Fix clip input check (#26683)

* Fix clip input check

* Fix default min/max value

* Allow both max and min to be None

* Register op change

* Revert OP signature change

paddle/fluid/operators/clip_op.h

@@ -66,7 +66,7 @@ template <typename DeviceContext, typename T>
 class ClipKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto max = static_cast<T>(context.Attr<float>("max"));
+    auto max = context.Attr<T>("max");
     Tensor max_cpu;
     if (context.HasInput("Max")) {
       auto* max_t = context.Input<Tensor>("Max");
@@ -77,9 +77,8 @@ class ClipKernel : public framework::OpKernel<T> {
       }
       max = max_data[0];
     }
-    max = static_cast<T>(max);
 
-    auto min = context.Attr<float>("min");
+    auto min = context.Attr<T>("min");
     Tensor min_cpu;
     if (context.HasInput("Min")) {
       auto* min_t = context.Input<Tensor>("Min");
@@ -90,9 +89,10 @@ class ClipKernel : public framework::OpKernel<T> {
       }
       min = min_data[0];
     }
-    min = static_cast<T>(min);
-    PADDLE_ENFORCE_LT(min, max, platform::errors::InvalidArgument(
-                                    "max should be greater than min. "
+
+    PADDLE_ENFORCE_LE(min, max,
+                      platform::errors::InvalidArgument(
+                          "max should be greater than or equal to min. "
                           "But received min = %f, max = %f",
                           min, max));
 
@@ -141,7 +141,7 @@ template <typename DeviceContext, typename T>
 class ClipGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto max = static_cast<T>(context.Attr<float>("max"));
+    auto max = context.Attr<T>("max");
     Tensor max_cpu;
     if (context.HasInput("Max")) {
       auto* max_t = context.Input<Tensor>("Max");
@@ -152,9 +152,8 @@ class ClipGradKernel : public framework::OpKernel<T> {
       }
       max = max_data[0];
     }
-    max = static_cast<T>(max);
 
-    auto min = context.Attr<float>("min");
+    auto min = context.Attr<T>("min");
     Tensor min_cpu;
     if (context.HasInput("Min")) {
       auto* min_t = context.Input<Tensor>("Min");
@@ -165,7 +164,6 @@ class ClipGradKernel : public framework::OpKernel<T> {
       }
       min = min_data[0];
     }
-    min = static_cast<T>(min);
 
     auto* d_out =
         context.Input<framework::LoDTensor>(framework::GradVarName("Out"));

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

@@ -93,6 +93,13 @@ class TestCase4(TestClipOp):
         self.inputs['Min'] = np.array([0.3]).astype('float32')
 
 
+class TestCase5(TestClipOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 16)
+        self.max = 0.5
+        self.min = 0.5
+
+
 class TestClipOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
@@ -112,6 +119,7 @@ class TestClipOpError(unittest.TestCase):
 
 class TestClipAPI(unittest.TestCase):
     def test_clip(self):
+        paddle.enable_static()
         data_shape = [1, 9, 9, 4]
         data = np.random.random(data_shape).astype('float32')
         images = fluid.data(name='image', shape=data_shape, dtype='float32')
@@ -128,15 +136,19 @@ class TestClipAPI(unittest.TestCase):
         out_4 = paddle.clip(images, max=0.7)
         out_5 = paddle.clip(images, min=min)
         out_6 = paddle.clip(images, max=max)
+        out_7 = paddle.clip(images, max=-1.)
+        out_8 = paddle.clip(images)
 
-        res1, res2, res3, res4, res5, res6 = exe.run(
+        res1, res2, res3, res4, res5, res6, res7, res8 = exe.run(
             fluid.default_main_program(),
             feed={
                 "image": data,
                 "min": np.array([0.2]).astype('float32'),
                 "max": np.array([0.8]).astype('float32')
             },
-            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
+            fetch_list=[
+                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
+            ])
 
         self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
         self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
@@ -144,6 +156,8 @@ class TestClipAPI(unittest.TestCase):
         self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
         self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
         self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
+        self.assertTrue(np.allclose(res7, data.clip(max=-1)))
+        self.assertTrue(np.allclose(res8, data))
 
     def test_clip_dygraph(self):
         place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
@@ -163,10 +177,8 @@ class TestClipAPI(unittest.TestCase):
         paddle.enable_static()
         x1 = fluid.data(name='x1', shape=[1], dtype="int16")
         x2 = fluid.data(name='x2', shape=[1], dtype="int8")
-        x3 = fluid.data(name='x3', shape=[1], dtype="float32")
         self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
         self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
-        self.assertRaises(Exception, paddle.clip, x=x3)
 
 
 if __name__ == '__main__':

python/paddle/tensor/math.py

@@ -15,6 +15,7 @@
 math functions
 """
 from __future__ import print_function
+import numpy as np
 
 from paddle.common_ops_import import *
 from paddle.tensor import cast
@@ -24,7 +25,6 @@ from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
 from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
-import sys
 
 # TODO: define math functions
 # yapf: disable
@@ -1611,11 +1611,15 @@ def clip(x, min=None, max=None, name=None):
             # [[4.5, 6.4]
     """
 
-    assert min is not None or max is not None, "either min or max should be defined."
+    np_dtype = np.float32
+    if x.dtype == VarDesc.VarType.FP64:
+        np_dtype = np.float64
+    fmin = float(np.finfo(np_dtype).min)
+    fmax = float(np.finfo(np_dtype).max)
 
     if in_dygraph_mode():
-        min = sys.float_info.min if min is None else min
-        max = sys.float_info.max if max is None else max
+        min = fmin if min is None else min
+        max = fmax if max is None else max
         return core.ops.clip(x, "min", min, "max", max)
 
     if min is not None:
@@ -1629,10 +1633,10 @@ def clip(x, min=None, max=None, name=None):
             check_dtype(max.dtype, 'max', ['float32', 'float64', 'int32'],
                         'clip', '(When the type of max in clip is Variable.)')
 
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'clip')
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'clip')
 
     inputs = {'X': x}
-    attrs = {'min': sys.float_info.min, 'max': sys.float_info.max}
+    attrs = {'min': fmin, 'max': fmax}
 
     if isinstance(min, Variable):
         min.stop_gradient = True