[Cherry-pick] Add truncated_normal/unique/swish/unbind yaml and polish Getting...

[Cherry-pick] Add truncated_normal/unique/swish/unbind yaml and polish Getting tensor place impl (#41539)

* [Phi] Polish truncated normal kernel and add yaml (#41280)

* polish truncated normal kernel

* add yaml

* add truncated normal kernel and add yaml

* polish unittests and yaml

* import dygraph mehtod

* add unique yaml and final state api (#41460)

* fix get tensor backend set bug (#41478)

* [Phi] Add unbind yaml and final state api (#41277)

* add unbind yaml

* fix unittest

* [Phi] Add swish yaml and final state api (#41479)

* add swish yaml and final state api

* skip mkldnn test

* fix grad mkldnn test

* add cherry-pick lost code

paddle/phi/api/lib/api_custom_impl.cc

@@ -475,6 +475,54 @@ std::tuple<Tensor, Tensor, Tensor> momentum_impl(
   return api_output;
 }
 
+std::vector<Tensor> unbind_impl(const Tensor& input, int axis) {
+  auto kernel_key_set = ParseKernelKeyByInputArgs(input);
+  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+
+  Backend kernel_backend = kernel_key.backend();
+  DataLayout kernel_layout = kernel_key.layout();
+  DataType kernel_data_type = kernel_key.dtype();
+
+  auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+      "unbind", {kernel_backend, kernel_layout, kernel_data_type});
+  VLOG(6) << "unbind API kernel key: [" << kernel_backend << ", "
+          << kernel_layout << ", " << kernel_data_type << "]";
+  VLOG(6) << "unbind API kernel: " << kernel;
+
+  auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
+
+  auto dense_input = PrepareData(input, kernel.InputAt(0), {});
+
+  // Calculate the number of out tensors
+  auto input_shape = input.dims();
+  if (axis < 0) {
+    axis = input_shape.size() + axis;
+  }
+  auto out_num = input_shape[axis];
+
+  std::vector<Tensor> out;
+  auto dense_outs = SetKernelOutput(out_num, kernel_backend, &out);
+  std::vector<phi::MetaTensor> meta_outs;
+  meta_outs.reserve(out_num);
+  std::vector<phi::MetaTensor*> meta_out_ptrs;
+  meta_out_ptrs.reserve(out_num);
+  for (int64_t i = 0; i < out_num; ++i) {
+    meta_outs.push_back(dense_outs[i]);
+    meta_out_ptrs.push_back(&meta_outs.back());
+  }
+
+  phi::UnbindInferMeta(MakeMetaTensor(*dense_input), axis, meta_out_ptrs);
+
+  using kernel_signature = void (*)(const phi::DeviceContext&,
+                                    const phi::DenseTensor&,
+                                    int,
+                                    std::vector<phi::DenseTensor*>&);
+  auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+  (*kernel_fn)(*dev_ctx, *dense_input, axis, dense_outs);
+
+  return out;
+}
+
 ////////////////// Backward(grad) api impls //////////////////////
 
 // TODO(chenweihang):  the original sum grad op can support higher-level

paddle/phi/api/lib/api_custom_impl.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
+
 #include "paddle/phi/api/include/tensor.h"
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/common/place.h"
@@ -73,6 +75,8 @@ std::tuple<Tensor, Tensor, Tensor> momentum_impl(
     bool multi_precision,
     float rescale_grad);
 
+std::vector<Tensor> unbind_impl(const Tensor& input, int axis);
+
 ////////////////// Backward(grad) api impls //////////////////////
 
 std::vector<Tensor> add_n_grad_impl(const std::vector<Tensor>& x,

paddle/phi/api/lib/kernel_dispatch.cc

@@ -14,18 +14,46 @@ limitations under the License. */
 
 #include "paddle/phi/api/lib/kernel_dispatch.h"
 
-#include "paddle/phi/api/include/context_pool.h"
-#include "paddle/phi/core/compat/convert_utils.h"
 #ifdef _MSC_VER
 #include <intrin.h>
 #endif
 
+#include "paddle/phi/api/include/context_pool.h"
+#include "paddle/phi/core/compat/convert_utils.h"
+#include "paddle/phi/core/string_tensor_utils.h"
+#include "paddle/phi/core/tensor_utils.h"
+
 namespace paddle {
 namespace experimental {
 namespace detail {
 
+// We need judge whether the allocation is nullptr,
+// whether the allocation is initialized, wo we need GetHolder method
+bool HasAllocation(const phi::TensorBase& t) {
+  if (phi::DenseTensor::classof(&t)) {
+    return phi::DenseTensorUtils::GetHolder(
+               static_cast<const phi::DenseTensor&>(t)) != nullptr;
+  } else if (phi::SelectedRows::classof(&t)) {
+    return phi::DenseTensorUtils::GetHolder(
+               static_cast<const phi::SelectedRows&>(t).value()) != nullptr;
+  } else if (phi::SparseCsrTensor::classof(&t)) {
+    return phi::DenseTensorUtils::GetHolder(
+               static_cast<const phi::SparseCsrTensor&>(t)
+                   .non_zero_elements()) != nullptr;
+  } else if (phi::SparseCooTensor::classof(&t)) {
+    return phi::DenseTensorUtils::GetHolder(
+               static_cast<const phi::SparseCooTensor&>(t)
+                   .non_zero_elements()) != nullptr;
+  } else if (phi::StringTensor::classof(&t)) {
+    return phi::StringTensorUtils::GetHolder(
+               static_cast<const phi::StringTensor&>(t)) != nullptr;
+  } else {
+    return false;
+  }
+}
+
 BackendSet GetTensorBackendSet(const phi::TensorBase& t) {
-  if (t.initialized()) {
+  if (HasAllocation(t)) {
     BackendSet backend_set(phi::TransToPhiBackend(t.place()));
     switch (t.layout()) {
       case DataLayout::MKLDNN:

paddle/phi/core/string_tensor_utils.h

@@ -23,6 +23,11 @@ class StringTensorUtils {
   static StringTensorMeta* GetMutableMeta(StringTensor* tensor) {
     return &(tensor->meta_);
   }
+
+  static const std::shared_ptr<phi::Allocation>& GetHolder(
+      const StringTensor& tensor) {
+    return tensor.holder_;
+  }
 };
 
 }  // namespace phi

paddle/phi/core/tensor_utils.h

@@ -25,6 +25,11 @@ class DenseTensorUtils {
     return &(tensor->meta_);
   }
 
+  static const std::shared_ptr<phi::Allocation>& GetHolder(
+      const DenseTensor& tensor) {
+    return tensor.holder_;
+  }
+
   static DenseTensor Slice(const DenseTensor& tensor,
                            int64_t begin_idx,
                            int64_t end_idx) {

paddle/phi/infermeta/unary.cc

@@ -2429,7 +2429,7 @@ void TransposeGradInferMeta(const MetaTensor& x,
 
 void UnbindInferMeta(const MetaTensor& x,
                      int axis,
-                     std::vector<MetaTensor>* outs) {
+                     std::vector<MetaTensor*> outs) {
   auto in_dims = x.dims();
   std::vector<int> out_dim;
   axis = axis < 0 ? in_dims.size() + axis : axis;
@@ -2438,11 +2438,11 @@ void UnbindInferMeta(const MetaTensor& x,
   }
   auto out_dims = phi::make_ddim(out_dim);
 
-  for (size_t i = 0; i < outs->size(); ++i) {
-    (*outs)[i].set_dtype(x.dtype());
-    (*outs)[i].set_dims(out_dims);
-    (*outs)[i].set_layout(x.layout());
-    (*outs)[i].share_lod(x);
+  for (size_t i = 0; i < outs.size(); ++i) {
+    outs[i]->set_dtype(x.dtype());
+    outs[i]->set_dims(out_dims);
+    outs[i]->set_layout(x.layout());
+    outs[i]->share_lod(x);
   }
 }
 

paddle/phi/infermeta/unary.h

@@ -365,7 +365,7 @@ void TrilTriuInferMeta(const MetaTensor& x,
 
 void UnbindInferMeta(const MetaTensor& x,
                      int axis,
-                     std::vector<MetaTensor>* outs);
+                     std::vector<MetaTensor*> outs);
 
 void UnchangedInferMeta(const MetaTensor& x, MetaTensor* out);
 

paddle/phi/kernels/cpu/truncated_gaussian_random_kernel.cc

@@ -21,10 +21,141 @@
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 
-#include "paddle/fluid/framework/generator.h"
-
 namespace phi {
 
+// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
+template <typename T>
+T Erfinv(T x) {
+  if (x < -1 || x > 1) {
+    return std::numeric_limits<T>::quiet_NaN();
+  } else if (x == 1.0) {
+    return std::numeric_limits<T>::infinity();
+  } else if (x == -1.0) {
+    return -std::numeric_limits<T>::infinity();
+  }
+
+  const T LN2 = 6.931471805599453094172321214581e-1;
+
+  const T A0 = 1.1975323115670912564578e0;
+  const T A1 = 4.7072688112383978012285e1;
+  const T A2 = 6.9706266534389598238465e2;
+  const T A3 = 4.8548868893843886794648e3;
+  const T A4 = 1.6235862515167575384252e4;
+  const T A5 = 2.3782041382114385731252e4;
+  const T A6 = 1.1819493347062294404278e4;
+  const T A7 = 8.8709406962545514830200e2;
+
+  const T B0 = 1.0000000000000000000e0;
+  const T B1 = 4.2313330701600911252e1;
+  const T B2 = 6.8718700749205790830e2;
+  const T B3 = 5.3941960214247511077e3;
+  const T B4 = 2.1213794301586595867e4;
+  const T B5 = 3.9307895800092710610e4;
+  const T B6 = 2.8729085735721942674e4;
+  const T B7 = 5.2264952788528545610e3;
+
+  const T C0 = 1.42343711074968357734e0;
+  const T C1 = 4.63033784615654529590e0;
+  const T C2 = 5.76949722146069140550e0;
+  const T C3 = 3.64784832476320460504e0;
+  const T C4 = 1.27045825245236838258e0;
+  const T C5 = 2.41780725177450611770e-1;
+  const T C6 = 2.27238449892691845833e-2;
+  const T C7 = 7.74545014278341407640e-4;
+
+  const T D0 = 1.4142135623730950488016887e0;
+  const T D1 = 2.9036514445419946173133295e0;
+  const T D2 = 2.3707661626024532365971225e0;
+  const T D3 = 9.7547832001787427186894837e-1;
+  const T D4 = 2.0945065210512749128288442e-1;
+  const T D5 = 2.1494160384252876777097297e-2;
+  const T D6 = 7.7441459065157709165577218e-4;
+  const T D7 = 1.4859850019840355905497876e-9;
+
+  const T E0 = 6.65790464350110377720e0;
+  const T E1 = 5.46378491116411436990e0;
+  const T E2 = 1.78482653991729133580e0;
+  const T E3 = 2.96560571828504891230e-1;
+  const T E4 = 2.65321895265761230930e-2;
+  const T E5 = 1.24266094738807843860e-3;
+  const T E6 = 2.71155556874348757815e-5;
+  const T E7 = 2.01033439929228813265e-7;
+
+  const T F0 = 1.414213562373095048801689e0;
+  const T F1 = 8.482908416595164588112026e-1;
+  const T F2 = 1.936480946950659106176712e-1;
+  const T F3 = 2.103693768272068968719679e-2;
+  const T F4 = 1.112800997078859844711555e-3;
+  const T F5 = 2.611088405080593625138020e-5;
+  const T F6 = 2.010321207683943062279931e-7;
+  const T F7 = 2.891024605872965461538222e-15;
+
+  T abs_x = abs(x);
+
+  if (abs_x <= 0.85) {
+    T r = 0.180625 - 0.25 * x * x;
+    T num =
+        (((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) *
+             r +
+         A0);
+    T den =
+        (((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) *
+             r +
+         B0);
+    return x * num / den;
+  }
+
+  T r = sqrt(LN2 - log(1.0 - abs_x));
+
+  T num, den;
+  if (r <= 5.0) {
+    r = r - 1.6;
+    num =
+        (((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) *
+             r +
+         C0);
+    den =
+        (((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) *
+             r +
+         D0);
+  } else {
+    r = r - 5.0;
+    num =
+        (((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) *
+             r +
+         E0);
+    den =
+        (((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) *
+             r +
+         F0);
+  }
+
+  if (x < 0) {
+    return -num / den;
+  } else {
+    return num / den;
+  }
+}
+
+template <typename T>
+struct TruncatedNormal {
+  T mean, std;
+  T a_normal_cdf;
+  T b_normal_cdf;
+  TruncatedNormal(T mean, T std) : mean(mean), std(std) {
+    auto normal_cdf = [](T x) {
+      return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
+    };
+    a_normal_cdf = normal_cdf(-2.0);
+    b_normal_cdf = normal_cdf(2.0);
+  }
+
+  T operator()(T value) const {
+    auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
+    return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean;
+  }
+};
+
 template <typename T, typename Context>
 void TruncatedGaussianRandomKernel(const Context& dev_ctx,
                                    const std::vector<int>& shape,
@@ -42,7 +173,13 @@ void TruncatedGaussianRandomKernel(const Context& dev_ctx,
   TruncatedNormal<T> truncated_normal(mean, std);
   int64_t size = tensor->numel();
 
-  auto engine = paddle::framework::GetCPURandomEngine(seed);
+  std::shared_ptr<std::mt19937_64> engine;
+  if (seed) {
+    engine = std::make_shared<std::mt19937_64>();
+    engine->seed(seed);
+  } else {
+    engine = dev_ctx.GetGenerator()->GetCPUEngine();
+  }
   for (int64_t i = 0; i < size; ++i) {
     data[i] = truncated_normal(dist(*engine));
   }

paddle/phi/kernels/gpu/truncated_gaussian_random_kernel.cu

@@ -24,8 +24,6 @@
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/kernel_registry.h"
 
-#include "paddle/fluid/framework/generator.h"
-
 namespace phi {
 
 template <typename T>
@@ -106,8 +104,7 @@ void TruncatedGaussianRandomKernel(const Context& dev_ctx,
   thrust::counting_iterator<int64_t> index_sequence_begin(0);
   int64_t size = tensor->numel();
 
-  int device_id = dev_ctx.GetPlace().GetDeviceId();
-  auto gen_cuda = paddle::framework::GetDefaultCUDAGenerator(device_id);
+  auto gen_cuda = dev_ctx.GetGenerator();
 
   if (gen_cuda->GetIsInitPy() && seed_flag) {
     auto seed_offset = gen_cuda->IncrementOffset(1);

paddle/phi/kernels/truncated_gaussian_random_kernel.h

@@ -14,149 +14,11 @@
 
 #pragma once
 
-#include <limits>
-#include <random>
-
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/core/dense_tensor.h"
-#include "paddle/phi/core/device_context.h"
-#include "paddle/phi/infermeta/nullary.h"
 
 namespace phi {
 
-// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
-template <typename T>
-T Erfinv(T x) {
-  if (x < -1 || x > 1) {
-    return std::numeric_limits<T>::quiet_NaN();
-  } else if (x == 1.0) {
-    return std::numeric_limits<T>::infinity();
-  } else if (x == -1.0) {
-    return -std::numeric_limits<T>::infinity();
-  }
-
-  const T LN2 = 6.931471805599453094172321214581e-1;
-
-  const T A0 = 1.1975323115670912564578e0;
-  const T A1 = 4.7072688112383978012285e1;
-  const T A2 = 6.9706266534389598238465e2;
-  const T A3 = 4.8548868893843886794648e3;
-  const T A4 = 1.6235862515167575384252e4;
-  const T A5 = 2.3782041382114385731252e4;
-  const T A6 = 1.1819493347062294404278e4;
-  const T A7 = 8.8709406962545514830200e2;
-
-  const T B0 = 1.0000000000000000000e0;
-  const T B1 = 4.2313330701600911252e1;
-  const T B2 = 6.8718700749205790830e2;
-  const T B3 = 5.3941960214247511077e3;
-  const T B4 = 2.1213794301586595867e4;
-  const T B5 = 3.9307895800092710610e4;
-  const T B6 = 2.8729085735721942674e4;
-  const T B7 = 5.2264952788528545610e3;
-
-  const T C0 = 1.42343711074968357734e0;
-  const T C1 = 4.63033784615654529590e0;
-  const T C2 = 5.76949722146069140550e0;
-  const T C3 = 3.64784832476320460504e0;
-  const T C4 = 1.27045825245236838258e0;
-  const T C5 = 2.41780725177450611770e-1;
-  const T C6 = 2.27238449892691845833e-2;
-  const T C7 = 7.74545014278341407640e-4;
-
-  const T D0 = 1.4142135623730950488016887e0;
-  const T D1 = 2.9036514445419946173133295e0;
-  const T D2 = 2.3707661626024532365971225e0;
-  const T D3 = 9.7547832001787427186894837e-1;
-  const T D4 = 2.0945065210512749128288442e-1;
-  const T D5 = 2.1494160384252876777097297e-2;
-  const T D6 = 7.7441459065157709165577218e-4;
-  const T D7 = 1.4859850019840355905497876e-9;
-
-  const T E0 = 6.65790464350110377720e0;
-  const T E1 = 5.46378491116411436990e0;
-  const T E2 = 1.78482653991729133580e0;
-  const T E3 = 2.96560571828504891230e-1;
-  const T E4 = 2.65321895265761230930e-2;
-  const T E5 = 1.24266094738807843860e-3;
-  const T E6 = 2.71155556874348757815e-5;
-  const T E7 = 2.01033439929228813265e-7;
-
-  const T F0 = 1.414213562373095048801689e0;
-  const T F1 = 8.482908416595164588112026e-1;
-  const T F2 = 1.936480946950659106176712e-1;
-  const T F3 = 2.103693768272068968719679e-2;
-  const T F4 = 1.112800997078859844711555e-3;
-  const T F5 = 2.611088405080593625138020e-5;
-  const T F6 = 2.010321207683943062279931e-7;
-  const T F7 = 2.891024605872965461538222e-15;
-
-  T abs_x = abs(x);
-
-  if (abs_x <= 0.85) {
-    T r = 0.180625 - 0.25 * x * x;
-    T num =
-        (((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) *
-             r +
-         A0);
-    T den =
-        (((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) *
-             r +
-         B0);
-    return x * num / den;
-  }
-
-  T r = sqrt(LN2 - log(1.0 - abs_x));
-
-  T num, den;
-  if (r <= 5.0) {
-    r = r - 1.6;
-    num =
-        (((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) *
-             r +
-         C0);
-    den =
-        (((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) *
-             r +
-         D0);
-  } else {
-    r = r - 5.0;
-    num =
-        (((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) *
-             r +
-         E0);
-    den =
-        (((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) *
-             r +
-         F0);
-  }
-
-  if (x < 0) {
-    return -num / den;
-  } else {
-    return num / den;
-  }
-}
-
-template <typename T>
-struct TruncatedNormal {
-  T mean, std;
-  T a_normal_cdf;
-  T b_normal_cdf;
-  TruncatedNormal(T mean, T std) : mean(mean), std(std) {
-    auto normal_cdf = [](T x) {
-      return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
-    };
-    a_normal_cdf = normal_cdf(-2.0);
-    b_normal_cdf = normal_cdf(2.0);
-  }
-
-  T operator()(T value) const {
-    auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
-    return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean;
-  }
-};
-
 template <typename T, typename Context>
 void TruncatedGaussianRandomKernel(const Context& dev_ctx,
                                    const std::vector<int>& shape,

python/paddle/fluid/initializer.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import math
 from . import framework
 from . import core
-from .framework import _non_static_mode, default_main_program
+from .framework import _non_static_mode, in_dygraph_mode, _in_legacy_dygraph, default_main_program, _current_expected_place
 import numpy as np
 from .core import VarDesc
 from . import unique_name
@@ -417,7 +417,18 @@ class TruncatedNormalInitializer(Initializer):
             out_dtype = var.dtype
             out_var = var
 
-        if framework._non_static_mode():
+        if in_dygraph_mode():
+            out_var = _C_ops.final_state_truncated_gaussian_random(
+                var.shape, self._mean, self._std_dev, self._seed, out_dtype,
+                _current_expected_place())
+            if var.dtype in [VarDesc.VarType.FP16, VarDesc.VarType.BF16]:
+                var_tmp = _C_ops.final_state_cast(out_var, var.dtype)
+                var_tmp._share_underline_tensor_to(var)
+            else:
+                out_var._share_underline_tensor_to(var)
+            return None
+
+        if _in_legacy_dygraph():
             out_var = _C_ops.truncated_gaussian_random(
                 'shape', var.shape, 'dtype', out_dtype, 'mean', self._mean,
                 'std', self._std_dev, 'seed', self._seed)

python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py

@@ -113,6 +113,7 @@ class TestMKLDNNSwishDim2(TestSwish):
         super(TestMKLDNNSwishDim2, self).setUp()
 
         self.attrs["use_mkldnn"] = True
+        self.check_eager = False
 
     def init_dtype(self):
         self.dtype = np.float32
@@ -284,6 +285,7 @@ class TestMKLDNNSwishDim4(TestSwish):
         self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
         self.outputs = {'Out': out}
         self.attrs = {"use_mkldnn": True, "beta": beta}
+        self.check_eager = False
 
     def init_dtype(self):
         self.dtype = np.float32

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

@@ -25,6 +25,7 @@ import paddle.nn.functional as F
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.framework import _test_eager_guard
 
 paddle.enable_static()
 
@@ -2928,7 +2929,9 @@ def ref_swish(x):
 class TestSwish(TestActivation):
     def setUp(self):
         self.op_type = "swish"
+        self.python_api = paddle.nn.functional.swish
         self.init_dtype()
+        self.check_eager = True
 
         np.random.seed(1024)
         x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
@@ -2940,7 +2943,10 @@ class TestSwish(TestActivation):
     def test_check_grad(self):
         if self.dtype == np.float16:
             return
-        self.check_grad(['X'], 'Out')
+        check_eager = False
+        if hasattr(self, 'check_eager'):
+            check_eager = self.check_eager
+        self.check_grad(['X'], 'Out', check_eager=check_eager)
 
 
 class TestSwishAPI(unittest.TestCase):
@@ -2975,6 +2981,10 @@ class TestSwishAPI(unittest.TestCase):
             self.assertEqual(np.allclose(out_ref, r.numpy()), True)
         paddle.enable_static()
 
+    def test_dygraph_final_state_api(self):
+        with _test_eager_guard():
+            self.test_dygraph_api()
+
     def test_fluid_api(self):
         paddle.enable_static()
         with fluid.program_guard(fluid.Program()):

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

@@ -17,10 +17,13 @@ from __future__ import print_function
 import unittest
 import numpy
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from op_test import OpTest
 from paddle.fluid.op import Operator
 from paddle.fluid.executor import Executor
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestTrunctedGaussianRandomOp(unittest.TestCase):
@@ -33,15 +36,16 @@ class TestTrunctedGaussianRandomOp(unittest.TestCase):
             "std": 1.,
             "seed": 10,
         }
-
         self.outputs = ["Out"]
 
     def test_cpu(self):
         self.gaussian_random_test(place=fluid.CPUPlace())
+        self.gaussian_random_test_eager(place=fluid.CPUPlace())
 
     def test_gpu(self):
         if core.is_compiled_with_cuda():
             self.gaussian_random_test(place=fluid.CUDAPlace(0))
+            self.gaussian_random_test_eager(place=fluid.CUDAPlace(0))
 
     def gaussian_random_test(self, place):
 
@@ -64,6 +68,17 @@ class TestTrunctedGaussianRandomOp(unittest.TestCase):
         self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
         self.assertAlmostEqual(numpy.var(tensor), 0.773, delta=0.1)
 
+    # TruncatedNormal.__call__ has no return value, so here call _C_ops api
+    # directly
+    def gaussian_random_test_eager(self, place):
+        with fluid.dygraph.guard(place):
+            with _test_eager_guard():
+                out = paddle._C_ops.final_state_truncated_gaussian_random(
+                    self.attrs["shape"], self.attrs["mean"], self.attrs["std"],
+                    self.attrs["seed"], core.VarDesc.VarType.FP32, place)
+                self.assertAlmostEqual(numpy.mean(out.numpy()), .0, delta=0.1)
+                self.assertAlmostEqual(numpy.var(out.numpy()), 0.773, delta=0.1)
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -17,9 +17,11 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest, convert_float_to_uint16
+import paddle
 import paddle.fluid as fluid
 import paddle.tensor as tensor
 from paddle.fluid import compiler, Program, program_guard, core
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestUnbind(unittest.TestCase):
@@ -39,6 +41,25 @@ class TestUnbind(unittest.TestCase):
         assert np.array_equal(res_1, input_1[0, 0:100])
         assert np.array_equal(res_2, input_1[1, 0:100])
 
+    def test_unbind_dygraph(self):
+        with fluid.dygraph.guard():
+            np_x = np.random.random([2, 3]).astype("float32")
+            x = paddle.to_tensor(np_x)
+            x.stop_gradient = False
+            [res_1, res_2] = paddle.unbind(x, 0)
+            self.assertTrue(np.array_equal(res_1, np_x[0, 0:100]))
+            self.assertTrue(np.array_equal(res_2, np_x[1, 0:100]))
+
+            out = paddle.add_n([res_1, res_2])
+
+            np_grad = np.ones(x.shape, np.float32)
+            out.backward()
+            self.assertTrue(np.array_equal(x.grad.numpy(), np_grad))
+
+    def test_unbind_dygraph_final_state(self):
+        with _test_eager_guard():
+            self.test_unbind_dygraph()
+
 
 class TestLayersUnbind(unittest.TestCase):
     def test_layers_unbind(self):
@@ -157,6 +178,7 @@ class TestUnbindOp4(TestUnbindOp):
 class TestUnbindBF16Op(OpTest):
     def setUp(self):
         self._set_op_type()
+        self.python_api = paddle.unbind
         self.dtype = self.get_dtype()
         self.axis = 0
         self.num = 3

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

@@ -21,6 +21,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestUniqueOp(OpTest):
@@ -251,6 +252,12 @@ class TestUniqueAPI(unittest.TestCase):
         self.assertTrue((counts.numpy() == np_counts).all(), True)
         paddle.enable_static()
 
+    def test_dygraph_final_state_api(self):
+        with _test_eager_guard():
+            self.test_dygraph_api_out()
+            self.test_dygraph_api_attr()
+            self.test_dygraph_attr_dtype()
+
     def test_static_graph(self):
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):

python/paddle/nn/functional/activation.py

@@ -1175,8 +1175,9 @@ def swish(x, name=None):
             x = paddle.to_tensor(np.array([-2., 0., 1.]))
             out = F.swish(x) # [-0.238406, 0., 0.731059]
     """
-
-    if in_dynamic_mode():
+    if in_dygraph_mode():
+        return _C_ops.final_state_swish(x, 1.0)
+    if _in_legacy_dygraph():
         return _C_ops.swish(x, 'beta', 1.0)
 
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'swish')

python/paddle/tensor/manipulation.py

@@ -1211,11 +1211,16 @@ def unique(x,
     else:
         axis = [axis]
     attr_dtype = convert_np_dtype_to_dtype_(dtype)
-    if paddle.in_dynamic_mode():
-        out, inverse, indices, counts = _C_ops.unique(
-            x, 'dtype', attr_dtype, 'return_index', return_index,
-            'return_inverse', return_inverse, 'return_counts', return_counts,
-            'axis', axis, "is_sorted", True)
+    if _non_static_mode():
+        if in_dygraph_mode():
+            out, indices, inverse, counts = _C_ops.final_state_unique(
+                x, return_index, return_inverse, return_counts, axis,
+                attr_dtype)
+        if _in_legacy_dygraph():
+            out, inverse, indices, counts = _C_ops.unique(
+                x, 'dtype', attr_dtype, 'return_index', return_index,
+                'return_inverse', return_inverse, 'return_counts',
+                return_counts, 'axis', axis, "is_sorted", True)
         outs = [out]
         if return_index:
             outs.append(indices)
@@ -1464,6 +1469,9 @@ def unbind(input, axis=0):
             # x3.shape [3, 5]
 
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_unbind(input, axis)
+
     if not isinstance(axis, (int)):
         raise TypeError("The type of 'axis'  must be int, but received %s." %
                         (type(axis)))
@@ -1472,7 +1480,7 @@ def unbind(input, axis=0):
     input_shape = input.shape
     axis_ = axis if axis >= 0 else len(input_shape) + axis
     num = input_shape[axis_]
-    if paddle.in_dynamic_mode():
+    if _in_legacy_dygraph():
         return _C_ops.unbind(input, num, 'axis', axis)
 
     helper = LayerHelper("unbind", **locals())

python/paddle/utils/code_gen/api.yaml

@@ -1744,6 +1744,17 @@
     data_type : x
   backward : sum_grad
 
+# The python API paddle.nn.functional.swish has no `bete` argument, it may be removed later
+- api : swish
+  args : (Tensor x, float beta=1.0)
+  output : Tensor(out)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : swish
+  backward : swish_grad
+
 # take_along_axis
 - api : take_along_axis
   args : (Tensor x, Tensor index, int axis)
@@ -1861,6 +1872,25 @@
     func : trunc
   backward : trunc_grad
 
+# python API: paddle.nn.initializer.TruncatedNormal
+- api : truncated_gaussian_random
+  args : (int[] shape, float mean, float std, int seed, DataType dtype=DataType::FLOAT32, Place place={})
+  output : Tensor
+  infer_meta :
+    func : TruncatedGaussianRandomInferMeta
+    param : [shape, mean, std, seed, dtype]
+  kernel :
+    func : truncated_gaussian_random
+    param : [shape, mean, std, seed, dtype]
+    backend : place
+    data_type : dtype
+
+- api : unbind
+  args : (Tensor input, int axis)
+  output : Tensor[]
+  invoke : unbind_impl(input, axis)
+  backward : unbind_grad
+
 # unfold
 - api : unfold
   args : (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)
@@ -1871,6 +1901,16 @@
     func : unfold
   backward : unfold_grad
 
+# The `axis` argument of Python API paddle.unique is not vector
+- api : unique
+  args : (Tensor x, bool return_index, bool return_inverse, bool return_counts, int[] axis, DataType dtype=DataType::INT64)
+  output : Tensor(out), Tensor(indices), Tensor(inverse), Tensor(counts)
+  infer_meta :
+    func : UniqueInferMeta
+  kernel :
+    func : unique
+    data_type : x
+
 - api : unsqueeze
   args : (Tensor x, IntArray axes)
   output : Tensor(xshape), Tensor(out)

python/paddle/utils/code_gen/backward.yaml

@@ -1317,6 +1317,16 @@
   kernel :
     func : sum_grad
 
+- backward_api : swish_grad
+  forward : swish (Tensor x, float beta=1.0) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad, float bete=1.0)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GeneralUnaryGradInferMeta
+    param : [x]
+  kernel :
+    func : swish_grad
+
 - backward_api : take_along_axis_grad
   forward : take_along_axis (Tensor x, Tensor index, int axis) -> Tensor(out)
   args : (Tensor x, Tensor index, Tensor out_grad, int axis)
@@ -1429,6 +1439,12 @@
   kernel :
     func : trunc_grad
 
+- backward_api : unbind_grad
+  forward : unbind (Tensor input, int axis) -> Tensor[](out)
+  args : (Tensor[] out_grad, int axis)
+  output : Tensor(input_grad)
+  invoke : stack(out_grad, axis)
+
 - backward_api : unfold_grad
   forward : unfold (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)