[Phi] Add shape and strided_slice yaml & Adapt eager mode (#41131)

* add several yaml

* polish strided slice kernel & add yaml

* reorder yaml

* add several yaml

* revert yaml config change

* resolve conflict

* Update test_strided_slice_op.py

paddle/fluid/operators/strided_slice_op.cc

@@ -228,7 +228,7 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(StridedSliceOpGradNoNeedBufferVarsInferer,
 namespace ops = paddle::operators;
 
 DECLARE_INFER_SHAPE_FUNCTOR(strided_slice, StridedSliceInferShape,
-                            PD_INFER_META(phi::StridedSliceInferMeta));
+                            PD_INFER_META(phi::StridedSliceRawInferMeta));
 
 REGISTER_OPERATOR(strided_slice, ops::StridedSliceOp, ops::StridedSliceOpMaker,
                   ops::StridedSliceOpGradMaker<paddle::framework::OpDesc>,

paddle/phi/infermeta/unary.cc

@@ -1922,15 +1922,15 @@ void SqueezeInferMeta(const MetaTensor& x,
   out->set_dtype(x.dtype());
 }
 
-void StridedSliceInferMeta(const MetaTensor& x,
-                           const std::vector<int>& axes,
-                           const IntArray& starts,
-                           const IntArray& ends,
-                           const IntArray& strides,
-                           const std::vector<int>& infer_flags,
-                           const std::vector<int>& decrease_axis,
-                           MetaTensor* out,
-                           MetaConfig config) {
+void StridedSliceRawInferMeta(const MetaTensor& x,
+                              const std::vector<int>& axes,
+                              const IntArray& starts,
+                              const IntArray& ends,
+                              const IntArray& strides,
+                              const std::vector<int>& infer_flags,
+                              const std::vector<int>& decrease_axis,
+                              MetaTensor* out,
+                              MetaConfig config) {
   auto in_dims = x.dims();
   PADDLE_ENFORCE_LT(
       in_dims.size(),
@@ -2052,6 +2052,19 @@ void StridedSliceInferMeta(const MetaTensor& x,
   out->set_dtype(x.dtype());
 }
 
+void StridedSliceInferMeta(const MetaTensor& x,
+                           const std::vector<int>& axes,
+                           const IntArray& starts,
+                           const IntArray& ends,
+                           const IntArray& strides,
+                           MetaTensor* out,
+                           MetaConfig config) {
+  std::vector<int> infer_flags(axes.size(), 1);
+  std::vector<int> decrease_axis;
+  StridedSliceRawInferMeta(
+      x, axes, starts, ends, strides, infer_flags, decrease_axis, out, config);
+}
+
 /*  Why not use SumRawInferMeta directly?
     Because we need make InferMetaFunction's args follow the design of api.yaml
 */

paddle/phi/infermeta/unary.h

@@ -284,13 +284,21 @@ void SqueezeInferMeta(const MetaTensor& x,
                       MetaTensor* xshape,
                       MetaTensor* out);
 
+void StridedSliceRawInferMeta(const MetaTensor& x,
+                              const std::vector<int>& axes,
+                              const IntArray& starts,
+                              const IntArray& ends,
+                              const IntArray& strides,
+                              const std::vector<int>& infer_flags,
+                              const std::vector<int>& decrease_axis,
+                              MetaTensor* out,
+                              MetaConfig config = MetaConfig());
+
 void StridedSliceInferMeta(const MetaTensor& x,
                            const std::vector<int>& axes,
                            const IntArray& starts,
                            const IntArray& ends,
                            const IntArray& strides,
-                           const std::vector<int>& infer_flags,
-                           const std::vector<int>& decrease_axis,
                            MetaTensor* out,
                            MetaConfig config = MetaConfig());
 

paddle/phi/kernels/cpu/strided_slice_grad_kernel.cc

@@ -19,10 +19,10 @@
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/impl/strided_slice_grad_kernel_impl.h"
 
-PD_REGISTER_KERNEL(strided_slice_grad,
+PD_REGISTER_KERNEL(strided_slice_raw_grad,
                    CPU,
                    ALL_LAYOUT,
-                   phi::StridedSliceGradKernel,
+                   phi::StridedSliceRawGradKernel,
                    bool,
                    int,
                    int64_t,

paddle/phi/kernels/cpu/strided_slice_kernel.cc

@@ -19,10 +19,10 @@
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/impl/strided_slice_kernel_impl.h"
 
-PD_REGISTER_KERNEL(strided_slice,
+PD_REGISTER_KERNEL(strided_slice_raw,
                    CPU,
                    ALL_LAYOUT,
-                   phi::StridedSliceKernel,
+                   phi::StridedSliceRawKernel,
                    bool,
                    int,
                    int64_t,

paddle/phi/kernels/gpu/strided_slice_grad_kernel.cu

@@ -19,10 +19,10 @@
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/impl/strided_slice_grad_kernel_impl.h"
 
-PD_REGISTER_KERNEL(strided_slice_grad,
+PD_REGISTER_KERNEL(strided_slice_raw_grad,
                    GPU,
                    ALL_LAYOUT,
-                   phi::StridedSliceGradKernel,
+                   phi::StridedSliceRawGradKernel,
                    bool,
                    int,
                    int64_t,

paddle/phi/kernels/gpu/strided_slice_kernel.cu

@@ -19,10 +19,10 @@
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/impl/strided_slice_kernel_impl.h"
 
-PD_REGISTER_KERNEL(strided_slice,
+PD_REGISTER_KERNEL(strided_slice_raw,
                    GPU,
                    ALL_LAYOUT,
-                   phi::StridedSliceKernel,
+                   phi::StridedSliceRawKernel,
                    bool,
                    int,
                    int64_t,

paddle/phi/kernels/impl/strided_slice_grad_kernel_impl.h

@@ -20,16 +20,16 @@
 namespace phi {
 
 template <typename T, typename Context>
-void StridedSliceGradKernel(const Context& dev_ctx,
-                            const DenseTensor& x,
-                            const DenseTensor& out_grad,
-                            const std::vector<int>& axes,
-                            const IntArray& starts,
-                            const IntArray& ends,
-                            const IntArray& strides,
-                            const std::vector<int>& infer_flags,
-                            const std::vector<int>& decrease_axis,
-                            DenseTensor* x_grad) {
+void StridedSliceRawGradKernel(const Context& dev_ctx,
+                               const DenseTensor& x,
+                               const DenseTensor& out_grad,
+                               const std::vector<int>& axes,
+                               const IntArray& starts,
+                               const IntArray& ends,
+                               const IntArray& strides,
+                               const std::vector<int>& infer_flags,
+                               const std::vector<int>& decrease_axis,
+                               DenseTensor* x_grad) {
   int rank = x.dims().size();
 #define SLICE_CASE(Rank)                                            \
   case Rank:                                                        \

paddle/phi/kernels/impl/strided_slice_kernel_impl.h

@@ -20,15 +20,15 @@
 namespace phi {
 
 template <typename T, typename Context>
-void StridedSliceKernel(const Context& dev_ctx,
-                        const DenseTensor& x,
-                        const std::vector<int>& axes,
-                        const IntArray& starts,
-                        const IntArray& ends,
-                        const IntArray& strides,
-                        const std::vector<int>& infer_flags,
-                        const std::vector<int>& decrease_axis,
-                        DenseTensor* out) {
+void StridedSliceRawKernel(const Context& dev_ctx,
+                           const DenseTensor& x,
+                           const std::vector<int>& axes,
+                           const IntArray& starts,
+                           const IntArray& ends,
+                           const IntArray& strides,
+                           const std::vector<int>& infer_flags,
+                           const std::vector<int>& decrease_axis,
+                           DenseTensor* out) {
   int rank = x.dims().size();
 #define SLICE_CASE(Rank)                                        \
   case Rank:                                                    \

paddle/phi/kernels/strided_slice_grad_kernel.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/strided_slice_grad_kernel.h"
+
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void StridedSliceGradKernel(const Context& dev_ctx,
+                            const DenseTensor& x,
+                            const DenseTensor& out_grad,
+                            const std::vector<int>& axes,
+                            const IntArray& starts,
+                            const IntArray& ends,
+                            const IntArray& strides,
+                            DenseTensor* x_grad) {
+  std::vector<int> infer_flags(axes.size(), 1);
+  std::vector<int> decrease_axis;
+  StridedSliceRawGradKernel<T, Context>(dev_ctx,
+                                        x,
+                                        out_grad,
+                                        axes,
+                                        starts,
+                                        ends,
+                                        strides,
+                                        infer_flags,
+                                        decrease_axis,
+                                        x_grad);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(strided_slice_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::StridedSliceGradKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(strided_slice_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::StridedSliceGradKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}
+#endif

paddle/phi/kernels/strided_slice_grad_kernel.h

@@ -19,6 +19,18 @@
 
 namespace phi {
 
+template <typename T, typename Context>
+void StridedSliceRawGradKernel(const Context& dev_ctx,
+                               const DenseTensor& x,
+                               const DenseTensor& out_grad,
+                               const std::vector<int>& axes,
+                               const IntArray& starts,
+                               const IntArray& ends,
+                               const IntArray& strides,
+                               const std::vector<int>& infer_flags,
+                               const std::vector<int>& decrease_axis,
+                               DenseTensor* x_grad);
+
 template <typename T, typename Context>
 void StridedSliceGradKernel(const Context& dev_ctx,
                             const DenseTensor& x,
@@ -27,8 +39,6 @@ void StridedSliceGradKernel(const Context& dev_ctx,
                             const IntArray& starts,
                             const IntArray& ends,
                             const IntArray& strides,
-                            const std::vector<int>& infer_flags,
-                            const std::vector<int>& decrease_axis,
                             DenseTensor* x_grad);
 
 template <typename T, typename Context>

paddle/phi/kernels/strided_slice_kernel.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/strided_slice_kernel.h"
+
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void StridedSliceKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        const std::vector<int>& axes,
+                        const IntArray& starts,
+                        const IntArray& ends,
+                        const IntArray& strides,
+                        DenseTensor* out) {
+  std::vector<int> infer_flags(axes.size(), 1);
+  std::vector<int> decrease_axis;
+  StridedSliceRawKernel<T, Context>(
+      dev_ctx, x, axes, starts, ends, strides, infer_flags, decrease_axis, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(strided_slice,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::StridedSliceKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(strided_slice,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::StridedSliceKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}
+#endif

paddle/phi/kernels/strided_slice_kernel.h

@@ -19,6 +19,17 @@
 
 namespace phi {
 
+template <typename T, typename Context>
+void StridedSliceRawKernel(const Context& dev_ctx,
+                           const DenseTensor& x,
+                           const std::vector<int>& axes,
+                           const IntArray& starts,
+                           const IntArray& ends,
+                           const IntArray& strides,
+                           const std::vector<int>& infer_flags,
+                           const std::vector<int>& decrease_axis,
+                           DenseTensor* out);
+
 template <typename T, typename Context>
 void StridedSliceKernel(const Context& dev_ctx,
                         const DenseTensor& x,
@@ -26,8 +37,6 @@ void StridedSliceKernel(const Context& dev_ctx,
                         const IntArray& starts,
                         const IntArray& ends,
                         const IntArray& strides,
-                        const std::vector<int>& infer_flags,
-                        const std::vector<int>& decrease_axis,
                         DenseTensor* out);
 
 template <typename T, typename Context>

python/paddle/fluid/layers/nn.py

@@ -11426,6 +11426,10 @@ def strided_slice(input, axes, starts, ends, strides):
             sliced_2 = fluid.layers.strided_slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2)
             # sliced_2 is input[:, 0:3:1, 0:2:1, 2:4:2].
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_strided_slice(input, axes, starts, ends,
+                                                strides)
+
     helper = LayerHelper('strided_slice', **locals())
 
     check_variable_and_dtype(input, 'input',
@@ -11590,7 +11594,11 @@ def shape(input):
             res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output])
             print(res) # [array([  3, 100, 100], dtype=int32)]
     """
-    if _non_static_mode():
+    if in_dygraph_mode():
+        out = _C_ops.final_state_shape(input)
+        out.stop_gradient = True
+        return out
+    if _in_legacy_dygraph():
         out = _C_ops.shape(input)
         out.stop_gradient = True
         return out

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
 from paddle.fluid import core
 from paddle.fluid.op import Operator
 
@@ -24,6 +25,7 @@ from paddle.fluid.op import Operator
 class TestShapeOp(OpTest):
     def setUp(self):
         self.op_type = "shape"
+        self.python_api = paddle.shape
         self.config()
         self.shape = [2, 3]
         input = np.zeros(self.shape)
@@ -34,7 +36,7 @@ class TestShapeOp(OpTest):
         self.shape = [2, 3]
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
 
 class case1(TestShapeOp):

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

@@ -58,6 +58,7 @@ class TestStrideSliceOp(OpTest):
     def setUp(self):
         self.initTestCase()
         self.op_type = 'strided_slice'
+        self.python_api = paddle.strided_slice
         self.output = strided_slice_native_forward(
             self.input, self.axes, self.starts, self.ends, self.strides)
 
@@ -72,10 +73,10 @@ class TestStrideSliceOp(OpTest):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(set(['Input']), 'Out')
+        self.check_grad(set(['Input']), 'Out', check_eager=True)
 
     def initTestCase(self):
         self.input = np.random.rand(100)
@@ -704,7 +705,7 @@ class TestStridedSliceTensorArray(unittest.TestCase):
         l2.sum().backward()
         grads_static = net.get_all_grads()
         net.clear_all_grad()
-        # compare result of dygraph and static 
+        # compare result of dygraph and static
         self.is_grads_equal(grads_static, grads_dy)
         self.assertTrue(
             np.array_equal(s1, s2),

python/paddle/utils/code_gen/api.yaml

@@ -951,6 +951,14 @@
     func : selu
   backward : selu_grad
 
+- api : shape
+  args : (Tensor input)
+  output : Tensor
+  infer_meta :
+    func : ShapeInferMeta
+  kernel :
+    func : shape, shape_sr
+
 # shard_index
 - api : shard_index
   args : (Tensor in, int index_num, int nshards, int shard_id, int ignore_value)
@@ -1070,6 +1078,15 @@
     func : square
   backward : square_grad
 
+- api : strided_slice
+  args : (Tensor x, int[] axes, IntArray starts, IntArray ends, IntArray strides)
+  output : Tensor
+  infer_meta :
+    func : StridedSliceInferMeta
+  kernel :
+    func : strided_slice
+  backward : strided_slice_grad
+
 - api : subtract
   args : (Tensor x, Tensor y)
   output : Tensor

python/paddle/utils/code_gen/backward.yaml

@@ -660,6 +660,16 @@
   kernel :
     func : square_grad
 
+- backward_api : strided_slice_grad
+  forward : strided_slice (Tensor x, int[] axes, IntArray starts, IntArray ends, IntArray strides) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad, int[] axes, IntArray starts, IntArray ends, IntArray strides)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GeneralUnaryGradInferMeta
+    param : [x]
+  kernel :
+    func : strided_slice_grad
+
 - backward_api : subtract_grad
   forward : subtract (Tensor x, Tensor y) -> Tensor(out)
   args : (Tensor x, Tensor y, Tensor out_grad, int axis = -1)