add sample_logits op

paddle/fluid/operators/CMakeLists.txt

@@ -66,7 +66,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
-set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler tree2col)
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler sample_prob tree2col)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search)
 if (WITH_GPU)
   set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)

paddle/fluid/operators/math/CMakeLists.txt

@@ -39,6 +39,7 @@ math_library(cross_entropy)
 math_library(cos_sim_functor)
 math_library(depthwise_conv)
 math_library(im2col)
+math_library(sample_prob)
 math_library(sampler)
 
 math_library(gru_compute DEPS activation_functions math_function)

paddle/fluid/operators/math/sample_prob.cc

@@ -0,0 +1,26 @@
+/* Copyright (c) 2016 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/math/sample_prob.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template class SampleWithProb<platform::CPUDeviceContext, float>;
+template class SampleWithProb<platform::CPUDeviceContext, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/sample_prob.cu

@@ -0,0 +1,188 @@
+/* Copyright (c) 2016 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 <thrust/random.h>
+#include <thrust/sort.h>
+#include <iostream>
+#include <vector>
+
+#include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/sample_prob.h"
+#include "paddle/fluid/operators/math/sampler.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+__device__ T gpu_adjust_prob(const T prob, const int num_samples,
+                             const int num_tries) {
+  if (num_samples == num_tries) {
+    return prob * num_samples;
+  } else {
+    return -expm1(num_tries * log1p(-prob));
+  }
+}
+
+class GPULogUniformSampler {
+ public:
+  __device__ int64_t Sample(float random, const int range,
+                            const float log_range) const;
+  __device__ float Probability(int64_t value, const float log_range) const;
+};
+
+__device__ int64_t GPULogUniformSampler::Sample(float random, const int range,
+                                                const float log_range) const {
+  // Got Log Uniform distribution from uniform distribution by
+  // inverse_transform_sampling method
+  const int64_t value = static_cast<int64_t>(exp(random * log_range)) - 1;
+  // Mathematically, value should be <= range_, but might not be due to some
+  // floating point roundoff, so we mod by range_.
+  return value % range;
+}
+
+__device__ float GPULogUniformSampler::Probability(
+    int64_t value, const float log_range) const {
+  // Given f(x) = 1/[(x+1) * log_range_]
+  // The value's  probability  is integral of f(x) from value to (value + 1)
+  return (log((value + 2.0) / (value + 1.0))) / log_range;
+}
+
+template <typename T>
+__global__ void SamplingCondidate(
+    const size_t n, const int num_tries, const int range, const float log_range,
+    const int num_true, const std::size_t num_samples,
+    const int64_t* label_data, int64_t* samples_data, T* probabilities_data) {
+  const int num_sampled_classes = num_true + num_samples;
+
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int step_size = 0;
+  GPULogUniformSampler sampler;
+
+  for (; idx < n; idx += blockDim.x * gridDim.x) {
+    int col_idx = idx % num_sampled_classes;
+    int row_idx = idx / num_sampled_classes;
+    if (col_idx < num_true) {
+      samples_data[idx] = label_data[row_idx * num_true + col_idx];
+    } else {
+      samples_data[idx] = samples_data[col_idx];
+    }
+    probabilities_data[idx] = sampler.Probability(samples_data[idx], log_range);
+    probabilities_data[idx] =
+        gpu_adjust_prob(probabilities_data[idx], num_samples, num_tries);
+  }
+}
+
+template <typename T>
+int UniqSampler(const Sampler& sampler, const std::size_t num_samples,
+                int64_t* samples_data) {
+  // sample num_samles unique samples for an example, note that they are not
+  // all negative samples
+  std::unordered_set<int64_t> tmp_samples;
+  tmp_samples.clear();
+  int num_tries = 0;
+  int j = 0;
+  while (j < num_samples) {
+    ++num_tries;
+    auto v = sampler.Sample();
+    auto insert_ok = tmp_samples.insert(v).second;
+    if (!insert_ok) {
+      continue;
+    }
+    samples_data[j] = v;
+    ++j;
+  }
+  return num_tries;
+}
+/*
+template <typename T>
+void Print(Tensor & t, std::string name) {
+  if (!FLAGS_debug_print) {
+    return;
+  }
+  VLOG(1) << "qxz print "<< name;
+  VLOG(1) << name << "size = " << t.numel();
+  size_t size = t.numel();
+  type *d = t.data<type>();
+#ifdef PADDLE_WITH_CUDA
+    std::vector<type> vec;
+    platform::DeviceContextPool::Instance().Get(t.place())->Wait();
+    if (platform::is_gpu_place(t.place())) {
+      vec.resize(size);
+      cudaMemcpy(vec.data(), d, sizeof(T) * size, cudaMemcpyDeviceToHost);
+      d = vec.data();
+    }
+#endif
+  VLOG(1) << name << " data_ptr = " << static_cast<void*>(d);
+  std::string out;
+  for (size_t i = 0; i < size; i++) {
+       out += std::to_string(d[i]);
+       out += ",";
+  }
+  VLOG(1) << out;
+}*/
+
+template <typename T>
+void GPUSampleWithProb<T>::operator()(
+    const platform::CUDADeviceContext& context, const int seed,
+    const int dict_size, const bool uniq, const std::size_t num_samples,
+    const Tensor* L, Tensor* S, Tensor* P) {
+  // UNDERSTAND: dimension issues
+  const auto lbl_dim = L->dims();
+  const int batch_size = lbl_dim[0];
+  const int num_true = lbl_dim[1];
+  const int num_sampled_classes = num_true + num_samples;
+  framework::DDim ret_dim{batch_size, num_sampled_classes};
+
+  // UNDERSTAND: raw data view
+  const int64_t* label_data = L->data<int64_t>();
+  int64_t* samples_data = S->data<int64_t>();
+  T* probabilities_data = P->data<T>();
+
+  int s_size = num_samples;
+  framework::DDim s_dim{s_size};
+  Tensor s;
+  int64_t* s_data = s.mutable_data<int64_t>(s_dim, platform::CPUPlace());
+
+  math::LogUniformSampler sampler(dict_size, seed);
+
+  int range = dict_size;
+  float log_range = log(range + 1);
+
+  int num_tries = UniqSampler<T>(sampler, num_samples, s_data);
+  VLOG(1) << "num_tries: " << num_tries;
+  PADDLE_ENFORCE(cudaMemcpy(samples_data + num_true, s_data,
+                            sizeof(int64_t) * num_samples,
+                            cudaMemcpyHostToDevice));
+
+  int threads = 512;
+  const size_t size = batch_size * num_sampled_classes;
+  int grid = (batch_size * num_sampled_classes + threads - 1) / threads;
+  SamplingCondidate<T><<<grid, threads, 0, context.stream()>>>(
+      size, num_tries, range, log_range, num_true, num_samples, label_data,
+      samples_data, probabilities_data);
+}
+
+template class GPUSampleWithProb<float>;
+template class GPUSampleWithProb<double>;
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/sample_prob.h

@@ -0,0 +1,118 @@
+/* Copyright (c) 2016 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 <iostream>
+#include <unordered_set>
+#include <vector>
+#include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/math/sampler.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+using Tensor = framework::Tensor;
+
+/* UNDERSTAND: utility function to adjust probability for unique sampling,
+return whatever as it is if not using unique samping */
+template <typename T>
+static T adjust_prob(const T prob, const int num_samples, const int num_tries) {
+  if (num_samples == num_tries) {
+    return prob * num_samples;
+  } else {
+    return -expm1(num_tries * log1p(-prob));
+  }
+}
+
+template <typename DeviceContext, typename T>
+class SampleWithProb {
+ public:
+  void operator()(const DeviceContext& context, const Sampler& sampler,
+                  const std::size_t num_samples, const Tensor* L, Tensor* S,
+                  Tensor* P) {
+    // UNDERSTAND: dimension issues
+    const auto lbl_dim = L->dims();
+    const int batch_size = lbl_dim[0];
+    const int num_true = lbl_dim[1];
+    const int num_sampled_classes = num_true + num_samples;
+    framework::DDim ret_dim{batch_size, num_sampled_classes};
+
+    // UNDERSTAND: raw data view
+    const int64_t* label_data = L->data<int64_t>();
+    int64_t* samples_data =
+        S->mutable_data<int64_t>(ret_dim, context.GetPlace());
+    T* probabilities_data = P->mutable_data<T>(ret_dim, context.GetPlace());
+
+    // temp sets for unique sampling
+    std::unordered_set<int64_t> tmp_samples;
+    int j = 0;  // column index
+    // add true labels, not that efficient
+    while (j < num_true) {
+      for (int i = 0; i < batch_size; ++i) {
+        auto samples_index = i * num_sampled_classes + j;
+        auto v = label_data[i * num_true + j];
+        samples_data[samples_index] = v;
+        probabilities_data[samples_index] = sampler.Probability(v);
+      }
+      ++j;
+    }
+
+    // sample num_samles unique samples for an example, note that they are not
+    // all negative samples
+    tmp_samples.clear();
+    int num_tries = 0;
+    while (j < num_sampled_classes) {
+      ++num_tries;
+      auto v = sampler.Sample();
+      auto insert_ok = tmp_samples.insert(v).second;
+      if (!insert_ok) {
+        continue;
+      }
+      auto p = sampler.Probability(v);
+      for (int i = 0; i < batch_size; ++i) {
+        auto samples_index = i * num_sampled_classes + j;
+        samples_data[samples_index] = v;
+        probabilities_data[samples_index] = p;
+      }
+      ++j;
+    }
+
+    // compute Q(y|x), because of unique sampling, probabilities need to be
+    // adjusted
+    for (int k = 0; k < num_sampled_classes; ++k) {
+      for (int i = 0; i < batch_size; ++i) {
+        auto samples_index = i * num_sampled_classes + k;
+        probabilities_data[samples_index] = adjust_prob(
+            probabilities_data[samples_index], num_samples, num_tries);
+      }
+    }
+  }
+};
+
+#ifdef PADDLE_WITH_CUDA
+template <typename T>
+class GPUSampleWithProb {
+ public:
+  void operator()(const platform::CUDADeviceContext& context, const int seed,
+                  const int dict_size, const bool uniq,
+                  const std::size_t num_samples, const Tensor* L, Tensor* S,
+                  Tensor* P);
+};
+#endif
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/sample_logits_op.cc

@@ -0,0 +1,248 @@
+/* Copyright (c) 2016 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/sample_logits_op.h"
+#include "paddle/fluid/operators/math/sample_prob.h"
+
+namespace paddle {
+namespace operators {
+
+class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Logits",
+             "(Tensor, default: Tensor<float>), The unscaled log probabilities "
+             "which is a 2-D tensor with shape [N x K]. N is the batch_size, "
+             "and K is the class number.");
+    AddInput("Label",
+             "(Tensor) The ground truth which is a 2-D tensor. Label is a "
+             "Tensor<int64> with shape [N x NT], where NT is the number of"
+             "true labels for each example.");
+    AddInput(
+        "CustomSamples",
+        "(Tensor, default: Tensor<int64_t>), A 2-D tensor with shaoe [N x "
+        "S+NT]."
+        "The customized sample labels with true labels at first. This tensor"
+        "is only use_custom_samples is true.")
+        .AsDispensable();
+    AddInput(
+        "CustomProbabilities",
+        "(Tensor, default: Tensor<float>), A 2-D tensor with shaoe [N x S+NT]."
+        "The customized sample probabilities with true labels at first. This "
+        "tensor is only use_custom_samples is true.")
+        .AsDispensable();
+    AddOutput(
+        "Samples",
+        "(Tensor, default: Tensor<int64_t>), A 2-D tensor with shape [N x "
+        "S+NT]."
+        "The outputs value of sampler by given the true label, where S is the "
+        "number of negative sample for each example. So Samples includes NT "
+        "true"
+        "labels and S negative labels for each example. This will be used in"
+        "backward calculation.")
+        .AsIntermediate();
+    AddOutput(
+        "Probabilities",
+        "(Tensor, default: Tensor<float>), A 2-D tensor with shape [N x "
+        "S+NT]."
+        "The outputs value of progabilites of samples by given the true label, "
+        "where S is the "
+        "number of negative sample for each example. So Samples includes NT "
+        "true"
+        "labels and S negative labels for each example.")
+        .AsIntermediate();
+    AddOutput("SampledLogits",
+              "(Tensor, default: Tensor<float>), A 2-D tensor with shape"
+              "[N x S+NT]. The outputs value of sampled softmax, which will be"
+              "used in backward calculation.")
+        .AsIntermediate();
+    AddOutput("SampledLabel",
+              "(Tensor, default: Tensor<int64>), A 2-D tensor. The cross "
+              "entropy loss with shape [N x NT].");
+    AddAttr<bool>(
+        "use_custom_samples",
+        "An indicator whether to use custom samples with probabilities, if True"
+        "the operator will use custom samples and custom probabilities"
+        "otherwise, the operator will generate them by itself.")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "uniq",
+        "An indicator whether to sample non-repetitive negtive labels, if True"
+        "the operator will sample negtive labels without replacement."
+        "otherwise, the operator will sample negtive labels with replacement.")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "remove_accidental_hits",
+        "An indicator whether to remove accidental hits when samples hits true"
+        "labels, the removal is implemented by subtracting the corresponding"
+        "logits by float_max to subpress their softmax to be zero.")
+        .SetDefault(true);
+    AddAttr<int>("num_samples", "The number of negative samples.");
+    AddAttr<int>("seed", "Random seed for generating samples").SetDefault(0);
+
+    AddComment(R"DOC(
+TODO(chenfeiyu): Write documentation for this Operator.
+Sampled Softmax With Cross Entropy Operator.
+
+Cross entropy loss with sampled softmax is used as the output layer extensively.
+This operator computes the softmax normalized values for each row of the input
+tensor, after which cross-entropy loss is computed. This provides a more
+numerically stable gradient.
+
+Because this operator performs a softmax on logits internally, it expects
+unscaled logits. This operator should not be used with the output of
+softmax operator since that would produce incorrect results.
+
+When the attribute soft_label is set false, this operators expects mutually
+exclusive hard labels, each sample in a batch is in exactly one class with a
+probability of 1.0. Each sample in the batch will have a single label.
+
+The equation is as follows:
+
+1) Hard label (one-hot label, so every sample has exactly one class)
+
+$$Loss_j =  -\text{Logit}_{Label_j} +
+\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right),
+j = 1,..., K$$
+
+2) Soft label (each sample can have a distribution over all classes)
+
+$$Loss_j =  -\sum_{i=0}^{K}\text{Label}_i \left(\text{Logit}_i -
+\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right),
+j = 1,...,K$$
+
+)DOC");
+  }
+};
+
+class SampleLogitsOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Logits"),
+                   "Input(Logits) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
+
+    PADDLE_ENFORCE(ctx->HasOutput("Samples"),
+                   "Output(Samples) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Probabilities"),
+                   "Output(Probabilities) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("SampledLogits"),
+                   "Output(SampledLogits) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("SampledLabel"),
+                   "Output(SampledLabel) should be not null.");
+
+    auto logits_dims = ctx->GetInputDim("Logits");
+    auto labels_dims = ctx->GetInputDim("Label");
+
+    PADDLE_ENFORCE_EQ(
+        logits_dims.size(), 2UL,
+        "The logits of softmax_with_cross_entropy should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
+                      "The labels should be a 2-D tensor.");
+
+    const int num_samples = ctx->Attrs().Get<int>("num_samples");
+    const int num_sampled_classes = labels_dims[1] + num_samples;
+    ctx->SetOutputDim("Samples", {logits_dims[0], num_sampled_classes});
+    ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
+    ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
+    ctx->SetOutputDim("SampledLabel", {logits_dims[0], labels_dims[1]});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Logits"));
+    framework::OpKernelType kt =
+        framework::OpKernelType(data_type, ctx.device_context());
+    // kt.place_ = platform::CPUPlace();
+    return kt;
+  }
+};
+
+// UNDERSTAND: InferShape for Grad
+class SampleLogitsOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Logits"),
+                   "Input(Logits) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Samples"),
+                   "Input(Samples) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
+                   "Input(SampledLogits) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
+                   "Input(SampledLogits@Grad) should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
+                   "Output(Logits@Grad) should be not null.");
+
+    auto logit_dims = ctx->GetInputDim("Logits");
+    auto label_dims = ctx->GetInputDim("Label");
+    PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
+                      "The label should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
+                      "The logits should be a 2-D tensor.");
+
+    ctx->SetOutputDim(framework::GradVarName("Logits"),
+                      ctx->GetInputDim("Logits"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto data_type = framework::GetDataTypeOfVar(
+        ctx.InputVar(framework::GradVarName("SampledLogits")));
+    framework::OpKernelType kt =
+        framework::OpKernelType(data_type, ctx.device_context());
+    // kt.place_ = platform::CPUPlace();
+    return kt;
+  }
+};
+
+// UNDERSTAND: what's the rule for making a GradMaker TODO
+class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* grad_op = new framework::OpDesc();
+    grad_op->SetType("sample_logits_grad");
+    grad_op->SetInput("Logits", Input("Logits"));
+    grad_op->SetInput("Label", Input("Label"));
+    grad_op->SetInput("Samples", Output("Samples"));
+    grad_op->SetInput("SampledLogits", Output("SampledLogits"));
+    grad_op->SetInput(framework::GradVarName("SampledLogits"),
+                      OutputGrad("SampledLogits"));
+    grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(sample_logits, ops::SampleLogitsOp, ops::SampleLogitsOpMaker,
+                  ops::SampleLogitsGradMaker);
+REGISTER_OPERATOR(sample_logits_grad, ops::SampleLogitsOpGrad);
+REGISTER_OP_CPU_KERNEL(sample_logits, ops::SampleLogitsKernel<float>,
+                       ops::SampleLogitsKernel<double>);
+REGISTER_OP_CPU_KERNEL(sample_logits_grad, ops::SampleLogitsGradKernel<float>,
+                       ops::SampleLogitsGradKernel<double>);

python/paddle/fluid/__init__.py

@@ -131,7 +131,7 @@ def __bootstrap__():
         'eager_delete_tensor_gb', 'fast_eager_deletion_mode',
         'allocator_strategy', 'reader_queue_speed_test_mode',
         'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir',
-        'inner_op_parallelism', 'enable_parallel_graph'
+        'inner_op_parallelism', 'enable_parallel_graph', 'debug_print'
     ]
     if 'Darwin' not in sysstr:
         read_env_flags.append('use_pinned_memory')

python/paddle/fluid/layers/nn.py

@@ -87,6 +87,7 @@ __all__ = [
     'transpose',
     'im2sequence',
     'nce',
+    'sample_logits',
     'hsigmoid',
     'beam_search',
     'row_conv',
@@ -5764,6 +5765,104 @@ def softmax_with_cross_entropy(logits,
     return loss
 
 
+def sample_logits(logits,
+                  label,
+                  num_samples,
+                  uniq=True,
+                  remove_accidental_hits=True,
+                  use_custom_samples=False,
+                  custom_samples=None,
+                  custom_probabilities=None,
+                  seed=0):
+    """
+    **Sampled Softmax With Cross Entropy Operator.**
+
+    Cross entropy loss with sampled softmax is used as the output layer for 
+    larger output classes extensively. This operator samples a number of samples
+    for each example(row), and computes the softmax normalized values for each 
+    row of the sampled tensor, after which cross-entropy loss is computed. 
+    This provides a more numerically stable gradient.
+
+    Because this operator performs a softmax on logits internally, it expects
+    unscaled logits. This operator should not be used with the output of
+    softmax operator since that would produce incorrect results.
+    
+    For examples with T true labels (T >= 1), we assume that each true label has
+    a probability of 1/T. For each sample, S samples are generated using a
+    log uniform distribution. True labels are concatenated with hese samples to
+    form T + S samples for each example. So, assume the shape of logits is
+    [N x K], the shape for samples is [N x (T+S)]. For each sampled label, a 
+    probability is calculated, which corresponds to the Q(y|x) in 
+    [Jean et al., 2014](http://arxiv.org/abs/1412.2007).
+    
+    Logits are sampled according to the sampled labels. Then if 
+    remove_accidental_hits is True, if a sample[i, j] accidentally hits true 
+    labels, then the corresponding sampled_logits[i, j] is minus by 1e20 to 
+    make its softmax result close to zero. Then samled logits are subtracted by
+    logQ(y|x), these sampled logits and re-indexed labels are used to compute 
+    a softmax with cross entropy.
+
+    Args:
+        logits (Variable): The unscaled log probabilities, which is a 2-D tensor
+            with shape [N x K]. N is the batch_size, and K is the class number.
+        label (Variable): The ground truth which is a 2-D tensor. Label is a 
+            Tensor<int64> with shape [N x T], where T is the number of true 
+            labels per example. 
+        num_samples (int): The number for each example, num_samples should be 
+            less than the number of class.
+        seed (int): The random seed for generating random number, which is used
+            in the process of sampling. Default is 0.
+        remove_accidental_hits (bool): A flag indicating whether to remove 
+            accidental hits when sampling. If True and if a sample[i, j] 
+            accidentally hits true labels, then the corresponding 
+            sampled_logits[i, j] is minus by 1e20 to make its softmax result 
+            close to zero. Default is True.
+
+    Returns:
+        Variable: Return the cross entropy loss which is a 2-D tensor with shape
+                  [N x 1].
+
+    Examples:
+        .. code-block:: python
+
+            logits = fluid.layers.data(name='data', shape=[256], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[5], dtype='int64')
+            fc = fluid.layers.fc(input=data, size=100)
+            out = fluid.layers.sampled_softmax_with_cross_entropy(
+                logits=fc, label=label, num_samples=25)
+    """
+    helper = LayerHelper('sample_logits', **locals())
+    samples = helper.create_variable_for_type_inference(dtype='int64')
+    probabilities = helper.create_variable_for_type_inference(
+        dtype=logits.dtype)
+    sampled_logits \
+        = helper.create_variable_for_type_inference(dtype=logits.dtype)
+    sampled_label = helper.create_variable_for_type_inference(dtype='int64')
+
+    helper.append_op(
+        type='sample_logits',
+        inputs={
+            'Logits': logits,
+            'Label': label,
+            'CustomSamples': custom_samples,
+            'CustomProbabilities': custom_probabilities
+        },
+        outputs={
+            'Samples': samples,
+            'Probabilities': probabilities,
+            'SampledLabel': sampled_label,
+            'SampledLogits': sampled_logits
+        },
+        attrs={
+            'use_custom_samples': use_custom_samples,
+            'uniq': uniq,
+            'remove_accidental_hits': remove_accidental_hits,
+            'num_samples': num_samples,
+            'seed': seed
+        })
+    return sampled_logits, sampled_label, samples, probabilities
+
+
 def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
     """
     This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`.

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

@@ -350,6 +350,7 @@ class OpTest(unittest.TestCase):
                 actual_t = np.array(actual)
                 expect = self.outputs[out_name]
                 expect_t = expect[0] if isinstance(expect, tuple) else expect
+                #import pdb; pdb.set_trace()
                 self.assertTrue(
                     np.allclose(
                         actual_t, expect_t, atol=atol, equal_nan=equal_nan),

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

@@ -374,6 +374,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
+    def test_sample_logits(self):
+        program = Program()
+        with program_guard(program):
+            logits = layers.data(name='Logits', shape=[256], dtype='float64')
+            label = layers.data(name='Label', shape=[5], dtype='int64')
+            num_samples = 25
+            output = layers.sample_logits(logits, label, num_samples)
+            self.assertIsNotNone(output)
+        print(str(program))
+
     @decorators.prog_scope()
     def test_nce(self):
         window_size = 5

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

@@ -156,8 +156,26 @@ def append_input_output(block, op_proto, np_list, is_input, dtype):
     return var_dict
 
 
+def var_cast(block, input):
+    if input.dtype == core.VarDesc.VarType.FP32 or input.dtype == core.VarDesc.VarType.FP32:
+        return input
+    out = block.create_var(dtype="float32", shape=[1])
+    op = block.append_op(
+        inputs={"X": input},
+        outputs={"Out": out},
+        type='cast',
+        attrs={
+            'out_dtype': core.VarDesc.VarType.FP32,
+            'in_dtype': input.dtype
+        })
+    op.desc.infer_var_type(block.desc)
+    op.desc.infer_shape(block.desc)
+    return out
+
+
 def append_loss_ops(block, output_names):
     mean_inputs = list(map(block.var, output_names))
+    mean_inputs = [var_cast(block, x) for x in mean_inputs]
 
     if len(mean_inputs) == 1:
         loss = block.create_var(dtype=mean_inputs[0].dtype, shape=[1])