Fix kl and summary bug (#27132)

* fix summary rnn

* fix kl_div bug when input shape is [1] and reduction is batchmean

paddle/fluid/operators/kldiv_loss_op.h

@@ -72,7 +72,11 @@ class KLDivLossKernel : public framework::OpKernel<T> {
       loss_t.device(place) = output;
     } else if ("batchmean" == reduction) {
       auto output_sum = output.sum();
-      loss_t.device(place) = output_sum / output_sum.constant(n);
+      if (n > 0) {
+        loss_t.device(place) = output_sum / output_sum.constant(n);
+      } else {
+        loss_t.device(place) = output_sum;
+      }
     } else if ("mean" == reduction) {
       loss_t.device(place) = output.mean();
     } else if ("sum" == reduction) {

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

@@ -24,7 +24,10 @@ def kldiv_loss(x, target, reduction):
     loss = np.where(target >= 0, output, np.zeros_like(x))
 
     if reduction == "batchmean":
-        return loss.sum() / x.shape[0]
+        if len(x.shape) > 0:
+            return loss.sum() / x.shape[0]
+        else:
+            return loss.sum()
     if reduction == "mean":
         return loss.mean()
     if reduction == "sum":
@@ -93,6 +96,9 @@ class TestKLDivLossDygraph(unittest.TestCase):
     def test_kl_loss_batchmean(self):
         self.run_kl_loss('batchmean')
 
+    def test_kl_loss_batchmean_shape(self):
+        self.run_kl_loss('batchmean', ())
+
     def test_kl_loss_mean(self):
         self.run_kl_loss('mean')
 

python/paddle/hapi/model_summary.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 import numpy as np
+import numbers
 
 import paddle
 import paddle.nn as nn
@@ -107,6 +109,11 @@ def summary(net, input_size, batch_size=None, dtypes=None):
     if batch_size is None:
         batch_size = -1
 
+    if not paddle.in_dynamic_mode():
+        warnings.warn(
+            "Your model was created in static mode, this may not get correct summary information!"
+        )
+
     result, params_info = summary_string(net, _input_size, batch_size, dtypes)
     print(result)
 
@@ -121,16 +128,16 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
 
     depth = len(list(model.sublayers()))
 
-    def register_hook(module):
-        def hook(module, input, output):
-            class_name = str(module.__class__).split(".")[-1].split("'")[0]
+    def register_hook(layer):
+        def hook(layer, input, output):
+            class_name = str(layer.__class__).split(".")[-1].split("'")[0]
 
             try:
-                module_idx = int(module._full_name.split('_')[-1])
+                layer_idx = int(layer._full_name.split('_')[-1])
             except:
-                module_idx = len(summary)
+                layer_idx = len(summary)
 
-            m_key = "%s-%i" % (class_name, module_idx + 1)
+            m_key = "%s-%i" % (class_name, layer_idx + 1)
             summary[m_key] = OrderedDict()
             summary[m_key]["input_shape"] = list(input[0].shape)
             summary[m_key]["input_shape"][0] = batch_size
@@ -142,23 +149,50 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
                 summary[m_key]["output_shape"][0] = batch_size
 
             params = 0
-            if hasattr(module, "weight") and hasattr(module.weight, "shape"):
-                params += np.prod(module.weight.shape)
-                summary[m_key]["trainable"] = module.weight.trainable or (
-                    not module.weight.stop_gradient)
-            if hasattr(module, "bias") and hasattr(module.bias, "shape"):
-                params += np.prod(module.bias.shape)
+
+            if paddle.in_dynamic_mode():
+                layer_state_dict = layer._parameters
+            else:
+                layer_state_dict = layer.state_dict()
+
+            for k, v in layer_state_dict.items():
+                params += np.prod(v.shape)
+
+                try:
+                    if (getattr(getattr(layer, k), 'trainable')) and (
+                            not getattr(getattr(layer, k), 'stop_gradient')):
+                        summary[m_key]["trainable"] = True
+                    else:
+                        summary[m_key]["trainable"] = False
+                except:
+                    summary[m_key]["trainable"] = True
+
             summary[m_key]["nb_params"] = params
 
-        if (not isinstance(module, nn.Sequential) and
-                not isinstance(module, nn.LayerList) and
-            (not (module == model) or depth < 1)):
+        if (not isinstance(layer, nn.Sequential) and
+                not isinstance(layer, nn.LayerList) and
+            (not (layer == model) or depth < 1)):
+
+            hooks.append(layer.register_forward_post_hook(hook))
+
+    def _check_input_size(input_sizes):
+        for input_size in input_sizes:
+            for item in input_size:
+                if not isinstance(item, numbers.Number):
+                    raise TypeError(
+                        "Expected item in input size be a number, but got {}".
+                        format(type(item)))
 
-            hooks.append(module.register_forward_post_hook(hook))
+                if item <= 0:
+                    raise ValueError(
+                        "Expected item in input size greater than zero, but got {}".
+                        format(item))
 
     if isinstance(input_size, tuple):
         input_size = [input_size]
 
+    _check_input_size(input_size)
+
     x = [
         paddle.rand(
             [2] + list(in_size), dtype=dtype)
@@ -197,7 +231,12 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
             "{0:,}".format(summary[layer]["nb_params"]), )
         total_params += summary[layer]["nb_params"]
 
-        total_output += np.prod(summary[layer]["output_shape"])
+        try:
+            total_output += np.prod(summary[layer]["output_shape"])
+        except:
+            for output_shape in summary[layer]["output_shape"]:
+                total_output += np.prod(output_shape)
+
         if "trainable" in summary[layer]:
             if summary[layer]["trainable"] == True:
                 trainable_params += summary[layer]["nb_params"]

python/paddle/nn/functional/loss.py

@@ -780,10 +780,10 @@ def kl_div(input, label, reduction='mean', name=None):
             input = np.random.uniform(-10, 10, shape).astype('float32')
             target = np.random.uniform(-10, 10, shape).astype('float32')
 
-            # 'batchmean' reduction, loss shape will be [N]
+            # 'batchmean' reduction, loss shape will be [1]
             pred_loss = F.kl_div(paddle.to_tensor(input),
                                  paddle.to_tensor(target), reduction='batchmean')
-            # shape=[5]
+            # shape=[1]
 
             # 'mean' reduction, loss shape will be [1]
             pred_loss = F.kl_div(paddle.to_tensor(input),

python/paddle/nn/layer/loss.py

@@ -627,10 +627,13 @@ class KLDivLoss(fluid.dygraph.Layer):
     $$l(x, y) = y * (\log(y) - x)$$
 
     Parameters:
-        reduction (str, optional): Indicate how to average the loss,
-            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
-            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
-            Default is ``'mean'``.
+        reduction (Tensor): Indicate how to average the loss,
+             the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``.
+             If `reduction` is ``'mean'``, the reduced mean loss is returned;
+             If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned;
+             if `reduction` is ``'sum'``, the reduced sum loss is returned;
+             if `reduction` is ``'none'``, no reduction will be apllied.
+             Default is ``'mean'``.
 
     Shape:
 
@@ -654,11 +657,11 @@ class KLDivLoss(fluid.dygraph.Layer):
             x = np.random.uniform(-10, 10, shape).astype('float32')
             target = np.random.uniform(-10, 10, shape).astype('float32')
 
-            # 'batchmean' reduction, loss shape will be [N]
+            # 'batchmean' reduction, loss shape will be [1]
             kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
             pred_loss = kldiv_criterion(paddle.to_tensor(x),
                                         paddle.to_tensor(target))
-            # shape=[5]
+            # shape=[1]
 
             # 'mean' reduction, loss shape will be [1]
             kldiv_criterion = nn.KLDivLoss(reduction='mean')
@@ -684,7 +687,7 @@ class KLDivLoss(fluid.dygraph.Layer):
         self.reduction = reduction
 
     def forward(self, input, label):
-        out = paddle.nn.functional.kl_div(input, label, self.reduction)
+        out = F.kl_div(input, label, self.reduction)
         return out
 
 

python/paddle/tests/test_model.py

@@ -523,6 +523,24 @@ class TestModelFunction(unittest.TestCase):
             model.summary(input_size=[(20)])
             model.summary(input_size=(20), batch_size=2)
 
+    def test_summary_nlp(self):
+        paddle.enable_static()
+        nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
+        paddle.summary(nlp_net, (1, 2))
+
+    def test_summary_error(self):
+        with self.assertRaises(TypeError):
+            nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
+            paddle.summary(nlp_net, (1, '2'))
+
+        with self.assertRaises(ValueError):
+            nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
+            paddle.summary(nlp_net, (-1, -1))
+
+        paddle.disable_static()
+        nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
+        paddle.summary(nlp_net, (1, 2))
+
     def test_export_deploy_model(self):
         for dynamic in [True, False]:
             fluid.enable_dygraph() if dynamic else None