Refine getitem of Variable (#20729)

* add support for __get_item__ of Variable test=develop

paddle/fluid/operators/strided_slice_op.cc

@@ -42,6 +42,7 @@ class StridedSliceOp : public framework::OperatorWithKernel {
     auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
     auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
     auto infer_flags = ctx->Attrs().Get<std::vector<int>>("infer_flags");
+    auto decrease_axis = ctx->Attrs().Get<std::vector<int>>("decrease_axis");
 
     auto starts_size = starts.size();
     auto ends_size = ends.size();
@@ -90,10 +91,32 @@ class StridedSliceOp : public framework::OperatorWithKernel {
     std::vector<int> out_dims_vector(in_dims.size(), -1);
     if (!tensor_input) {
       StridedSliceOutDims(starts, ends, strides, axes, infer_flags, in_dims,
-                          out_dims_vector.data(), axes.size(), true);
+                          decrease_axis, out_dims_vector.data(), axes.size(),
+                          true);
     }
     framework::DDim out_dims(framework::make_ddim(out_dims_vector));
+    // generate new shape
+    if (decrease_axis.size() > 0) {
+      std::vector<int> new_out_shape;
+      for (size_t i = 0; i < decrease_axis.size(); ++i) {
+        if (ctx->IsRuntime() && infer_flags[i] != -1) {
+          PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
+                            "decrease dim should be 1");
+        }
+        out_dims[decrease_axis[i]] = 0;
+      }
 
+      for (int i = 0; i < out_dims.size(); ++i) {
+        if (out_dims[i] != 0) {
+          new_out_shape.push_back(out_dims[i]);
+        }
+      }
+      if (new_out_shape.size() == 0) {
+        new_out_shape.push_back(1);
+      }
+
+      out_dims = framework::make_ddim(new_out_shape);
+    }
     ctx->SetOutputDim("Out", out_dims);
     ctx->ShareLoD("Input", /*->*/ "Out");
   }
@@ -177,6 +200,8 @@ class StridedSliceOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>(
         "infer_flags", "(list<int>) Flags of inferring dims in attributes.")
         .SetDefault({});
+    AddAttr<std::vector<int>>("decrease_axis", "(list<int>) decrease_axis")
+        .SetDefault({});
     AddComment(R"DOC(
 Strided Slice Operator.
 Instead of calling this op directly most users will want to use the
@@ -212,10 +237,12 @@ class StridedSliceOpGrad : public framework::OperatorWithKernel {
   framework::OpKernelType GetKernelTypeForVar(
       const std::string &var_name, const Tensor &tensor,
       const framework::OpKernelType &expected_kernel_type) const override {
-    if (var_name == "StartsTensor" || var_name == "EndsTensor") {
+    if (var_name == "StartsTensor" || var_name == "EndsTensor" ||
+        var_name == "StridesTensor") {
       return expected_kernel_type;
     }
-    if (var_name == "StartsTensorList" || var_name == "EndsTensorList") {
+    if (var_name == "StartsTensorList" || var_name == "EndsTensorList" ||
+        var_name == "StridesTensorList") {
       return expected_kernel_type;
     }
     return framework::OpKernelType(expected_kernel_type.data_type_,

paddle/fluid/operators/strided_slice_op.h

@@ -27,22 +27,34 @@ static void StridedSliceOutDims(
     const std::vector<int>& starts, const std::vector<int>& ends,
     const std::vector<int>& strides, const std::vector<int>& axes,
     const std::vector<int>& infer_flags, const framework::DDim in_dims,
-    int* out_dims_vector, const size_t size, bool infer_shape) {
+    const std::vector<int>& decrease_axis, int* out_dims_vector,
+    const size_t size, bool infer_shape) {
   for (int i = 0; i < in_dims.size(); i++) {
     out_dims_vector[i] = in_dims[i];
   }
   int stride_index, start_index, end_index;
   for (size_t i = 0; i < size; i++) {
     int axes_index = axes[i];
+    start_index = starts[i];
+    end_index = ends[i];
+    stride_index = strides[i];
+    bool decrease_axis_affect = false;
+    if (start_index == -1 && end_index == 0 && infer_flags[i] == -1) {
+      auto ret = std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
+      if (ret != decrease_axis.end()) {
+        decrease_axis_affect = true;
+      }
+    }
+    if (decrease_axis_affect) {
+      out_dims_vector[axes_index] = 1;
+      continue;
+    }
     if (infer_shape && infer_flags[i] == -1) {
       out_dims_vector[axes_index] = -1;
       continue;
     }
 
-    PADDLE_ENFORCE_NE(strides[i], 0, "stride must not to be zero");
-    start_index = starts[i];
-    end_index = ends[i];
-    stride_index = strides[i];
+    PADDLE_ENFORCE_NE(stride_index, 0, "stride must not to be zero");
     int axis_size = in_dims[axes_index];
     if (axis_size < 0) {
       continue;
@@ -77,6 +89,8 @@ static void StridedSliceOutDims(
 
 static void StridedSliceFunctor(int* starts, int* ends, int* strides, int* axes,
                                 int* reverse_axis, const framework::DDim dims,
+                                const std::vector<int>& infer_flags,
+                                const std::vector<int>& decrease_axis,
                                 const size_t size) {
   for (size_t axis = 0; axis < size; axis++) {
     int axis_size = dims[axes[axis]];
@@ -86,6 +100,15 @@ static void StridedSliceFunctor(int* starts, int* ends, int* strides, int* axes,
       ends[axis_index] = 1;
       strides[axis_index] = 1;
     }
+    bool decrease_axis_affect = false;
+    if (starts[axis_index] == -1 && ends[axis_index] == 0 &&
+        infer_flags[axis_index] == -1) {
+      auto ret = std::find(decrease_axis.begin(), decrease_axis.end(),
+                           axes[axis_index]);
+      if (ret != decrease_axis.end()) {
+        decrease_axis_affect = true;
+      }
+    }
     // stride must not be zero
     if (starts[axis_index] < 0) {
       starts[axis_index] = starts[axis_index] + axis_size;
@@ -94,6 +117,13 @@ static void StridedSliceFunctor(int* starts, int* ends, int* strides, int* axes,
     if (ends[axis_index] < 0) {
       ends[axis_index] = ends[axis_index] + axis_size;
     }
+    if (decrease_axis_affect) {
+      if (strides[axis_index] < 0) {
+        ends[axis_index] = starts[axis_index] - 1;
+      } else {
+        ends[axis_index] = starts[axis_index] + 1;
+      }
+    }
     if (strides[axis_index] < 0) {
       reverse_axis[axis_index] = 1;
       strides[axis_index] = -strides[axis_index];
@@ -151,6 +181,7 @@ class StridedSliceKernel : public framework::OpKernel<T> {
     auto strides = context.Attr<std::vector<int>>("strides");
     auto axes = context.Attr<std::vector<int>>("axes");
     auto infer_flags = context.Attr<std::vector<int>>("infer_flags");
+    auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
 
     auto starts_indices = Eigen::DSizes<Eigen::DenseIndex, D>();
     auto ends_indices = Eigen::DSizes<Eigen::DenseIndex, D>();
@@ -187,12 +218,14 @@ class StridedSliceKernel : public framework::OpKernel<T> {
 
     std::vector<int> out_dims_vector(in_dims.size(), -1);
     StridedSliceOutDims(starts, ends, strides, axes, infer_flags, in_dims,
-                        out_dims_vector.data(), axes.size(), false);
+                        decrease_axis, out_dims_vector.data(), axes.size(),
+                        false);
     framework::DDim out_dims(framework::make_ddim(out_dims_vector));
 
     std::vector<int> reverse_vector(starts.size(), 0);
     StridedSliceFunctor(starts.data(), ends.data(), strides.data(), axes.data(),
-                        reverse_vector.data(), in_dims, starts.size());
+                        reverse_vector.data(), in_dims, infer_flags,
+                        decrease_axis, starts.size());
 
     for (size_t axis = 0; axis < D; axis++) {
       starts_indices[axis] = 0;
@@ -209,8 +242,28 @@ class StridedSliceKernel : public framework::OpKernel<T> {
     }
 
     framework::Tensor tmp;
-    tmp.mutable_data<T>(out_dims, context.GetPlace());
 
+    auto out_dims_origin = out_dims;
+    if (decrease_axis.size() > 0) {
+      std::vector<int> new_out_shape;
+      for (size_t i = 0; i < decrease_axis.size(); ++i) {
+        PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
+                          "decrease dim should be 1");
+        out_dims_origin[decrease_axis[i]] = 0;
+      }
+
+      for (int i = 0; i < out_dims_origin.size(); ++i) {
+        if (out_dims_origin[i] != 0) {
+          new_out_shape.push_back(out_dims_origin[i]);
+        }
+      }
+      if (new_out_shape.size() == 0) {
+        new_out_shape.push_back(1);
+      }
+      out_dims_origin = framework::make_ddim(new_out_shape);
+    }
+
+    tmp.mutable_data<T>(out_dims, context.GetPlace());
     out->Resize(out_dims);
     out->mutable_data<T>(context.GetPlace());
     auto in_t =
@@ -225,6 +278,10 @@ class StridedSliceKernel : public framework::OpKernel<T> {
     tmp_t.device(place) =
         in_t.stridedSlice(starts_indices, ends_indices, strides_indices);
     out_t.device(place) = tmp_t.reverse(reverse_axis);
+
+    if (decrease_axis.size() > 0) {
+      out->Resize(out_dims_origin);
+    }
   }
 };
 
@@ -276,6 +333,8 @@ class StridedSliceGradKernel : public framework::OpKernel<T> {
     auto ends = context.Attr<std::vector<int>>("ends");
     auto strides = context.Attr<std::vector<int>>("strides");
     auto axes = context.Attr<std::vector<int>>("axes");
+    auto infer_flags = context.Attr<std::vector<int>>("infer_flags");
+    auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
 
     auto list_new_ends_tensor =
         context.MultiInput<framework::Tensor>("EndsTensorList");
@@ -313,7 +372,8 @@ class StridedSliceGradKernel : public framework::OpKernel<T> {
     std::vector<int> reverse_vector(starts.size(), 0);
 
     StridedSliceFunctor(starts.data(), ends.data(), strides.data(), axes.data(),
-                        reverse_vector.data(), out_dims, starts.size());
+                        reverse_vector.data(), out_dims, infer_flags,
+                        decrease_axis, starts.size());
 
     for (size_t axis = 0; axis < D; axis++) {
       starts_indices[axis] = 0;

python/paddle/fluid/framework.py

@@ -1414,9 +1414,11 @@ class Variable(object):
         slice_axis = []
         slice_start = []
         slice_end = []
+        slice_step = []
+        use_strided_slice = False
         reverse_axis = []
 
-        def fill_constant(shape, dtype, value, force_cpu=False, out=None):
+        def fill_constant(shape, value, force_cpu=False, out=None):
             self.block.append_op(
                 type='fill_constant',
                 inputs={},
@@ -1425,7 +1427,7 @@ class Variable(object):
                     'shape': shape,
                     'dtype': out.dtype,
                     'value': float(value),
-                    'force_cpu': force_cpu or force_init_on_cpu()
+                    'force_cpu': force_cpu
                 },
                 stop_gradient=True)
             out.stop_gradient = True
@@ -1435,15 +1437,17 @@ class Variable(object):
             if isinstance(slice_item, slice):
                 start = slice_item.start
                 end = slice_item.stop
-                step = slice_item.step if slice_item.step else 1
+                step = slice_item.step
 
-                assert (step == 1 or step == -1)
+                if start is None and end is None and step is None:
+                    continue
 
-                if step == -1:
-                    reverse_axis.append(dim)
-                    assert (start is None and end is None)
+                if step is None:
+                    step = 1
 
                 if start is None and end is None:
+                    assert (step == -1)
+                    reverse_axis.append(dim)
                     continue
 
                 if start is None:
@@ -1452,16 +1456,21 @@ class Variable(object):
                 if end is None:
                     end = 10000000
 
+                if step != 1:
+                    use_strided_slice = True
+
                 slice_axis.append(dim)
                 slice_start.append(start)
                 slice_end.append(end)
+                slice_step.append(step)
             else:
                 decrease_axis.append(dim)
                 slice_axis.append(dim)
                 slice_start.append(slice_item)
+                slice_step.append(1)
                 if isinstance(slice_item, Variable):
                     temp_1 = self.block.create_var(dtype='int32')
-                    fill_constant([1], 'int32', 1, force_cpu=True, out=temp_1)
+                    fill_constant([1], 1, force_cpu=True, out=temp_1)
                     temp_end = self.block.create_var(dtype='int32')
                     self.block.append_op(
                         type='elementwise_add',
@@ -1489,8 +1498,7 @@ class Variable(object):
                 else:
                     assert (isinstance(dim, int))
                     temp_out = self.block.create_var(dtype='int32')
-                    fill_constant(
-                        [1], 'int32', dim, force_cpu=True, out=temp_out)
+                    fill_constant([1], dim, force_cpu=True, out=temp_out)
                     new_list_tensor.append(temp_out)
             return new_list_tensor
 
@@ -1501,8 +1509,9 @@ class Variable(object):
             'ends': [],
             'decrease_axis': decrease_axis
         }
+        if (use_strided_slice == True):
+            attrs['strides'] = []
         infer_flags = list(1 for i in range(len(slice_axis)))
-
         # starts
         if not contain_var(slice_start):
             attrs['starts'] = slice_start
@@ -1525,11 +1534,23 @@ class Variable(object):
                     infer_flags[i] = -1
                 else:
                     attrs['ends'].append(dim)
+        # strides
+        if use_strided_slice == True:
+            if not contain_var(slice_step):
+                attrs['strides'] = slice_step
+            else:
+                inputs['StridesTensorList'] = get_new_list_tensor(slice_step)
+                for i, dim in enumerate(slice_step):
+                    if isinstance(dim, Variable):
+                        attrs['strides'].append(-1)
+                        infer_flags[i] = -1
+                    else:
+                        attrs['strides'].append(dim)
         # infer_flags
         attrs['infer_flags'] = infer_flags
 
         out = self
-        if len(slice_axis) > 0:
+        if use_strided_slice == False and len(slice_axis) > 0:
             # append slice_op here
             slice_out_var = self.block.create_var(
                 name=unique_name.generate_with_ignorable_key(self.name +
@@ -1543,6 +1564,18 @@ class Variable(object):
                 attrs=attrs)
 
             out = slice_out_var
+        elif use_strided_slice == True and len(slice_axis) > 0:
+            strided_slice_out_var = self.block.create_var(
+                name=unique_name.generate_with_ignorable_key(self.name +
+                                                             "_strided_slice"),
+                dtype=self.dtype)
+            self.block.append_op(
+                type="strided_slice",
+                inputs=inputs,
+                outputs={'Out': [strided_slice_out_var]},
+                attrs=attrs)
+
+            out = strided_slice_out_var
 
         if len(reverse_axis) > 0:
             reverse_out_var = self.block.create_var(

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

@@ -438,7 +438,7 @@ class TestStridedSliceOp_strides_Tensor(OpTest):
 
 
 # Test python API
-class TestSliceAPI(OpTest):
+class TestStridedSliceAPI(OpTest):
     def test_1(self):
         input = np.random.random([3, 4, 5, 6]).astype("float32")
         minus_1 = fluid.layers.fill_constant([1], "int32", -1)
@@ -455,7 +455,6 @@ class TestSliceAPI(OpTest):
             shape=[3, 4, 5, 6],
             append_batch_size=False,
             dtype="float32")
-
         out_1 = fluid.layers.strided_slice(
             x,
             axes=[0, 1, 2],
@@ -477,9 +476,9 @@ class TestSliceAPI(OpTest):
         out_4 = fluid.layers.strided_slice(
             x, axes=[0, 1, 2], starts=starts, ends=ends, strides=strides)
 
-        out_5 = x[-3:3, 0:100, 2:-1]
-        out_6 = x[minus_3:3, 0:100, :, 2:-1]
-        out_7 = x[minus_1, 0:100, :, 2:minus_1]
+        out_5 = x[-3:3, 0:100:2, -1:2:-1]
+        out_6 = x[minus_3:3:1, 0:100:2, :, minus_1:2:minus_1]
+        out_7 = x[minus_1, 0:100:2, :, -1:2:-1]
 
         exe = fluid.Executor(place=fluid.CPUPlace())
         res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
@@ -491,14 +490,13 @@ class TestSliceAPI(OpTest):
                 'strides': np.array([1, 1, 1]).astype("int32")
             },
             fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
-
         assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
         assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
         assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
         assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
-        assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
-        assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
-        assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1])
+        assert np.array_equal(res_5, input[-3:3, 0:100:2, -1:2:-1, :])
+        assert np.array_equal(res_6, input[-3:3, 0:100:2, :, -1:2:-1])
+        assert np.array_equal(res_7, input[-1, 0:100:2, :, -1:2:-1])
 
 
 if __name__ == "__main__":