add nll_loss op test=develop (#23758) (#24101)

* add nll_loss op test=develop

paddle/fluid/operators/nll_loss_op.cc

+/* 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/nll_loss_op.h"
+#include <memory>
+#include <string>
+
+namespace paddle {
+namespace operators {
+
+class NLLLossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasOutput("Total_weight"), "Output", "Total_weight",
+                   "NLLLoss");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto label_dims = ctx->GetInputDim("Label");
+    auto reduction = ctx->Attrs().Get<std::string>("reduction");
+
+    PADDLE_ENFORCE_EQ(x_dims.size() == 2 || x_dims.size() == 4, true,
+                      platform::errors::InvalidArgument(
+                          "The tensor rank of Input(X) must be 2 or 4."));
+    bool contain_unknown_dim = framework::contain_unknown_dim(x_dims) ||
+                               framework::contain_unknown_dim(label_dims);
+    bool check = ctx->IsRuntime() || !contain_unknown_dim;
+    if (check) {
+      PADDLE_ENFORCE_EQ(
+          x_dims[0], label_dims[0],
+          platform::errors::InvalidArgument(
+              "ShapeError: Expected input batch_size to match label batch_size,"
+              "But received: the Input(x) batch_size is [%s], the Input(label) "
+              " batch_size is [%s].",
+              x_dims[0], label_dims[0]));
+      if (ctx->HasInput("Weight")) {
+        auto w_dims = ctx->GetInputDim("Weight");
+        PADDLE_ENFORCE_EQ(w_dims.size(), 1,
+                          platform::errors::InvalidArgument(
+                              "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."));
+      }
+    }
+    if (x_dims.size() == 2) {
+      if (reduction == "none") {
+        ctx->SetOutputDim("Out", {x_dims[0]});
+      } else {
+        ctx->SetOutputDim("Out", {1});
+      }
+    } else if (x_dims.size() == 4) {
+      PADDLE_ENFORCE_EQ(label_dims.size(), 3,
+                        platform::errors::InvalidArgument(
+                            "The tensor rank of Input(Label) must be 3."));
+      auto input0 = x_dims[0];
+      auto input2 = x_dims[2];
+      auto input3 = x_dims[3];
+      auto label0 = label_dims[0];
+      auto label1 = label_dims[1];
+      auto label2 = label_dims[2];
+      PADDLE_ENFORCE_EQ(
+          input0 == label0 && input2 == label1 && input3 == label2, true,
+          platform::errors::InvalidArgument("Input(X) tensor shape should "
+                                            "match to Input(Label) tensor "
+                                            "shape."));
+      if (reduction == "none") {
+        ctx->SetOutputDim("Out", {x_dims[0], x_dims[2], x_dims[3]});
+      } else {
+        ctx->SetOutputDim("Out", {1});
+      }
+    }
+    ctx->SetOutputDim("Total_weight", {1});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+};
+
+class NLLLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor, default Tensor<float>) A tensor whose last dimension "
+             "size is equal to the number of classes. It  is expected to "
+             "contain log-probabilities of each class. "
+             "The X tensor's shape has to be either [batch_size, C] or"
+             "[batch_size, C, dim1, ..., dimK] in with K >= 1 in the case "
+             " K-dimensional loss.");
+    AddInput("Label",
+             "(Tensor, default Tensor<int64_t>) A tensor which represents the "
+             "the ground truth. It contains the class index in the range "
+             "[0, C-1] where C = number of classes. The Lable tensor's "
+             "shape has to be (batch_size), or "
+             "(batch_size, dim1, ..., dimK) "
+             "with K >= 1 in the case K-dimensional loss.");
+    AddInput("Weight",
+             "(Tensor, optional) A tensor should be a 1D tensor assigning "
+             "weight to each of the classes. It's shape must be [C], where "
+             "C is the class number.")
+        .AsDispensable();
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>) A tensor that represents the "
+              "NLL loss.");
+    AddOutput("Total_weight",
+              "(Tensor, default Tensor<float>) A tensor saves the total"
+              "weight value in the forward process.");
+    AddAttr<int64_t>("ignore_index",
+                     "(int64_t, default -100), Specifies a target value that is"
+                     "ignored and does not contribute to the input gradient.")
+        .SetDefault(-100);
+    AddAttr<std::string>(
+        "reduction",
+        "(string, default mean), Specifies the reduction to apply"
+        "to the output. The options include \"none\", \"mean\","
+        "\"sum\".")
+        .SetDefault("mean");
+    AddComment(R"DOC(
+NLL(Negative Log Likelihood) Loss Operator.
+
+This operator computes the NLL loss according to the inputs.
+The loss can be described as:
+
+$Out[i] = -X[Label[i]]*Weight[Label[i]]$
+
+It can also be used for higher dimension inputs, such as 2D images, by 
+providing an input of shape (batch_size, C, d1, d2, ..., dK), with 
+K >= 1, where K is the number of dimensions, and a Label of 
+appropriate shape. In the case of images, it computes NLL loss 
+per-pixel.
+
+)DOC");
+  }
+};
+
+class NLLLossGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "NLLLoss");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "NLLLoss");
+
+    auto reduction = ctx->Attrs().Get<std::string>("reduction");
+    auto x_dims = ctx->GetInputDim("X");
+    auto label_dims = ctx->GetInputDim("Label");
+    auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+    bool contain_unknown_dim = framework::contain_unknown_dim(x_dims) ||
+                               framework::contain_unknown_dim(dout_dims);
+    bool check = ctx->IsRuntime() || !contain_unknown_dim;
+
+    if (check) {
+      auto batch_size = x_dims[0];
+      if (x_dims.size() == 2) {
+        PADDLE_ENFORCE_EQ(dout_dims.size(), 1,
+                          platform::errors::InvalidArgument(
+                              "The dimensions of Input(Out@Grad) must be 1"));
+        if (reduction == "none") {
+          PADDLE_ENFORCE_EQ(
+              dout_dims[0], batch_size,
+              platform::errors::InvalidArgument(
+                  "The unreduced size ofInput(Out@Grad) must be the "
+                  "same as batch_size."));
+        } else {
+          PADDLE_ENFORCE_EQ(
+              dout_dims[0], 1,
+              platform::errors::InvalidArgument(
+                  "The reduced size of Input(Out@Grad) must be 1"));
+        }
+      } else if (x_dims.size() == 4) {
+        if (reduction == "none") {
+          PADDLE_ENFORCE_EQ(
+              dout_dims.size(), 3,
+              platform::errors::InvalidArgument(
+                  "The dimensions of Input(Out@Grad) must be 3,But got [%s].",
+                  dout_dims.size()));
+          PADDLE_ENFORCE_EQ(
+              dout_dims[0] == label_dims[0] && dout_dims[1] == label_dims[1] &&
+                  dout_dims[2] == label_dims[2],
+              true, platform::errors::InvalidArgument(
+                        "The dimensions of Input(Out@Grad) must be match "
+                        "to Input(Label) dimensions."));
+        } else {
+          PADDLE_ENFORCE_EQ(
+              dout_dims[0], 1,
+              platform::errors::InvalidArgument(
+                  "The reduced size of Input(Out@Grad) must be 1"));
+        }
+      }
+    }
+
+    auto x_grad_name = framework::GradVarName("X");
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, x_dims);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+};
+
+template <typename T>
+class NLLLossGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("nll_loss_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("Label", this->Input("Label"));
+    op->SetInput("Total_weight", this->Output("Total_weight"));
+
+    if (this->HasInput("Weight")) {
+      op->SetInput("Weight", this->Input("Weight"));
+    }
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(nll_loss, ops::NLLLossOp, ops::NLLLossOpMaker,
+                  ops::NLLLossGradMaker<paddle::framework::OpDesc>,
+                  ops::NLLLossGradMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(nll_loss_grad, ops::NLLLossGradOp);
+REGISTER_OP_CPU_KERNEL(
+    nll_loss, ops::NLLLossOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::NLLLossOpKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    nll_loss_grad,
+    ops::NLLLossGradOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::NLLLossGradOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/nll_loss_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 <algorithm>
+#include <string>
+#include "cub/cub.cuh"
+#include "paddle/fluid/operators/math.h"
+#include "paddle/fluid/operators/nll_loss_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+static constexpr int kNumCUDAThreads = 512;
+static constexpr int kNumMaxinumNumBlocks = 4096;
+static const int NTHREADS = 32;
+
+static inline int NumBlocks(const int N) {
+  return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
+                  kNumMaxinumNumBlocks);
+}
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+template <typename T>
+__global__ void GPUNLLLossForward1D_no_reduce(T* out_data, const T* x_data,
+                                              const int64_t* label_data,
+                                              const T* weight_data,
+                                              const int64_t batch_size,
+                                              const int64_t n_classes,
+                                              const int64_t ignore_index) {
+  CUDA_1D_KERNEL_LOOP(i, batch_size) {
+    const int64_t cur_label = label_data[i];
+    if (cur_label == ignore_index) {
+      out_data[i] = 0;
+      continue;
+    }
+    const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+    out_data[i] = -x_data[i * n_classes + cur_label] * cur_weight;
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossForward1D_with_reduce(
+    T* out_data, T* total_weight_data, const T* x_data,
+    const int64_t* label_data, const T* weight_data, const int64_t batch_size,
+    const int64_t n_classes, const int64_t size_average,
+    const int64_t ignore_index) {
+  __shared__ T sharedInputs[NTHREADS], sharedWeights[NTHREADS];
+  sharedInputs[threadIdx.x] = 0;
+  sharedWeights[threadIdx.x] = 0;
+  int i;
+  for (i = threadIdx.x; i < batch_size; i += NTHREADS) {
+    const auto cur_label = label_data[i];
+    if (cur_label != ignore_index) {
+      const auto cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+      sharedInputs[threadIdx.x] -=
+          x_data[i * n_classes + cur_label] * cur_weight;
+      sharedWeights[threadIdx.x] += cur_weight;
+    }
+  }
+  __syncthreads();
+
+  if (threadIdx.x == 0) {
+    *out_data = *total_weight_data = 0;
+    T output_val = 0;
+    T total_weight_val = 0;
+    for (i = 0; i < NTHREADS; ++i) {
+      output_val += sharedInputs[i];
+      total_weight_val += sharedWeights[i];
+    }
+    *total_weight_data = total_weight_val;
+    *out_data = output_val;
+
+    if (size_average && *total_weight_data != 0) {
+      *out_data = output_val / total_weight_val;
+    }
+  }
+}
+
+// Reduce N values concurrently, i.e. suppose N = 2, and there are 4 threads:
+// (1, 2), (3, 4), (5, 6), (7, 8), then the return in threadVals for thread 0
+// is (1 + 3 + 5 + 7, 2 + 4 + 6 + 8) = (16, 20)
+//
+// If smem is not used again, there is no need to __syncthreads before this
+// call. However, if smem will be used, e.g., this function is called in a loop,
+// then __syncthreads is needed either before or afterwards to prevent non-0
+// threads overriding smem in the next loop before num-0 thread reads from it.
+template <typename T, typename ReduceOp, int N>
+__device__ void reduceNValuesInBlock(T* smem, T threadVals[N],
+                                     const unsigned int numVals,
+                                     ReduceOp reduceOp, T init) {
+  if (numVals == 0) {
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      threadVals[i] = init;
+    }
+    return;
+  }
+
+  // We store each of the N values contiguously, so if N = 2, all values for
+  // the first threadVal for each thread in the block are stored followed by
+  // all of the values for the second threadVal for each thread in the block
+  if (threadIdx.x < numVals) {
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      smem[i * numVals + threadIdx.x] = threadVals[i];
+    }
+  }
+  __syncthreads();
+
+  // Number of lanes in the final reduction --> this is used to determine
+  // where to put the outputs of each of the n things we are reducing. If
+  // nLP = 32, then we have the 32 outputs for the first threadVal,
+  // followed by the 32 outputs for the second threadVal, etc.
+  const unsigned int numLanesParticipating = min(numVals, warpSize);
+
+  if (numVals > warpSize && ((threadIdx.x / warpSize) == 0)) {
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      threadVals[i] = threadIdx.x < numVals ? threadVals[i] : init;
+    }
+
+    for (int i = warpSize + threadIdx.x; i < numVals; i += warpSize) {
+#pragma unroll
+      for (int j = 0; j < N; ++j) {
+        threadVals[j] = reduceOp(threadVals[j], smem[j * numVals + i]);
+      }
+    }
+
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      smem[i * numLanesParticipating + threadIdx.x] = threadVals[i];
+    }
+  }
+  __syncthreads();
+
+  if (threadIdx.x == 0) {
+    if (numLanesParticipating == 32) {
+#pragma unroll
+      for (int i = 0; i < N; ++i) {
+#pragma unroll
+        for (int j = 1; j < 32; ++j) {
+          threadVals[i] = reduceOp(threadVals[i], smem[i * 32 + j]);
+        }
+      }
+    } else {
+#pragma unroll
+      for (int i = 0; i < N; ++i) {
+        for (int j = 1; j < numLanesParticipating; ++j) {
+          threadVals[i] = reduceOp(threadVals[i], smem[i * numVals + j]);
+        }
+      }
+    }
+  }
+}
+
+// Block-wide reduction in shared memory helper; only threadIdx.x == 0 will
+// return the reduced value
+//
+// If smem is not used again, there is no need to __syncthreads before this
+// call. However, if smem will be used, e.g., this function is called in a loop,
+// then __syncthreads is needed either before or afterwards to prevent non-0
+// threads overriding smem in the next loop before num-0 thread reads from it.
+template <typename T, typename ReduceOp>
+__device__ T reduceBlock(T* smem, const unsigned int numVals, T threadVal,
+                         ReduceOp reduceOp, T init) {
+  reduceNValuesInBlock<T, ReduceOp, 1>(smem, &threadVal, numVals, reduceOp,
+                                       init);
+  return threadVal;
+}
+
+template <typename T>
+__global__ void GPUNLLLossForward2D_no_reduce(
+    T* out_data, const T* x_data, const int64_t* label_data,
+    const T* weight_data, const int64_t batch_size, const int64_t n_classes,
+    const int64_t in_dim2, const int64_t in_dim3, const int64_t ignore_index) {
+  const int64_t map_size = in_dim2 * in_dim3;
+  const int64_t sample_size = n_classes * map_size;
+  const int64_t out_numel = batch_size * map_size;
+  CUDA_1D_KERNEL_LOOP(i, out_numel) {
+    const int64_t b = i % batch_size;
+    const int64_t h = (i / batch_size) % in_dim2;
+    const int64_t w = (i / (batch_size * in_dim2)) % in_dim3;
+
+    const int64_t index = b * map_size + h * in_dim3 + w;
+    const int64_t cur_label = label_data[index];
+    if (cur_label == ignore_index) {
+      out_data[index] = 0;
+      continue;
+    }
+    const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+    out_data[index] =
+        -x_data[b * sample_size + cur_label * map_size + h * in_dim3 + w] *
+        cur_weight;
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossForward2D_with_reduce(
+    T* out_data, T* total_weight_data, const T* x_data,
+    const int64_t* label_data, const T* weight_data, const int64_t batch_size,
+    const int64_t n_classes, const int64_t map_nelem,
+    const int64_t blocks_per_sample, const int64_t ignore_index) {
+  __shared__ T partial_sums[kNumCUDAThreads];
+  int64_t i;
+  T input_sum = 0;
+  T acc_weight = 0;
+  *out_data = 0;
+  *total_weight_data = 0;
+
+  int64_t sample = blockIdx.x / blocks_per_sample;
+  int64_t toffset = sample * map_nelem;
+  int64_t ioffset = sample * map_nelem * n_classes;
+  int64_t step = blockDim.x * blocks_per_sample;
+  for (i = (blockIdx.x % blocks_per_sample) * blockDim.x + threadIdx.x;
+       i < map_nelem; i += step) {
+    const int64_t cur_label = label_data[toffset + i];
+    if (cur_label != ignore_index) {
+      const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+      input_sum -= x_data[ioffset + i + map_nelem * cur_label] * cur_weight;
+      acc_weight += cur_weight;
+    }
+  }
+
+  input_sum =
+      reduceBlock(partial_sums, blockDim.x, input_sum, thrust::plus<T>(), (T)0);
+  __syncthreads();
+  acc_weight = reduceBlock(partial_sums, blockDim.x, acc_weight,
+                           thrust::plus<T>(), (T)0);
+
+  if (threadIdx.x == 0) {
+    paddle::platform::CudaAtomicAdd(total_weight_data, acc_weight);
+    paddle::platform::CudaAtomicAdd(out_data, input_sum);
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossForward2D_size_average(T* out_data,
+                                                 T* total_weight_data) {
+  if (*total_weight_data != 0) {
+    *out_data /= *total_weight_data;
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossBackward1D_no_reduce(
+    T* dx_data, const int64_t* label_data, const T* weight_data,
+    const T* dout_data, const int64_t batch_size, const int64_t n_classes,
+    const int64_t ignore_index) {
+  CUDA_1D_KERNEL_LOOP(i, batch_size) {
+    const int64_t cur_label = label_data[i];
+    if (cur_label == ignore_index) {
+      continue;
+    }
+    const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+    dx_data[i * n_classes + cur_label] = -dout_data[i] * cur_weight;
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossBackward1D_with_reduce(
+    T* dx_data, const T* total_weight_data, const int64_t* label_data,
+    const T* weight_data, const T* dout_data, const int64_t batch_size,
+    const int64_t n_classes, const int64_t size_average,
+    const int64_t ignore_index) {
+  if (*total_weight_data <= 0) {
+    return;
+  }
+  int i;
+  const T norm = size_average ? (T)(1 / *total_weight_data) : (T)1;
+  for (i = threadIdx.x; i < batch_size; i += NTHREADS) {
+    const int64_t cur_label = label_data[i];
+    if (cur_label != ignore_index) {
+      const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+      dx_data[i * n_classes + cur_label] = -cur_weight * dout_data[0] * norm;
+    }
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossBackward2D_no_reduce(
+    T* dx_data, const int64_t* label_data, const T* weight_data,
+    const T* dout_data, const int64_t batch_size, const int64_t n_classes,
+    const int64_t in_dim2, const int64_t in_dim3, const int64_t ignore_index) {
+  const int64_t map_size = in_dim2 * in_dim3;
+  const int64_t sample_size = n_classes * map_size;
+  const int64_t out_numel = batch_size * map_size;
+  CUDA_1D_KERNEL_LOOP(i, out_numel) {
+    const int64_t b = i % batch_size;
+    const int64_t h = (i / batch_size) % in_dim2;
+    const int64_t w = (i / (batch_size * in_dim2)) % in_dim3;
+    const int64_t index = b * map_size + h * in_dim3 + w;
+    const int64_t cur_label = label_data[index];
+    if (cur_label == ignore_index) {
+      continue;
+    }
+    const T cur_weight = weight_data ? weight_data[cur_label] : (T)1;
+    dx_data[b * sample_size + cur_label * map_size + h * in_dim3 + w] =
+        -dout_data[index] * cur_weight;
+  }
+}
+
+template <typename T>
+__global__ void GPUNLLLossBackward2D_with_reduce(
+    T* dx_data, const T* total_weight_data, const int64_t* label_data,
+    const T* weight_data, const T* dout_data, const int64_t batch_size,
+    const int64_t n_classes, const int64_t map_nelem,
+    const int64_t blocks_per_sample, const int64_t size_average,
+    const int64_t ignore_index) {
+  if (*total_weight_data <= 0) {
+    return;
+  }
+  int64_t i;
+  const T norm = size_average ? (T)(1 / *total_weight_data) : (T)1;
+  int sample = blockIdx.x / blocks_per_sample;
+  int step = blockDim.x * blocks_per_sample;
+  int toffset = sample * map_nelem;
+  int ioffset = sample * map_nelem * n_classes;
+  for (i = (blockIdx.x % blocks_per_sample) * blockDim.x + threadIdx.x;
+       i < map_nelem; i += step) {
+    const int64_t cur_label = label_data[toffset + i];
+    if (cur_label != ignore_index) {
+      dx_data[ioffset + i + map_nelem * cur_label] =
+          -(weight_data ? weight_data[cur_label] : (T)1) * norm * dout_data[0];
+    }
+  }
+}
+
+template <typename DeviceContext, typename T>
+class NLLLossCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* out = ctx.Output<Tensor>("Out");
+    auto* total_weight = ctx.Output<Tensor>("Total_weight");
+    auto ignore_index = ctx.Attr<int64_t>("ignore_index");
+    auto reduction = ctx.Attr<std::string>("reduction");
+
+    auto x_data = x->data<T>();
+    auto out_data = out->mutable_data<T>(ctx.GetPlace());
+    auto total_weight_data = total_weight->mutable_data<T>(ctx.GetPlace());
+    auto label_data = labels->data<int64_t>();
+    auto weight_data = weight ? weight->data<T>() : nullptr;
+    cudaMemset(total_weight_data, 0, sizeof(T));
+    auto x_dims = x->dims();
+    auto batch_size = x_dims[0];
+    auto n_classes = x_dims[1];
+    int64_t size_average = (int64_t)(reduction == "mean");
+
+    if (x_dims.size() == 2) {
+      int blocks = NumBlocks(batch_size);
+      int threads = kNumCUDAThreads;
+      auto& dev_ctx = ctx.cuda_device_context();
+      if (reduction == "none") {
+        GPUNLLLossForward1D_no_reduce<
+            T><<<blocks, threads, 0, dev_ctx.stream()>>>(
+            out_data, x_data, label_data, weight_data, batch_size, n_classes,
+            ignore_index);
+      } else {
+        GPUNLLLossForward1D_with_reduce<
+            T><<<1, NTHREADS, 0, dev_ctx.stream()>>>(
+            out_data, total_weight_data, x_data, label_data, weight_data,
+            batch_size, n_classes, size_average, ignore_index);
+      }
+    } else if (x_dims.size() == 4) {
+      const auto in_dim2 = x_dims[2];
+      const auto in_dim3 = x_dims[3];
+      const auto map_size = in_dim2 * in_dim3;
+      const auto out_numel = batch_size * in_dim2 * in_dim3;
+      int blocks = NumBlocks(out_numel);
+      int threads = kNumCUDAThreads;
+      auto& dev_ctx = ctx.cuda_device_context();
+      if (reduction == "none") {
+        GPUNLLLossForward2D_no_reduce<
+            T><<<blocks, threads, 0, dev_ctx.stream()>>>(
+            out_data, x_data, label_data, weight_data, batch_size, n_classes,
+            in_dim2, in_dim3, ignore_index);
+      } else {
+        int blocks_per_sample = NumBlocks(map_size) / 128;
+        blocks_per_sample = (blocks_per_sample == 0) ? 1 : blocks_per_sample;
+        int total_blocks = blocks_per_sample * batch_size;
+        GPUNLLLossForward2D_with_reduce<
+            T><<<total_blocks, threads, 0, dev_ctx.stream()>>>(
+            out_data, total_weight_data, x_data, label_data, weight_data,
+            batch_size, n_classes, map_size, blocks_per_sample, ignore_index);
+        if (size_average) {
+          GPUNLLLossForward2D_size_average<T><<<1, 1, 0, dev_ctx.stream()>>>(
+              out_data, total_weight_data);
+        }
+      }
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class NLLLossGradCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* total_weight = ctx.Input<Tensor>("Total_weight");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    auto dout_data = dout->data<T>();
+    auto label_data = labels->data<int64_t>();
+    auto weight_data = weight ? weight->data<T>() : nullptr;
+    auto total_weight_data = total_weight->data<T>();
+    auto ignore_index = ctx.Attr<int64_t>("ignore_index");
+    auto reduction = ctx.Attr<std::string>("reduction");
+    cudaMemset(dx_data, 0, dx->numel() * sizeof(T));
+
+    int64_t size_average = (int64_t)(reduction == "mean");
+    auto x_dims = x->dims();
+    auto batch_size = x_dims[0];
+    auto n_classes = x_dims[1];
+
+    if (x_dims.size() == 2) {
+      int blocks = NumBlocks(batch_size);
+      int threads = kNumCUDAThreads;
+      auto& dev_ctx = ctx.cuda_device_context();
+      if (reduction == "none") {
+        GPUNLLLossBackward1D_no_reduce<
+            T><<<blocks, threads, 0, dev_ctx.stream()>>>(
+            dx_data, label_data, weight_data, dout_data, batch_size, n_classes,
+            ignore_index);
+      } else {
+        GPUNLLLossBackward1D_with_reduce<
+            T><<<1, NTHREADS, 0, dev_ctx.stream()>>>(
+            dx_data, total_weight_data, label_data, weight_data, dout_data,
+            batch_size, n_classes, size_average, ignore_index);
+      }
+    } else if (x_dims.size() == 4) {
+      const auto in_dim2 = x_dims[2];
+      const auto in_dim3 = x_dims[3];
+      const auto map_size = in_dim2 * in_dim3;
+      const auto out_numel = batch_size * in_dim2 * in_dim3;
+
+      int blocks = NumBlocks(out_numel);
+      int threads = kNumCUDAThreads;
+      auto& dev_ctx = ctx.cuda_device_context();
+      if (reduction == "none") {
+        GPUNLLLossBackward2D_no_reduce<
+            T><<<blocks, threads, 0, dev_ctx.stream()>>>(
+            dx_data, label_data, weight_data, dout_data, batch_size, n_classes,
+            in_dim2, in_dim3, ignore_index);
+      } else {
+        int blocks_per_sample = NumBlocks(map_size) / 128;
+        blocks_per_sample = (blocks_per_sample == 0) ? 1 : blocks_per_sample;
+        int total_blocks = blocks_per_sample * batch_size;
+        GPUNLLLossBackward2D_with_reduce<
+            T><<<total_blocks, threads, 0, dev_ctx.stream()>>>(
+            dx_data, total_weight_data, label_data, weight_data, dout_data,
+            batch_size, n_classes, map_size, blocks_per_sample, size_average,
+            ignore_index);
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    nll_loss,
+    ops::NLLLossCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::NLLLossCUDAKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    nll_loss_grad,
+    ops::NLLLossGradCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::NLLLossGradCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/nll_loss_op.h

+/* 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. */
+
+#pragma once
+#include <memory>
+#include <string>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+static void nll_loss_1D(T* out_data, T* total_weight_data, const T* x_data,
+                        const int64_t* label_data, const T* weight_data,
+                        const int64_t batch_size, const int64_t n_classes,
+                        const std::string reduction,
+                        const int64_t ignore_index) {
+  if (reduction == "none") {
+    for (int64_t i = 0; i < batch_size; ++i) {
+      const auto cur_label = label_data[i];
+      if (cur_label == ignore_index) {
+        out_data[i] = 0;
+        continue;
+      }
+      PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
+                        platform::errors::InvalidArgument(
+                            "label should not be out of bounds."));
+
+      const auto cur_weight =
+          weight_data ? weight_data[cur_label] : static_cast<T>(1);
+      out_data[i] = -x_data[i * n_classes + cur_label] * cur_weight;
+    }
+    return;
+  }
+
+  T output_val = 0;
+  T total_weight_val = 0;
+
+  for (int64_t i = 0; i < batch_size; i++) {
+    const auto cur_label = label_data[i];
+    if (cur_label == ignore_index) {
+      out_data[i] = 0;
+      continue;
+    }
+    PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
+                      platform::errors::InvalidArgument(
+                          "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;
+    output_val -= x_data[i * n_classes + cur_label] * cur_weight;
+  }
+  if (reduction == "mean" && total_weight_val != 0) {
+    output_val /= total_weight_val;
+  }
+  *out_data = output_val;
+  *total_weight_data = total_weight_val;
+}
+
+template <typename T>
+static void nll_loss_2D(T* out_data, T* total_weight_data, const T* x_data,
+                        const int64_t* label_data, const T* weight_data,
+                        const int64_t batch_size, const int64_t n_classes,
+                        const int64_t in_dim2, const int64_t in_dim3,
+                        const std::string reduction,
+                        const int64_t ignore_index) {
+  const auto map_size = in_dim2 * in_dim3;
+  const auto sample_size = n_classes * map_size;
+  if (reduction == "none") {
+    for (int i = 0; i < batch_size; i++) {
+      for (int h = 0; h < in_dim2; h++) {
+        for (int w = 0; w < in_dim3; w++) {
+          const auto index = i * map_size + h * in_dim3 + w;
+          const auto cur_label = label_data[index];
+          if (cur_label == ignore_index) {
+            out_data[index] = 0;
+            continue;
+          }
+          PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
+                            platform::errors::InvalidArgument(
+                                "label should nor 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 +
+                                    h * in_dim3 + w] *
+                            cur_weight;
+        }
+      }
+    }
+    return;
+  }
+
+  T output_val = 0;
+  T total_weight_val = 0;
+
+  for (int i = 0; i < batch_size; i++) {
+    for (int h = 0; h < in_dim2; h++) {
+      for (int w = 0; w < in_dim3; w++) {
+        const auto index = i * map_size + h * in_dim3 + w;
+        const auto cur_label = label_data[index];
+        if (cur_label == ignore_index) {
+          out_data[index] = 0;
+          continue;
+        }
+        PADDLE_ENFORCE_EQ(cur_label >= 0 && cur_label < n_classes, true,
+                          platform::errors::InvalidArgument(
+                              "label should nor be out of bounds."));
+        const auto cur_weight =
+            weight_data ? weight_data[cur_label] : static_cast<T>(1);
+        total_weight_val += cur_weight;
+        output_val -=
+            x_data[i * sample_size + cur_label * map_size + h * in_dim3 + w] *
+            cur_weight;
+      }
+    }
+  }
+
+  if (reduction == "mean" && total_weight_val != 0) {
+    output_val /= total_weight_val;
+  }
+  *out_data = output_val;
+  *total_weight_data = total_weight_val;
+}
+
+template <typename DeviceContext, typename T>
+class NLLLossOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* out = ctx.Output<Tensor>("Out");
+    auto* total_weight = ctx.Output<Tensor>("Total_weight");
+    auto reduction = ctx.Attr<std::string>("reduction");
+    auto ignore_index = ctx.Attr<int64_t>("ignore_index");
+
+    auto x_data = x->data<T>();
+    auto label_data = labels->data<int64_t>();
+    auto weight_data = weight ? weight->data<T>() : nullptr;
+    auto out_data = out->mutable_data<T>(ctx.GetPlace());
+    auto total_weight_data = total_weight->mutable_data<T>(ctx.GetPlace());
+    *total_weight_data = 0;
+
+    auto x_dims = x->dims();
+    const auto batch_size = x_dims[0];
+    const auto n_classes = x_dims[1];
+
+    if (x_dims.size() == 2) {
+      nll_loss_1D<T>(out_data, total_weight_data, x_data, label_data,
+                     weight_data, batch_size, n_classes, reduction,
+                     ignore_index);
+    } else if (x_dims.size() == 4) {
+      const auto in_dim2 = x_dims[2];
+      const auto in_dim3 = x_dims[3];
+      nll_loss_2D<T>(out_data, total_weight_data, x_data, label_data,
+                     weight_data, batch_size, n_classes, in_dim2, in_dim3,
+                     reduction, ignore_index);
+    }
+  }
+};
+
+template <typename T>
+static void nll_loss_grad_1D(T* dx_data, const T* dout_data,
+                             const int64_t* label_data, const T* weight_data,
+                             const T* total_weight_data,
+                             const int64_t batch_size, const int64_t n_classes,
+                             const std::string reduction,
+                             const int64_t ignore_index) {
+  if (reduction == "none") {
+    for (int i = 0; i < batch_size; i++) {
+      const auto cur_label = label_data[i];
+      if (cur_label == ignore_index) {
+        continue;
+      }
+      const auto cur_weight =
+          weight_data ? weight_data[cur_label] : static_cast<T>(1);
+      dx_data[i * n_classes + cur_label] = -dout_data[i] * cur_weight;
+    }
+    return;
+  }
+
+  const T dout_val = *dout_data;
+  const T total_weight_val = *total_weight_data;
+  for (int i = 0; i < batch_size; i++) {
+    const auto cur_label = label_data[i];
+    if (cur_label == ignore_index) {
+      continue;
+    }
+    const auto cur_weight =
+        weight_data ? weight_data[cur_label] : static_cast<T>(1);
+    dx_data[i * n_classes + cur_label] = -dout_val * cur_weight;
+    if (reduction == "mean") {
+      dx_data[i * n_classes + cur_label] /= total_weight_val;
+    }
+  }
+}
+
+template <typename T>
+static void nll_loss_grad_2D(T* dx_data, const T* dout_data,
+                             const int64_t* label_data, const T* weight_data,
+                             const T* total_weight_data,
+                             const int64_t batch_size, const int64_t n_classes,
+                             const int64_t in_dim2, const int64_t in_dim3,
+                             const std::string reduction,
+                             const int64_t ignore_index) {
+  const auto map_size = in_dim2 * in_dim3;
+  const auto sample_size = n_classes * map_size;
+
+  if (reduction == "none") {
+    for (int i = 0; i < batch_size; i++) {
+      for (int h = 0; h < in_dim2; h++) {
+        for (int w = 0; w < in_dim3; w++) {
+          const auto index = i * map_size + h * in_dim3 + w;
+          const auto cur_label = label_data[index];
+          if (cur_label == ignore_index) {
+            continue;
+          }
+          const auto cur_weight =
+              weight_data ? weight_data[cur_label] : static_cast<T>(1);
+          dx_data[i * sample_size + cur_label * map_size + h * in_dim3 + w] =
+              -cur_weight * dout_data[index];
+        }
+      }
+    }
+    return;
+  }
+
+  const T dout_val = *dout_data;
+  const T total_weight_val = *total_weight_data;
+  for (int i = 0; i < batch_size; i++) {
+    for (int h = 0; h < in_dim2; h++) {
+      for (int w = 0; w < in_dim3; w++) {
+        const auto index = i * map_size + h * in_dim3 + w;
+        const auto cur_label = label_data[index];
+        if (cur_label == ignore_index) {
+          continue;
+        }
+        const auto cur_weight =
+            weight_data ? weight_data[cur_label] : static_cast<T>(1);
+        const auto dx_index =
+            i * sample_size + cur_label * map_size + h * in_dim3 + w;
+        dx_data[dx_index] = -dout_val * cur_weight;
+        if (reduction == "mean") {
+          dx_data[dx_index] /= total_weight_val;
+        }
+      }
+    }
+  }
+}
+
+template <typename DeviceContext, typename T>
+class NLLLossGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* total_weight = ctx.Input<Tensor>("Total_weight");
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto ignore_index = ctx.Attr<int64_t>("ignore_index");
+    auto reduction = ctx.Attr<std::string>("reduction");
+
+    auto dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    auto dout_data = dout->data<T>();
+    auto label_data = labels->data<int64_t>();
+    auto weight_data = weight ? weight->data<T>() : nullptr;
+    auto total_weight_data = total_weight->data<T>();
+    memset(dx_data, 0, dx->numel() * sizeof(T));
+
+    const auto x_dims = x->dims();
+    const auto batch_size = x_dims[0];
+    const auto n_classes = x_dims[1];
+
+    if (x_dims.size() == 2) {
+      nll_loss_grad_1D(dx_data, dout_data, label_data, weight_data,
+                       total_weight_data, batch_size, n_classes, reduction,
+                       ignore_index);
+    } else if (x_dims.size() == 4) {
+      const auto in_dim2 = x_dims[2];
+      const auto in_dim3 = x_dims[3];
+      nll_loss_grad_2D(dx_data, dout_data, label_data, weight_data,
+                       total_weight_data, batch_size, n_classes, in_dim2,
+                       in_dim3, reduction, ignore_index);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/nn/__init__.py

@@ -66,7 +66,7 @@ from .layer.loss import L1Loss  #DEFINE_ALIAS
 from .layer import loss  #DEFINE_ALIAS
 from .layer import conv  #DEFINE_ALIAS
 from .layer.conv import Conv2D, Conv2DTranspose, Conv3D, Conv3DTranspose  #DEFINE_ALIAS
-# from .layer.loss import NLLLoss   #DEFINE_ALIAS
+from .layer.loss import NLLLoss  #DEFINE_ALIAS
 from .layer.loss import BCELoss  #DEFINE_ALIAS
 # from .layer.learning_rate import CosineDecay   #DEFINE_ALIAS
 # from .layer.learning_rate import ExponentialDecay   #DEFINE_ALIAS

python/paddle/nn/layer/loss.py

@@ -19,7 +19,7 @@ __all__ = [
     'CrossEntropyLoss',
     'MSELoss',
     'L1Loss',
-    #    'NLLLoss',
+    'NLLLoss',
     'BCELoss'
 ]
 
@@ -446,3 +446,145 @@ class BCELoss(fluid.dygraph.Layer):
             return fluid.layers.reduce_mean(out)
         else:
             return out
+
+
+class NLLLoss(fluid.dygraph.Layer):
+    """
+    This op accepts input and target label and returns negative log likelihood 
+    cross error. It is useful to train a classification problem with C classes.
+     
+    The input for the loss is epected to contain log-probabilities of
+    each classes. It hs to be a Tensor of size either (batch_size, C) or 
+    (batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case.
+    The label for the loss should be a class index in the range [0, C-1]
+    where C is the number of classes. If ignore_index is specified, the
+    specified target value does not contribute to the input gradient.
+    
+    If the optional argument `weight` is provided, it should be a 1D Tensor
+    assigning weight to each of the classed. This is particularly useful
+    when you have an unbalanced training set.
+ 
+    The loss is calculated as follows.
+    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:
+
+    .. math::
+        \ell(x, y) = L = \{l_1,\dots,l_N\}^\\top, \quad
+        l_n = - w_{y_n} x_{n,y_n}, \quad
+        w_{c} = \\text{weight}[c] \cdot \mathbb{1}\{c \\not= \\text{ignore\\_index}\},
+
+    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
+    (default ``'mean'``), then
+
+    .. math::
+        \ell(x, y) = \\begin{cases}
+            \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, &
+            \\text{if reduction} = \\text{'mean';}\\\\
+            \\sum_{n=1}^N l_n,  &
+            \\text{if reduction} = \\text{'sum'.}
+        \\end{cases}
+
+    Parameters:
+        input (Variable): Input tensor, the data type is float32, float64. 
+        label (Variable): Label tensor, the data type is int64_t.
+        weight (Variable, optional): Weight tensor, a manual rescaling weight given
+            to each class. If given, it has to be a Tensor of size `C`. Otherwise,
+            it treated as if having all ones. the data type is 
+            float32, float64, Default is ``'None'``.
+        reduction (str, optional): Indicate how to average the loss, 
+            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
+            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; 
+            Default is ``'mean'``.
+        ignore_index (int64, optional): Specifies a target value that is ignored
+            and does not contribute to the input gradient.
+
+    Returns:
+        The tensor variable storing the nll_loss.
+
+    Return type: Variable.
+    
+    Examples:
+
+        .. code-block:: python
+
+            # declarative mode
+            import paddle.fluid as fluid
+            import numpy as np
+            import paddle
+
+            input_np = np.random.random(size=(10, 10)).astype(np.float32)
+            label_np = np.random.randint(0, 10, size=(10,)).astype(np.int64)
+            prog = fluid.Program()
+            startup_prog = fluid.Program()
+            place = fluid.CPUPlace()
+            with fluid.program_guard(prog, startup_prog):
+                input = fluid.data(name='input', shape=[10, 10], dtype='float32')
+                label = fluid.data(name='label', shape=[10], dtype='int64')
+                nll_loss = paddle.nn.loss.NLLLoss()
+                res = nll_loss(input, label)
+
+                exe = fluid.Executor(place)
+                static_result = exe.run(
+                    prog,
+                    feed={"input": input_np,
+                          "label": label_np},
+                    fetch_list=[res])
+            print(static_result)
+            
+            # imperative mode
+            import paddle.fluid.dygraph as dg
+            with dg.guard(place) as g:
+                input = dg.to_variable(input_np)
+                label = dg.to_variable(label_np)
+                output = nll_loss(input, label)
+                print(output.numpy())
+    """
+
+    def __init__(self, weight=None, reduction='mean', ignore_index=-100):
+        super(NLLLoss, self).__init__()
+        self.weight = weight
+        self.reduction = reduction
+        self.ignore_index = ignore_index
+
+    def forward(self, input, label):
+        dtype = self._helper.input_dtype(input)
+
+        fluid.data_feeder.check_variable_and_dtype(
+            input, 'input', ['float32', 'float64'], 'nll_loss')
+        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
+                                                   'nll_loss')
+
+        if self.reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "The value of 'reduction' in nll_loss should be 'sum', 'mean' or 'none', but "
+                "received %s, which is not allowed." % self.reduction)
+
+        x_shape = list(input.shape)
+        n = x_shape[0]
+        c = x_shape[1]
+        x_dims = len(x_shape)
+        if x_dims < 2:
+            raise ValueError('Expected 2 or more dimensions (got {})'.format(
+                x_dims))
+        if x_dims != 2 and x_dims != 4:
+            input = fluid.layers.reshape(input, shape=[n, c, 1, -1])
+            label = fluid.layers.reshape(label, shape=[n, 1, -1])
+            out_shape = [n] + x_shape[2:]
+
+        inputs = {'X': input, 'Label': label}
+        attrs = {'reduction': self.reduction, 'ignore_index': self.ignore_index}
+
+        if self.weight is not None:
+            if isinstance(self.weight, fluid.framework.Variable):
+                inputs['Weight'] = self.weight
+
+        out = self._helper.create_variable_for_type_inference(dtype=input.dtype)
+        total_weight = self._helper.create_variable_for_type_inference(
+            dtype=input.dtype)
+        outputs = {'Out': out, 'Total_weight': total_weight}
+
+        self._helper.append_op(
+            type='nll_loss', inputs=inputs, outputs=outputs, attrs=attrs)
+        if x_dims != 2 and x_dims != 4 and self.reduction == 'none':
+            out = fluid.layers.reshape(out, shape=out_shape)
+
+        return out