Update paddle.clamp (#25906)

* Update `paddle.clamp` rename to `paddle.clip` add fast path for dygraph mode remove `out` rename `input` -> `x` update doc sample * Fix leftover `Variable` wording * Indent doc with spaces * Remove `:alias` in docs * Update `enable_imperative` -> `disable_static` * Remove `imperative` also trigger CI * Update tests for better coverage * Rebase to fix `cosine_similarity` * Fix `cosine_similarity` some more

Author: willthefrog

python/paddle/__init__.py

@@ -181,7 +181,7 @@
 from .tensor.math import erf #DEFINE_ALIAS
 from .tensor.math import addcmul #DEFINE_ALIAS
 from .tensor.math import addmm #DEFINE_ALIAS
-from .tensor.math import clamp #DEFINE_ALIAS
+from .tensor.math import clip #DEFINE_ALIAS
 from .tensor.math import trace #DEFINE_ALIAS
 from .tensor.math import kron #DEFINE_ALIAS
 from .tensor.math import prod #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -12205,8 +12205,6 @@ def logical_not(x, out=None, name=None):
 @templatedoc()
 def clip(x, min, max, name=None):
  """
- :alias_main: paddle.nn.clip
-:alias: paddle.nn.clip,paddle.nn.clip.clip
 :old_api: paddle.fluid.layers.clip
 
  ${comment}

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

@@ -16,6 +16,7 @@
 
 import unittest
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 from op_test import OpTest
@@ -109,5 +110,64 @@ def test_dtype():
  self.assertRaises(TypeError, test_dtype)
 
 
+class TestClipAPI(unittest.TestCase):
+ def test_clip(self):
+ data_shape = [1, 9, 9, 4]
+ data = np.random.random(data_shape).astype('float32')
+ images = fluid.data(name='image', shape=data_shape, dtype='float32')
+ min = fluid.data(name='min', shape=[1], dtype='float32')
+ max = fluid.data(name='max', shape=[1], dtype='float32')
+
+ place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+ ) else fluid.CPUPlace()
+ exe = fluid.Executor(place)
+
+ out_1 = paddle.clip(images, min=min, max=max)
+ out_2 = paddle.clip(images, min=0.2, max=0.9)
+ out_3 = paddle.clip(images, min=0.3)
+ out_4 = paddle.clip(images, max=0.7)
+ out_5 = paddle.clip(images, min=min)
+ out_6 = paddle.clip(images, max=max)
+
+ res1, res2, res3, res4, res5, res6 = 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])
+
+ self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
+ self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
+ self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
+ 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)))
+
+ def test_clip_dygraph(self):
+ place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+ ) else fluid.CPUPlace()
+ paddle.disable_static(place)
+ data_shape = [1, 9, 9, 4]
+ data = np.random.random(data_shape).astype('float32')
+ images = paddle.to_variable(data, dtype='float32')
+
+ out_1 = paddle.clip(images, min=0.2, max=0.8)
+ out_2 = paddle.clip(images, min=0.2, max=0.9)
+
+ self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
+ self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
+
+ def test_errors(self):
+ 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__':
  unittest.main()

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

@@ -29,10 +29,10 @@ def setUp(self):
  if core.is_compiled_with_cuda():
  self.places.append(paddle.CUDAPlace(0))
 
- def _get_numpy_out(self, x1, x2, dim=1, eps=1e-8):
- w12 = np.sum(x1 * x2, axis=dim)
- w1 = np.sum(x1 * x1, axis=dim)
- w2 = np.sum(x2 * x2, axis=dim)
+ def _get_numpy_out(self, x1, x2, axis=1, eps=1e-8):
+ w12 = np.sum(x1 * x2, axis=axis)
+ w1 = np.sum(x1 * x1, axis=axis)
+ w2 = np.sum(x2 * x2, axis=axis)
  n12 = np.sqrt(np.clip(w1 * w2, eps * eps, None))
  cos_sim = w12 / n12
  return cos_sim
@@ -42,22 +42,22 @@ def check_static_result(self, place):
 
  with program_guard(Program(), Program()):
  shape = [10, 15]
- dim = 1
+ axis = 1
  eps = 1e-8
  np.random.seed(0)
  np_x1 = np.random.rand(*shape).astype(np.float32)
  np_x2 = np.random.rand(*shape).astype(np.float32)
 
  x1 = paddle.data(name="x1", shape=shape)
  x2 = paddle.data(name="x2", shape=shape)
- result = F.cosine_similarity(x1, x2, dim=dim, eps=eps)
+ result = F.cosine_similarity(x1, x2, axis=axis, eps=eps)
  exe = Executor(place)
  fetches = exe.run(default_main_program(),
  feed={"x1": np_x1,
  "x2": np_x2},
  fetch_list=[result])
 
- np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+ np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
  self.assertTrue(np.allclose(fetches[0], np_out))
 
  def test_static(self):
@@ -68,33 +68,33 @@ def test_dygraph_1(self):
  paddle.disable_static()
 
  shape = [10, 15]
- dim = 1
+ axis = 1
  eps = 1e-8
  np.random.seed(1)
  np_x1 = np.random.rand(*shape).astype(np.float32)
  np_x2 = np.random.rand(*shape).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+ np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
  tesnor_x1 = paddle.to_variable(np_x1)
  tesnor_x2 = paddle.to_variable(np_x2)
- y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+ y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
  self.assertTrue(np.allclose(y.numpy(), np_out))
 
  def test_dygraph_2(self):
  paddle.disable_static()
 
  shape = [12, 13]
- dim = 0
+ axis = 0
  eps = 1e-6
  np.random.seed(1)
  np_x1 = np.random.rand(*shape).astype(np.float32)
  np_x2 = np.random.rand(*shape).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+ np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
  tesnor_x1 = paddle.to_variable(np_x1)
  tesnor_x2 = paddle.to_variable(np_x2)
- y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+ y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
  self.assertTrue(np.allclose(y.numpy(), np_out))
 
@@ -103,16 +103,16 @@ def test_dygraph_3(self):
 
  shape1 = [10, 12, 10]
  shape2 = [10, 1, 10]
- dim = 2
+ axis = 2
  eps = 1e-6
  np.random.seed(1)
  np_x1 = np.random.rand(*shape1).astype(np.float32)
  np_x2 = np.random.rand(*shape2).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+ np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
  tesnor_x1 = paddle.to_variable(np_x1)
  tesnor_x2 = paddle.to_variable(np_x2)
- y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+ y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
  self.assertTrue(np.allclose(y.numpy(), np_out))
 

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

@@ -28,9 +28,9 @@
 from ...fluid.layers import squeeze #DEFINE_ALIAS
 from ...fluid.layers import unsqueeze #DEFINE_ALIAS
 from ...fluid.layers import elementwise_mul #DEFINE_ALIAS
-from ...tensor import clamp #DEFINE_ALIAS
-from ...tensor import sum #DEFINE_ALIAS
-from ...tensor import sqrt #DEFINE_ALIAS
+from ...tensor import clip
+from ...tensor import sum
+from ...tensor import sqrt
 
 #from ...fluid.layers import fc #DEFINE_ALIAS
 from ...fluid.layers import pad_constant_like #DEFINE_ALIAS
@@ -635,17 +635,17 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
  return out
 
 
-def cosine_similarity(x1, x2, dim=1, eps=1e-8):
+def cosine_similarity(x1, x2, axis=1, eps=1e-8):
  """
- Compute cosine similarity between x1 and x2 along dim.
+ Compute cosine similarity between x1 and x2 along axis.
 
  Parameters:
  x1 (Tensor): First input. float32/double.
  x2 (Tensor): Second input. float32/double.
- dim (int): Dimension of vectors to compute cosine similarity. Default is 1.
+ axis (int): Dimension of vectors to compute cosine similarity. Default is 1.
  eps(float): Small value to avoid division by zero. Default is 1e-8.
  
- Returns: a Tensor representing cosine similarity between x1 and x2 along dim.
+ Returns: a Tensor representing cosine similarity between x1 and x2 along axis.
  Return Type: Tensor
 
  Examples:
@@ -659,7 +659,7 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-8):
  [0.9098952 0.15715368 0.8671125 0.3156102 ]
  [0.4427798 0.54136837 0.5276275 0.32394758]
  [0.3769419 0.8535014 0.48041078 0.9256797 ]]
- dim = 1
+ axis = 1
  eps = 1e-8
  Out: [0.5275037 0.8368967 0.75037485 0.9245899]
 
@@ -675,14 +675,14 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-8):
  x2 = np.random.rand(2,3)
  x1 = paddle.to_tensor(x1)
  x2 = paddle.to_tensor(x2)
- result = paddle.nn.functional.cosine_similarity(x1, x2, dim=0)
+ result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0)
  print(result.numpy())
  # [0.99806249 0.9817672 0.94987036]
  
  """
- w12 = sum(elementwise_mul(x1, x2), axis=dim)
- w1 = sum(elementwise_mul(x1, x1), axis=dim)
- w2 = sum(elementwise_mul(x2, x2), axis=dim)
- n12 = sqrt(clamp(w1 * w2, min=eps * eps))
+ w12 = sum(elementwise_mul(x1, x2), axis=axis)
+ w1 = sum(elementwise_mul(x1, x1), axis=axis)
+ w2 = sum(elementwise_mul(x2, x2), axis=axis)
+ n12 = sqrt(clip(w1 * w2, min=eps * eps))
  cos_sim = w12 / n12
  return cos_sim

python/paddle/nn/functional/common.py

@@ -154,7 +154,7 @@
 from .math import erf #DEFINE_ALIAS
 from .math import addcmul #DEFINE_ALIAS
 from .math import addmm #DEFINE_ALIAS
-from .math import clamp #DEFINE_ALIAS
+from .math import clip #DEFINE_ALIAS
 from .math import trace #DEFINE_ALIAS
 from .math import kron #DEFINE_ALIAS
 from .math import prod #DEFINE_ALIAS