support if logic for Variable in dygraph (#22892)

* support if logic for Variable in dygraph, test=develop

* fix test_learning_rate_scheduler.py, test=develop

* fix optest, test=develop

* fix error message, test=develop

* fix optest,test=develop

* fix comment, test=develop

python/paddle/fluid/dygraph/nn.py

@@ -1661,7 +1661,7 @@ class GRUUnit(layers.Layer):
             'HiddenPrev': [hidden],
             'Weight': [self.weight]
         }
-        if self.bias:
+        if self.bias is not None:
             inputs['Bias'] = [self.bias]
         attrs = {
             'activation': self.activation,
@@ -2114,7 +2114,7 @@ class BilinearTensorProduct(layers.Layer):
 
     def forward(self, x, y):
         self._inputs = {"X": x, "Y": y, "Weight": self.weight}
-        if self.bias:
+        if self.bias is not None:
             self._inputs["Bias"] = self.bias
         if self._name is not None:
             out = self._helper.create_variable(

python/paddle/fluid/dygraph/parallel.py

@@ -420,7 +420,7 @@ class DataParallel(layers.Layer):
         grad_vars = []
         for param in self._layers.parameters():
             # NOTE(zcd): The grad_ivar maybe no generated.
-            if param.trainable and param._grad_ivar():
+            if param.trainable and (param._grad_ivar() is not None):
                 g_var = param._grad_ivar()
                 grad_vars.append(g_var)
                 assert g_var not in grad_var_set

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -204,9 +204,20 @@ def monkey_patch_varbase():
                 return 'name %s, shape: %s, not inited' % (self.name,
                                                            self.shape)
 
-    for method_name, method in (("set_value", set_value), ("block", block),
-                                ("backward", backward), ("gradient", gradient),
-                                ("__str__", __str__), ("to_string", to_string)):
+    def __nonzero__(self):
+        numel = np.prod(self.shape)
+        assert numel == 1, "When Variable is used as the condition of if/while , Variable can only contain one element."
+        tensor = self.value().get_tensor()
+        assert tensor._is_initialized(), "tensor not initialized"
+        return bool(np.all(tensor.__array__() > 0))
+
+    def __bool__(self):
+        return self.__nonzero__()
+
+    for method_name, method in (
+        ("__bool__", __bool__), ("__nonzero__", __nonzero__),
+        ("set_value", set_value), ("block", block), ("backward", backward),
+        ("gradient", gradient), ("__str__", __str__), ("to_string", to_string)):
         setattr(core.VarBase, method_name, method)
 
     # patch math methods for varbase

python/paddle/fluid/dygraph_utils.py

@@ -55,7 +55,7 @@ def _append_bias_in_dygraph(input, bias=None, axis=1):
 
     Return the Variable after bias operation
     """
-    if not bias:
+    if bias is None:
         return input
 
     return core.ops.elementwise_add(input, bias, 'axis', axis)

python/paddle/fluid/tests/unittests/dygraph_to_static/ifelse_simple_func.py

@@ -182,8 +182,8 @@ class NetWithControlFlowIf(fluid.dygraph.Layer):
 
 def if_with_and_or(x_v, label=None):
     batch_size = fluid.layers.shape(x_v)
-    if x_v and (fluid.layers.mean(x_v).numpy()[0] > 0 or
-                label is not None) and batch_size[0] > 1 and True:
+    if x_v is not None and (fluid.layers.mean(x_v).numpy()[0] > 0 or
+                            label is not None) and batch_size[0] > 1 and True:
         x_v = x_v - 1
     else:
         x_v = x_v + 1
@@ -198,16 +198,16 @@ def if_with_and_or_1(x, y=None):
     batch_size = fluid.layers.shape(x)
     if batch_size[0] > 1 and y is not None:
         x = x + 1
-    if y or batch_size[0] > 1:
+    if y is not None or batch_size[0] > 1:
         x = x - 1
     return x
 
 
 def if_with_and_or_2(x, y=None):
     batch_size = fluid.layers.shape(x)
-    if x and batch_size[0] > 1 and y is not None:
+    if x is not None and batch_size[0] > 1 and y is not None:
         x = x + 1
-    if batch_size[0] > 1 or y or x is not None:
+    if batch_size[0] > 1 or y is not None or x is not None:
         x = x - 1
     return x
 
@@ -215,9 +215,10 @@ def if_with_and_or_2(x, y=None):
 def if_with_and_or_3(x, y=None):
     batch_size = fluid.layers.shape(x)
     mean_res = fluid.layers.mean(x)
-    if x and batch_size[0] > 1 and y is not None and mean_res.numpy()[0] > 0:
+    if x is not None and batch_size[0] > 1 and y is not None and mean_res.numpy(
+    )[0] > 0:
         x = x + 1
-    if mean_res.numpy()[0] > 0 and (x and batch_size[0] > 1) and y:
+    if mean_res.numpy()[0] > 0 and (x is not None and batch_size[0] > 1) and y:
         x = x - 1
     return x
 
@@ -225,8 +226,10 @@ def if_with_and_or_3(x, y=None):
 def if_with_and_or_4(x, y=None):
     batch_size = fluid.layers.shape(x)
     mean_res = fluid.layers.mean(x)
-    if (x and batch_size[0] > 1) or (y is not None and mean_res.numpy()[0] > 0):
+    if (x is not None and batch_size[0] > 1) or (y is not None and
+                                                 mean_res.numpy()[0] > 0):
         x = x + 1
-    if (x or batch_size[0] > 1) and (y is not None or mean_res.numpy()[0] > 0):
+    if (x is not None or batch_size[0] > 1) and (y is not None or
+                                                 mean_res.numpy()[0] > 0):
         x = x - 1
     return x

python/paddle/fluid/tests/unittests/dygraph_to_static/test_ifelse.py

@@ -100,7 +100,7 @@ def dyfunc_ifExp_with_while(x):
     def body(i, ten, y):
         # It will be converted into `layers.cond` as followed.
         # map_func(lambda x: fluid.layers.cond(i==0, lambda: x, lambda: add_fn(x), y)
-        y = map_func(lambda x: x if i == 0 else add_fn(x), y)
+        y = map_func(lambda x: x if (i == 0) is not None else add_fn(x), y)
         i += 1
         return [i, ten, y]
 

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -50,7 +50,7 @@ class PrePostProcessLayer(Layer):
         self.functors = []
         for cmd in self.process_cmd:
             if cmd == "a":  # add residual connection
-                self.functors.append(lambda x, y: x + y if y else x)
+                self.functors.append(lambda x, y: x + y if y is not None else x)
             elif cmd == "n":  # add layer normalization
                 self.functors.append(
                     self.add_sublayer(
@@ -118,7 +118,7 @@ class MultiHeadAttention(Layer):
         # scale dot product attention
         product = layers.matmul(
             x=q, y=k, transpose_y=True, alpha=self.d_model**-0.5)
-        if attn_bias:
+        if attn_bias is not None:
             product += attn_bias
         weights = layers.softmax(product)
         if self.dropout_rate:

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

@@ -364,7 +364,7 @@ class PrePostProcessLayer(Layer):
     def forward(self, prev_out, out, process_cmd, dropout_rate=0.):
         for cmd in process_cmd:
             if cmd == "a":  # add residual connection
-                out = out + prev_out if prev_out else out
+                out = out + prev_out if prev_out is not None else out
             elif cmd == "n":  # add layer normalization
                 out = self._layer_norm(out)
             elif cmd == "d":  # add dropout
@@ -443,7 +443,7 @@ class MultiHeadAttentionLayer(Layer):
             y=transpose_k,
             transpose_y=True,
             alpha=self._d_model**-0.5)
-        if attn_bias:
+        if attn_bias is not None:
             product += attn_bias
         weights = fluid.layers.softmax(product)
         if self._dropout_rate:

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import copy
 import math
+import numpy as np
 import unittest
 
 import paddle.fluid as fluid
@@ -319,7 +320,8 @@ class TestLinearWamrupLearningRateDecayDygraphMode(unittest.TestCase):
 
                 t = lr()
 
-                self.assertEqual(t[0], right_result[i])
+                self.assertTrue(
+                    np.allclose((t.numpy())[0].item(), right_result[i]))
 
 
 class TestLinearWamrupLearningRateDecayDygraphModeTypeCheck(unittest.TestCase):

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

@@ -189,6 +189,25 @@ class TestVarBase(unittest.TestCase):
                 np.array_equal(var.numpy(),
                                fluid.framework._var_base_to_np(var)))
 
+    def test_if(self):
+        with fluid.dygraph.guard():
+            var1 = fluid.dygraph.to_variable(np.array([[[0]]]))
+            var2 = fluid.dygraph.to_variable(np.array([[[1]]]))
+
+            var1_bool = False
+            var2_bool = False
+
+            if var1:
+                var1_bool = True
+
+            if var2:
+                var2_bool = True
+
+            assert var1_bool == False, "if var1 should be false"
+            assert var2_bool == True, "if var2 should be true"
+            assert bool(var1) == False, "bool(var1) is False"
+            assert bool(var2) == True, "bool(var2) is True"
+
 
 if __name__ == '__main__':
     unittest.main()