ret;
+}
+
 void Reduce::create_megdnn_opr() {
     set_megdnn_opr(intl::get_megdnn_handle(comp_node())->
             create_operator<megdnn::Reduce>());

src/opr/include/megbrain/opr/basic_arith.h

@@ -335,6 +335,7 @@ MGB_DEFINE_OPR_CLASS(Reduce, intl::DynamicOutputIfInputDynamic<
         void add_input_layout_constraint() override final;
         void scn_do_execute() override final;
         void init_output_static_infer_desc() override final;
+        NodeProp* do_make_node_prop() const override;
 
         void create_megdnn_opr() override;
         void record_execute_deps(ExecDependencyArray& deps) override;

src/opr/test/basic_arith/reduction.cpp

@@ -900,4 +900,61 @@ TEST(TestBasicArithReduction, StaticInferValueDType) {
     run_test(F16, F16, ParamType::FLOAT_O16xC32);
 }
 
+TEST(TestBasicArithReduction, EmptyInput) {
+    using Param = opr::Reduce::Param;
+    using Mode = opr::Reduce::Mode;
+
+    auto check_allow_empty = [](const Param& param, const TensorShape& inpshp, double target_val) {
+        HostTensorGenerator<> gen;
+        auto graph = ComputingGraph::make();
+        auto host_x = gen(inpshp);
+        auto x = opr::Host2DeviceCopy::make(*graph, host_x),
+             y = opr::Reduce::make(x, param, {});
+        HostTensorND host_y;
+        auto func = graph->compile({make_callback_copy(y, host_y)});
+        func->execute().wait();
+        if (!host_y.shape().is_empty()) {
+            size_t size = host_y.layout().total_nr_elems();
+
+#define cb(DType)                                        \
+    if (host_y.layout().dtype == DType()) {              \
+        using ctype = typename DTypeTrait<DType>::ctype; \
+        auto ptr = host_y.ptr<ctype>();                  \
+        ctype target = static_cast<ctype>(target_val);   \
+        for (size_t i = 0; i < size; ++i) {              \
+            ASSERT_TRUE(ptr[i] == target);               \
+        }                                                \
+    }
+    MEGDNN_FOREACH_COMPUTING_DTYPE(cb)
+#undef cb
+
+        } else {
+            ASSERT_TRUE(host_y.empty());
+        }
+    };
+
+    auto check_forbid_empty = [](const Param& param, const TensorShape& inpshp) {
+        HostTensorGenerator<> gen;
+        auto graph = ComputingGraph::make();
+        auto host_x = gen(inpshp);
+        auto x = opr::Host2DeviceCopy::make(*graph, host_x),
+             y = opr::Reduce::make(x, param, {});
+        HostTensorND host_y;
+        auto func = graph->compile({make_callback_copy(y, host_y)});
+        ASSERT_ANY_THROW(func->execute().wait());
+    };
+
+    check_allow_empty({Mode::SUM, 0, {}}, {0}, 0);
+    check_allow_empty({Mode::SUM, -1, {}}, {2, 0, 3}, 0);
+    check_allow_empty({Mode::SUM, 1, {}}, {2, 0, 3}, 0);
+    check_allow_empty({Mode::PRODUCT, 0, {}}, {0, 1, 2}, 1);
+    check_allow_empty({Mode::PRODUCT, 1, {}}, {0, 0, 0}, 1);
+    check_allow_empty({Mode::PRODUCT, 2, {}}, {0, 0, 0}, 1);
+
+    check_forbid_empty({Mode::MAX, 0, {}}, {0});
+    check_forbid_empty({Mode::MIN, -1, {}}, {0, 1, 2});
+    check_forbid_empty({Mode::MEAN, 0, {}}, {0, 0});
+    check_forbid_empty({Mode::SUM_SQR, 1, {}}, {2, 1, 0});
+}
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}