[PaddlePaddle hackathon] Add randint_like (#36169)

* add randint like

* rm .cc .cu

* Update unity_build_rule.cmake

* try to make test pass

* use python

* update

* update randint_like

* rename test_randint_like_op -> test_randint_like

* update

* update randint like docs

* update randint like

* update

* update

* add bool

* update randint like test

* update

* update

python/paddle/__init__.py

@@ -230,6 +230,7 @@ from .tensor.random import uniform  # noqa: F401
 from .tensor.random import randn  # noqa: F401
 from .tensor.random import rand  # noqa: F401
 from .tensor.random import randint  # noqa: F401
+from .tensor.random import randint_like  # noqa: F401
 from .tensor.random import randperm  # noqa: F401
 from .tensor.search import argmax  # noqa: F401
 from .tensor.search import argmin  # noqa: F401
@@ -376,6 +377,7 @@ __all__ = [  # noqa
            'ParamAttr',
            'stanh',
            'randint',
+           'randint_like',
            'assign',
            'gather',
            'scale',

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

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+from paddle.static import program_guard, Program
+
+
+# Test python API
+class TestRandintLikeAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_bool = np.zeros((10, 12)).astype("bool")
+        self.x_int32 = np.zeros((10, 12)).astype("int32")
+        self.x_int64 = np.zeros((10, 12)).astype("int64")
+        self.x_float16 = np.zeros((10, 12)).astype("float16")
+        self.x_float32 = np.zeros((10, 12)).astype("float32")
+        self.x_float64 = np.zeros((10, 12)).astype("float64")
+
+        self.dtype = ["bool", "int32", "int64", "float16", "float32", "float64"]
+        self.place=paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+            # results are from [-100, 100).
+            x_bool = paddle.fluid.data(
+                name="x_bool", shape=[10, 12], dtype="bool")
+            x_int32 = paddle.fluid.data(
+                name="x_int32", shape=[10, 12], dtype="int32")
+            x_int64 = paddle.fluid.data(
+                name="x_int64", shape=[10, 12], dtype="int64")
+            x_float16 = paddle.fluid.data(
+                name="x_float16", shape=[10, 12], dtype="float16")
+            x_float32 = paddle.fluid.data(
+                name="x_float32", shape=[10, 12], dtype="float32")
+            x_float64 = paddle.fluid.data(
+                name="x_float64", shape=[10, 12], dtype="float64")
+
+            exe = paddle.static.Executor(self.place)
+
+            # x dtype is bool output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist1 = [
+                paddle.randint_like(
+                    x_bool, low=-10, high=10, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs1 = exe.run(feed={'x_bool': self.x_bool}, fetch_list=outlist1)
+            for out, dtype in zip(outs1, self.dtype):
+                self.assertTrue(out.dtype, np.dtype(dtype))
+                self.assertTrue(((out >= -10) & (out <= 10)).all(), True)
+
+            # x dtype is int32 output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist2 = [
+                paddle.randint_like(
+                    x_int32, low=-5, high=10, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs2 = exe.run(feed={'x_int32': self.x_int32}, fetch_list=outlist2)
+            for out, dtype in zip(outs2, self.dtype):
+                self.assertTrue(out.dtype, np.dtype(dtype))
+                self.assertTrue(((out >= -5) & (out <= 10)).all(), True)
+
+            # x dtype is int64 output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist3 = [
+                paddle.randint_like(
+                    x_int64, low=-100, high=100, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs3 = exe.run(feed={'x_int64': self.x_int64}, fetch_list=outlist3)
+            for out, dtype in zip(outs3, self.dtype):
+                self.assertTrue(out.dtype, np.dtype(dtype))
+                self.assertTrue(((out >= -100) & (out <= 100)).all(), True)
+
+            # x dtype is float16 output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist4 = [
+                paddle.randint_like(
+                    x_float16, low=-3, high=25, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs4 = exe.run(feed={'x_float16': self.x_float16},
+                            fetch_list=outlist4)
+            for out, dtype in zip(outs4, self.dtype):
+                self.assertTrue(out.dtype, np.dtype(dtype))
+                self.assertTrue(((out >= -3) & (out <= 25)).all(), True)
+
+            # x dtype is float32 output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist5 = [
+                paddle.randint_like(
+                    x_float32, low=-25, high=25, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs5 = exe.run(feed={'x_float32': self.x_float32},
+                            fetch_list=outlist5)
+            for out, dtype in zip(outs5, self.dtype):
+                self.assertTrue(out.dtype, np.dtype(dtype))
+                self.assertTrue(((out >= -25) & (out <= 25)).all(), True)
+
+            # x dtype is float64 output dtype in ["bool", "int32", "int64", "float16", "float32", "float64"]
+            outlist6 = [
+                paddle.randint_like(
+                    x_float64, low=-16, high=16, dtype=dtype)
+                for dtype in self.dtype
+            ]
+            outs6 = exe.run(feed={'x_float64': self.x_float64},
+                            fetch_list=outlist6)
+            for out, dtype in zip(outs6, self.dtype):
+                self.assertTrue(out.dtype, dtype)
+                self.assertTrue(((out >= -16) & (out <= 16)).all(), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        # x dtype ["bool", "int32", "int64", "float16", "float32", "float64"]
+        for x in [
+                self.x_bool, self.x_int32, self.x_int64, self.x_float16,
+                self.x_float32, self.x_float64
+        ]:
+            x_inputs = paddle.to_tensor(x)
+            # self.dtype ["bool", "int32", "int64", "float16", "float32", "float64"]
+            for dtype in self.dtype:
+                out = paddle.randint_like(
+                    x_inputs, low=-100, high=100, dtype=dtype)
+                self.assertTrue(out.numpy().dtype, np.dtype(dtype))
+                self.assertTrue(((out.numpy() >= -100) &
+                                 (out.numpy() <= 100)).all(), True)
+
+        paddle.enable_static()
+
+    def test_errors(self):
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+            x_bool = paddle.fluid.data(
+                name="x_bool", shape=[10, 12], dtype="bool")
+            x_int32 = paddle.fluid.data(
+                name="x_int32", shape=[10, 12], dtype="int32")
+            x_int64 = paddle.fluid.data(
+                name="x_int64", shape=[10, 12], dtype="int64")
+            x_float16 = paddle.fluid.data(
+                name="x_float16", shape=[10, 12], dtype="float16")
+            x_float32 = paddle.fluid.data(
+                name="x_float32", shape=[10, 12], dtype="float32")
+            x_float64 = paddle.fluid.data(
+                name="x_float64", shape=[10, 12], dtype="float64")
+
+            # x dtype is bool
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_bool, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(ValueError, paddle.randint_like, x_bool, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(ValueError, paddle.randint_like, x_bool, low=-5)
+
+            # x dtype is int32
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_int32, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(ValueError, paddle.randint_like, x_int32, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(ValueError, paddle.randint_like, x_int32, low=-5)
+
+            # x dtype is int64
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_int64, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(ValueError, paddle.randint_like, x_int64, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(ValueError, paddle.randint_like, x_int64, low=-5)
+
+            # x dtype is float16
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float16, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float16, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float16, low=-5)
+
+            # x dtype is float32
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float32, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float32, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float32, low=-5)
+
+            # x dtype is float64
+            # low is 5 and high is 5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float64, low=5, high=5)
+            # low(default value) is 0 and high is -5, low must less then high
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float64, high=-5)
+            # if high is None, low must be greater than 0
+            self.assertRaises(
+                ValueError, paddle.randint_like, x_float64, low=-5)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -197,6 +197,7 @@ from .random import uniform_  # noqa: F401
 from .random import randn  # noqa: F401
 from .random import rand  # noqa: F401
 from .random import randint  # noqa: F401
+from .random import randint_like  # noqa: F401
 from .random import randperm  # noqa: F401
 from .search import argmax  # noqa: F401
 from .search import argmin  # noqa: F401

python/paddle/tensor/random.py

@@ -661,6 +661,180 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
     return out
 
 
+def randint_like(x, low=0, high=None, dtype=None, name=None):
+    """
+    This OP returns a Tensor filled with random integers from a discrete uniform
+    distribution in the range [``low``, ``high``), with the same shape as ``x``.
+    (use ``dtype`` if ``dtype`` is not None) 
+    If ``high`` is None (the default), the range is [0, ``low``).
+
+    Args:
+        x (Tensor): The input tensor which specifies shape. The dtype of ``x`` 
+            can be bool, int32, int64, float16, float32, float64.
+        low (int): The lower bound on the range of random values to generate.
+            The ``low`` is included in the range. If ``high`` is None, the
+            range is [0, ``low``). Default is 0.
+        high (int, optional): The upper bound on the range of random values to
+            generate, the ``high`` is excluded in the range. Default is None
+            (see above for behavior if high = None). Default is None.
+        dtype (str|np.dtype, optional): The data type of the
+            output tensor. Supported data types: bool, int32, int64, float16, 
+            float32, float64. If ``dytpe`` is None, the data type is the
+            same as x's data type. Default is None.
+        name (str, optional): The default value is None.  Normally there is no
+            need for user to set this property.  For more information, please
+            refer to :ref:`api_guide_Name`.
+
+    Returns: 
+        Tensor: A Tensor filled with random integers from a discrete uniform
+        distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            # example 1:
+            # dtype is None and the dtype of x is float16
+            x = paddle.zeros((1,2)).astype("float16")
+            out1 = paddle.randint_like(x, low=-5, high=5)
+            print(out1)
+            print(out1.dtype)
+            # [[0, -3]]  # random
+            # paddle.float16
+
+            # example 2:
+            # dtype is None and the dtype of x is float32
+            x = paddle.zeros((1,2)).astype("float32")
+            out2 = paddle.randint_like(x, low=-5, high=5)
+            print(out2)
+            print(out2.dtype)
+            # [[0, -3]]  # random
+            # paddle.float32
+
+            # example 3:
+            # dtype is None and the dtype of x is float64
+            x = paddle.zeros((1,2)).astype("float64")
+            out3 = paddle.randint_like(x, low=-5, high=5)
+            print(out3)
+            print(out3.dtype)
+            # [[0, -3]]  # random
+            # paddle.float64
+
+            # example 4:
+            # dtype is None and the dtype of x is int32
+            x = paddle.zeros((1,2)).astype("int32")
+            out4 = paddle.randint_like(x, low=-5, high=5)
+            print(out4)
+            print(out4.dtype)
+            # [[0, -3]]  # random
+            # paddle.int32
+
+            # example 5:
+            # dtype is None and the dtype of x is int64
+            x = paddle.zeros((1,2)).astype("int64")
+            out5 = paddle.randint_like(x, low=-5, high=5)
+            print(out5)
+            print(out5.dtype)
+            # [[0, -3]]  # random
+            # paddle.int64
+
+            # example 6:
+            # dtype is float64 and the dtype of x is float32
+            x = paddle.zeros((1,2)).astype("float32")
+            out6 = paddle.randint_like(x, low=-5, high=5, dtype="float64")
+            print(out6)
+            print(out6.dtype)
+            # [[0, -1]]  # random
+            # paddle.float64
+
+            # example 7:
+            # dtype is bool and the dtype of x is float32
+            x = paddle.zeros((1,2)).astype("float32")
+            out7 = paddle.randint_like(x, low=-5, high=5, dtype="bool")
+            print(out7)
+            print(out7.dtype)
+            # [[0, -1]]  # random
+            # paddle.bool
+
+            # example 8:
+            # dtype is int32 and the dtype of x is float32
+            x = paddle.zeros((1,2)).astype("float32")
+            out8 = paddle.randint_like(x, low=-5, high=5, dtype="int32")
+            print(out8)
+            print(out8.dtype)
+            # [[0, -1]]  # random
+            # paddle.int32
+
+            # example 9:
+            # dtype is int64 and the dtype of x is float32
+            x = paddle.zeros((1,2)).astype("float32")
+            out9 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
+            print(out9)
+            print(out9.dtype)
+            # [[0, -1]]  # random
+            # paddle.int64
+
+            # example 10:
+            # dtype is int64 and the dtype of x is bool
+            x = paddle.zeros((1,2)).astype("bool")
+            out10 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
+            print(out10)
+            print(out10.dtype)
+            # [[0, -1]]  # random
+            # paddle.int64
+
+    """
+    if high is None:
+        if low <= 0:
+            raise ValueError(
+                "If high is None, low must be greater than 0, but received low = {0}.".
+                format(low))
+        high = low
+        low = 0
+    if dtype is None:
+        dtype = x.dtype
+    if not isinstance(dtype, core.VarDesc.VarType):
+        dtype = convert_np_dtype_to_dtype_(dtype)
+    shape = x.shape
+
+    if low >= high:
+        raise ValueError(
+            "randint_like's low must less then high, but received low = {0}, "
+            "high = {1}".format(low, high))
+
+    if in_dygraph_mode():
+        shape = utils.convert_shape_to_list(shape)
+        out = _C_ops.randint('shape', shape, 'low', low, 'high', high, 'seed',
+                             0, 'dtype', core.VarDesc.VarType.INT64)
+        out = paddle.cast(out, dtype)
+        return out
+
+    check_shape(shape, 'randint_like')
+    check_dtype(dtype, 'dtype',
+                ['bool', 'float16', 'float32', 'float64', 'int32',
+                 'int64'], 'randint_like')
+
+    inputs = dict()
+    attrs = {
+        'low': low,
+        'high': high,
+        'seed': 0,
+        'dtype': core.VarDesc.VarType.INT64
+    }
+    utils.get_shape_tensor_inputs(
+        inputs=inputs, attrs=attrs, shape=shape, op_type='randint_like')
+
+    helper = LayerHelper("randint", **locals())
+    out = helper.create_variable_for_type_inference(
+        dtype=core.VarDesc.VarType.INT64)
+    helper.append_op(
+        type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs)
+    out.stop_gradient = True
+    out = paddle.cast(out, dtype)
+    return out
+
+
 def randperm(n, dtype="int64", name=None):
     """
     This OP returns a 1-D Tensor filled with random permutation values from 0

tools/parallel_UT_rule.py

@@ -417,11 +417,12 @@ TETRAD_PARALLEL_JOB_NEW = [
     'test_fusion_group_op', 'test_imperative_layer_apply',
     'test_executor_return_tensor_not_overwriting',
     'test_optimizer_in_control_flow', 'test_lookup_table_op', 'test_randint_op',
-    'test_convert_call', 'test_sigmoid_cross_entropy_with_logits_op',
-    'copy_cross_scope_test', 'test_normalization_wrapper',
-    'test_pretrained_model', 'test_flip', 'test_cosine_similarity_api',
-    'test_cumsum_op', 'test_range', 'test_log_loss_op', 'test_where_index',
-    'test_tril_triu_op', 'test_lod_reset_op', 'test_lod_tensor', 'test_addmm_op',
+    'test_randint_like', 'test_convert_call',
+    'test_sigmoid_cross_entropy_with_logits_op', 'copy_cross_scope_test',
+    'test_normalization_wrapper', 'test_pretrained_model', 'test_flip',
+    'test_cosine_similarity_api', 'test_cumsum_op', 'test_range',
+    'test_log_loss_op', 'test_where_index', 'test_tril_triu_op',
+    'test_lod_reset_op', 'test_lod_tensor', 'test_addmm_op',
     'test_index_select_op', 'test_nvprof', 'test_index_sample_op',
     'test_unstack_op', 'test_increment', 'strided_memcpy_test',
     'test_target_assign_op', 'test_trt_dynamic_shape_transformer_prune',