Complete sequence_pad_op and its CPU kernel. Add unittests

paddle/fluid/operators/math/sequence_padding.cc

@@ -70,9 +70,10 @@ class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
                   std::vector<T> pad_value = {0}, int pad_seq_len = -1,
                   int lod_level = 0, bool norm_by_times = false,
                   const PadLayout layout = kBatchLengthWidth) {
-    auto seq_offsets = framework::ToAbsOffset(seq_tensor.lod())[lod_level];
-    auto seq_tensor_dims = seq_tensor.dims();
-    auto pad_tensor_dims = pad_tensor->dims();
+    auto seq_lod = seq_tensor.lod();
+    const auto seq_offsets = framework::ToAbsOffset(seq_lod)[lod_level];
+    const auto& seq_tensor_dims = seq_tensor.dims();
+    const auto& pad_tensor_dims = pad_tensor->dims();
     if (pad_seq_len == -1) {
       pad_seq_len = MaximumSequenceLength(seq_offsets);
     }
@@ -91,12 +92,21 @@ class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
 
     // fill padding value
     T* pad_data = pad_tensor->data<T>();
-    for (int i = 0; i < pad_tensor->numel() / step_width; ++i) {
-      memcpy(pad_data, pad_value.data(), step_width * sizeof(T));
+    for (int i = 0; i < pad_tensor->numel(); i += step_width) {
+      memcpy(pad_data + i, pad_value.data(), step_width * sizeof(T));
     }
 
     CopyValidData<T>(pad_tensor, &seq_tensor, seq_offsets, pad_seq_len,
                      step_width, norm_by_times, kSeqToPad, layout);
+
+    // Set pad_tensor's lod info if possible
+    if (layout == kBatchLengthWidth) {
+      framework::LoD pad_lod(seq_lod.begin() + lod_level, seq_lod.end());
+      for (size_t i = 0; i < pad_lod[0].size(); ++i) {
+        pad_lod[0][i] = i * pad_seq_len;
+      }
+      pad_tensor->set_lod(pad_lod);
+    }
   }
 };
 
@@ -109,8 +119,8 @@ class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
                   int lod_level = 0, bool norm_by_times = false,
                   const PadLayout& layout = kBatchLengthWidth) {
     auto seq_offsets = framework::ToAbsOffset(seq_tensor->lod())[lod_level];
-    auto seq_tensor_dims = seq_tensor->dims();
-    auto pad_tensor_dims = pad_tensor.dims();
+    const auto& seq_tensor_dims = seq_tensor->dims();
+    const auto& pad_tensor_dims = pad_tensor.dims();
     if (pad_seq_len == -1) {
       pad_seq_len = MaximumSequenceLength(seq_offsets);
     }

paddle/fluid/operators/math/sequence_padding.h

@@ -44,9 +44,6 @@ inline static void CheckDims(const framework::DDim& seq_tensor_dims,
                     "Value of 1st dimension of the sequence tensor should be "
                     "equal to sum of lengths of all sequences.");
 
-  PADDLE_ENFORCE(seq_tensor_dims.size() == 1 || seq_tensor_dims.size() == 2,
-                 "seq_tensor's rank should be 1 or 2.");
-
   PADDLE_ENFORCE(seq_tensor_dims.size() + 1 == pad_tensor_dims.size() ||
                      seq_tensor_dims.size() == pad_tensor_dims.size(),
                  "pad_tensor's rank should be 1 greater than seq_tensor's "

paddle/fluid/operators/sequence_pad_op.cc

@@ -21,82 +21,85 @@ class SequencePadOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
+ protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of SequencePadOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("PadValue"),
+                   "Input(PadValue) of SequencePadOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SequencePadOp should not be null.");
 
     auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(x_dims.size(), 2,
+                      "The rank of Input(x) can't be less than 2.");
+    auto time_step_dims = framework::slice_ddim(x_dims, 1, x_dims.size());
+    auto pad_value_dims = ctx->GetInputDim("PadValue");
+    PADDLE_ENFORCE(pad_value_dims == framework::make_ddim({1}) ||
+                       pad_value_dims == time_step_dims,
+                   "The Input(PadValue) must be a scalar or a tensor whose "
+                   "shape equals to time steps in sequences");
 
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2,
-                      "Only support 2-D tensor, rank of Input(X) should be 2.");
-
-    int lod_level = ctx->Attrs().Get<int>("lod_level");
-
-    int64_t max_len = -1;
-    int64_t seq_num = -1;
-    int x_lod_size = -1;
+    int batch_dim_size = -1;
 
     if (ctx->IsRuntime()) {
+      // run time
       framework::Variable* x_var =
           boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
-
-      auto& x_lod = x_var->Get<LoDTensor>().lod();
-
-      x_lod_size = x_lod.size();
-
-      auto x_abs_offset = framework::ToAbsOffset(x_lod)[lod_level];
-
-      PADDLE_ENFORCE_EQ(x_dims[0], static_cast<int64_t>(x_abs_offset.back()),
-                        "The first dimension of `X` should be equal to sum "
-                        "of all sequences' length.");
-
-      seq_num = x_abs_offset.size() - 1;
-
-      for (int64_t i = 1; i <= seq_num; ++i) {
-        int64_t seq_len = x_abs_offset[i] - x_abs_offset[i - 1];
-        max_len = max_len < seq_len ? seq_len : max_len;
+      const auto& x_lod = x_var->Get<LoDTensor>().lod();
+      PADDLE_ENFORCE(!x_lod.empty(), "The Input(X) must hold lod info.");
+      const auto& x_lod_0 = x_lod[0];
+      PADDLE_ENFORCE_GE(x_lod_0.size(), 2,
+                        "The Input(X)'s lod info is corrupted.");
+      PADDLE_ENFORCE_EQ(
+          x_dims[0], static_cast<int64_t>(x_lod_0.back()),
+          "The Input(X)'s lod info mismatches the actual tensor shape.");
+
+      int seq_num = x_lod_0.size() - 1;
+      int max_seq_len = math::MaximumSequenceLength(x_lod_0);
+      int padded_length = ctx->Attrs().Get<int>("padded_length");
+      if (padded_length == -1) {
+        padded_length = max_seq_len;
       }
+      PADDLE_ENFORCE_GE(padded_length, max_seq_len,
+                        "The Attr(padded_length) must be -1 or an int greater "
+                        "than the length of the longest original sequence.");
+      batch_dim_size = padded_length * seq_num;
     } else {
+      // compile time
       framework::VarDesc* x_desc =
           boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
-      x_lod_size = x_desc->GetLoDLevel();
-    }
-
-    PADDLE_ENFORCE(lod_level >= 0 && lod_level < x_lod_size,
-                   "Invalid `lod_level` which should be at least 0 and less "
-                   "than maximum lod level of `X`");
-
-    ctx->SetOutputDim("Out", {seq_num, max_len, x_dims[1]});
+      PADDLE_ENFORCE_GE(x_desc->GetLoDLevel(), 1);
     }
 
- protected:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-        ctx.device_context());
+    auto out_dims = x_dims;
+    out_dims[0] = batch_dim_size;
+    ctx->SetOutputDim("Out", out_dims);
   }
 };
 
 class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SequencePadOpMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
+  void Make() override {
     AddInput("X",
              "(LoDTensor, default LoDTensor<float>) Input variable which "
-             "should contain lod information. Length of each sequence would "
-             "be computed from the most bottom level lod.");
-    AddOutput("Out",
-              "(Tensor) Output variable which would be a common tensor "
-              "without lod. Each sequence would be padded to the maximum "
-              "length.");
-    AddAttr<float>("lod_level",
-                   "(int, default 0) Specify which level lod to referred to.");
-    AddAttr<float>("pad_value",
-                   "(float, default 0.0) Specify which value to be padded to "
-                   "the end of each sequence.");
+             "should contain lod information.");
+    AddInput("PadValue",
+             "(LoDTensor), this Tensor holds values that will be fill into "
+             "padded steps. It can be a scalar or a tensor whose shape equals "
+             "to time steps in sequences. If it's a scalar, it will be "
+             "automatically broadcasted to the shape of time step.");
+    AddOutput(
+        "Out",
+        "(LoDTensor) The output vairable, which contains padded sequences.");
+    AddAttr<int>(
+        "padded_length",
+        "The length of padded sequences. It can be setted to -1 or "
+        "any positive int. When it is -1, all sequences will be padded up to "
+        "the length of the longest one among them; when it a certain positive "
+        "value, it must be greater than the length of the longest original "
+        "sequence.")
+        .SetDefault(-1);
     AddComment(R"DOC(
 
     )DOC");

paddle/fluid/operators/sequence_pad_op.cu

@@ -17,7 +17,13 @@ limitations under the License. */
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     sequence_pad,
-    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     sequence_pad_grad,
-    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/sequence_pad_op.h

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/operators/math/math_function.h"
@@ -24,68 +26,24 @@ namespace operators {
 using LoDTensor = framework::LoDTensor;
 using LoD = framework::LoD;
 
-template <typename DeviceContext, typename T>
-struct CopyFunctor {
-  LoDTensor* lod_tensor_;
-  LoDTensor* pad_tensor_;
-  const LoD& ref_lod_;
-  const DeviceContext& ctx_;
-  bool is_lod_to_pad_;
-
-  CopyFunctor(LoDTensor* lod_tensor, const LoD& ref_lod, LoDTensor* pad_tensor,
-              const DeviceContext& ctx, bool is_lod_to_pad)
-      : lod_tensor_(lod_tensor),
-        pad_tensor_(pad_tensor),
-        ref_lod_(ref_lod),
-        ctx_(ctx),
-        is_lod_to_pad_(is_lod_to_pad) {}
-
-  void operator()() const {
-    /*
-    auto seq_num = ref_lod_.size() - 1;
-    auto max_len = pad_tensor_->dims()[0] / seq_num;
-
-    PADDLE_ENFORCE_EQ(max_len * seq_num, pad_tensor_->dims()[0],
-                      "First dimension of padded tensor should be equal to "
-                      "maximum sequence length mulplied by sequence number.");
-
-    for (size_t i = 1; i < ref_lod_.size(); ++i) {
-      auto seq_start = ref_lod_[i - 1];
-      auto seq_end = ref_lod_[i];
-      auto pad_start = (i - 1) * max_len;
-      auto pad_end = pad_start + (seq_end - seq_start);
-      auto sub_lod_tensor = lod_tensor_->Slice(seq_start, seq_end);
-      auto sub_pad_tensor = pad_tensor_->Slice(pad_start, pad_end);
-      if (is_lod_to_pad_) {
-        framework::TensorCopy(sub_lod_tensor, ctx.GetPlace(), &sub_pad_tensor);
-      } else {
-        framework::TensorCopy(sub_pad_tensor, ctx.GetPlace(), &sub_lod_tensor);
-      }
-    }
-    */
-  }
-};
-
 template <typename DeviceContext, typename T>
 class SequencePadOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    /*
-    auto* x = ctx.Input<LoDTensor>("X");
-    auto* out_ptr = ctx.Output<LoDTensor>("Out");
-
-    out_ptr->mutable_data<T>(ctx.GetPlace());
+    const auto* x = ctx.Input<LoDTensor>("X");
+    auto* out = ctx.Output<LoDTensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
 
-    // Resize();
+    const auto* pad_value = ctx.Input<LoDTensor>("PadValue");
+    const T* pad_value_data = pad_value->data<T>();
+    std::vector<T> pad_value_vec(pad_value_data,
+                                 pad_value_data + pad_value->numel());
 
-    T pad_value = static_cast<T>(ctx.Attr<float>("pad_value"));
+    int padded_length = ctx.Attr<int>("padded_length");
 
     math::PaddingLoDTensorFunctor<DeviceContext, T>()(
-        ctx.template device_context<DeviceContext>(), *x, *, false);
-
-    math::SetConstant<DeviceContext, T> set_func;
-    set_func(ctx.template device_context<DeviceContext>(), out_ptr, pad_value);
-    */
+        ctx.template device_context<DeviceContext>(), *x, out, pad_value_vec,
+        padded_length, 0, false, math::kBatchLengthWidth);
   }
 };
 
@@ -93,26 +51,17 @@ template <typename DeviceContext, typename T>
 class SequencePadGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    /*
-    auto* x_ptr = ctx.Input<LoDTensor>("X");
-    auto* g_out_ptr = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
-    auto* g_x_ptr = ctx.Output<LoDTensor>(framework::GradVarName("X"));
-
-    math::SetConstant<DeviceContext, T> set_func;
-    set_func(ctx.template device_context<DeviceContext>(),
-             g_x_ptr,
-             static_cast<T>(0));
+    auto* d_x = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    if (d_x) {
+      const auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
+      d_x->mutable_data<T>(ctx.GetPlace());
 
-    auto& x_lod = x_ptr->lod();
-    auto& x_last_level_lod = x_lod[x_lod.size() - 1];
+      int padded_length = ctx.Attr<int>("padded_length");
 
-    CopyFunctor copy_func<DeviceContext, T>(g_out_ptr,
-                                            x_last_level_lod,
-                                            g_x_ptr,
-                                            ctx,
-                                            false);
-    copy_func();
-    */
+      math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
+          ctx.template device_context<DeviceContext>(), *d_out, d_x,
+          padded_length, 0, false, math::kBatchLengthWidth);
+    }
   }
 };
 

python/paddle/fluid/tests/unittests/test_sequence_pad_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestSequencePadOp(OpTest):
+    def set_attr(self):
+        self.x_shape = [12, 4]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0]
+        self.padded_length = -1
+        self.dtype = 'float32'
+
+    def set_data(self):
+        x_data = np.random.uniform(0.1, 0.5, self.x_shape).astype(self.dtype)
+        pad_value_data = np.array(self.pad_value).astype(self.dtype)
+        self.inputs = {
+            'X': (x_data, self.x_len_lod),
+            'PadValue': pad_value_data
+        }
+        self.attrs = {'padded_length': self.padded_length}
+
+    def compute(self):
+        # get padded length
+        padded_length = self.padded_length
+        x_len_lod_0 = self.x_len_lod[0]
+        if padded_length == -1:
+            max_seq_len = 0
+            for l in x_len_lod_0:
+                max_seq_len = max(max_seq_len, l)
+            padded_length = max_seq_len
+
+        # do padding
+        x_data = self.inputs['X'][0]
+        pad_value_data = self.inputs['PadValue']
+        if pad_value_data.shape == (1, ):
+            pad_value_data = np.broadcast_to(
+                pad_value_data, shape=x_data.shape[1:])
+        padded_sequences = []
+        start_idx = 0
+        for l in x_len_lod_0:
+            end_idx = start_idx + l
+            seq = x_data[start_idx:end_idx]
+            to_pad_len = padded_length - l
+            for _ in range(to_pad_len):
+                seq = np.append(seq, pad_value_data[np.newaxis, :], axis=0)
+            padded_sequences.append(seq)
+            start_idx = end_idx
+
+        out_len_lod = self.x_len_lod[:]
+        out_len_lod_0 = [padded_length] * len(x_len_lod_0)
+        out_len_lod[0] = out_len_lod_0
+        out_data = np.concatenate(padded_sequences, axis=0)
+        self.outputs = {'Out': (out_data, out_len_lod)}
+
+    def setUp(self):
+        self.op_type = 'sequence_pad'
+        self.set_attr()
+        self.set_data()
+        self.compute()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+
+class TestSequencePadOp2(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 4]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0, 2.0, 3.0, 4.0]
+        self.padded_length = -1
+        self.dtype = 'float32'
+
+
+class TestSequencePadOp3(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 4]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0]
+        self.padded_length = 7
+        self.dtype = 'float32'
+
+
+class TestSequencePadOp4(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 4]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0, 2.0, 3.0, 4.0]
+        self.padded_length = 7
+        self.dtype = 'float32'
+
+
+class TestSequencePadOp5(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 2, 2]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0]
+        self.padded_length = -1
+        self.dtype = 'float32'
+
+
+class TestSequencePadOp6(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 2, 2]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [[1.0, 2.0], [3.0, 4.0]]
+        self.padded_length = -1
+        self.dtype = 'float32'
+
+
+class TestSequencePadOp7(TestSequencePadOp):
+    def set_attr(self):
+        self.x_shape = [12, 2, 2]
+        self.x_len_lod = [[2, 3, 4, 3]]
+        self.pad_value = [1.0]
+        self.padded_length = 7
+        self.dtype = 'float32'