Make word2vec uses parallel.do when CI

paddle/operators/sum_op.h

@@ -68,32 +68,59 @@ 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();
 
       // 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

@@ -12,76 +12,145 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import unittest
+import os
 
-PASS_NUM = 100
-EMBED_SIZE = 32
-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')
-
-embed_first = fluid.layers.embedding(
-    input=first_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_second = fluid.layers.embedding(
-    input=second_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_third = fluid.layers.embedding(
-    input=third_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_forth = fluid.layers.embedding(
-    input=forth_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-
-concat_embed = fluid.layers.concat(
-    input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
-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)
-avg_cost = fluid.layers.mean(x=cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
-
-train_reader = paddle.batch(
-    paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(
-    feed_list=[first_word, second_word, third_word, forth_word, next_word],
-    place=place)
-
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_cost_np = exe.run(fluid.default_main_program(),
-                              feed=feeder.feed(data),
-                              fetch_list=[avg_cost])
-        if avg_cost_np[0] < 5.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
+
+def main(use_cuda, is_sparse, parallel):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    PASS_NUM = 100
+    EMBED_SIZE = 32
+    HIDDEN_SIZE = 256
+    N = 5
+    BATCH_SIZE = 32
+    IS_SPARSE = is_sparse
+
+    def __network__(words):
+        embed_first = fluid.layers.embedding(
+            input=words[0],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_second = fluid.layers.embedding(
+            input=words[1],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_third = fluid.layers.embedding(
+            input=words[2],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_forth = fluid.layers.embedding(
+            input=words[3],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+
+        concat_embed = fluid.layers.concat(
+            input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+        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=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)
+
+    train_reader = paddle.batch(
+        paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(
+        feed_list=[first_word, second_word, third_word, forth_word, next_word],
+        place=place)
+
+    exe.run(fluid.default_startup_program())
+
+    for pass_id in range(PASS_NUM):
+        for data in train_reader():
+            avg_cost_np = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+            if avg_cost_np[0] < 5.0:
+                return
+    raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
+
+
+FULL_TEST = os.getenv('FULL_TEST',
+                      '0').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(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__)
+
+    setattr(W2VTest, fn_name, fn)
+
+
+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()