Add isfinite v2 op (#26344)

add the isnan, isfinite, isinf api for the paddle 2.0

paddle/fluid/framework/tensor_util.cc

@@ -420,6 +420,61 @@ inline void Any(const framework::Tensor& tensor, Predicate predicate,
   platform::VisitPlace(place, visitor);
 }
 
+template <typename Predicate, typename DevCtx>
+struct AllDTypeVisitor {
+  Predicate predicate_;
+  const Tensor& tensor_;
+  const DevCtx& ctx_;
+  Tensor* out_;
+
+  AllDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
+                  Tensor* out)
+      : predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
+
+  template <typename T>
+  void apply() const {
+    auto t = EigenVector<T>::Flatten(tensor_);
+    auto o = EigenVector<bool>::Flatten(*out_);
+    o.device(*ctx_.eigen_device()) = predicate_(t);
+  }
+};
+
+template <typename Predicate, typename DevCtx>
+inline void AllImpl(Predicate predicate, const framework::Tensor& tensor,
+                    const DevCtx& ctx, framework::Tensor* out) {
+  VisitDataType(tensor.type(), AllDTypeVisitor<Predicate, DevCtx>(
+                                   predicate, tensor, ctx, out));
+}
+
+template <typename Predicate>
+class AllOutVisitor : public boost::static_visitor<> {
+ private:
+  const framework::Tensor& tensor_;
+  mutable framework::Tensor* out_;
+  Predicate predicate_;
+
+ public:
+  AllOutVisitor(const framework::Tensor& tensor, Predicate predicate,
+                framework::Tensor* out)
+      : tensor_(tensor), out_(out), predicate_(predicate) {}
+
+  template <typename Place>
+  void operator()(const Place& place) const {
+    auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
+    out_->Resize(tensor_.dims());
+    out_->mutable_data<bool>(place);
+    AllImpl(predicate_, tensor_, *ctx, out_);
+  }
+};
+
+template <typename Predicate>
+inline void All(const framework::Tensor& tensor, Predicate predicate,
+                framework::Tensor* out) {
+  AllOutVisitor<Predicate> visitor(tensor, predicate, out);
+  auto place = tensor.place();
+  platform::VisitPlace(place, visitor);
+}
+
 struct ContainsNANPredicate {
   template <typename T>
   auto operator()(const T& eigen_vec) const
@@ -440,6 +495,12 @@ void TensorContainsNAN(const framework::Tensor& tensor,
   Any(tensor, predicate, out);
 }
 
+void TensorContainsNANV2(const framework::Tensor& tensor,
+                         framework::Tensor* out) {
+  ContainsNANPredicate predicate;
+  All(tensor, predicate, out);
+}
+
 struct ContainsInfPredicate {
   template <typename T>
   auto operator()(const T& eigen_vec) const
@@ -460,6 +521,12 @@ void TensorContainsInf(const framework::Tensor& tensor,
   Any(tensor, predicate, out);
 }
 
+void TensorContainsInfV2(const framework::Tensor& tensor,
+                         framework::Tensor* out) {
+  ContainsInfPredicate predicate;
+  All(tensor, predicate, out);
+}
+
 // NOTE(dzhwinter):
 // Isfinite need a AllVisitor to loop through all the elements.
 // We choose two cuda call instead of one allvisitor. The AllVisitor
@@ -472,8 +539,8 @@ bool TensorIsfinite(const framework::Tensor& tensor) {
 
 #ifdef PADDLE_WITH_CUDA
 template <typename T>
-static inline void __global__ BothFalse(const T* cmp, T* out) {
-  out[0] = (!cmp[0]) && (!out[0]);
+static inline void __global__ BothFalse(const T* cmp, T* out, int element_num) {
+  CUDA_KERNEL_LOOP(i, element_num) { out[i] = (!cmp[i]) && (!out[i]); }
 }
 #endif
 
@@ -495,22 +562,40 @@ struct BothFalseVisitor : public boost::static_visitor<> {
   void VisitorImpl(const platform::CUDAPlace& gpu) const {
 #ifdef PADDLE_WITH_CUDA
     auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(gpu);
-    BothFalse<bool><<<1, 1, 0, ctx->stream()>>>(in_.data<bool>(),
-                                                out_->mutable_data<bool>(gpu));
+    constexpr int MAX_BLOCK_DIM = 512;
+    const int MAX_GRID_DIM = ctx->GetMaxPhysicalThreadCount() / MAX_BLOCK_DIM;
+    int element_num = in_.numel();
+    int block_size = (element_num >= MAX_BLOCK_DIM)
+                         ? MAX_BLOCK_DIM
+                         : (1 << static_cast<int>(std::log2(element_num)));
+    int grid_size = element_num / block_size;
+    grid_size = (grid_size >= MAX_GRID_DIM) ? MAX_GRID_DIM : grid_size;
+    BothFalse<bool><<<grid_size, block_size, 0, ctx->stream()>>>(
+        in_.data<bool>(), out_->mutable_data<bool>(gpu), element_num);
 #endif
   }
 
   void VisitorImpl(const platform::CPUPlace& cpu) const {
-    bool lhs = !in_.data<bool>()[0];
-    bool rhs = !out_->mutable_data<bool>(cpu)[0];
-    out_->mutable_data<bool>(cpu)[0] = lhs && rhs;
+    int num = in_.numel();
+    const bool* in_ptr = in_.data<bool>();
+    bool* out_ptr = out_->data<bool>();
+    for (int i = 0; i < num; ++i) {
+      bool lhs = !in_ptr[i];
+      bool rhs = !out_ptr[i];
+      out_ptr[i] = lhs && rhs;
+    }
   }
 
   void VisitorImpl(
       const platform::CUDAPinnedPlace& cpu /* equals to cpu*/) const {
-    bool lhs = !in_.data<bool>()[0];
-    bool rhs = !out_->mutable_data<bool>(cpu)[0];
-    out_->mutable_data<bool>(cpu)[0] = lhs && rhs;
+    int num = in_.numel();
+    const bool* in_ptr = in_.data<bool>();
+    bool* out_ptr = out_->data<bool>();
+    for (int i = 0; i < num; ++i) {
+      bool lhs = !in_ptr[i];
+      bool rhs = !out_ptr[i];
+      out_ptr[i] = lhs && rhs;
+    }
   }
 };
 
@@ -523,6 +608,15 @@ void TensorIsfinite(const framework::Tensor& tensor, framework::Tensor* out) {
   platform::VisitPlace(place, visitor);
 }
 
+void TensorIsfiniteV2(const framework::Tensor& tensor, framework::Tensor* out) {
+  framework::Tensor tmp;
+  TensorContainsInfV2(tensor, &tmp);
+  TensorContainsNANV2(tensor, out);
+  BothFalseVisitor visitor(tmp, out);
+  auto place = tensor.place();
+  platform::VisitPlace(place, visitor);
+}
+
 void TensorToStream(std::ostream& os, const Tensor& tensor,
                     const platform::DeviceContext& dev_ctx) {
   {  // the 1st field, uint32_t version

paddle/fluid/framework/tensor_util.h

@@ -76,6 +76,13 @@ void TensorFromStream(std::istream& is, Tensor* tensor,
                       const platform::DeviceContext& dev_ctx,
                       const size_t& seek, const std::vector<int64_t>& shape);
 
+// store the bool result tensor in out tensor
+void TensorContainsNANV2(const framework::Tensor& tensor,
+                         framework::Tensor* out);
+void TensorContainsInfV2(const framework::Tensor& tensor,
+                         framework::Tensor* out);
+void TensorIsfiniteV2(const framework::Tensor& tensor, framework::Tensor* out);
+
 // convert dlpack's DLTensor to tensor
 void TensorFromDLPack(const ::DLTensor& dl_tensor, framework::Tensor* dst);
 

paddle/fluid/operators/isfinite_v2_op.cc

+// Copyright (c) 2018 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/fluid/operators/isfinite_v2_op.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/operators/common_infer_shape_functions.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace plat = paddle::platform;
+
+namespace paddle {
+namespace operators {
+
+class OverflowV2Op : public framework::OperatorWithKernel {
+ public:
+  OverflowV2Op(const std::string &type,
+               const framework::VariableNameMap &inputs,
+               const framework::VariableNameMap &outputs,
+               const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "isfinitev2");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "isfinitev2");
+    UnaryOpUnchangedInferShape(ctx);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    int dtype = -1;
+    auto *x_var = ctx.InputVar("X");
+    if (x_var->IsType<framework::LoDTensor>()) {
+      dtype = x_var->Get<framework::LoDTensor>().type();
+    } else if (x_var->IsType<framework::SelectedRows>()) {
+      dtype = x_var->Get<framework::SelectedRows>().value().type();
+    } else {
+      PADDLE_THROW(plat::errors::InvalidArgument(
+          "Cannot find the input data type by all input data"));
+    }
+    return framework::OpKernelType(framework::proto::VarType::Type(dtype),
+                                   ctx.GetPlace());
+  }
+};
+
+class OverflowV2OpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) The input tensors of overflowv2 operator.");
+    AddOutput("Out",
+              "(Tensor) The output tensor of overflowv2 operator. "
+              "Same size compare to input tensor");
+    AddComment(string::Sprintf(R"DOC(
+Overflow %s operator.
+
+$$Out = %s(X)$$
+
+Check whether each element of X is Inf or Nan, return the bool result of each
+element of X as a tensor.
+
+%s
+)DOC",
+                               GetName(), GetComments()));
+  }
+
+ protected:
+  virtual std::string GetName() const = 0;
+  virtual std::string GetComments() const = 0;
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+#define REGISTER_V2OP_MAKER(op_type, comment)                         \
+  namespace paddle {                                                  \
+  namespace operators {                                               \
+  class _##op_type##OverflowV2OpMaker                                 \
+      : public ::paddle::operators::OverflowV2OpMaker {               \
+   protected:                                                         \
+    std::string GetName() const { return #op_type; }                  \
+    std::string GetComments() const { return comment; }               \
+  };                                                                  \
+  }                                                                   \
+  }                                                                   \
+  REGISTER_OPERATOR(                                                  \
+      op_type, ops::OverflowV2Op, ops::_##op_type##OverflowV2OpMaker, \
+      paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>, \
+      paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>)
+
+#define REGISTER_OVERFLOW_CPU_KERNEL(op_type, functor)                       \
+  REGISTER_OP_CPU_KERNEL(                                                    \
+      op_type, ops::OverflowKernel<paddle::platform::CPUDeviceContext, int,  \
+                                   ops::functor>,                            \
+      ops::OverflowKernel<paddle::platform::CPUDeviceContext, int64_t,       \
+                          ops::functor>,                                     \
+      ops::OverflowKernel<paddle::platform::CPUDeviceContext, float,         \
+                          ops::functor>,                                     \
+      ops::OverflowKernel<paddle::platform::CPUDeviceContext, double,        \
+                          ops::functor>,                                     \
+      ops::OverflowKernel<paddle::platform::CPUDeviceContext, plat::float16, \
+                          ops::functor>);
+
+REGISTER_V2OP_MAKER(isinf_v2, "isinfv2(X)");
+REGISTER_V2OP_MAKER(isnan_v2, "isnanv2(X)");
+REGISTER_V2OP_MAKER(isfinite_v2, "isfinitev2(X)");
+
+REGISTER_OVERFLOW_CPU_KERNEL(isinf_v2, InfinityV2Functor);
+REGISTER_OVERFLOW_CPU_KERNEL(isnan_v2, NANV2Functor);
+REGISTER_OVERFLOW_CPU_KERNEL(isfinite_v2, IsfiniteV2Functor);

paddle/fluid/operators/isfinite_v2_op.cu

+// Copyright (c) 2020 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/fluid/operators/isfinite_v2_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+#define REGISTER_OVERFLOW_CUDA_KERNEL(op_type, functor)                       \
+  REGISTER_OP_CUDA_KERNEL(                                                    \
+      op_type, ops::OverflowKernel<paddle::platform::CUDADeviceContext, int,  \
+                                   ops::functor>,                             \
+      ops::OverflowKernel<paddle::platform::CUDADeviceContext, int64_t,       \
+                          ops::functor>,                                      \
+      ops::OverflowKernel<paddle::platform::CUDADeviceContext, float,         \
+                          ops::functor>,                                      \
+      ops::OverflowKernel<paddle::platform::CUDADeviceContext, double,        \
+                          ops::functor>,                                      \
+      ops::OverflowKernel<paddle::platform::CUDADeviceContext, plat::float16, \
+                          ops::functor>);
+
+REGISTER_OVERFLOW_CUDA_KERNEL(isinf_v2, InfinityV2Functor);
+REGISTER_OVERFLOW_CUDA_KERNEL(isnan_v2, NANV2Functor);
+REGISTER_OVERFLOW_CUDA_KERNEL(isfinite_v2, IsfiniteV2Functor);

paddle/fluid/operators/isfinite_v2_op.h

+// Copyright (c) 2018 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.
+
+#pragma once
+
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/isfinite_op.h"
+#include "paddle/fluid/platform/float16.h"
+#include "paddle/fluid/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+struct InfinityV2Functor {
+  void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
+    framework::TensorContainsInfV2(tensor, out);
+  }
+};
+
+struct NANV2Functor {
+  void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
+    framework::TensorContainsNANV2(tensor, out);
+  }
+};
+
+struct IsfiniteV2Functor {
+  void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
+    framework::TensorIsfiniteV2(tensor, out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/nll_loss_op.cc

@@ -55,8 +55,8 @@ class NLLLossOp : public framework::OperatorWithKernel {
                               "Input(Weight) should be a 1D tensor."));
         PADDLE_ENFORCE_EQ(x_dims[1], w_dims[0],
                           platform::errors::InvalidArgument(
-                              "Input(Weight) Tensor's size should match"
-                              "to the class numer."));
+                              "Input(Weight) Tensor's size should match "
+                              "to the the total number of classes."));
       }
     }
     if (x_dims.size() == 2) {

paddle/fluid/operators/nll_loss_op.h

@@ -91,7 +91,7 @@ static void nll_loss_2D(T* out_data, T* total_weight_data, const T* x_data,
           }
           PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
                             platform::errors::InvalidArgument(
-                                "label should nor be out of bounds."));
+                                "label should not be out of bounds."));
           const auto cur_weight =
               weight_data ? weight_data[cur_label] : static_cast<T>(1);
           out_data[index] = -x_data[i * sample_size + cur_label * map_size +
@@ -117,7 +117,7 @@ static void nll_loss_2D(T* out_data, T* total_weight_data, const T* x_data,
         }
         PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
                           platform::errors::InvalidArgument(
-                              "label should nor be out of bounds."));
+                              "label should not be out of bounds."));
         const auto cur_weight =
             weight_data ? weight_data[cur_label] : static_cast<T>(1);
         total_weight_val += cur_weight;

python/paddle/__init__.py

@@ -91,7 +91,7 @@ from .tensor.logic import equal  #DEFINE_ALIAS
 from .tensor.logic import greater_equal  #DEFINE_ALIAS
 from .tensor.logic import greater_than  #DEFINE_ALIAS
 from .tensor.logic import is_empty  #DEFINE_ALIAS
-from .tensor.logic import isfinite  #DEFINE_ALIAS
+#from .tensor.logic import isfinite  #DEFINE_ALIAS
 from .tensor.logic import less_equal  #DEFINE_ALIAS
 from .tensor.logic import less_than  #DEFINE_ALIAS
 from .tensor.logic import logical_and  #DEFINE_ALIAS
@@ -193,6 +193,9 @@ from .tensor.math import addmm  #DEFINE_ALIAS
 from .tensor.math import clip  #DEFINE_ALIAS
 from .tensor.math import trace  #DEFINE_ALIAS
 from .tensor.math import kron  #DEFINE_ALIAS
+from .tensor.math import isfinite  #DEFINE_ALIAS
+from .tensor.math import isinf  #DEFINE_ALIAS
+from .tensor.math import isnan  #DEFINE_ALIAS
 from .tensor.math import prod  #DEFINE_ALIAS
 from .tensor.random import standard_normal
 from .tensor.random import normal

python/paddle/fluid/tests/unittests/test_isfinite_v2_op.py

+# Copyright (c) 2020 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.
+
+import paddle
+import paddle.fluid as fluid
+import unittest
+import numpy as np
+
+
+def run_static(x_np, dtype, op_str, use_gpu=False):
+    paddle.enable_static()
+    startup_program = fluid.Program()
+    main_program = fluid.Program()
+    place = paddle.CPUPlace()
+    if use_gpu and fluid.core.is_compiled_with_cuda():
+        place = paddle.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    with fluid.program_guard(main_program, startup_program):
+        x = paddle.data(name='x', shape=x_np.shape, dtype=dtype)
+        res = getattr(paddle.tensor, op_str)(x)
+        exe.run(startup_program)
+        static_result = exe.run(main_program,
+                                feed={'x': x_np},
+                                fetch_list=[res])
+    return static_result
+
+
+def run_dygraph(x_np, op_str, use_gpu=True):
+    place = paddle.CPUPlace()
+    if use_gpu and fluid.core.is_compiled_with_cuda():
+        place = paddle.CUDAPlace(0)
+    paddle.disable_static(place)
+    x = paddle.to_variable(x_np)
+    dygraph_result = getattr(paddle.tensor, op_str)(x)
+    return dygraph_result
+
+
+def np_data_generator(low, high, np_shape, type, sv_list, op_str, *args,
+                      **kwargs):
+    x_np = np.random.uniform(low, high, np_shape).astype(getattr(np, type))
+    # x_np.shape[0] >= len(sv_list)
+    if type in ['float16', 'float32', 'float64']:
+        for i, v in enumerate(sv_list):
+            x_np[i] = v
+    ori_shape = x_np.shape
+    x_np = x_np.reshape((np.product(ori_shape), ))
+    np.random.shuffle(x_np)
+    x_np = x_np.reshape(ori_shape)
+    result_np = getattr(np, op_str)(x_np)
+    return x_np, result_np
+
+
+TEST_META_DATA = [
+    {
+        'low': 0.1,
+        'high': 1,
+        'np_shape': [8, 17, 5, 6, 7],
+        'type': 'float16',
+        'sv_list': [np.inf, np.nan]
+    },
+    {
+        'low': 0.1,
+        'high': 1,
+        'np_shape': [11, 17],
+        'type': 'float32',
+        'sv_list': [np.inf, np.nan]
+    },
+    {
+        'low': 0.1,
+        'high': 1,
+        'np_shape': [2, 3, 4, 5],
+        'type': 'float64',
+        'sv_list': [np.inf, np.nan]
+    },
+    {
+        'low': 0,
+        'high': 100,
+        'np_shape': [11, 17, 10],
+        'type': 'int32',
+        'sv_list': [np.inf, np.nan]
+    },
+    {
+        'low': 0,
+        'high': 999,
+        'np_shape': [132],
+        'type': 'int64',
+        'sv_list': [np.inf, np.nan]
+    },
+]
+
+
+def test(test_case, op_str, use_gpu=False):
+    for meta_data in TEST_META_DATA:
+        meta_data = dict(meta_data)
+        meta_data['op_str'] = op_str
+        x_np, result_np = np_data_generator(**meta_data)
+        static_result = run_static(x_np, meta_data['type'], op_str, use_gpu)
+        dygraph_result = run_dygraph(x_np, op_str, use_gpu)
+        test_case.assertTrue((static_result == result_np).all())
+        test_case.assertTrue((dygraph_result.numpy() == result_np).all())
+
+
+class TestCPUNormal(unittest.TestCase):
+    def test_inf(self):
+        test(self, 'isinf')
+
+    def test_nan(self):
+        test(self, 'isnan')
+
+    def test_finite(self):
+        test(self, 'isfinite')
+
+
+class TestCUDANormal(unittest.TestCase):
+    def test_inf(self):
+        test(self, 'isinf', True)
+
+    def test_nan(self):
+        test(self, 'isnan', True)
+
+    def test_finite(self):
+        test(self, 'isfinite', True)
+
+
+class TestError(unittest.TestCase):
+    def test_bad_input(self):
+        paddle.enable_static()
+        with fluid.program_guard(fluid.Program()):
+
+            def test_isinf_bad_x():
+                x = [1, 2, 3]
+                result = paddle.tensor.isinf(x)
+
+            self.assertRaises(TypeError, test_isinf_bad_x)
+
+            def test_isnan_bad_x():
+                x = [1, 2, 3]
+                result = paddle.tensor.isnan(x)
+
+            self.assertRaises(TypeError, test_isnan_bad_x)
+
+            def test_isfinite_bad_x():
+                x = [1, 2, 3]
+                result = paddle.tensor.isfinite(x)
+
+            self.assertRaises(TypeError, test_isfinite_bad_x)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -58,7 +58,7 @@ from .logic import equal  #DEFINE_ALIAS
 from .logic import greater_equal  #DEFINE_ALIAS
 from .logic import greater_than  #DEFINE_ALIAS
 from .logic import is_empty  #DEFINE_ALIAS
-from .logic import isfinite  #DEFINE_ALIAS
+#from .logic import isfinite  #DEFINE_ALIAS
 from .logic import less_equal  #DEFINE_ALIAS
 from .logic import less_than  #DEFINE_ALIAS
 from .logic import logical_and  #DEFINE_ALIAS
@@ -161,6 +161,9 @@ from .math import addmm  #DEFINE_ALIAS
 from .math import clip  #DEFINE_ALIAS
 from .math import trace  #DEFINE_ALIAS
 from .math import kron  #DEFINE_ALIAS
+from .math import isfinite  #DEFINE_ALIAS
+from .math import isinf  #DEFINE_ALIAS
+from .math import isnan  #DEFINE_ALIAS
 from .math import prod  #DEFINE_ALIAS
 from .random import standard_normal
 from .random import normal

python/paddle/tensor/math.py

@@ -126,7 +126,10 @@ __all__ = [
         'addmm',
         'clip',
         'trace',
-        'kron'
+        'kron',
+        'isfinite',
+        'isinf',
+        'isnan'
 ]
 # yapf: enable.
 
@@ -1669,6 +1672,100 @@ def cumsum(x, axis=None, dtype=None, name=None):
     _cum_sum_ = generate_layer_fn('cumsum')
     return _cum_sum_(**kwargs)
 
+def isfinite(x, name=None):
+    """
+
+    Return whether every element of input tensor is finite number or not.
+
+    Args:
+        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        `Tensor`, the bool result which shows every element of `x` whether it is finite number or not.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
+            x = paddle.to_tensor(x_np)
+            out = paddle.tensor.isfinite(x)
+            print(out.numpy())  # [False  True  True False  True False False]
+    """
+    if in_dygraph_mode():
+        return core.ops.isfinite_v2(x)
+    helper = LayerHelper("isfinite_v2", **locals())
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isfinite')
+    out = helper.create_variable_for_type_inference('bool')
+    helper.append_op(type="isfinite_v2", inputs={"X": x}, outputs={"Out": out})
+    return out
+
+def isinf(x, name=None):
+    """
+
+    Return whether every element of input tensor is `+/-INF` or not.
+
+    Args:
+        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        `Tensor`, the bool result which shows every element of `x` whether it is `+/-INF` or not.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
+            x = paddle.to_tensor(x_np)
+            out = paddle.tensor.isinf(x)
+            print(out.numpy())  # [ True False False  True False False False]
+    """
+    if in_dygraph_mode():
+        return core.ops.isinf_v2(x)
+    helper = LayerHelper("isinf_v2", **locals())
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isinf')
+    out = helper.create_variable_for_type_inference(dtype='bool')
+    helper.append_op(type="isinf_v2", inputs={"X": x}, outputs={"Out": out})
+    return out
+
+def isnan(x, name=None):
+    """
+
+    Return whether every element of input tensor is `NaN` or not.
+
+    Args:
+        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        `Tensor`, the bool result which shows every element of `x` whether it is `NaN` or not.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
+            x = paddle.to_tensor(x_np)
+            out = paddle.tensor.isnan(x)
+            print(out.numpy())  # [False False False False False  True  True]
+    """
+    if in_dygraph_mode():
+        return core.ops.isnan_v2(x)
+    helper = LayerHelper("isnan_v2", **locals())
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isnan')
+    out = helper.create_variable_for_type_inference(dtype='bool')
+    helper.append_op(type="isnan_v2", inputs={"X": x}, outputs={"Out": out})
+    return out
+
+
 def prod(x, axis=None, keepdim=False, dtype=None, name=None):
     """
     Compute the product of tensor elements over the given axis.
@@ -1694,7 +1791,7 @@ def prod(x, axis=None, keepdim=False, dtype=None, name=None):
     Raises:
         ValueError: The :attr:`dtype` must be float32, float64, int32 or int64.
         TypeError: The type of :attr:`axis` must be int, list or tuple.
-
+    
     Examples:
         .. code-block:: python