Fix Reduce functor (#12262)

* Fix Reduce and Gather

* Fix unit test

Author: chengduo

paddle/fluid/framework/details/reduce_and_gather.h

@@ -35,14 +35,16 @@ struct ReduceLoDTensor {
     PADDLE_ENFORCE(!src_tensors_.empty());
     auto &t0 = *src_tensors_[0];
     PADDLE_ENFORCE_NE(t0.numel(), 0);
+
     dst_tensor_.Resize(t0.dims());
     T *dst = dst_tensor_.mutable_data<T>(platform::CPUPlace());
-    if (dst != t0.data<T>()) {
-      std::copy(t0.data<T>(), t0.data<T>() + t0.numel(), dst);
-    }
 
-    for (size_t i = 1; i < src_tensors_.size(); ++i) {
+    for (size_t i = 0; i < src_tensors_.size(); ++i) {
       auto &t = *src_tensors_[i];
+      if (dst == t.data<T>()) {
+        continue;
+      }
+
       PADDLE_ENFORCE_EQ(t.dims(), t0.dims());
       PADDLE_ENFORCE_EQ(t.type(), t0.type());
       std::transform(t.data<T>(), t.data<T>() + t.numel(), dst, dst,

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

@@ -102,37 +102,79 @@ def setUpClass(cls):
             fluid.recordio_writer.convert_reader_to_recordio_file(
                 MNIST_RECORDIO_FILE, reader, feeder)
 
+    def _init_data(self, random=True):
+        np.random.seed(5)
+        if random:
+            img = np.random.random(size=[32, 784]).astype(np.float32)
+        else:
+            img = np.ones(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
+        return img, label
+
+    # simple_fc
     def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
         if use_cuda and not core.is_compiled_with_cuda():
             return
         self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
         self.check_network_convergence(
             simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
 
-        img = np.zeros(shape=[32, 784], dtype='float32')
-        label = np.ones(shape=[32, 1], dtype='int64')
+        img, label = self._init_data()
+
         self.check_network_convergence(
             simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
             use_reduce=use_reduce)
 
+    def check_simple_fc_convergence_with_Reduce(self, use_cuda):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
+        self.check_network_convergence(
+            simple_fc_net, use_cuda=use_cuda, use_reduce=True)
+        self.check_network_convergence(
+            simple_fc_net,
+            use_cuda=use_cuda,
+            allow_op_delay=True,
+            use_reduce=True)
+
+        img, label = self._init_data()
+
+        all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
+            simple_fc_net,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            use_reduce=False)
+        reduce_first_loss, reduce_last_loss = self.check_network_convergence(
+            simple_fc_net,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            use_reduce=True)
+
+        for loss in zip(all_reduce_first_loss, reduce_first_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(all_reduce_last_loss, reduce_last_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+
     def test_simple_fc(self):
         # use_cuda
         self.check_simple_fc_convergence(True)
         self.check_simple_fc_convergence(False)
 
     def test_simple_fc_with_new_strategy(self):
         # use_cuda, use_reduce
-        self.check_simple_fc_convergence(True, True)
-        self.check_simple_fc_convergence(False, True)
+        self.check_simple_fc_convergence_with_Reduce(True)
+        self.check_simple_fc_convergence_with_Reduce(False)
 
-    def check_simple_fc_parallel_accuracy(self, use_cuda, use_reduce=False):
+    def check_simple_fc_parallel_accuracy(self, use_cuda):
         if use_cuda and not core.is_compiled_with_cuda():
             return
-        img = np.zeros(shape=[32, 784], dtype='float32')
-        label = np.ones(shape=[32, 1], dtype='int64')
+
+        img, label = self._init_data(random=False)
+
         single_first_loss, single_last_loss = self.check_network_convergence(
             method=simple_fc_net,
             seed=1000,
@@ -146,8 +188,7 @@ def check_simple_fc_parallel_accuracy(self, use_cuda, use_reduce=False):
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
-            use_parallel_executor=True,
-            use_reduce=use_reduce)
+            use_parallel_executor=True)
 
         for p_f in parallel_first_loss:
             self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
@@ -158,32 +199,53 @@ def test_simple_fc_parallel_accuracy(self):
         self.check_simple_fc_parallel_accuracy(True)
         self.check_simple_fc_parallel_accuracy(False)
 
-    def test_simple_fc_parallel_accuracy_with_new_strategy(self):
-        # use_cuda, use_reduce
-        self.check_simple_fc_parallel_accuracy(True, True)
-        self.check_simple_fc_parallel_accuracy(False, True)
-
-    def check_batchnorm_fc_convergence(self, use_cuda, use_reduce=False):
+    def check_batchnorm_fc_convergence(self, use_cuda):
         if use_cuda and not core.is_compiled_with_cuda():
             return
+
         self.check_network_convergence(fc_with_batchnorm, use_cuda=use_cuda)
-        img = np.zeros(shape=[32, 784], dtype='float32')
-        label = np.ones(shape=[32, 1], dtype='int64')
+
+        img, label = self._init_data()
+
+        self.check_network_convergence(
+            fc_with_batchnorm,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda)
+
+    def check_batchnorm_fc_convergence_use_reduce(self, use_cuda):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
         self.check_network_convergence(
+            fc_with_batchnorm, use_cuda=use_cuda, use_reduce=True)
+
+        img, label = self._init_data()
+
+        all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
             fc_with_batchnorm,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
-            use_reduce=use_reduce)
+            use_reduce=False)
+        reduce_first_loss, reduce_last_loss = self.check_network_convergence(
+            fc_with_batchnorm,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            use_reduce=True)
+
+        for loss in zip(all_reduce_first_loss, reduce_first_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(all_reduce_last_loss, reduce_last_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
 
     def test_batchnorm_fc(self):
         self.check_batchnorm_fc_convergence(True)
         self.check_batchnorm_fc_convergence(False)
 
     def test_batchnorm_fc_with_new_strategy(self):
-        # use_cuda, use_reduce
-        self.check_batchnorm_fc_convergence(True, True)
-        self.check_batchnorm_fc_convergence(False, True)
+        self.check_batchnorm_fc_convergence_use_reduce(True)
+        self.check_batchnorm_fc_convergence_use_reduce(False)
 
 
 if __name__ == '__main__':