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

+/* 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

+/* 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

+/* 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

+/* 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>);

paddle/fluid/operators/sample_logits_op.cu

+/* 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 <string>
+#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/math/math_function.h"
+#include "paddle/fluid/operators/math/sample_prob.h"
+#include "paddle/fluid/operators/math/softmax.h"
+#include "paddle/fluid/operators/sample_logits_op.h"
+
+namespace paddle {
+namespace operators {
+
+DEFINE_bool(debug_print, true, "run debug mode");
+
+// UNDERSTAND: something like take_along_axis in numpy.
+template <typename T>
+__global__ void GPUTakeAlongD1(size_t size, const int batch_size,
+                               const int array_slice_size,
+                               const int idx_slice_size, const T* p_array,
+                               const int64_t* p_index, T* p_value) {
+  const auto value_slice_size = idx_slice_size;
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int step_size = blockDim.x * gridDim.x;
+
+  for (; idx < size; idx += step_size) {
+    int i = idx / idx_slice_size;
+    auto array_index = p_index[idx];
+    p_value[idx] = p_array[i * array_slice_size + array_index];
+  }
+}
+
+// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
+// indices, scatter is done in += way.
+template <typename T>
+__global__ void GPUPutAlongD1(size_t size, const int batch_size,
+                              const int array_slice_size,
+                              const int idx_slice_size, T* p_array,
+                              const int64_t* p_index, const T* p_value) {
+  const auto value_slice_size = idx_slice_size;
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int step_size = blockDim.x * gridDim.x;
+
+  // size == batch_size
+  for (; idx < size; idx += step_size) {
+    int i = idx;
+    for (int j = 0; j < idx_slice_size; ++j) {
+      auto array_index = p_index[i * idx_slice_size + j];
+      p_array[i * array_slice_size + array_index] +=
+          p_value[i * idx_slice_size + j];
+    }
+  }
+}
+
+// UNDERSTAND: set label as 0,1,...,num_true-1
+template <typename T>
+__global__ void GPUSetLabel(size_t size, const int num_true, int64_t* p_array) {
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int step_size = blockDim.x * gridDim.x;
+
+  for (; idx < size; idx += step_size) {
+    p_array[idx] = idx % num_true;
+  }
+}
+
+// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
+// logits by a float max, here 1e20
+template <typename T>
+__global__ void gpu_compute_remove_accidental_hits(const int size,
+                                                   const int num_true,
+                                                   const int idx_slice_size,
+                                                   const int64_t* p_index,
+                                                   T* p_value) {
+  const auto value_slice_size = idx_slice_size;
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int step_size = blockDim.x * gridDim.x;
+
+  for (; idx < size; idx += step_size) {
+    int i = idx / idx_slice_size;
+    if (idx % idx_slice_size < num_true) continue;
+    for (int j = 0; j < num_true; ++j) {
+      const auto true_idx = i * idx_slice_size + j;
+      if (p_index[true_idx] == p_index[idx]) {
+        p_value[idx] -= 1e20;
+        break;
+      }
+    }
+  }
+}
+
+template <typename T>
+class SampleLogitsCUDAKernel : public framework::OpKernel<T> {
+ public:
+  using Tensor = framework::Tensor;
+  template <typename type>
+  void Print(const Tensor& t, std::string name) const {
+    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;
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    // get necessary inputs
+    const Tensor* logits = context.Input<Tensor>("Logits");
+    const Tensor* label = context.Input<Tensor>("Label");
+    VLOG(3) << "Enter SampleLogitsCUDAKernel";
+
+    // get necessary outputs
+    Tensor* samples = context.Output<Tensor>("Samples");
+    Tensor* probabilities = context.Output<Tensor>("Probabilities");
+    Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
+    Tensor* sampled_label = context.Output<Tensor>("SampledLabel");
+
+    // shapes
+    const auto batch_size = logits->dims()[0];
+    const auto num_classes = logits->dims()[1];
+    const auto label_dim = label->dims();
+    const auto num_true = label_dim[1];
+    const auto samples_dim = samples->dims();
+
+    // attrs
+    const auto num_samples = context.Attr<int>("num_samples");
+    const bool use_custom_samples = context.Attr<bool>("use_custom_samples");
+    const bool uniq = context.Attr<bool>("uniq");
+    const bool remove_accidental_hits =
+        context.Attr<bool>("remove_accidental_hits");
+
+    // device contexts
+    auto& dev_ctx = context.cuda_device_context();
+
+    // UNDERSTAND: allocate memories for temporaries
+    sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
+    math::SetConstant<platform::CUDADeviceContext, T> set_zero;
+    set_zero(dev_ctx, sampled_logits, static_cast<T>(0));
+
+    auto sampled_label_data =
+        sampled_label->mutable_data<int64_t>(label_dim, context.GetPlace());
+    int threads = 512;
+    size_t size = batch_size * num_true;
+    int grid = (size + threads - 1) / threads;
+    GPUSetLabel<
+        T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
+        size, num_true, sampled_label_data);
+
+    if (use_custom_samples) {
+      const Tensor* custom_samples = context.Input<Tensor>("CustomSamples");
+      const Tensor* custom_probabilities =
+          context.Input<Tensor>("CustomProbabilities");
+      samples->ShareDataWith(*custom_samples);
+      probabilities->ShareDataWith(*custom_probabilities);
+    } else {
+      samples->mutable_data<int64_t>(context.GetPlace());
+      probabilities->mutable_data<T>(samples_dim, context.GetPlace());
+      // UNDERSTAND: sampling
+      const auto seed = context.Attr<int>("seed");
+      auto sampler_with_prob = math::GPUSampleWithProb<T>();
+      Print<int64_t>(*samples, std::string("samples1"));
+      sampler_with_prob(context.cuda_device_context(), seed, num_classes, uniq,
+                        num_samples, label, samples, probabilities);
+    }
+    Print<int64_t>(*samples, std::string("samples2"));
+    Print<T>(*probabilities, std::string("probabilities"));
+
+    // UNDERSTAND: gather sampled logits and remove accidental hits if needed
+    const auto num_take = samples->dims()[1];
+    const auto array_dims = logits->dims();
+    const auto idx_dims = samples->dims();
+
+    const T* p_array = logits->data<T>();
+    const int64_t* p_index = samples->data<int64_t>();
+    T* p_value = sampled_logits->data<T>();
+
+    // src slice size
+    const auto array_slice_size = array_dims[1];
+    // index slice size
+    const auto idx_slice_size = idx_dims[1];
+
+    size = batch_size * num_take;
+    grid = (size + threads - 1) / threads;
+    GPUTakeAlongD1<
+        T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
+        size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
+        p_value);
+    Print<T>(*sampled_logits, std::string("sampled_logits"));
+
+    if (remove_accidental_hits) {
+      const size_t size = batch_size * (num_true + num_samples);
+      int grid = (size + threads - 1) / threads;
+      gpu_compute_remove_accidental_hits<
+          T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
+          size, num_true, idx_slice_size, p_index, p_value);
+      Print<T>(*sampled_logits,
+               std::string("sampled_logits_remove_accidental_hits"));
+    }
+
+    // subtracted sampled logits with logQ(y|x)
+    auto probs = EigenMatrix<T>::From(*probabilities);
+    auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
+    smp_logits.device(*dev_ctx.eigen_device()) =
+        (smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
+            .unaryExpr(TolerableValue<T>());
+    Print<T>(*sampled_logits, std::string("sampled_logits_res"));
+  }
+};
+
+template <typename T>
+class SampleLogitsGradCUDAKernel : public framework::OpKernel<T> {
+ public:
+  using Tensor = framework::Tensor;
+  template <typename type>
+  void Print(const Tensor& t, std::string name) const {
+    if (!FLAGS_debug_print) {
+      return;
+    }
+    VLOG(1) << "qxz print " << name;
+    VLOG(1) << name << "size = " << t.numel();
+    size_t size = t.numel();
+    const 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<const void*>(d);
+    std::string out;
+    for (size_t i = 0; i < size; i++) {
+      out += std::to_string(d[i]);
+      out += ",";
+    }
+    VLOG(1) << out;
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
+    const Tensor* samples = context.Input<Tensor>("Samples");
+    const Tensor* sampled_logits_grad =
+        context.Input<Tensor>(framework::GradVarName("SampledLogits"));
+    logits_grad->mutable_data<T>(context.GetPlace());
+
+    auto& dev_ctx = context.cuda_device_context();
+    math::SetConstant<platform::CUDADeviceContext, T> set_zero;
+    set_zero(dev_ctx, logits_grad, static_cast<T>(0));
+
+    // UNDERSTAND: scatter it back to logit_grad
+    const auto batch_size = samples->dims()[0];
+    const auto num_put = samples->dims()[1];
+    const auto array_dims = logits_grad->dims();
+    const auto idx_dims = samples->dims();
+
+    T* p_array = logits_grad->data<T>();
+    const int64_t* p_index = samples->data<int64_t>();
+    const T* p_value = sampled_logits_grad->data<T>();
+
+    // src slice size
+    const auto array_slice_size = array_dims[1];
+    // index slice size
+    const auto idx_slice_size = idx_dims[1];
+
+    int threads = 128;
+    const size_t size = batch_size;
+    int grid = (size + threads - 1) / threads;
+
+    Print<T>(*sampled_logits_grad, std::string("sampled_logits_grad"));
+    Print<int64_t>(*samples, std::string("samples"));
+    GPUPutAlongD1<
+        T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
+        size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
+        p_value);
+    Print<T>(*logits_grad, std::string("logits_grad"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(sample_logits, ops::SampleLogitsCUDAKernel<float>,
+                        ops::SampleLogitsCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(sample_logits_grad,
+                        ops::SampleLogitsGradCUDAKernel<float>,
+                        ops::SampleLogitsGradCUDAKernel<double>);

paddle/fluid/operators/sample_logits_op.h

+/* 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 <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/math/math_function.h"
+#include "paddle/fluid/operators/math/sample_prob.h"
+#include "paddle/fluid/operators/math/softmax.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename T>
+struct TolerableValue {
+  HOSTDEVICE T operator()(const T& x) const {
+    PADDLE_ASSERT(std::is_floating_point<T>::value);
+    const T kApproInf = 1e20;
+    if (x == INFINITY) return kApproInf;
+    if (x == -INFINITY) return -kApproInf;
+    return x;
+  }
+};
+
+// UNDERSTAND: something like take_along_axis in numpy.
+template <typename T>
+static void CPUTakeAlongD1(const platform::DeviceContext& ctx,
+                           const framework::Tensor& array,
+                           const framework::Tensor& index,
+                           framework::Tensor* value) {
+  PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
+  // UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
+  PADDLE_ENFORCE(index.dims().size() == 2 && array.dims().size() == 2 &&
+                 index.dims()[0] == array.dims()[0] &&
+                 index.dims() == value->dims());
+
+  const auto batch_size = index.dims()[0];
+  const auto num_take = index.dims()[1];
+  const auto array_dims = array.dims();
+  const auto idx_dims = index.dims();
+
+  // UNDERSTAND: no allocations here
+  const T* p_array = array.data<T>();
+  const int64_t* p_index = index.data<int64_t>();
+  T* p_value = value->data<T>();
+
+  // src slice size
+  const auto array_slice_size = array_dims[1];
+
+  // index slice size
+  const auto idx_slice_size = idx_dims[1];
+  const auto value_slice_size = idx_slice_size;
+
+  for (int i = 0; i < batch_size; ++i) {
+    for (int j = 0; j < num_take; ++j) {
+      auto array_index = p_index[i * idx_slice_size + j];
+      p_value[i * value_slice_size + j] =
+          p_array[i * array_slice_size + array_index];
+    }
+  }
+}
+
+// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
+// indices, scatter is done in += way.
+template <typename T>
+static void CPUPutAlongD1(const platform::DeviceContext& ctx,
+                          framework::Tensor* array,
+                          const framework::Tensor& index,
+                          const framework::Tensor& value) {
+  PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
+  // UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
+  PADDLE_ENFORCE(index.dims().size() == 2 && array->dims().size() == 2 &&
+                 index.dims()[0] == array->dims()[0] &&
+                 index.dims() == value.dims());
+  const auto batch_size = index.dims()[0];
+  const auto num_put = index.dims()[1];
+  auto array_dims = array->dims();
+  auto idx_dims = index.dims();
+
+  // UNDERSTAND: no allocations here
+  T* p_array = array->data<T>();
+  const int64_t* p_index = index.data<int64_t>();
+  const T* p_value = value.data<T>();
+
+  // slice sizes
+  const auto array_slice_size = array_dims[1];
+  const auto idx_slice_size = idx_dims[1];
+  const auto value_slice_size = idx_slice_size;
+
+  for (int i = 0; i < batch_size; ++i) {
+    for (int j = 0; j < num_put; ++j) {
+      auto array_index = p_index[i * idx_slice_size + j];
+      p_array[i * array_slice_size + array_index] +=
+          p_value[i * value_slice_size + j];
+    }
+  }
+}
+
+// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
+// logits by a float max, here 1e20
+template <typename T>
+static void compute_remove_accidental_hits(const platform::DeviceContext& ctx,
+                                           framework::Tensor* sampled_logits,
+                                           const framework::Tensor& samples,
+                                           const int num_true) {
+  const auto batch_size = sampled_logits->dims()[0];
+  const auto num_sampled_classes = sampled_logits->dims()[1];
+  T* sampled_logits_data = sampled_logits->data<T>();
+  const auto samples_data = samples.data<int64_t>();
+
+  std::unordered_set<int64_t> tmp_true_labels;
+  for (int i = 0; i < batch_size; ++i) {
+    tmp_true_labels.clear();
+    tmp_true_labels.insert(samples_data + i * num_sampled_classes,
+                           samples_data + i * num_sampled_classes + num_true);
+    for (int j = num_true; j < num_sampled_classes; ++j) {
+      const auto idx = i * num_sampled_classes + j;
+      if (tmp_true_labels.find(samples_data[idx]) != tmp_true_labels.end())
+        sampled_logits_data[idx] -= 1e20;
+    }
+  }
+}
+
+template <typename T>
+class SampleLogitsKernel : public framework::OpKernel<T> {
+ public:
+  using Tensor = framework::Tensor;
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
+                   "This kernel only runs on CPU.");
+    VLOG(3) << "Enter SampleLogitsKernel";
+    // get necessary inputs
+    const Tensor* logits = context.Input<Tensor>("Logits");
+    const Tensor* label = context.Input<Tensor>("Label");
+
+    // get necessary outputs
+    Tensor* samples = context.Output<Tensor>("Samples");
+    Tensor* probabilities = context.Output<Tensor>("Probabilities");
+    Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
+    Tensor* sampled_label = context.Output<Tensor>("SampledLabel");
+
+    // shapes
+    const auto batch_size = logits->dims()[0];
+    const auto num_classes = logits->dims()[1];
+    const auto label_dim = label->dims();
+    const auto num_true = label_dim[1];
+    const auto samples_dim = samples->dims();
+
+    // attrs
+    const auto num_samples = context.Attr<int>("num_samples");
+    const bool use_custom_samples = context.Attr<bool>("use_custom_samples");
+    const bool remove_accidental_hits =
+        context.Attr<bool>("remove_accidental_hits");
+
+    // device contexts
+    auto& dev_ctx =
+        context.template device_context<platform::CPUDeviceContext>();
+
+    // UNDERSTAND: allocate memories for temporaries
+    sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
+    auto sampled_label_data =
+        sampled_label->mutable_data<int64_t>(label_dim, context.GetPlace());
+    for (int i = 0; i < batch_size; ++i)
+      for (int j = 0; j < num_true; ++j)
+        sampled_label_data[i * num_true + j] = j;
+
+    if (use_custom_samples) {
+      const Tensor* custom_samples = context.Input<Tensor>("CustomSamples");
+      const Tensor* custom_probabilities =
+          context.Input<Tensor>("CustomProbabilities");
+      samples->ShareDataWith(*custom_samples);
+      probabilities->ShareDataWith(*custom_probabilities);
+    } else {
+      samples->mutable_data<int64_t>(context.GetPlace());
+      probabilities->mutable_data<T>(samples_dim, context.GetPlace());
+      // UNDERSTAND: sampling
+      const auto seed = context.Attr<int>("seed");
+      auto sampler_with_prob =
+          math::SampleWithProb<platform::CPUDeviceContext, T>();
+      sampler_with_prob(dev_ctx, math::LogUniformSampler(num_classes, seed),
+                        num_samples, label, samples, probabilities);
+    }
+
+    // UNDERSTAND: gather sampled logits and remove accidental hits if needed
+    CPUTakeAlongD1<T>(dev_ctx, *logits, *samples, sampled_logits);
+    if (remove_accidental_hits) {
+      compute_remove_accidental_hits<T>(dev_ctx, sampled_logits, *samples,
+                                        num_true);
+    }
+
+    /* Debug
+    const auto num_sampled_classes = samples_dim[1];
+    std::cout << "Sampled Logits" << std::endl;
+    const auto sampled_logits_data = sampled_logits->data<T>();
+    for (int i = 0; i < sampled_logits->numel(); ++i) {
+      std::cout << sampled_logits_data[i] << ", ";
+      if ((i + 1) % num_sampled_classes == 0)
+        std::cout << std::endl;
+    }
+    std::cout << std::endl;
+    */
+    /* Debug
+    std::cout << "Samples" << std::endl;
+    const auto samples_data = samples->data<int64_t>();
+    for (int i = 0; i < samples->numel(); ++i) {
+      std::cout << samples_data[i] << ", ";
+      if ((i + 1) % num_sampled_classes == 0)
+        std::cout << std::endl;
+    }
+    std::cout << std::endl;
+    */
+    /* Debug
+    std::cout << "Probabilities" << std::endl;
+    const auto probabilities_data = probabilities->data<T>();
+    for (int i = 0; i < probabilities->numel(); ++i) {
+      std::cout << probabilities_data[i] << ", ";
+      if ((i + 1) % num_sampled_classes == 0)
+        std::cout << std::endl;
+    }
+    std::cout << std::endl;
+    */
+    // subtracted sampled logits with logQ(y|x)
+    auto probs = EigenMatrix<T>::From(*probabilities);
+    auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
+    smp_logits.device(*dev_ctx.eigen_device()) =
+        (smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
+            .unaryExpr(TolerableValue<T>());
+  }
+};
+
+template <typename T>
+class SampleLogitsGradKernel : public framework::OpKernel<T> {
+ public:
+  using Tensor = framework::Tensor;
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
+    const Tensor* samples = context.Input<Tensor>("Samples");
+    const Tensor* sampled_logits_grad =
+        context.Input<Tensor>(framework::GradVarName("SampledLogits"));
+    logits_grad->mutable_data<T>(context.GetPlace());
+
+    auto& dev_ctx =
+        context.template device_context<platform::CPUDeviceContext>();
+    math::SetConstant<platform::CPUDeviceContext, T> set_zero;
+    set_zero(dev_ctx, logits_grad, static_cast<T>(0));
+
+    // const bool remove_accidental_hits =
+    //    context.Attr<bool>("remove_accidental_hits");
+
+    // UNDERSTAND: scatter it back to logit_grad
+    CPUPutAlongD1<T>(dev_ctx, logits_grad, *samples, *sampled_logits_grad);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

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/test_sample_logits.py

+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class Sampler(object):
+    def __init__(self, range, seed):
+        self.range_ = range
+        self.seed_ = seed
+        np.random.seed(self.seed_)
+
+    def sample(self):
+        rasie("No Implementation!")
+
+    def probability(self, value):
+        raise ("No Implementation!")
+
+
+class LogUniformSampler(Sampler):
+    def __init__(self, range, seed):
+        super(LogUniformSampler, self).__init__(range, seed)
+        self.log_range_ = np.log(self.range_ + 1)
+
+    def sample(self):
+        value = int(np.exp(np.random.uniform(0.0, self.log_range_)) - 1)
+        return value % self.range_
+
+    def probability(self, value):
+        return np.log((value + 2.0) / (value + 1.0)) / self.log_range_
+
+
+def adjust_prob(prob, num_samples, num_tries):
+    if num_samples == num_tries:
+        return prob * num_samples
+    else:
+        return -np.expm1(num_tries * np.log1p(-prob))
+
+
+def take_along_axis1(array, index):
+    out = np.zeros_like(index, dtype=array.dtype)
+    n_row, n_col = index.shape
+    for i in range(n_row):
+        for j in range(n_col):
+            out[i, j] = array[i, index[i, j]]
+    return out
+
+
+def sample_prob(sampler, num_samples, label):
+    batch_size, num_true = label.shape
+    num_sampled_classes = num_samples + num_true
+
+    samples = np.zeros((batch_size, num_sampled_classes), dtype=np.int64)
+    probabilities = np.zeros(
+        (batch_size, num_sampled_classes), dtype=np.float64)
+
+    tmp_samples = set()
+    num_tries = 0
+    j = 0
+    while j < num_true:
+        for i in range(batch_size):
+            samples[i, j] = label[i, j]
+            probabilities[i, j] = sampler.probability(label[i, j])
+        j += 1
+    while j < num_sampled_classes:
+        v = sampler.sample()
+        num_tries += 1
+        if v not in tmp_samples:
+            tmp_samples.add(v)
+            for i in range(batch_size):
+                samples[i, j] = v
+                probabilities[i, j] = sampler.probability(v)
+            j += 1
+    for k in range(num_sampled_classes):
+        for i in range(batch_size):
+            probabilities[i, k] = adjust_prob(probabilities[i, k], num_samples,
+                                              num_tries)
+    return (samples, probabilities)
+
+
+def compute_remove_accidental_hits(sampled_logits, samples, num_true):
+    batch_size, num_sampled_classes = samples.shape
+    for i in range(batch_size):
+        true_labels = set(samples[i, np.arange(num_true)])
+        for j in range(num_true, num_sampled_classes):
+            if samples[i, j] in true_labels:
+                sampled_logits[i, j] -= 1e20
+
+
+def sample_logits(logits,
+                  label,
+                  num_samples,
+                  seed,
+                  remove_accidental_hits,
+                  use_custom_samples,
+                  custom_samples=None,
+                  custom_probabilities=None):
+    batch_size, num_classes = logits.shape
+    num_true = label.shape[1]
+    num_sampled_classes = num_true + num_samples
+
+    if use_custom_samples:
+        samples = custom_samples
+        probabilities = custom_probabilities
+    else:
+        sampler = LogUniformSampler(num_classes, seed)
+        samples, probabilities = sample_prob(sampler, num_samples, label)
+    sampled_logits = take_along_axis1(logits, samples)
+
+    #print(samples)
+    #print(probabilities)
+    #print(sampled_logits)
+    if remove_accidental_hits:
+        compute_remove_accidental_hits(sampled_logits, samples, num_true)
+    sampled_logits -= np.log(probabilities)
+    sampled_label = np.tile(np.arange(num_true), (batch_size, 1))
+    return (sampled_logits, samples, sampled_label, probabilities)
+
+
+class TestSampleLogitsOp(OpTest):
+    '''
+    Test SampleLogitsOp, but with random results precomputed
+    in python and just test the non-random part.
+    '''
+
+    def generate_data(self, logits, label, num_samples, seed,
+                      remove_accidental_hits, use_custom_samples,
+                      custom_samples, custom_probabilities):
+        self.attrs = {
+            'num_samples': num_samples,
+            'use_custom_samples': use_custom_samples,
+            'remove_accidental_hits': remove_accidental_hits,
+            'seed': seed
+        }
+        self.inputs = {
+            'Logits': logits,
+            'Label': label,
+            'CustomSamples': custom_samples,
+            'CustomProbabilities': custom_probabilities
+        }
+
+    def set_data(self, batch_size, num_classes, num_true, num_samples, seed,
+                 remove_accidental_hits):
+        logits = np.random.randn(batch_size, num_classes)
+        label = np.stack([
+            np.random.choice(
+                range(0, num_classes), num_true, replace=False)
+            for _ in range(batch_size)
+        ])
+        sampler = LogUniformSampler(num_classes, seed)
+        custom_samples, custom_probabilities = \
+            sample_prob(sampler, num_samples, label)
+        use_custom_samples = True
+        remove_accidental_hits = remove_accidental_hits
+        self.generate_data(logits, label, num_samples, seed,
+                           remove_accidental_hits, use_custom_samples,
+                           custom_samples, custom_probabilities)
+
+    def compute(self):
+        out = sample_logits(self.inputs["Logits"], self.inputs["Label"],
+                            self.attrs["num_samples"], self.attrs["seed"],
+                            self.attrs["remove_accidental_hits"],
+                            self.attrs["use_custom_samples"],
+                            self.inputs["CustomSamples"],
+                            self.inputs["CustomProbabilities"])
+
+        self.outputs = {
+            'SampledLogits': out[0],
+            'Samples': out[1],
+            'SampledLabel': out[2],
+            'Probabilities': out[3]
+        }
+
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        batch_size = 5
+        num_classes = 20
+        num_true = 5
+        num_samples = 10
+        seed = 10
+        remove_accidental_hits = True
+        self.set_data(batch_size, num_classes, num_true, num_samples, seed,
+                      remove_accidental_hits)
+        self.compute()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        pass
+        self.check_grad(
+            ["Logits"], ["SampledLogits", "Samples"], max_relative_error=0.02)
+
+
+class TestSampleLogitsOp2(TestSampleLogitsOp):
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        batch_size = 5
+        num_classes = 20
+        num_true = 5
+        num_samples = 10
+        seed = 10
+        remove_accidental_hits = False
+        self.set_data(batch_size, num_classes, num_true, num_samples, seed,
+                      remove_accidental_hits)
+        self.compute()
+
+
+class TestSampleLogitsOp3(TestSampleLogitsOp):
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        batch_size = 5
+        num_classes = 100
+        num_true = 5
+        num_samples = 25
+        seed = 10
+        remove_accidental_hits = True
+        self.set_data(batch_size, num_classes, num_true, num_samples, seed,
+                      remove_accidental_hits)
+        self.compute()
+
+
+class TestSampleLogitsOp4(TestSampleLogitsOp):
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        batch_size = 5
+        num_classes = 100
+        num_true = 5
+        num_samples = 25
+        seed = 10
+        remove_accidental_hits = False
+        self.set_data(batch_size, num_classes, num_true, num_samples, seed,
+                      remove_accidental_hits)
+        self.compute()
+
+
+class TestSampleLogitsOpV2(OpTest):
+    '''
+    Test SampleLogitsOp, but with random results precomputed
+    in C++ and copied to python and just test the non-random part.
+    '''
+
+    def generate_data(self, logits, label, num_samples, seed,
+                      remove_accidental_hits, use_custom_samples):
+        self.attrs = {
+            'num_samples': num_samples,
+            'use_custom_samples': use_custom_samples,
+            'remove_accidental_hits': remove_accidental_hits,
+            'seed': seed
+        }
+        self.inputs = {'Logits': logits, 'Label': label}
+
+    def set_data(self, num_classes, num_samples, seed, remove_accidental_hits):
+        label = np.array([[6, 12, 15, 5, 1], [0, 9, 4, 1, 10],
+                          [0, 2, 10, 16, 13], [14, 4, 7, 2, 1],
+                          [3, 18, 11, 8, 14]])
+        batch_size, num_true = label.shape
+        use_custom_samples = False
+
+        num_sampled_classes = num_samples + num_true
+        logits = np.random.randn(batch_size, num_classes)
+
+        remove_accidental_hits = remove_accidental_hits
+        self.generate_data(logits, label, num_samples, seed,
+                           remove_accidental_hits, use_custom_samples)
+
+        # python and c++ use different random generator
+        # use fetched samples from c++ for python code
+        self.fetched_samples = np.array(
+            [[6, 12, 15, 5, 1, 5, 15, 1, 0, 8, 3, 14, 2, 13, 4],
+             [0, 9, 4, 1, 10, 5, 15, 1, 0, 8, 3, 14, 2, 13, 4],
+             [0, 2, 10, 16, 13, 5, 15, 1, 0, 8, 3, 14, 2, 13, 4],
+             [14, 4, 7, 2, 1, 5, 15, 1, 0, 8, 3, 14, 2, 13, 4],
+             [3, 18, 11, 8, 14, 5, 15, 1, 0, 8, 3, 14, 2, 13, 4]])
+        fectched_num_tries = 21
+
+        probabilities = np.zeros(
+            (batch_size, num_sampled_classes), dtype=np.float64)
+
+        sampler = LogUniformSampler(num_classes, seed)
+        for j in range(num_sampled_classes):
+            for i in range(batch_size):
+                probabilities[i, j] = sampler.probability(self.fetched_samples[
+                    i, j])
+                probabilities[i, j] = adjust_prob(
+                    probabilities[i, j], num_samples, fectched_num_tries)
+        self.probabilities = probabilities
+
+    def compute(self):
+        out = sample_logits(self.inputs["Logits"], self.inputs["Label"],
+                            self.attrs["num_samples"], self.attrs["seed"],
+                            self.attrs["remove_accidental_hits"], True,
+                            self.fetched_samples, self.probabilities)
+        self.outputs = {
+            'SampledLogits': out[0],
+            'Samples': out[1],
+            'SampledLabel': out[2],
+            'Probabilities': out[3]
+        }
+
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        num_samples = 10
+        num_classes = 20
+        seed = 10
+        remove_accidental_hits = True
+
+        self.set_data(num_classes, num_samples, seed, remove_accidental_hits)
+        self.compute()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        pass
+        self.check_grad(
+            ["Logits"], ["SampledLogits", "Samples"], max_relative_error=0.02)
+
+
+class TestSampleLogitsOpV3(OpTest):
+    '''
+    Test SampleLogitsOp, but with random results precomputed
+    in C++ and copied to python and just test the non-random part.
+    '''
+
+    def generate_data(self, logits, label, num_samples, seed,
+                      remove_accidental_hits, use_custom_samples):
+        self.attrs = {
+            'num_samples': num_samples,
+            'use_custom_samples': use_custom_samples,
+            'remove_accidental_hits': remove_accidental_hits,
+            'seed': seed
+        }
+        self.inputs = {'Logits': logits, 'Label': label}
+
+    def set_data(self, num_classes, num_samples, seed, remove_accidental_hits):
+        self.fetched_samples = np.array([[
+            52,
+            3,
+            12,
+            74,
+            28,
+            1,
+            79,
+            2,
+            42,
+            8,
+            13,
+            0,
+            18,
+            88,
+            49,
+            14,
+            46,
+            39,
+            57,
+            26,
+            75,
+            9,
+            50,
+            16,
+            66,
+            6,
+            23,
+            5,
+            11,
+            17,
+            54,
+            35,
+            20,
+            53,
+            10,
+            47,
+            80,
+            38,
+            7,
+            4,
+            31,
+            15,
+            19,
+            58,
+            22,
+            34,
+            41,
+            73,
+            62,
+            95,
+            25,
+            70,
+            37,
+            30,
+            65,
+            27,
+            51,
+            43,
+            32,
+            99,
+            21,
+            56,
+            29,
+            40,
+            69,
+            55,
+            98,
+            77,
+            67,
+            33,
+            89,
+            63,
+            81,
+            59,
+            48,
+            91,
+            68,
+            72,
+            61,
+            52,
+            86,
+        ], [
+            2,
+            3,
+            12,
+            74,
+            28,
+            1,
+            79,
+            2,
+            42,
+            8,
+            13,
+            0,
+            18,
+            88,
+            49,
+            14,
+            46,
+            39,
+            57,
+            26,
+            75,
+            9,
+            50,
+            16,
+            66,
+            6,
+            23,
+            5,
+            11,
+            17,
+            54,
+            35,
+            20,
+            53,
+            10,
+            47,
+            80,
+            38,
+            7,
+            4,
+            31,
+            15,
+            19,
+            58,
+            22,
+            34,
+            41,
+            73,
+            62,
+            95,
+            25,
+            70,
+            37,
+            30,
+            65,
+            27,
+            51,
+            43,
+            32,
+            99,
+            21,
+            56,
+            29,
+            40,
+            69,
+            55,
+            98,
+            77,
+            67,
+            33,
+            89,
+            63,
+            81,
+            59,
+            48,
+            91,
+            68,
+            72,
+            61,
+            52,
+            86,
+        ], [
+            2,
+            3,
+            12,
+            74,
+            28,
+            1,
+            79,
+            2,
+            42,
+            8,
+            13,
+            0,
+            18,
+            88,
+            49,
+            14,
+            46,
+            39,
+            57,
+            26,
+            75,
+            9,
+            50,
+            16,
+            66,
+            6,
+            23,
+            5,
+            11,
+            17,
+            54,
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+            59,
+            48,
+            91,
+            68,
+            72,
+            61,
+            52,
+            86,
+        ]])
+        fectched_num_tries = 323
+
+        label = self.fetched_samples[:, 0:1]
+        batch_size, num_true = label.shape
+        use_custom_samples = False
+
+        #import pdb; pdb.set_trace()
+        num_sampled_classes = num_samples + num_true
+        logits = np.random.randn(batch_size, num_classes)
+
+        remove_accidental_hits = remove_accidental_hits
+        self.generate_data(logits, label, num_samples, seed,
+                           remove_accidental_hits, use_custom_samples)
+
+        # python and c++ use different random generator
+        # use fetched samples from c++ for python code
+        probabilities = np.zeros(
+            (batch_size, num_sampled_classes), dtype=np.float64)
+
+        sampler = LogUniformSampler(num_classes, seed)
+        for j in range(num_sampled_classes):
+            for i in range(batch_size):
+                probabilities[i, j] = sampler.probability(self.fetched_samples[
+                    i, j])
+                probabilities[i, j] = adjust_prob(
+                    probabilities[i, j], num_samples, fectched_num_tries)
+        self.probabilities = probabilities
+
+    def compute(self):
+        out = sample_logits(self.inputs["Logits"], self.inputs["Label"],
+                            self.attrs["num_samples"], self.attrs["seed"],
+                            self.attrs["remove_accidental_hits"], True,
+                            self.fetched_samples, self.probabilities)
+        self.outputs = {
+            'SampledLogits': out[0],
+            'Samples': out[1],
+            'SampledLabel': out[2],
+            'Probabilities': out[3]
+        }
+
+    def setUp(self):
+        self.op_type = 'sample_logits'
+        num_samples = 80
+        num_classes = 100
+        seed = 123
+        remove_accidental_hits = True
+
+        self.set_data(num_classes, num_samples, seed, remove_accidental_hits)
+        self.compute()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        pass
+        self.check_grad(
+            ["Logits"], ["SampledLogits", "Samples"], max_relative_error=0.02)
+
+
+if __name__ == '__main__':
+    unittest.main()

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])