feat(mgb/opr): let CondTake support empty input

GitOrigin-RevId: dfb401a945d5d75909f7b78448b3713623c28a2c

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

@@ -18,6 +18,7 @@ import megengine.amp as amp
 import megengine.core.ops.builtin as builtin
 import megengine.core.tensor.dtype as dtype
 import megengine.functional as F
+import megengine.jit as jit
 from megengine import Parameter, Tensor, is_cuda_available, tensor
 from megengine.core._trace_option import use_symbolic_shape
 from megengine.core.autodiff.grad import Grad
@@ -859,6 +860,35 @@ def test_condtake():
     np.testing.assert_equal(idx.numpy(), np.where(y.reshape(-1))[0])
 
 
+# @pytest.mark.parametrize("is_symbolic", [None, False, True])
+def test_condtake(is_symbolic=None):
+    shapes = [
+        (3, 3, 3),
+        (0,),
+        (3, 0, 3),
+    ]
+
+    def fn(mask, data):
+        return F.cond_take(mask, data)
+
+    if is_symbolic is not None:
+        fn = jit.trace(symbolic=is_symbolic)(fn)
+
+    for shp in shapes:
+        x_np = np.random.randn(*shp).astype("float32")
+        mask_np = x_np > 0
+        x = tensor(x_np)
+        mask = tensor(mask_np)
+        ref_out = x_np[mask_np]
+        ref_idx = mask_np.flatten().nonzero()[0]
+        for i in range(3):
+            out, idx = fn(mask, x)
+            np.testing.assert_equal(out.numpy(), ref_out)
+            np.testing.assert_equal(idx.numpy(), ref_idx)
+            if is_symbolic is None:
+                break
+
+
 def test_condtake_is_same():
     op1 = builtin.CondTake()
     op2 = builtin.CondTake()

imperative/src/impl/ops/cond_take.cpp

@@ -45,25 +45,30 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
 
     auto&& inp = inputs[0];
     auto&& msk = inputs[1];
+    SmallVector<TensorPtr> out;
     mgb_assert(inp->layout().eq_shape(msk->layout()),
                "input shape does not match mask shape");
     mgb_assert(msk->get_value().dtype().enumv() == DTypeEnum::Bool,
                "mask dtype must be bool");
-    DnnOprCaller<megdnn::CondTake> dnn_op(inp->comp_node());
-    dnn_op.op->param().val = 1;
-
-    TensorLayout m_layout({dnn_op.op->get_workspace_in_bytes(inp->layout())},
-                           dtype::Byte());
-
-    auto dnn_workspace = dnn_op.create_workspace(m_layout);
     MegDNNDynOutMallocImpl<2> policy{inp->comp_node()};
-
-    dnn_op.op->exec(inp->dev_tensor().as_megdnn(),
-                  msk->dev_tensor().as_megdnn(),
-                  dnn_workspace,
-                  &policy);
-
-    SmallVector<TensorPtr> out;
+    if (inp->layout().is_empty()) {
+        // empty tensor
+        policy.alloc_output(0, inp->layout().dtype, {0}, nullptr);
+        policy.alloc_output(1, dtype::Int32(), {0}, nullptr);
+    } else {
+        DnnOprCaller<megdnn::CondTake> dnn_op(inp->comp_node());
+        dnn_op.op->param().val = 1;
+
+        TensorLayout m_layout({dnn_op.op->get_workspace_in_bytes(inp->layout())},
+                            dtype::Byte());
+
+        auto dnn_workspace = dnn_op.create_workspace(m_layout);
+
+        dnn_op.op->exec(inp->dev_tensor().as_megdnn(),
+                    msk->dev_tensor().as_megdnn(),
+                    dnn_workspace,
+                    &policy);
+    }
     out.push_back(policy.at(0));
     out.push_back(policy.at(1));
     return out;

src/opr/impl/misc.cpp

@@ -264,6 +264,15 @@ CondTake::CondTake(VarNode *data, VarNode *mask,
     }
 }
 
+CondTake::NodeProp* CondTake::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);
+    ret->add_dep_type_existing_var(input(1),
+                                   NodeProp::DepType::VALUE_ALLOW_EMPTY);
+    return ret;
+}
+
 #if MGB_ENABLE_GRAD
 MGB_IMPL_OPR_GRAD(CondTake) {
     mgb_assert(out_grad.size() == 3 && !out_grad[2]);
@@ -305,11 +314,21 @@ void CondTake::add_input_layout_constraint() {
 }
 
 void CondTake::scn_do_execute() {
+    auto&& data = input(0)->dev_tensor();
+    auto&& mask = input(1)->dev_tensor();
     intl::MegDNNDynOutMallocImpl dyn_malloc{this, comp_node()};
-    megdnn_opr()->exec(input(0)->dev_tensor().as_megdnn(),
-                       input(1)->dev_tensor().as_megdnn(),
-                       intl::get_megdnn_workspace_from_var(output().back()),
-                       &dyn_malloc);
+    if (data.layout().is_empty()) {
+        mgb_assert(data.layout().eq_shape(mask.layout()),
+                "CondTake shape differs: data=%s mask=%s",
+                data.layout().TensorShape::to_string().c_str(),
+                mask.layout().TensorShape::to_string().c_str());
+        dyn_malloc.alloc_output(0, data.layout().dtype, {0}, nullptr);
+        dyn_malloc.alloc_output(1, dtype::Int32(), {0}, nullptr);
+    } else {
+        megdnn_opr()->exec(data.as_megdnn(), mask.as_megdnn(),
+                        intl::get_megdnn_workspace_from_var(output().back()),
+                        &dyn_malloc);
+    }
 }
 
 /* ================= TopK =================  */

src/opr/include/megbrain/opr/misc.h

@@ -151,6 +151,7 @@ MGB_DEFINE_OPR_CLASS(CondTake, intl::CondTakeBase) // {
     void init_output_static_infer_desc() override;
     void scn_do_execute() override;
     void add_input_layout_constraint() override;
+    NodeProp* do_make_node_prop() const override;
 
     public:
         CondTake(VarNode *data, VarNode *mask,

src/opr/test/misc.cpp

@@ -256,20 +256,25 @@ TEST(TestOprMisc, CondTake) {
         run(mki({100}));
 }
 
-TEST(TestOprMisc, CondTakeEmptyOut) {
+TEST(TestOprMisc, CondTakeEmptyIO) {
     using Param = opr::CondTake::Param;
     HostTensorGenerator<> gen;
-    auto host_x = gen({1});
-    host_x->ptr<float>()[0] = 1;
-    auto graph = ComputingGraph::make();
-    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
-    auto out = opr::CondTake::make(x, x, {Param::Mode::LT});
-    HostTensorND host_out0, host_out1;
-    auto func = graph->compile({make_callback_copy(out[0], host_out0),
-            make_callback_copy(out[1], host_out1)});
-    func->execute();
-    ASSERT_EQ(TensorShape{0}, host_out0.shape());
-    ASSERT_EQ(TensorShape{0}, host_out1.shape());
+    auto check = [&](const TensorShape& shp) {
+        auto host_x = gen(shp);
+        auto graph = ComputingGraph::make();
+        auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+        auto y = x + 1;
+        auto out = opr::CondTake::make(x, y, {Param::Mode::EQ});
+        HostTensorND host_out0, host_out1;
+        auto func = graph->compile({make_callback_copy(out[0], host_out0),
+                make_callback_copy(out[1], host_out1)});
+        func->execute();
+        ASSERT_EQ(TensorShape{0}, host_out0.shape());
+        ASSERT_EQ(TensorShape{0}, host_out1.shape());
+    };
+    check({1});
+    check({0});
+    check({1, 0});
 }
 
 TEST(TestOprMisc, TopKValueOnly) {