add ContextProjectionBackward, ContextProjectionBackwardData, ContextProjectionBackwardWeightw

paddle/function/CMakeLists.txt

@@ -19,7 +19,6 @@ if(WITH_TESTING)
     add_simple_unittest(CrossMapNormalOpTest)
     add_unittest(ContextProjectionOpTest
         ContextProjectionOpTest.cpp
-        ContextProjectionOpGpu.cu
         ../gserver/tests/TestUtil.cpp)
 endif()
 endif()

paddle/function/context_projection_op.cpp

@@ -41,7 +41,7 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(Tensor& output,
       !weight.getData()
           ? nullptr
           : std::make_shared<CpuMatrix>(
-                weight.getData(), weight.dims_[0], input.dims_[1]);
+                weight.getData(), weight.dims_[0], weight.dims_[1]);
   CpuIVector seq_vec(sequence.dims_[0],
                      reinterpret_cast<int*>(sequence.getData()));
   CHECK_EQ(out_mat->getWidth(), in_mat->getWidth() * context_length);
@@ -125,12 +125,207 @@ private:
   bool is_padding_;
 };
 
+template <>
+void ContextProjectionBackward<DEVICE_TYPE_CPU>(Tensor& out_grad,
+                                                const Tensor& in_grad,
+                                                const Tensor& w_grad,
+                                                const Tensor& sequence,
+                                                size_t context_length,
+                                                int context_start,
+                                                size_t begin_pad,
+                                                bool is_padding) {
+  CHECK(out_grad.getData() && sequence.getData());
+  CHECK_EQ(out_grad.dims_.size(), 2);
+  CHECK_EQ(in_grad.dims_.size(), 2);
+  CHECK_EQ(w_grad.dims_.size(), 2);
+  CHECK_EQ(sequence.dims_.size(), 1);
+
+  auto out_grad_mat = std::make_shared<CpuMatrix>(
+      out_grad.getData(), out_grad.dims_[0], out_grad.dims_[1]);
+  const auto in_grad_mat =
+      !in_grad.getData()
+          ? nullptr
+          : std::make_shared<CpuMatrix>(
+                in_grad.getData(), in_grad.dims_[0], in_grad.dims_[1]);
+  const auto w_grad_mat =
+      !w_grad.getData()
+          ? nullptr
+          : std::make_shared<CpuMatrix>(
+                w_grad.getData(), w_grad.dims_[0], w_grad.dims_[1]);
+  CpuIVector seq_vec(sequence.dims_[0],
+                     reinterpret_cast<int*>(sequence.getData()));
+  CHECK_EQ(out_grad_mat->getWidth(), in_grad_mat->getWidth() * context_length);
+
+  size_t input_dim = in_grad_mat ? in_grad_mat->getWidth()
+                                 : w_grad_mat ? w_grad_mat->getWidth() : 0;
+  CHECK_EQ(out_grad_mat->getWidth(), input_dim * context_length);
+
+  const int* starts = seq_vec.getData();
+  size_t num_sequences = seq_vec.getSize() - 1;
+  for (size_t i = 0; i < num_sequences; ++i) {
+    for (size_t j = 0; j < context_length; ++j) {
+      int begin = starts[i] + context_start + j;
+      int end = starts[i + 1] + context_start + j;
+      int dst_begin = starts[i];
+      int dst_end = starts[i + 1];
+      if (begin < starts[i]) {
+        int64_t pad_size =
+            std::min(starts[i] - begin, starts[i + 1] - starts[i]);
+        if (is_padding && w_grad_mat) {
+          MatrixPtr mat = out_grad_mat->subMatrix(starts[i], pad_size);
+          MatrixPtr sub = w_grad_mat->subMatrix(j, pad_size);
+          sub->addAtOffset(*mat, j * input_dim);
+        }
+        dst_begin = starts[i] + pad_size;
+        begin = starts[i];
+      }
+      if (end > starts[i + 1]) {
+        int64_t pad_size =
+            std::min(end - starts[i + 1], starts[i + 1] - starts[i]);
+        if (is_padding && w_grad_mat) {
+          MatrixPtr mat =
+              out_grad_mat->subMatrix(starts[i + 1] - pad_size, pad_size);
+          MatrixPtr sub = w_grad_mat->subMatrix(
+              begin_pad + context_start + j - pad_size, pad_size);
+          sub->addAtOffset(*mat, j * input_dim);
+        }
+        dst_end = starts[i + 1] - pad_size;
+        end = starts[i + 1];
+      }
+      if (end <= begin) continue;
+      if (!in_grad_mat) continue;
+      MatrixPtr src = in_grad_mat->subMatrix(begin, end - begin);
+      MatrixPtr dst = out_grad_mat->subMatrix(dst_begin, dst_end - dst_begin);
+      src->addAtOffset(*dst, j * input_dim);
+    }
+  }
+}
+
+/**
+ * \param inputs[0] input value.
+ * \param inputs[1] input weight.
+ * \param inputs[2] input sequence.
+ * \param outputs[0] output value.
+ */
+template <DeviceType Device>
+class ContextProjectionBackwardFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    context_length_ = config.get<size_t>("context_length");
+    context_start_ = config.get<int>("context_start");
+    begin_pad_ = config.get<size_t>("begin_pad");
+    is_padding_ = config.get<bool>("is_padding");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(3, inputs.size());
+    CHECK_EQ(1, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    ContextProjectionBackward<Device>((Tensor&)outputs[0],
+                                      inputs[0],
+                                      inputs[1],
+                                      inputs[2],
+                                      context_length_,
+                                      context_start_,
+                                      begin_pad_,
+                                      is_padding_);
+  }
+
+private:
+  size_t context_length_;
+  int context_start_;
+  size_t begin_pad_;
+  bool is_padding_;
+};
+
+/**
+ * \param inputs[0] input grad.
+ * \param inputs[1] input sequence.
+ * \param outputs[0] output grad.
+ */
+template <DeviceType Device>
+class ContextProjectionBackwardDataFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    context_length_ = config.get<size_t>("context_length");
+    context_start_ = config.get<int>("context_start");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(2, inputs.size());
+    CHECK_EQ(1, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    ContextProjectionBackwardData<Device>((Tensor&)outputs[0],
+                                          (Tensor&)inputs[0],
+                                          inputs[1],
+                                          context_length_,
+                                          context_start_);
+  }
+
+private:
+  size_t context_length_;
+  int context_start_;
+};
+
+/**
+ * \param inputs[0] weight grad.
+ * \param inputs[1] input sequence.
+ * \param outputs[0] output grad.
+ */
+template <DeviceType Device>
+class ContextProjectionBackwardWeightFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    context_length_ = config.get<size_t>("context_length");
+    context_start_ = config.get<int>("context_start");
+    begin_pad_ = config.get<size_t>("begin_pad");
+    total_pad_ = config.get<size_t>("total_pad");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(2, inputs.size());
+    CHECK_EQ(1, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    ContextProjectionBackwardWeight<Device>((Tensor&)outputs[0],
+                                            (Tensor&)inputs[0],
+                                            inputs[1],
+                                            context_length_,
+                                            context_start_,
+                                            total_pad_,
+                                            begin_pad_);
+  }
+
+private:
+  size_t context_length_;
+  int context_start_;
+  size_t begin_pad_;
+  size_t total_pad_;
+};
+
 REGISTER_TYPED_FUNC(ContextProjectionForward,
                     CPU,
                     ContextProjectionForwardFunc);
+REGISTER_TYPED_FUNC(ContextProjectionBackward,
+                    CPU,
+                    ContextProjectionBackwardFunc);
 #ifndef PADDLE_ONLY_CPU
 REGISTER_TYPED_FUNC(ContextProjectionForward,
                     GPU,
                     ContextProjectionForwardFunc);
+REGISTER_TYPED_FUNC(ContextProjectionBackwardData,
+                    GPU,
+                    ContextProjectionBackwardDataFunc);
+REGISTER_TYPED_FUNC(ContextProjectionBackwardWeight,
+                    GPU,
+                    ContextProjectionBackwardWeightFunc);
 #endif
 }  // namespace paddle

paddle/function/context_projection_op.h

@@ -25,9 +25,10 @@ namespace paddle {
  * \param[in]   input             input data.
  * \param[in]   weight            input weight.
  * \param[in]   sequence          input data.
- * \param[in]   context_length     consecutive rows for concatenation.
- * \param[in]   begin_pad          context start position.
- * \param[in]   is_padding         whether padding 0 or not.
+ * \param[in]   context_length    consecutive rows for concatenation.
+ * \param[in]   context_start     context start position.
+ * \param[in]   begin_pad         begining pad position.
+ * \param[in]   is_padding        whether padding 0 or not.
  *
  */
 template <DeviceType Device>
@@ -40,4 +41,43 @@ void ContextProjectionForward(Tensor& output,
                               size_t begin_pad,
                               bool is_padding);
 
+/**
+ * \brief   Context Projection Backward.
+ *
+ * \param[out]  outputs           output gradient.
+ * \param[in]   input             input gradient.
+ * \param[in]   weight            input weight gradient.
+ * \param[in]   sequence          input data.
+ * \param[in]   context_length    consecutive rows for concatenation.
+ * \param[in]   context_start     context start position.
+ * \param[in]   begin_pad         begining pad position.
+ * \param[in]   is_padding        whether padding 0 or not.
+ *
+ */
+template <DeviceType Device>
+void ContextProjectionBackward(Tensor& out_grad,
+                               const Tensor& in_grad,
+                               const Tensor& w_grad,
+                               const Tensor& sequence,
+                               size_t context_length,
+                               int context_start,
+                               size_t begin_pad,
+                               bool is_padding);
+
+template <DeviceType Device>
+void ContextProjectionBackwardData(Tensor& out_grad,
+                                   Tensor& in_grad,
+                                   const Tensor& sequence,
+                                   size_t context_length,
+                                   int context_start);
+
+template <DeviceType Device>
+void ContextProjectionBackwardWeight(Tensor& out_grad,
+                                     Tensor& w_grad,
+                                     const Tensor& sequence,
+                                     size_t context_length,
+                                     int context_start,
+                                     size_t total_pad,
+                                     size_t begin_pad);
+
 }  // namespace paddle

paddle/function/context_projection_op_gpu.cu

@@ -134,4 +134,214 @@ void ContextProjectionForward<DEVICE_TYPE_GPU>(Tensor& output,
                                 is_padding);
 }
 
+__global__ void KeContextProjectionBackwardData(real* out_grad,
+                                                const int* sequence,
+                                                real* in_grad,
+                                                int input_dim,
+                                                int context_length,
+                                                int context_start) {
+  int idx = threadIdx.x;
+  int block_size = blockDim.x;
+  int sequenceId = blockIdx.x;
+  int seq_start = sequence[sequenceId];
+  int seq_end = sequence[sequenceId+1];
+  real value = 0;
+
+  int instances = seq_end - seq_start + context_length - 1;
+  out_grad += seq_start * input_dim * context_length;
+  in_grad += seq_start * input_dim;
+  for (int k = 0; k <= input_dim / block_size; k++) {
+    if (idx < input_dim) {
+      for (int i = 0; i < instances; i++) {
+        if ((i + context_start) < 0) {
+          continue;
+        } else if ((i + context_start) >= (seq_end - seq_start)) {
+          continue;
+        } else {
+          // value = 0;
+          value = in_grad[(i + context_start) * input_dim + idx];
+        }
+
+        int outx = (i - context_length) < 0 ? i : (context_length - 1);
+        int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1));
+        real* output_r =
+          out_grad + outy * input_dim * context_length + outx * input_dim;
+        for (int j = outy; j < seq_end - seq_start; j++) {
+          value += output_r[idx];
+          if (j - outy == outx) break;
+          output_r += (context_length - 1) * input_dim;
+        }
+        in_grad[(i + context_start) * input_dim + idx] = value;
+      }
+    }
+    idx += block_size;
+  }
+}
+
+void hl_context_projection_backward_data(real* out_grad,
+                                         const int* sequence,
+                                         real* input_grad,
+                                         int num_sequences,
+                                         int input_dim,
+                                         int context_length,
+                                         int context_start) {
+  CHECK_NOTNULL(out_grad);
+  CHECK_NOTNULL(sequence);
+  CHECK_NOTNULL(input_grad);
+
+  int block_size = 128;
+  int blocks_x = num_sequences;
+  int blocks_y = 1;
+  dim3 threads(block_size, 1);
+  dim3 grid(blocks_x, blocks_y);
+  KeContextProjectionBackwardData<<< grid, threads, 0, STREAM_DEFAULT >>>
+    (out_grad, sequence, input_grad, input_dim, context_length, context_start);
+  CHECK_SYNC("hl_context_projection_backward_data failed");
+}
+
+template <>
+void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(Tensor& out_grad,
+                                               Tensor& in_grad,
+                                               const Tensor& sequence,
+                                               size_t context_length,
+                                               int context_start) {
+  CHECK(in_grad.getData() && out_grad.getData() && sequence.getData());
+  CHECK_EQ(out_grad.dims_.size(), 2);
+  CHECK_EQ(in_grad.dims_.size(), 2);
+  CHECK_EQ(sequence.dims_.size(), 1);
+  CHECK_EQ(out_grad.dims_[1], in_grad.dims_[1] * context_length);
+
+  hl_context_projection_backward_data(out_grad.getData(),
+                reinterpret_cast<int*>(sequence.getData()),
+                                      in_grad.getData(),
+                                      sequence.dims_[0] - 1,
+                                      in_grad.dims_[1],
+                                      context_length,
+                                      context_start);
+}
+
+template<int THREADS_X, int THREADS_Y>
+__global__ void KeContextProjectionBackwardWeight(real* out_grad,
+                                                  const int* sequence,
+                                                  real* w_grad,
+                                                  int num_sequences,
+                                                  int w_dim,
+                                                  int context_length,
+                                                  int context_start,
+                                                  int begin_pad) {
+  __shared__ real sum_s[THREADS_Y][THREADS_X];
+  int pad_of_block = (w_dim + THREADS_X - 1) / THREADS_X;
+  const int idx = threadIdx.x;
+  const int idy = threadIdx.y;
+  int padId = blockIdx.x / pad_of_block;
+  int weight_idx = idx + THREADS_X * (blockIdx.x % pad_of_block);
+  int instanceId;
+  real value = 0;
+  real* output_r;
+
+  sum_s[idy][idx] = 0.0f;
+  if (weight_idx < w_dim) {
+    for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) {
+      int seq_start = sequence[seqId];
+      int seq_end = sequence[seqId+1];
+      output_r = out_grad + seq_start * w_dim * context_length;
+
+      if (context_start < 0) {
+        if (padId + context_start < 0) {
+          instanceId = padId;
+        } else {
+          // begin_pad > 0;
+          instanceId = (padId - begin_pad) +
+            (seq_end - seq_start) - context_start;
+        }
+      } else {
+        if (padId + (seq_end - seq_start) < context_start) {
+          continue;
+        } else {
+          // begin_pad == 0;
+          instanceId = padId + (seq_end - seq_start) - context_start;
+        }
+      }
+
+      int outx = (instanceId - context_length) < 0 ?
+                 instanceId : (context_length - 1);
+      int outy = (instanceId - context_length) < 0 ?
+                 0 : (instanceId - (context_length - 1));
+      output_r += outy * w_dim * context_length + outx * w_dim;
+      for (int j = outy; j < seq_end - seq_start; j++) {
+        value += output_r[weight_idx];
+        if (j - outy == outx) break;
+        output_r += (context_length - 1) * w_dim;
+      }
+    }
+    sum_s[idy][idx] = value;
+  }
+  __syncthreads();
+
+  for (int stride = THREADS_Y/2; stride > 0; stride = stride/2) {
+    if (idy < stride) {
+      sum_s[idy][idx] += sum_s[idy + stride][idx];
+    }
+    __syncthreads();
+  }
+  __syncthreads();
+
+  if (weight_idx < w_dim) {
+    if (idy == 0) {
+      w_grad[padId * w_dim + weight_idx] += sum_s[0][idx];
+    }
+  }
+}
+
+void hl_context_projection_backward_weight(real* out_grad,
+                                           const int* sequence,
+                                           real* w_grad,
+                                           int num_sequences,
+                                           int w_dim,
+                                           size_t total_pad,
+                                           int context_length,
+                                           int context_start,
+                                           int begin_pad) {
+  CHECK_NOTNULL(out_grad);
+  CHECK_NOTNULL(sequence);
+  CHECK_NOTNULL(w_grad);
+
+  int threads_x = 32;
+  int threads_y = 32;
+  int blocks_x = total_pad * ((w_dim + threads_x - 1) / threads_x);
+  dim3 threads(threads_x, threads_y);
+  dim3 grid(blocks_x, 1);
+
+  KeContextProjectionBackwardWeight<32, 32>
+    <<< grid, threads, 0, STREAM_DEFAULT >>>
+    (out_grad, sequence, w_grad, num_sequences, w_dim,
+     context_length, context_start, begin_pad);
+  CHECK_SYNC("hl_context_projection_backward_weight failed");
+}
+
+template <>
+void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(Tensor& out_grad,
+                                                      Tensor& w_grad,
+                                                      const Tensor& sequence,
+                                                      size_t context_length,
+                                                      int context_start,
+                                                      size_t total_pad,
+                                                      size_t begin_pad) {
+  CHECK(w_grad.getData() && out_grad.getData());
+  CHECK_EQ(out_grad.dims_.size(), 2);
+  CHECK_EQ(w_grad.dims_.size(), 2);
+  CHECK_EQ(sequence.dims_.size(), 1);
+  CHECK_EQ(out_grad.dims_[1], w_grad.dims_[1] * context_length);
+
+  hl_context_projection_backward_weight(out_grad.getData(),
+                    reinterpret_cast<int*>(sequence.getData()),
+                                        w_grad.getData(),
+                                        sequence.dims_[0] - 1,
+                                        w_grad.dims_[1],
+                                        total_pad,
+                                        context_length,
+                                        context_start,
+                                        begin_pad);
+}
+
 }  // namespace paddle

paddle/function/context_projection_op_test.cpp

@@ -77,7 +77,100 @@ void testMatrixProjectionForward(int context_start,
   autotest::TensorCheckEqual(cpu_out, gpu_out);
 }
 
-TEST(ContextProjectionForward, projection) {
+void testMatrixProjectionBackward(int context_start,
+                                  int context_length,
+                                  bool is_padding,
+                                  size_t batch_size,
+                                  size_t input_dim) {
+  size_t pad = std::max(0, -context_start) +
+               std::max(0, (int)(context_start + context_length - 1));
+  if (pad == 0) is_padding = false;
+
+  std::shared_ptr<FunctionBase> cpu_func(
+      FunctionBase::funcRegistrar_.createByType(
+          "ContextProjectionBackward-CPU"));
+  FuncConfig cpu_config;
+  cpu_config.set("context_length", context_length)
+      .set("context_start", context_start)
+      .set("begin_pad", std::max(0, -context_start))
+      .set("is_padding", is_padding);
+  cpu_func->init(cpu_config);
+
+  std::shared_ptr<FunctionBase> gpu_data_func(
+      FunctionBase::funcRegistrar_.createByType(
+          "ContextProjectionBackwardData-GPU"));
+  FuncConfig gpu_data_config;
+  gpu_data_config.set("context_length", context_length)
+      .set("context_start", context_start);
+  gpu_data_func->init(gpu_data_config);
+
+  std::shared_ptr<FunctionBase> gpu_w_func(
+      FunctionBase::funcRegistrar_.createByType(
+          "ContextProjectionBackwardWeight-GPU"));
+  FuncConfig gpu_w_config;
+  gpu_w_config.set("context_length", context_length)
+      .set("context_start", context_start)
+      .set("begin_pad", std::max(0, -context_start))
+      .set("total_pad", pad);
+  gpu_w_func->init(gpu_w_config);
+
+  CpuMatrix cpu_in_grad(batch_size, input_dim);
+  cpu_in_grad.randomizeUniform();
+  GpuMatrix gpu_in_grad(batch_size, input_dim);
+  gpu_in_grad.copyFrom(cpu_in_grad);
+
+  CpuMatrix cpu_out_grad(batch_size, input_dim * context_length);
+  cpu_out_grad.randomizeUniform();
+  GpuMatrix gpu_out_grad(batch_size, input_dim * context_length);
+  gpu_out_grad.copyFrom(cpu_out_grad);
+
+  IVectorPtr cpu_seq;
+  generateSequenceStartPositions(batch_size, cpu_seq);
+  IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true);
+  gpu_seq->copyFrom(*cpu_seq);
+
+  auto cpu_w_grad =
+      is_padding ? std::make_shared<CpuMatrix>(pad, input_dim) : nullptr;
+  auto gpu_w_grad =
+      is_padding ? std::make_shared<GpuMatrix>(pad, input_dim) : nullptr;
+  if (is_padding) {
+    cpu_w_grad->randomizeUniform();
+    gpu_w_grad->copyFrom(*cpu_w_grad);
+  }
+
+  cpu_func->calc({Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}),
+                  Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr,
+                         Dims{pad, input_dim}),
+                  Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
+                         Dims{cpu_seq->getSize()})},
+                 {Tensor(cpu_out_grad.getData(),
+                         Dims{batch_size, input_dim * context_length})},
+                 {});
+
+  gpu_data_func->calc(
+      {Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}),
+       Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
+              Dims{gpu_seq->getSize()})},
+      {Tensor(gpu_out_grad.getData(),
+              Dims{batch_size, input_dim * context_length})},
+      {});
+
+  if (is_padding && gpu_w_grad) {
+    gpu_w_func->calc({Tensor(gpu_w_grad->getData(), Dims{pad, input_dim}),
+                      Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
+                             Dims{gpu_seq->getSize()})},
+                     {Tensor(gpu_out_grad.getData(),
+                             Dims{batch_size, input_dim * context_length})},
+                     {});
+  }
+
+  autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad);
+  if (is_padding) {
+    autotest::TensorCheckErr(*cpu_w_grad, *gpu_w_grad);
+  }
+}
+
+TEST(ContextProjection, projection) {
   for (auto context_start : {-5, -3, -1, 0, 3}) {
     for (auto context_length : {1, 2, 5, 7}) {
       for (auto trainable_padding : {false, true}) {
@@ -93,6 +186,11 @@ TEST(ContextProjectionForward, projection) {
                                         trainable_padding,
                                         batch_size,
                                         input_dim);
+            testMatrixProjectionBackward(context_start,
+                                         context_length,
+                                         trainable_padding,
+                                         batch_size,
+                                         input_dim);
           }
         }
       }