add topk cast (#41304)

python/paddle/fluid/layers/nn.py

@@ -4791,7 +4791,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0]
             fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0]
     """
-    helper = LayerHelper('reduce_prod', **locals())
+
     if dim is not None and not isinstance(dim, list):
         if isinstance(dim, tuple):
             dim = list(dim)
@@ -4801,6 +4801,12 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             raise TypeError(
                 "The type of axis must be int, list or tuple, but received {}".
                 format(type(dim)))
+    if in_dygraph_mode():
+        return _C_ops.final_state_reduce_prod(
+            input, dim if dim != None and dim != [] else [0], keep_dim, True if
+            dim == None or dim == [] or len(dim) == len(input.shape) else False)
+
+    helper = LayerHelper('reduce_prod', **locals())
     check_variable_and_dtype(
         input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod')
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())

python/paddle/fluid/layers/tensor.py

@@ -21,7 +21,7 @@ import warnings
 from ..layer_helper import LayerHelper
 from ..param_attr import ParamAttr
 from ..initializer import Initializer
-from ..framework import convert_np_dtype_to_dtype_, _non_static_mode, _varbase_creator, device_guard, _in_legacy_dygraph
+from ..framework import convert_np_dtype_to_dtype_, _non_static_mode, _varbase_creator, device_guard, _in_legacy_dygraph, in_dygraph_mode
 from ..framework import Variable
 from ..initializer import Constant
 from ..core import VarDesc
@@ -243,6 +243,11 @@ def cast(x, dtype):
             x = paddle.to_tensor([2, 3, 4], 'float64')
             y = paddle.cast(x, 'uint8')
     """
+    if in_dygraph_mode():
+        if not isinstance(dtype, core.VarDesc.VarType):
+            dtype = convert_np_dtype_to_dtype_(dtype)
+        return _C_ops.final_state_cast(x, dtype)
+
     if _non_static_mode():
         if not isinstance(dtype, core.VarDesc.VarType):
             dtype = convert_np_dtype_to_dtype_(dtype)

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

@@ -1559,6 +1559,8 @@ class OpTest(unittest.TestCase):
 
             def _compare_numpy(self, name, actual_np, expect_np):
                 with _test_eager_guard():
+                    print(actual_np)
+                    print(expect_np)
                     super()._compare_numpy(name, actual_np, expect_np)
 
             def convert_uint16_to_float_ifneed(self, actual_np, expect_np):

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

@@ -22,6 +22,7 @@ import paddle.fluid.core as core
 import paddle.fluid as fluid
 from paddle.fluid import compiler, Program, program_guard
 from op_test import OpTest, convert_uint16_to_float, convert_float_to_uint16
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestCastOpFp32ToFp64(OpTest):
@@ -115,6 +116,20 @@ class TestCastOpError(unittest.TestCase):
             self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')
 
 
+class TestCastOpEager(unittest.TestCase):
+    def test_eager(self):
+        with paddle.fluid.dygraph.base.guard():
+            with _test_eager_guard():
+                x = paddle.ones([2, 2], dtype="float16")
+                x.stop_gradient = False
+                out = paddle.cast(x, "float32")
+                self.assertTrue(
+                    np.array_equal(out, np.ones([2, 2]).astype("float32")))
+                out.backward()
+                self.assertTrue(np.array_equal(x.gradient(), x.numpy()))
+                self.assertTrue(x.gradient().dtype == np.float16)
+
+
 if __name__ == '__main__':
     paddle.enable_static()
     unittest.main()

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

@@ -241,6 +241,7 @@ class TestMin8DOp(OpTest):
 class TestProdOp(OpTest):
     def setUp(self):
         self.op_type = "reduce_prod"
+        self.python_api = paddle.prod
         self.init_data_type()
         self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.data_type)}
         self.outputs = {'Out': self.inputs['X'].prod(axis=0)}

python/paddle/fluid/tests/unittests/test_top_k_v2_op.py

@@ -57,10 +57,10 @@ class TestTopkOp(OpTest):
         self.outputs = {'Out': output, 'Indices': indices}
 
     def test_check_output(self):
-        self.check_output(check_eager=False)
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(set(['X']), 'Out', check_eager=False)
+        self.check_grad(set(['X']), 'Out', check_eager=True)
 
 
 class TestTopkOp1(TestTopkOp):

python/paddle/tensor/search.py

@@ -858,6 +858,12 @@ def topk(x, k, axis=None, largest=True, sorted=True, name=None):
 
     """
 
+    if in_dygraph_mode():
+        if axis == None:
+            axis = -1
+        out, indices = _C_ops.final_state_top_k(x, k, axis, largest, sorted)
+        return out, indices
+
     if _non_static_mode():
         if axis is None:
             out, indices = _C_ops.top_k_v2(x, 'k',

python/paddle/utils/code_gen/api.yaml

@@ -1123,7 +1123,8 @@
   infer_meta :
     func : ReduceInferMetaBase
   kernel :
-    func : reduce_prod
+    func : prod_raw
+  backward : reduce_prod_grad
 
 - api : relu
   args : (Tensor x)

python/paddle/utils/code_gen/backward.yaml

@@ -721,6 +721,16 @@
   kernel :
     func : reciprocal_grad
 
+- backward_api : reduce_prod_grad
+  forward : reduce_prod (Tensor x, int64_t[] dims, bool keep_dim, bool reduce_all) -> Tensor(out)
+  args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims,  bool keep_dim, bool reduce_all)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : reduce_prod_grad
+
 - backward_api : relu_double_grad
   forward : relu_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x)
   args : (Tensor out, Tensor grad_x_grad)