Merge pull request #12887 from chenwhql/sequence_enumerate_op

Feat: add sequence enumerate op

paddle/fluid/API.spec

@@ -172,6 +172,7 @@ paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'],
 paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
 paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None))
 paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
+paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))

paddle/fluid/operators/sequence_enumerate_op.cc

+//   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.
+
+#include "paddle/fluid/operators/sequence_enumerate_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SequenceEnumerateOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(
+        ctx->HasInput("X"),
+        "Input(X) of SequecceEnumerate operator should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("Out"),
+        "Output(X) of SequenceEnumerate operator should not be null.");
+
+    const auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(
+        x_dims.size(), 2UL,
+        "Input(X) of SequenceEnumerate operator's rank should be 2.");
+    PADDLE_ENFORCE_EQ(
+        x_dims[1], 1UL,
+        "Input(X) of SequenceEnumerate operator's 2nd dimension should be 1.");
+
+    const auto win_size = ctx->Attrs().Get<int>("win_size");
+    ctx->SetOutputDim("Out", {x_dims[0], win_size});
+    ctx->ShareLoD("X", "Out");
+  }
+};
+
+class SequenceEnumerateOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(2-D LoDTensor with the 2nd dimension equal to 1) "
+             "Input LoDTensor of SequenceEnumerate operator.");
+    AddOutput("Out",
+              "(2-D LoDTensor with the 2nd dimension equal to win_size) "
+              "Output LoDTensor of SequenceEnumerate operator.");
+    AddAttr<int>("win_size", "(int) The enumerate sequence window size.")
+        .AddCustomChecker([](const int& win_size) {
+          PADDLE_ENFORCE(win_size >= 2,
+                         "The window size should be not less than 2.");
+        });
+    AddAttr<int>("pad_value", "(int) The enumerate sequence padding value.")
+        .SetDefault(0);
+    AddComment(R"DOC(
+Sequence Enumerate Operator.
+
+Generate a new sequence for the input index sequence, which enumerates all the
+sub-sequences with length `win_size` of the input. 
+The enumerated sequence has the same 1st dimension with variable `input`, and
+the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
+    
+Examples:
+Case 1:
+  Input:
+    X.lod = [[0, 3, 5]]
+    X.data = [[1], [2], [3], [4], [5]]
+    X.dims = [5, 1]
+  Attrs:
+    win_size = 2
+    pad_value = 0
+  Output:
+    Out.lod = [[0, 3, 5]]
+    Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
+    Out.dims = [5, 2]
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(sequence_enumerate, ops::SequenceEnumerateOp,
+                             ops::SequenceEnumerateOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    sequence_enumerate,
+    ops::SequenceEnumerateKernel<paddle::platform::CPUDeviceContext, int32_t>,
+    ops::SequenceEnumerateKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/sequence_enumerate_op.cu

+//   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.
+
+#include <thrust/device_vector.h>
+#include <thrust/host_vector.h>
+#include "paddle/fluid/operators/sequence_enumerate_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+
+namespace paddle {
+namespace operators {
+using platform::PADDLE_CUDA_NUM_THREADS;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+__global__ void CalcOutPut(const T* in_data, const size_t* in_lod,
+                           const size_t lod_len, const int64_t win_size,
+                           const int64_t pad_value, T* out_data) {
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  if (index < in_lod[lod_len - 1]) {
+    int end_idx = 0;
+    // Get LoD interval of index
+    for (int i = 1; i < lod_len; ++i) {
+      if (index < in_lod[i]) {
+        end_idx = in_lod[i];
+        break;
+      }
+    }
+    for (size_t i = 0; i < win_size; ++i) {
+      int word_pos = index + i;
+      out_data[index * win_size + i] =
+          word_pos < end_idx ? in_data[word_pos] : pad_value;
+    }
+  }
+}
+
+template <typename T>
+class SequenceEnumerateOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int win_size = context.Attr<int>("win_size");
+    int pad_value = context.Attr<int>("pad_value");
+
+    auto in_dims = in->dims();
+    auto in_lod = in->lod();
+
+    PADDLE_ENFORCE_EQ(
+        static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
+        "The actual input data's size mismatched with LoD information.");
+
+    /* Generate enumerate sequence set */
+    auto stream = context.cuda_device_context().stream();
+    auto lod0 = in_lod[0];
+    auto in_len = in->numel();
+    auto in_data = in->data<T>();
+    auto out_data = out->mutable_data<T>(context.GetPlace());
+    // Copy LoD to GPU
+    const size_t* dev_in_lod_ptr = lod0.CUDAData(context.GetPlace());
+    // Calc output tensor
+    CalcOutPut<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
+                 PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
+        in_data, dev_in_lod_ptr, lod0.size(), win_size, pad_value, out_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_CUDA_KERNEL(
+    sequence_enumerate,
+    paddle::operators::SequenceEnumerateOpCUDAKernel<int32_t>,
+    paddle::operators::SequenceEnumerateOpCUDAKernel<int64_t>);

paddle/fluid/operators/sequence_enumerate_op.h

+//   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.
+
+#pragma once
+
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+using LoDTensor = framework::LoDTensor;
+
+template <typename DeviceContext, typename T>
+class SequenceEnumerateKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int win_size = context.Attr<int>("win_size");
+    int pad_value = context.Attr<int>("pad_value");
+
+    auto in_dims = in->dims();
+    auto in_lod = in->lod();
+
+    PADDLE_ENFORCE_EQ(
+        static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
+        "The actual input data's size mismatched with LoD information.");
+
+    // Generate enumerate sequence set
+    auto lod0 = in_lod[0];
+    auto in_data = in->data<T>();
+    auto out_data = out->mutable_data<T>(context.GetPlace());
+    for (size_t i = 0; i < lod0.size() - 1; ++i) {
+      for (size_t idx = lod0[i]; idx < lod0[i + 1]; ++idx) {
+        for (int word_idx = 0; word_idx < win_size; ++word_idx) {
+          size_t word_pos = idx + word_idx;
+          out_data[win_size * idx + word_idx] =
+              word_pos < lod0[i + 1] ? in_data[word_pos] : pad_value;
+        }
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/nn.py

@@ -111,6 +111,7 @@ __all__ = [
     'stack',
     'pad2d',
     'unstack',
+    'sequence_enumerate',
 ]
 
 
@@ -5823,6 +5824,51 @@ def flatten(x, axis=1, name=None):
     return out
 
 
+def sequence_enumerate(input, win_size, pad_value=0, name=None):
+    """
+    Generate a new sequence for the input index sequence, which enumerates all the
+    sub-sequences with length `win_size` of the input. 
+    The enumerated sequence has the same 1st dimension with variable `input`, and
+    the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
+    
+    Examples:
+    Case 1:
+      Input:
+        X.lod = [[0, 3, 5]]
+        X.data = [[1], [2], [3], [4], [5]]
+        X.dims = [5, 1]
+      Attrs:
+        win_size = 2
+        pad_value = 0
+      Output:
+        Out.lod = [[0, 3, 5]]
+        Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
+        Out.dims = [5, 2]
+
+    Args:
+        input (Variable): The input variable which is a index sequence.
+        win_size (int): The window size for enumerating all sub-sequences.
+        pad_value (int): The padding value, default 0.
+
+    Returns:
+        Variable: The enumerate sequence variable which is a LoDTensor.
+
+    Examples:
+        .. code-block:: python
+
+            x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
+            out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
+    """
+    helper = LayerHelper('sequence_enumerate', **locals())
+    out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
+    helper.append_op(
+        type='sequence_enumerate',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={'win_size': win_size,
+               'pad_value': pad_value})
+
+
 def sequence_mask(x, maxlen=None, dtype='int64', name=None):
     """
     **SequenceMask Layer**
@@ -5902,6 +5948,7 @@ def stack(x, axis=0):
     helper.append_op(
         type='stack', inputs={'X': x}, outputs={'Y': out},
         attrs={'axis': axis})
+
     return out
 
 

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

@@ -549,6 +549,13 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_sequence_enumerate(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
+            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_sequence_enumerate_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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def sequence_enumerate(input_seq, in_lod, win_size, pad_value):
+    lod0 = [0]
+    for i in range(0, len(in_lod[0])):
+        lod0.append(lod0[i] + in_lod[0][i])
+    out_seq = []
+    for i in range(0, len(lod0) - 1):
+        for idx in range(lod0[i], lod0[i + 1]):
+            single_seq = []
+            for word_idx in range(win_size):
+                word_pos = idx + word_idx
+                dat = input_seq[word_pos] if word_pos < lod0[i+1] \
+                    else pad_value
+                single_seq.append(dat)
+            out_seq.append(single_seq)
+    return out_seq
+
+
+class TestSequenceEnumerateOp(OpTest):
+    def setUp(self):
+        self.op_type = "sequence_enumerate"
+        self.init_test_case()
+        self.inputs = {'X': (self.in_seq, self.lod)}
+        self.attrs = {'win_size': self.win_size, 'pad_value': self.pad_value}
+        self.outputs = {'Out': (self.out_seq, self.lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def init_test_case(self):
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        self.win_size = 2
+        self.pad_value = 0
+        out_seq = sequence_enumerate(self.in_seq, self.lod, self.win_size,
+                                     self.pad_value)
+        self.out_seq = np.array(out_seq).astype("int32")
+
+
+class TesSequenceEnumerateOpInt64(TestSequenceEnumerateOp):
+    def init_test_case(self):
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int64")
+        self.lod = [[9, 4, 11, 6]]
+        self.win_size = 2
+        self.pad_value = 0
+        out_seq = sequence_enumerate(self.in_seq, self.lod, self.win_size,
+                                     self.pad_value)
+        self.out_seq = np.array(out_seq).astype("int64")
+
+
+class TestSequenceEnumerateOpLargeWinSize(TestSequenceEnumerateOp):
+    def init_test_case(self):
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        self.win_size = 5
+        self.pad_value = 0
+        out_seq = sequence_enumerate(self.in_seq, self.lod, self.win_size,
+                                     self.pad_value)
+        self.out_seq = np.array(out_seq).astype("int32")
+
+
+class TestSequenceEnumerateOpMaxWinSize(TestSequenceEnumerateOp):
+    def init_test_case(self):
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        self.win_size = 30
+        self.pad_value = 0
+        out_seq = sequence_enumerate(self.in_seq, self.lod, self.win_size,
+                                     self.pad_value)
+        self.out_seq = np.array(out_seq).astype("int32")
+
+
+class TestSequenceEnumerateOpLargePadValue(TestSequenceEnumerateOp):
+    def init_test_case(self):
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        self.win_size = 5
+        self.pad_value = 5
+        out_seq = sequence_enumerate(self.in_seq, self.lod, self.win_size,
+                                     self.pad_value)
+        self.out_seq = np.array(out_seq).astype("int32")
+
+
+if __name__ == "__main__":
+    unittest.main()