Add expand equal all yaml (#41540)

* add expand, poisson

* add poison grad

* add expand equal_all poisson triangular solve yaml

paddle/fluid/operators/expand_v2_op.cc

@@ -16,7 +16,11 @@ limitations under the License. */
 #include <memory>
 #include <string>
 #include <vector>
+
+#include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/unary.h"
 
 #define MAX_RANK_SUPPORTED 6
 
@@ -29,70 +33,6 @@ class ExpandV2Op : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandV2");
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ExpandV2");
-    auto x_dims = ctx->GetInputDim("X");
-    auto expand_shape = ctx->Attrs().Get<std::vector<int>>("shape");
-
-    if (expand_shape.size() == 0) {
-      expand_shape = std::vector<int>(x_dims.size(), -1);
-    }
-
-    PADDLE_ENFORCE_GE(
-        expand_shape.size(), static_cast<size_t>(x_dims.size()),
-        platform::errors::InvalidArgument(
-            "The number of elements (%d) of 'shape' for "
-            "expand_v2 op must be greater than or equal to the rank "
-            "(%d) of the input.",
-            expand_shape.size(), static_cast<size_t>(x_dims.size())));
-    PADDLE_ENFORCE_LE(expand_shape.size(), MAX_RANK_SUPPORTED,
-                      platform::errors::InvalidArgument(
-                          "The number of elements (%d) of 'shape' for "
-                          "must not be greater than %d.",
-                          expand_shape.size(), MAX_RANK_SUPPORTED));
-    PADDLE_ENFORCE_GE(expand_shape.size(), 1,
-                      platform::errors::InvalidArgument(
-                          "The number of elements (%d) of 'shape' for "
-                          "must be a positive integer.",
-                          expand_shape.size()));
-
-    auto out_rank =
-        std::max(static_cast<size_t>(x_dims.size()), expand_shape.size());
-    std::vector<int64_t> out_shape(out_rank);
-    auto x_dim_vec = phi::vectorize<int>(x_dims);
-    auto diff = expand_shape.size() - x_dim_vec.size();
-    x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
-    for (size_t i = 0; i < expand_shape.size(); ++i) {
-      if (x_dims[i] == -1) {
-        out_shape[i] = -1;
-      } else if (expand_shape[i] == -1) {
-        if (static_cast<size_t>(x_dims.size()) > i) {
-          out_shape[i] = x_dims[i];
-        } else {
-          out_shape[i] = -1;
-        }
-      } else if (expand_shape[i] == -2) {
-        // We use -2 to represent the element in expand_shape is a var.
-        out_shape[i] = -1;
-      } else {
-        PADDLE_ENFORCE_GT(
-            expand_shape[i], 0,
-            platform::errors::InvalidArgument(
-                "The %uth element of 'shape' for expand_v2 op must be "
-                "greater than 0, but the value given is %d.",
-                i, expand_shape[i]));
-        out_shape[i] = expand_shape[i];
-      }
-    }
-
-    ctx->SetOutputDim("Out", phi::make_ddim(out_shape));
-    if (out_shape[0] == x_dims[0]) {
-      ctx->ShareLoD("X", "Out");
-    }
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
@@ -291,10 +231,14 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(ExpandV2GradNoNeedBufVarsInferer, "X");
 }  // namespace operators
 }  // namespace paddle
 
+DECLARE_INFER_SHAPE_FUNCTOR(expand_v2, ExpandInferShapeFunctor,
+                            PD_INFER_META(phi::ExpandInferMeta));
+
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(expand_v2, ops::ExpandV2Op, ops::ExpandV2OpMaker,
                   ops::ExpandV2GradOpMaker<paddle::framework::OpDesc>,
-                  ops::ExpandV2GradOpMaker<paddle::imperative::OpBase>);
+                  ops::ExpandV2GradOpMaker<paddle::imperative::OpBase>,
+                  ExpandInferShapeFunctor);
 REGISTER_OPERATOR(expand_v2_grad, ops::ExpandV2GradOp,
                   ops::ExpandV2DoubleGradOpMaker<paddle::framework::OpDesc>,
                   ops::ExpandV2DoubleGradOpMaker<paddle::imperative::OpBase>,

paddle/phi/infermeta/unary.cc

@@ -405,6 +405,78 @@ void EighInferMeta(const MetaTensor& x,
   out_v->set_dims(input_dim);
 }
 
+void ExpandInferMeta(const MetaTensor& x,
+                     const IntArray& shape,
+                     MetaTensor* out) {
+#define MAX_RANK_SUPPORTED 6
+  auto x_dims = x.dims();
+  auto expand_shape = shape.GetData();
+
+  if (expand_shape.size() == 0) {
+    expand_shape = std::vector<int64_t>(x_dims.size(), -1);
+  }
+
+  PADDLE_ENFORCE_GE(
+      expand_shape.size(),
+      static_cast<size_t>(x_dims.size()),
+      phi::errors::InvalidArgument(
+          "The number of elements (%d) of 'shape' for "
+          "expand_v2 op must be greater than or equal to the rank "
+          "(%d) of the input.",
+          expand_shape.size(),
+          static_cast<size_t>(x_dims.size())));
+  PADDLE_ENFORCE_LE(
+      expand_shape.size(),
+      MAX_RANK_SUPPORTED,
+      phi::errors::InvalidArgument("The number of elements (%d) of 'shape' for "
+                                   "must not be greater than %d.",
+                                   expand_shape.size(),
+                                   MAX_RANK_SUPPORTED));
+  PADDLE_ENFORCE_GE(
+      expand_shape.size(),
+      1,
+      phi::errors::InvalidArgument("The number of elements (%d) of 'shape' for "
+                                   "must be a positive integer.",
+                                   expand_shape.size()));
+
+  auto out_rank =
+      std::max(static_cast<size_t>(x_dims.size()), expand_shape.size());
+  std::vector<int64_t> out_shape(out_rank);
+  auto x_dim_vec = phi::vectorize<int>(x_dims);
+  auto diff = expand_shape.size() - x_dim_vec.size();
+  x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
+  for (size_t i = 0; i < expand_shape.size(); ++i) {
+    if (x_dims[i] == -1) {
+      out_shape[i] = -1;
+    } else if (expand_shape[i] == -1) {
+      if (static_cast<size_t>(x_dims.size()) > i) {
+        out_shape[i] = x_dims[i];
+      } else {
+        out_shape[i] = -1;
+      }
+    } else if (expand_shape[i] == -2) {
+      // We use -2 to represent the element in expand_shape is a var.
+      out_shape[i] = -1;
+    } else {
+      PADDLE_ENFORCE_GT(
+          expand_shape[i],
+          0,
+          phi::errors::InvalidArgument(
+              "The %uth element of 'shape' for expand_v2 op must be "
+              "greater than 0, but the value given is %d.",
+              i,
+              expand_shape[i]));
+      out_shape[i] = expand_shape[i];
+    }
+  }
+
+  out->set_dims(make_ddim(out_shape));
+  out->set_dtype(x.dtype());
+  if (out_shape[0] == x_dims[0]) {
+    out->share_lod(x);
+  }
+}
+
 void FlattenInferMeta(const MetaTensor& x,
                       int start_axis,
                       int stop_axis,

paddle/phi/infermeta/unary.h

@@ -85,6 +85,10 @@ void EighInferMeta(const MetaTensor& x,
                    MetaTensor* out_w,
                    MetaTensor* out_v);
 
+void ExpandInferMeta(const MetaTensor& x,
+                     const IntArray& shape,
+                     MetaTensor* out);
+
 void FlattenInferMeta(const MetaTensor& x,
                       int start_axis,
                       int stop_axis,

paddle/phi/kernels/impl/poisson_grad_kernel_impl.h

@@ -20,7 +20,9 @@
 namespace phi {
 
 template <typename T, typename Context>
-void PoissonGradKernel(const Context& ctx, DenseTensor* x_grad) {
+void PoissonGradKernel(const Context& ctx,
+                       const DenseTensor& out_grad,
+                       DenseTensor* x_grad) {
   ctx.template Alloc<T>(x_grad);
   phi::funcs::SetConstant<Context, T> functor;
   functor(ctx, x_grad, static_cast<T>(0));

paddle/phi/kernels/poisson_grad_kernel.h

@@ -20,6 +20,8 @@
 namespace phi {
 
 template <typename T, typename Context>
-void PoissonGradKernel(const Context& ctx, DenseTensor* x_grad);
+void PoissonGradKernel(const Context& ctx,
+                       const DenseTensor& out_grad,
+                       DenseTensor* x_grad);
 
 }  // namespace phi

paddle/phi/ops/compat/poisson_sig.cc

@@ -18,7 +18,8 @@ namespace phi {
 
 KernelSignature PoissonGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
-  return KernelSignature("poisson_grad", {}, {}, {GradVarName("X")});
+  return KernelSignature(
+      "poisson_grad", {GradVarName("Out")}, {}, {GradVarName("X")});
 }
 
 }  // namespace phi

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

@@ -28,12 +28,13 @@ def create_test_not_equal_class(op_type, typename, callback):
             x = np.random.random(size=(10, 7)).astype(typename)
             y = np.random.random(size=(10, 7)).astype(typename)
             z = callback(x, y)
+            self.python_api = paddle.tensor.equal_all
             self.inputs = {'X': x, 'Y': y}
             self.outputs = {'Out': z}
             self.op_type = op_type
 
         def test_output(self):
-            self.check_output()
+            self.check_output(check_eager=True)
 
     cls_name = "{0}_{1}_{2}".format(op_type, typename, 'not_equal_all')
     Cls.__name__ = cls_name
@@ -46,12 +47,13 @@ def create_test_not_shape_equal_class(op_type, typename, callback):
             x = np.random.random(size=(10, 7)).astype(typename)
             y = np.random.random(size=(10)).astype(typename)
             z = callback(x, y)
+            self.python_api = paddle.tensor.equal_all
             self.inputs = {'X': x, 'Y': y}
             self.outputs = {'Out': z}
             self.op_type = op_type
 
         def test_output(self):
-            self.check_output()
+            self.check_output(check_eager=True)
 
     cls_name = "{0}_{1}_{2}".format(op_type, typename, 'not_shape_equal_all')
     Cls.__name__ = cls_name
@@ -63,12 +65,13 @@ def create_test_equal_class(op_type, typename, callback):
         def setUp(self):
             x = y = np.random.random(size=(10, 7)).astype(typename)
             z = callback(x, y)
+            self.python_api = paddle.tensor.equal_all
             self.inputs = {'X': x, 'Y': y}
             self.outputs = {'Out': z}
             self.op_type = op_type
 
         def test_output(self):
-            self.check_output()
+            self.check_output(check_eager=True)
 
     cls_name = "{0}_{1}_{2}".format(op_type, typename, 'equal_all')
     Cls.__name__ = cls_name
@@ -82,12 +85,13 @@ def create_test_dim1_class(op_type, typename, callback):
             x = np.array([True, False, True]).astype(typename)
             x = np.array([False, False, True]).astype(typename)
             z = callback(x, y)
+            self.python_api = paddle.tensor.equal_all
             self.inputs = {'X': x, 'Y': y}
             self.outputs = {'Out': z}
             self.op_type = op_type
 
         def test_output(self):
-            self.check_output()
+            self.check_output(check_eager=True)
 
     cls_name = "{0}_{1}_{2}".format(op_type, typename, 'equal_all')
     Cls.__name__ = cls_name

python/paddle/fluid/tests/unittests/test_expand_v2_op.py

@@ -40,10 +40,10 @@ class TestExpandV2OpRank1(OpTest):
         self.expand_times = [1]
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+        self.check_grad(['X'], 'Out', check_eager=True)
 
 
 class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1):

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

@@ -18,6 +18,7 @@ import numpy as np
 from op_test import OpTest
 import math
 import os
+from paddle.fluid.framework import _test_eager_guard
 
 paddle.enable_static()
 paddle.seed(100)
@@ -96,11 +97,18 @@ class TestPoissonAPI(unittest.TestCase):
             self.assertTrue(np.min(y_np) >= 0)
 
     def test_dygraph(self):
-        paddle.disable_static()
+        with paddle.fluid.dygraph.base.guard():
             x = paddle.randn([10, 10], dtype='float32')
             y = paddle.poisson(x)
             self.assertTrue(np.min(y.numpy()) >= 0)
-        paddle.enable_static()
+
+            with _test_eager_guard():
+                x = paddle.randn([10, 10], dtype='float32')
+                x.stop_gradient = False
+                y = paddle.poisson(x)
+                y.backward()
+                self.assertTrue(np.min(y.numpy()) >= 0)
+                self.assertTrue(np.array_equal(np.zeros_like(x), x.gradient()))
 
     def test_fixed_random_number(self):
         # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'

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

@@ -47,6 +47,7 @@ class TestTriangularSolveOp(OpTest):
 
     def setUp(self):
         self.op_type = "triangular_solve"
+        self.python_api = paddle.tensor.linalg.triangular_solve
         self.config()
 
         self.inputs = {
@@ -62,10 +63,10 @@ class TestTriangularSolveOp(OpTest):
         self.outputs = {'Out': self.output}
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out')
+        self.check_grad(['X', 'Y'], 'Out', check_eager=True)
 
 
 # 2D(broadcast) + 3D, test 'transpose'

python/paddle/tensor/linalg.py

@@ -2834,6 +2834,10 @@ def triangular_solve(x,
         print(out)
         # [7, -2, -5]
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_triangular_solve(x, y, upper, transpose,
+                                                   unitriangular)
+
     if paddle.in_dynamic_mode():
         return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',
                                        transpose, 'unitriangular',

python/paddle/tensor/logic.py

@@ -301,6 +301,9 @@ def equal_all(x, y, name=None):
           result2 = paddle.equal_all(x, z)
           print(result2) # result2 = [False ]
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_equal_all(x, y)
+
     if paddle.in_dynamic_mode():
         return _C_ops.equal_all(x, y)
 

python/paddle/tensor/manipulation.py

@@ -2000,6 +2000,9 @@ def expand(x, shape, name=None):
             print(out)
             # [[1, 2, 3], [1, 2, 3]]
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_expand(x, shape)
+
     if paddle.in_dynamic_mode():
         return _C_ops.expand_v2(x, 'shape', shape)
 

python/paddle/utils/code_gen/api.yaml

@@ -603,6 +603,14 @@
   kernel :
     func : equal
 
+- api : equal_all
+  args : (Tensor x, Tensor y)
+  output : Tensor
+  infer_meta :
+    func : CompareAllInferMeta
+  kernel :
+    func : equal_all
+
 # erf
 - api : erf
   args : (Tensor x)
@@ -633,6 +641,16 @@
     func : exp
   backward : exp_grad
 
+# expand
+- api : expand
+  args : (Tensor x, IntArray shape)
+  output : Tensor
+  infer_meta :
+    func : ExpandInferMeta
+  kernel :
+    func : expand
+  backward : expand_grad
+
 # expand_as
 - api : expand_as
   args : (Tensor x, Tensor y, int[] target_shape)
@@ -1513,7 +1531,7 @@
     func : pixel_shuffle
   backward : pixel_shuffle_grad
 
-# poisson  // no need grad
+# poisson
 - api : poisson
   args : (Tensor x)
   output : Tensor
@@ -1521,6 +1539,7 @@
     func : UnchangedInferMeta
   kernel :
     func : poisson
+  backward : poisson_grad
 
 - api : pool2d
   args : (Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
@@ -2066,7 +2085,7 @@
     func : TriangularSolveInferMeta
   kernel :
     func : triangular_solve
-  # backward : triangular_solve_grad
+  backward : triangular_solve_grad
 
 - api : tril_triu
   args : (Tensor x,  int diagonal,  bool lower)

python/paddle/utils/code_gen/backward.yaml

@@ -492,6 +492,16 @@
     func : expand_as_grad
   no_need_buffer : x
 
+- backward_api : expand_grad
+  forward : expand (Tensor x, IntArray shape) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad, IntArray shape)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : expand_grad
+
 - backward_api : expm1_grad
   forward : expm1 (Tensor x) -> Tensor(out)
   args : (Tensor out, Tensor out_grad)
@@ -1159,6 +1169,16 @@
   kernel :
     func : pixel_shuffle_grad
 
+- backward_api : poisson_grad
+  forward : poisson (Tensor x) -> Tensor(out)
+  args : (Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [out_grad]
+  kernel :
+    func : poisson_grad
+
 - backward_api : pool2d_grad
   forward : pool2d(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
   args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
@@ -1685,6 +1705,16 @@
   kernel :
     func : transpose_grad
 
+- backward_api : triangular_solve_grad
+  forward : triangular_solve (Tensor x, Tensor y, bool upper, bool tranpose, bool unitriangular) -> Tensor(out)
+  args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, bool upper, bool tranpose, bool unitriangular)
+  output : Tensor(x_grad), Tensor(y_grad)
+  infer_meta :
+    func : GeneralBinaryGradInferMeta
+    param : [x, y]
+  kernel :
+    func : triangular_solve_grad
+
 - backward_api : tril_triu_grad
   forward : tril_triu(Tensor x,  int diagonal,  bool lower) -> Tensor(out)
   args : (Tensor out_grad,  int diagonal,  bool lower)