Correctly handling multiple inputs and integer inputs for recurrent_g… (#114)

* Correctly handling multiple inputs and integer inputs for recurrent_group

* Fix ScatterAgentLayer for generation

* Revert sequence_(nest)_rnn.conf

paddle/cuda/src/hl_cuda_cublas.cc

@@ -217,7 +217,7 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
   } else {
     LOG(FATAL) << "parameter transa error!";
   }
-  CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS);
+  CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS) << hl_cublas_get_error_string(stat);
   CHECK_SYNC("hl_matrix_mul failed");
 }
 
@@ -266,7 +266,7 @@ void hl_matrix_mul_vector(real *A_d, hl_trans_op_t trans,
     LOG(FATAL) << "parameter transa error!";
   }
 
-  CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS);
+  CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS) << hl_cublas_get_error_string(stat);
   CHECK_SYNC("hl_matrix_mul_vector");
 }
 

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -497,20 +497,21 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
     int idSize = 0;
     // connect in_links
     for (size_t j = 0; j < inFrameLines_.size(); ++j) {
+      Info& info = info_[shareInlinkInfo ? 0 : j];
       // idSize denotes the sum number of tokens in each length i
-      idSize = info_[j].idIndex[i + 1] - info_[j].idIndex[i];
+      idSize = info.idIndex[i + 1] - info.idIndex[i];
       InFrameLine inFrameLine = inFrameLines_[j];
       auto scatterAgent =
           dynamic_cast<ScatterAgentLayer*>(inFrameLine.agents[i].get());
       scatterAgent->setRealLayerAndOutput(inFrameLine.inLayer,
-                                          inFrameLine.outArg, info_[j].allIds,
-                                          info_[j].idIndex[i], idSize);
+                                          inFrameLine.outArg, info.allIds,
+                                          info.idIndex[i], idSize);
       if (hasSubseq) {
         // size: the length of subsequence
         int size =
-            info_[j].seqStartPosIndex[i + 1] - info_[j].seqStartPosIndex[i];
-        scatterAgent->setSequenceStartPositions(info_[j].sequenceStartPositions,
-                                                info_[j].seqStartPosIndex[i],
+            info.seqStartPosIndex[i + 1] - info.seqStartPosIndex[i];
+        scatterAgent->setSequenceStartPositions(info.sequenceStartPositions,
+                                                info.seqStartPosIndex[i],
                                                 size);
       }
     }
@@ -744,10 +745,13 @@ void RecurrentGradientMachine::selectRowsOneTime(LayerPtr layer,
                                                  const IVectorPtr& allIds,
                                                  Argument* arg,
                                                  PassType passType) {
-  const MatrixPtr& realV = layer->getOutputValue();
+  Argument& src = layer->getOutput();
+  if (src.value) {
+    const MatrixPtr& realV = src.value;
     int height = realV->getHeight();
     int width = realV->getWidth();
-  Matrix::resizeOrCreate(arg->value, height, width, /* trans */ false, useGpu_);
+    Matrix::resizeOrCreate(
+      arg->value, height, width, /* trans */ false, useGpu_);
     arg->value->zeroMem();
     arg->value->selectRows(*realV, *allIds);
     if (passType != PASS_TEST) {
@@ -755,6 +759,11 @@ void RecurrentGradientMachine::selectRowsOneTime(LayerPtr layer,
                              useGpu_);
       arg->grad->zeroMem();
     }
+  }
+  if (src.ids) {
+    IVector::resizeOrCreate(arg->ids, src.ids->getSize(), useGpu_);
+    arg->ids->selectFrom(*src.ids, *allIds);
+  }
 }
 
 void RecurrentGradientMachine::createSeqPos(

paddle/gserver/layers/AgentLayer.cpp

@@ -139,15 +139,16 @@ void ScatterAgentLayer::forward(PassType passType) {
   Layer::forward(passType);
   CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
 
-  if (realLayer_->getOutput().ids) {  // ids scatter
-    IVector::resizeOrCreate(output_.ids, ids_->getSize(), useGpu_);
-    output_.ids->selectFrom(*realLayer_->getOutput().ids, *ids_);
-  } else {  // value scatter
   int width = this->getSize();
-    if (realOutArg_.value) {
-      output_.subArgFrom(realOutArg_, /* offset */ idIndex_ * width, idSize_,
+  if (realOutArg_.value || realOutArg_.ids) {
+    output_.subArgFrom(realOutArg_, /* offset */ idIndex_, idSize_,
                        width, useGpu_);
   } else {  // used in generation
+    if (realLayer_->getOutput().ids) {
+      IVector::resizeOrCreate(output_.ids, ids_->getSize(), useGpu_);
+      output_.ids->selectFrom(*realLayer_->getOutput().ids, *ids_);
+    }
+    if (realLayer_->getOutput().value) {
       int height = ids_->getSize();
       resetOutput(height, width);
 
@@ -213,18 +214,17 @@ void SequenceGatherAgentLayer::forward(PassType passType) {
 void SequenceScatterAgentLayer::forward(PassType passType) {
   Layer::forward(passType);
   CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
-  CHECK(!realLayer_->getOutput().ids) << "Not supported";
 
   const Argument& input = realLayer_->getOutput();
-  CHECK_EQ(input.value->getWidth(), this->getSize());
+  CHECK_EQ(realLayer_->getSize(), this->getSize());
   int width = this->getSize();
 
   AsyncGpuBlock asyncGpuBlock;
   REGISTER_TIMER_INFO("SequenceAgentLayerForward", getName().c_str());
 
-  if (realOutArg_.value) {
+  if (realOutArg_.value || realOutArg_.ids) {
     CHECK(realOutArg_.sequenceStartPositions);
-    output_.subArgFrom(realOutArg_, /* offset */ idIndex_ * width, idSize_,
+    output_.subArgFrom(realOutArg_, /* offset */ idIndex_, idSize_,
                        width, useGpu_, /* trans */ false, /* seqFlag */ true,
                        /* seqStart */ seqStartPosIndex_,
                        /* seqSize */ numSequences_);

paddle/gserver/tests/CMakeLists.txt

@@ -56,7 +56,6 @@ add_test(NAME test_RecurrentGradientMachine
     COMMAND .set_python_path.sh -d
             ${PROJ_ROOT}/python:${PROJ_ROOT}/paddle/gserver/tests
             ${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine
-            --use_gpu=false
     WORKING_DIRECTORY ${PROJ_ROOT}/paddle)
 
 add_unittest_without_exec(test_NetworkCompare

paddle/gserver/tests/sequence_nest_rnn_multi_input.conf

+#edit-mode: -*- python -*-
+# Copyright (c) 2016 Baidu, Inc. 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 paddle.trainer_config_helpers import *
+
+######################## data source ################################
+define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
+                        test_list=None,
+                        module='rnn_data_provider',
+                        obj='process_subseq')
+
+
+settings(batch_size=2, learning_rate=0.01)
+######################## network configure ################################
+dict_dim = 10
+word_dim = 8
+hidden_dim = 8
+label_dim = 3
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(input=data, size=word_dim)
+
+# This hierachical RNN is designed to be equivalent to the simple RNN in
+# sequence_rnn.conf
+
+def outer_step(wid, x):
+    outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
+    def inner_step(y, wid):
+        z = embedding_layer(input=wid, size=word_dim)
+        inner_mem = memory(name="inner_rnn_state",
+                           size=hidden_dim,
+                           boot_layer=outer_mem)
+        out = fc_layer(input=[y, z, inner_mem],
+                        size=hidden_dim,
+                        act=TanhActivation(),
+                        bias_attr=True,
+                        name="inner_rnn_state")
+        return out
+
+    inner_rnn_output = recurrent_group(
+        step=inner_step,
+        name="inner",
+        input=[x, wid])
+    last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
+
+    # "return last" should also work. But currently RecurrentGradientMachine
+    # does not handle it correctly. Current implementation requires that
+    # all the out links are from sequences. However, it does not report error
+    # when the out links are not sequences.
+    return inner_rnn_output
+
+out = recurrent_group(
+    name="outer",
+    step=outer_step,
+    input=[SubsequenceInput(data), SubsequenceInput(emb)])
+
+rep = last_seq(input=out)
+prob = fc_layer(size=label_dim,
+                input=rep,
+                act=SoftmaxActivation(),
+                bias_attr=True)
+
+outputs(classification_cost(input=prob,
+                            label=data_layer(name="label", size=label_dim)))

paddle/gserver/tests/sequence_rnn_multi_input.conf

+#edit-mode: -*- python -*-
+# Copyright (c) 2016 Baidu, Inc. 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 paddle.trainer_config_helpers import *
+
+######################## data source ################################
+define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
+                        test_list=None,
+                        module='rnn_data_provider',
+                        obj='process_seq')
+
+
+settings(batch_size=2, learning_rate=0.01)
+######################## network configure ################################
+dict_dim = 10
+word_dim = 8
+hidden_dim = 8
+label_dim = 3
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(input=data, size=word_dim)
+
+def step(y, wid):
+    z = embedding_layer(input=wid, size=word_dim)
+    mem = memory(name="rnn_state", size=hidden_dim)
+    out = fc_layer(input=[y, z, mem],
+                    size=hidden_dim,
+                    act=TanhActivation(),
+                    bias_attr=True,
+                    name="rnn_state")
+    return out
+
+out = recurrent_group(
+    name="rnn",
+    step=step,
+    input=[emb, data])
+
+rep = last_seq(input=out)
+prob = fc_layer(size=label_dim,
+                input=rep,
+                act=SoftmaxActivation(),
+                bias_attr=True)
+
+outputs(classification_cost(input=prob,
+                            label=data_layer(name="label", size=label_dim)))

paddle/gserver/tests/test_RecurrentGradientMachine.cpp

@@ -92,7 +92,11 @@ void CalCost(const string& conf, const string& dir, real* cost,
   rmDir(dir.c_str());
 }
 
-void test(const string& conf1, const string& conf2, double eps) {
+void test(const string& conf1, const string& conf2, double eps, bool useGpu) {
+  if (!paddle::version::isWithGpu() && useGpu) {
+    return;
+  }
+  FLAGS_use_gpu = useGpu;
   int num_passes = 5;
   real* cost1 = new real[num_passes];
   const string dir1 = "gserver/tests/t1";
@@ -113,17 +117,28 @@ void test(const string& conf1, const string& conf2, double eps) {
 }
 
 TEST(RecurrentGradientMachine, HasSubSequence) {
+  for (bool useGpu : {false, true}) {
     test("gserver/tests/sequence_layer_group.conf",
          "gserver/tests/sequence_nest_layer_group.conf",
-       1e-5);
+         1e-5, useGpu);
+  }
 }
 
 TEST(RecurrentGradientMachine, rnn) {
+  for (bool useGpu : {false, true}) {
     test("gserver/tests/sequence_rnn.conf",
          "gserver/tests/sequence_nest_rnn.conf",
-       0);
+         1e-6, useGpu);
+  }
 }
 
+TEST(RecurrentGradientMachine, rnn_multi_input) {
+  for (bool useGpu : {false, true}) {
+    test("gserver/tests/sequence_rnn_multi_input.conf",
+         "gserver/tests/sequence_nest_rnn_multi_input.conf",
+         1e-6, useGpu);
+  }
+}
 
 int main(int argc, char** argv) {
   if (paddle::version::isWithPyDataProvider()) {

paddle/parameter/Argument.cpp

@@ -554,11 +554,16 @@ void Argument::degradeSequence(const Argument& input, bool useGpu) {
 void Argument::subArgFrom(const Argument& input, size_t offset, size_t height,
                           size_t width, bool useGpu, bool trans, bool seqFlag,
                           size_t seqStart, size_t seqSize) {
-  value = Matrix::create(input.value->getData() + offset, height, width, trans,
-                         useGpu);
+  if (input.value) {
+    value = Matrix::create(input.value->getData() + offset * width,
+                           height, width, trans, useGpu);
+  }
+  if (input.ids) {
+    ids = IVector::create(input.ids->getData() + offset, height, useGpu);
+  }
   if (input.grad) {
-    grad = Matrix::create(input.grad->getData() + offset, height, width, trans,
-                          useGpu);
+    grad = Matrix::create(input.grad->getData() + offset * width,
+                          height, width, trans, useGpu);
   }
   if (seqFlag) {
     sequenceStartPositions = std::make_shared<ICpuGpuVector>(

paddle/parameter/Argument.h

@@ -177,11 +177,11 @@ struct Argument {
   }
 
   /**
-   * @brief (value, grad, sequenceStartPositions) of output are subset of
+   * @brief (value, ids, grad, sequenceStartPositions) of output are subset of
    *        input. Note that, output share the same memory of input.
    *
    * @param input[in]       input
-   * @param offset[in]      offset of input.value
+   * @param offset[in]      offset in terms of rows
    * @param height[in]      height of output.value
    * @param width[in]       width of output.value
    * @param useGpu[in]

python/paddle/trainer_config_helpers/layers.py

@@ -216,7 +216,7 @@ def check_input(input):
     """
 
     if isinstance(input, LayerOutput):
-        return [LayerOutput]
+        return [input]
     assert isinstance(input, list)
     for inp in input:
         assert isinstance(inp, LayerOutput)
@@ -764,7 +764,7 @@ def print_layer(input, name=None):
     :type input: LayerOutput|list|tuple
     :return: No return
     """
-    check_input(input)
+    input = check_input(input)
 
     Layer(
         name=name,