Support settiem by Bool index

paddle/fluid/pybind/imperative.cc

@@ -499,12 +499,12 @@ static void ParseIndexingSlice(
       none_axes->push_back(dim);
     } else if (PyList_Check(slice_item)) {
       *list_select_flag = true;
-      if (size != 1) {
-        PADDLE_THROW(platform::errors::InvalidArgument(
-            "When index contains a list, its length is excepted to 1, "
-            "but received %d",
-            size));
-      }
+      PADDLE_ENFORCE_EQ(
+          size, 1,
+          platform::errors::InvalidArgument(
+              "When index contains a list, its length is excepted to 1, "
+              "but received %d",
+              size));
       bool all_bool = true;
       int list_size = PyList_GET_SIZE(slice_item);
       for (int j = 0; j < list_size; ++j) {
@@ -517,12 +517,13 @@ static void ParseIndexingSlice(
         }
       }
       if (all_bool) {
-        if (list_size != shape[0]) {
-          PADDLE_THROW(platform::errors::InvalidArgument(
-              "The dimension of bool index doesn't match indexed array along "
-              "dimension 0, the target dimension is %d, but received %d.",
-              shape[0], list_size));
-        }
+        PADDLE_ENFORCE_EQ(
+            list_size, shape[0],
+            platform::errors::InvalidArgument(
+                "The dimension of bool index doesn't match indexed array along "
+                "dimension 0, the target dimension is %d, but received %d.",
+                shape[0], list_size));
+
         for (int j = 0; j < list_size; ++j) {
           PyObject *list_item = PyList_GetItem(slice_item, j);
           if (list_item == Py_True) {
@@ -818,7 +819,7 @@ void BindImperative(py::module *m_ptr) {
       .def("__setitem_varbase__",
            [](std::shared_ptr<imperative::VarBase> &self, py::handle _index,
               py::object &value_obj) {
-             VLOG(4) << "Call __setitem__";
+             VLOG(4) << "Call __setitem_varbase__";
 
              auto self_tensor =
                  self->MutableVar()->GetMutable<framework::LoDTensor>();
@@ -871,7 +872,6 @@ void BindImperative(py::module *m_ptr) {
              // TODO(liym27): Try not to call TensorToPyArray because it always
              // copys data to cpu place, which reduces performance.
              if (parse_index && value_is_tensor) {
-               VLOG(4) << "index is integer/slice/ellipsis and value is tensor";
                std::vector<int> axes, starts, ends, steps, decrease_axes,
                    none_axes, infer_flags, list_select_idxs;
                // if index is a list, list_select_flag will be true
@@ -880,6 +880,7 @@ void BindImperative(py::module *m_ptr) {
                                   &steps, &decrease_axes, &none_axes,
                                   &infer_flags, &list_select_idxs,
                                   &list_select_flag);
+
                framework::AttributeMap attrs = {
                    {"axes", axes},
                    {"starts", starts},

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -587,14 +587,25 @@ def _is_list_tuple(item):
             return self._getitem_index_not_tensor(item)
 
     def __setitem__(self, item, value):
+        def contain_tensor_or_list(item):
+            if not isinstance(item, tuple):
+                item = [item]
 
-        if contain_tensor(item):
-            # 1. Call _setitem_impl_ when item contains tensor.
-            # Why not call a c++ function ? Because item can't be parsed when it contains tensor.
+            for slice_item in item:
+                if isinstance(slice_item, list):
+                    return True
+                elif isinstance(slice_item, Variable):
+                    return True
+
+            return False
+
+        if contain_tensor_or_list(item):
+            # To reuse code with static graph,
+            # Call _setitem_impl_ when item contains tensor or list.
             return _setitem_impl_(self, item, value)
 
         else:
-            # 2. Call c++ func __setitem_varbase__ to speedup.
+            # Call c++ func __setitem_varbase__ to speedup.
             return self.__setitem_varbase__(item, value)
 
     for method_name, method in (

python/paddle/fluid/tests/unittests/test_set_value_op.py

@@ -408,6 +408,61 @@ def _get_answer(self):
         self.data[None, :, 1, ..., None] = np.zeros(self.shape)[0, 0, :, None]
 
 
+# 1.5 item is list or Tensor of bol
+class TestSetValueItemBool1(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[[True, False]] = self.value
+
+    def _get_answer(self):
+        self.data[[True, False]] = self.value
+
+
+class TestSetValueItemBool2(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[[False, False]] = self.value
+
+    def _get_answer(self):
+        self.data[[False, False]] = self.value
+
+
+class TestSetValueItemBool3(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[[False, True]] = np.zeros(self.shape[2])
+
+    def _get_answer(self):
+        self.data[[False, True]] = np.zeros(self.shape[2])
+
+
+class TestSetValueItemBool4(TestSetValueApi):
+    def _call_setitem(self, x):
+        idx = paddle.assign(np.array([False, True]))
+        x[idx] = np.zeros(self.shape[2])
+
+    def _get_answer(self):
+        self.data[np.array([False, True])] = np.zeros(self.shape[2])
+
+
+class TestSetValueItemBool5(TestSetValueApi):
+    def _call_setitem(self, x):
+        idx = paddle.assign(
+            np.array([[False, True, False], [True, True, False]]))
+        x[idx] = self.value
+
+    def _get_answer(self):
+        self.data[np.array([[False, True, False], [True, True, False]
+                            ])] = self.value
+
+
+class TestSetValueItemBool6(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0, ...] = 0
+        x[x > 0] = self.value
+
+    def _get_answer(self):
+        self.data[0, ...] = 0
+        self.data[self.data > 0] = self.value
+
+
 # 2. Test different type of value: int, float, numpy.ndarray, Tensor
 # 2.1 value is int32, int64, float32, float64, bool
 
@@ -830,6 +885,21 @@ def _ellipsis_error(self):
             one = paddle.ones([1])
             x[::one] = self.value
 
+    def _bool_list_error(self):
+        with self.assertRaises(TypeError):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            x[[True, False, 0]] = 0
+
+        with self.assertRaises(IndexError):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            x[[True, False], [True, False]] = 0
+
+    def _bool_tensor_error(self):
+        with self.assertRaises(IndexError):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            idx = paddle.assign([True, False, True])
+            x[idx] = 0
+
     def _broadcast_mismatch(self):
         program = paddle.static.Program()
         with paddle.static.program_guard(program):
@@ -846,6 +916,8 @@ def test_error(self):
             self._value_type_error()
             self._dtype_error()
             self._step_error()
+            self._bool_list_error()
+            self._bool_tensor_error()
         self._broadcast_mismatch()
 
 

python/paddle/fluid/variable_index.py

@@ -509,16 +509,6 @@ def _setitem_impl_(var, item, value):
             start = slice_item
             end = slice_item + 1 if slice_item != -1 else MAX_INTEGER
             step = 1
-        elif isinstance(slice_item, list):
-            if not is_list_tuple(slice_item, int):
-                raise TypeError(
-                    "Only support int or list in index list. But revceived {}.".
-                    format(slice_item))
-            slice_info.update(slice_item)
-            continue
-        elif isinstance(slice_item, (Variable, np.ndarray)):
-            slice_info.update(slice_item)
-            continue
 
         elif isinstance(slice_item, slice):
             start = slice_item.start
@@ -547,10 +537,43 @@ def _setitem_impl_(var, item, value):
 
             if end is None:
                 end = MAX_INTEGER if step > 0 else (0 - MAX_INTEGER)
+        elif isinstance(slice_item, list):
+            if is_list_tuple(slice_item, int):
+                slice_info.update(slice_item)
+                continue
+
+            for i in slice_item:
+                if not isinstance(i, bool):
+                    raise TypeError("Doesn't support {} in index list.".format(
+                        type(i)))
+
+            if len(item) != 1:
+                raise IndexError(
+                    "When index contains a bool list, its length must be 1, but received {}.".
+                    format(len(item)))
+
+            from .layers import assign
+            idx_tensor = assign(slice_item)
+            return set_value_for_bool_tensor(var, idx_tensor, value)
+
+        elif isinstance(slice_item, np.ndarray):
+            slice_info.update(slice_item)
+            continue
+
+        elif isinstance(slice_item, Variable):
+            if slice_item.dtype == core.VarDesc.VarType.BOOL:
+                if len(item) != 1:
+                    raise IndexError(
+                        "When index contains a bool tensor, its length must be 1, but received {}.".
+                        format(len(item)))
+                return set_value_for_bool_tensor(var, slice_item, value)
+            else:
+                slice_info.update(slice_item)
+                continue
         else:
             raise IndexError(
-                "Valid index accept int, slice, ellipsis or None, but received {}.".
-                format(slice_item))
+                "Valid index accept int, slice, ellipsis, None, list of bool, Variable, "
+                "but received {}.".format(slice_item))
 
         axes.append(dim)
         starts.append(start)
@@ -632,3 +655,47 @@ def _setitem_impl_(var, item, value):
         type="set_value", inputs=inputs, outputs={'Out': var}, attrs=attrs)
 
     return var
+
+
+# the item is a tensor of bool 
+def set_value_for_bool_tensor(var, item, value):
+
+    # TODO(zyfncg): Now scatter_nd_add only support float32 and float64 tensor, 
+    # so in the current version we also only support float32 and float64 tensor, 
+    # this problem will be fixed in the future.
+    if var.dtype != core.VarDesc.VarType.FP32 and var.dtype != core.VarDesc.VarType.FP64:
+        raise TypeError("Only support float and double tensor for bool index, "
+                        "but received {}.".format(var.dtype))
+
+    if len(item.shape) > len(var.shape):
+        raise IndexError("The dims of bool index doesn't match indexed array, "
+                         "the dims of bool index except to be equal or less "
+                         "than {}, but received {}.".format(
+                             len(var.shape), len(item.shape)))
+    for i, dim_len in enumerate(item.shape):
+        if dim_len != var.shape[i]:
+            raise IndexError(
+                "The dimension of bool index doesn't match indexed array along "
+                "dimension {}, the target dimension is {}, but received {}.".
+                format(i, var.shape[i], dim_len))
+
+    def idx_not_empty(var, item, value):
+        from .framework import Variable
+        from .layers import assign
+        from .layers.nn import where
+        from ..tensor import gather_nd, scatter_nd_add
+
+        if not isinstance(value, Variable):
+            value = assign(value).cast(var.dtype)
+
+        idx = where(item)
+        gather_val = gather_nd(var, idx)
+        gather_val_new = value - gather_val
+        out = scatter_nd_add(var, idx, gather_val_new)
+        var[:] = out
+
+    from .layers.control_flow import cond
+    # If all the bool index is False, just do nothing
+    cond(item.any(), lambda: idx_not_empty(var, item, value))
+
+    return var