Test word2vec for parallel.do

* Polish sum_op support SelectedRows in_place

paddle/operators/sum_op.h

@@ -68,7 +68,32 @@ class SumKernel : public framework::OpKernel<T> {
         }
       }
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      PADDLE_ENFORCE(!in_place, "SelectedRows not support inplace sum now");
+      std::unique_ptr<framework::SelectedRows> in0;
+      if (in_place) {
+        // If is in_place, we store the input[0] to in0
+        auto &in_sel0 = in_vars[0]->Get<SelectedRows>();
+        auto &rows = in_sel0.rows();
+#ifdef PADDLE_WITH_CUDA
+        std::vector<int64_t> rows_in_cpu;
+        rows_in_cpu.reserve(rows.size());
+        for (auto item : rows) {
+          rows_in_cpu.push_back(item);
+        }
+        in0.reset(new framework::SelectedRows(rows_in_cpu, in_sel0.height()));
+#else
+        in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
+#endif
+        in0->mutable_value()->ShareDataWith(in_sel0.value());
+      }
+
+      auto get_selected_row = [&](size_t i) -> const SelectedRows & {
+        if (i == 0 && in0) {
+          return *in0.get();
+        } else {
+          return in_vars[i]->Get<SelectedRows>();
+        }
+      };
+
       auto *out = context.Output<SelectedRows>("Out");
       out->mutable_rows()->clear();
       auto *out_value = out->mutable_value();
@@ -76,24 +101,26 @@ class SumKernel : public framework::OpKernel<T> {
       // Runtime InferShape
       size_t first_dim = 0;
       for (int i = 0; i < N; i++) {
-        first_dim += in_vars[i]->Get<SelectedRows>().rows().size();
+        auto &sel_row = get_selected_row(i);
+        first_dim += sel_row.rows().size();
       }
-      auto in_dim = in_vars[0]->Get<SelectedRows>().value().dims();
-      auto in_dim_vec = framework::vectorize(in_dim);
-      in_dim_vec[0] = static_cast<int64_t>(first_dim);
+      auto in_dim =
+          framework::vectorize(get_selected_row(N - 1).value().dims());
+      in_dim[0] = static_cast<int64_t>(first_dim);
 
-      out_value->Resize(framework::make_ddim(in_dim_vec));
+      out_value->Resize(framework::make_ddim(in_dim));
       out_value->mutable_data<T>(context.GetPlace());
 
       math::SelectedRowsAddTo<DeviceContext, T> functor;
 
       int64_t offset = 0;
       for (int i = 0; i < N; i++) {
-        PADDLE_ENFORCE_EQ(out->height(),
-                          in_vars[i]->Get<SelectedRows>().height());
-        functor(context.template device_context<DeviceContext>(),
-                in_vars[i]->Get<SelectedRows>(), offset, out);
-        offset += in_vars[i]->Get<SelectedRows>().value().numel();
+        auto &sel_row = get_selected_row(i);
+
+        PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
+        functor(context.template device_context<DeviceContext>(), sel_row,
+                offset, out);
+        offset += sel_row.value().numel();
       }
     } else if (out_var->IsType<framework::LoDTensorArray>()) {
       auto &out_array = *out_var->GetMutable<framework::LoDTensorArray>();

python/paddle/v2/fluid/tests/book/test_word2vec.py

@@ -15,9 +15,10 @@
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import unittest
+import os
 
 
-def main_impl(use_cuda):
+def main(use_cuda, is_sparse, parallel):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
 
@@ -26,37 +27,29 @@ def main_impl(use_cuda):
     HIDDEN_SIZE = 256
     N = 5
     BATCH_SIZE = 32
-    IS_SPARSE = True
-
-    word_dict = paddle.dataset.imikolov.build_dict()
-    dict_size = len(word_dict)
-
-    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
-    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
-    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
-    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
-    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+    IS_SPARSE = is_sparse
 
+    def __network__(words):
         embed_first = fluid.layers.embedding(
-        input=first_word,
+            input=words[0],
             size=[dict_size, EMBED_SIZE],
             dtype='float32',
             is_sparse=IS_SPARSE,
             param_attr='shared_w')
         embed_second = fluid.layers.embedding(
-        input=second_word,
+            input=words[1],
             size=[dict_size, EMBED_SIZE],
             dtype='float32',
             is_sparse=IS_SPARSE,
             param_attr='shared_w')
         embed_third = fluid.layers.embedding(
-        input=third_word,
+            input=words[2],
             size=[dict_size, EMBED_SIZE],
             dtype='float32',
             is_sparse=IS_SPARSE,
             param_attr='shared_w')
         embed_forth = fluid.layers.embedding(
-        input=forth_word,
+            input=words[3],
             size=[dict_size, EMBED_SIZE],
             dtype='float32',
             is_sparse=IS_SPARSE,
@@ -67,9 +60,37 @@ def main_impl(use_cuda):
         hidden1 = fluid.layers.fc(input=concat_embed,
                                   size=HIDDEN_SIZE,
                                   act='sigmoid')
-    predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
-    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
+        predict_word = fluid.layers.fc(input=hidden1,
+                                       size=dict_size,
+                                       act='softmax')
+        cost = fluid.layers.cross_entropy(input=predict_word, label=words[4])
         avg_cost = fluid.layers.mean(x=cost)
+        return avg_cost
+
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+
+    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
+    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
+    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
+    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
+    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+
+    if not parallel:
+        avg_cost = __network__(
+            [first_word, second_word, third_word, forth_word, next_word])
+    else:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            avg_cost = __network__(
+                map(pd.read_input, [
+                    first_word, second_word, third_word, forth_word, next_word
+                ]))
+            pd.write_output(avg_cost)
+
+        avg_cost = fluid.layers.mean(x=pd())
+
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
     sgd_optimizer.minimize(avg_cost)
 
@@ -94,22 +115,42 @@ def main_impl(use_cuda):
     raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
 
 
-def main(*args, **kwargs):
+FULL_TEST = os.getenv('FULL_TEST',
+                      '1').lower() in ['true', '1', 't', 'y', 'yes', 'on']
+SKIP_REASON = "Only run minimum number of tests in CI server, to make CI faster"
+
+
+class W2VTest(unittest.TestCase):
+    pass
+
+
+def inject_test_method(use_cuda, is_sparse, parallel):
+    fn_name = "test_{0}_{1}_{2}".format("cuda" if use_cuda else "cpu", "sparse"
+                                        if is_sparse else "dense", "parallel"
+                                        if parallel else "normal")
+
+    def __impl__(*args, **kwargs):
         prog = fluid.Program()
         startup_prog = fluid.Program()
         scope = fluid.core.Scope()
         with fluid.scope_guard(scope):
             with fluid.program_guard(prog, startup_prog):
-            main_impl(*args, **kwargs)
+                main(use_cuda=use_cuda, is_sparse=is_sparse, parallel=parallel)
 
+    if use_cuda and is_sparse and parallel:
+        fn = __impl__
+    else:
+        # skip the other test when on CI server
+        fn = unittest.skipUnless(
+            condition=FULL_TEST, reason=SKIP_REASON)(__impl__)
 
-class W2VTest(unittest.TestCase):
-    def test_cpu_normal(self):
-        main(use_cuda=False)
+    setattr(W2VTest, fn_name, fn)
 
-    def test_gpu_normal(self):
-        main(use_cuda=True)
 
+for use_cuda in (False, True):
+    for is_sparse in (False, True):
+        for parallel in (False, True):
+            inject_test_method(use_cuda, is_sparse, parallel)
 
 if __name__ == '__main__':
     unittest.main()