From 5b573c58e2d172204f40f81e434bba5d6865db5b Mon Sep 17 00:00:00 2001 From: zhupengyang Date: Fri, 10 Jul 2020 09:35:46 +0800 Subject: [PATCH] randperm API: remove out, devive, stop_gradient; add name (#25410) --- paddle/fluid/operators/randperm_op.cc | 3 +- paddle/fluid/operators/randperm_op.cu | 3 +- .../fluid/tests/unittests/test_randperm_op.py | 117 ++++++------------ python/paddle/tensor/random.py | 84 ++++--------- 4 files changed, 66 insertions(+), 141 deletions(-) diff --git a/paddle/fluid/operators/randperm_op.cc b/paddle/fluid/operators/randperm_op.cc index 919a05a0d99..deafd651e90 100644 --- a/paddle/fluid/operators/randperm_op.cc +++ b/paddle/fluid/operators/randperm_op.cc @@ -92,4 +92,5 @@ template using kernel = paddle::operators::RandpermKernel; -REGISTER_OP_CPU_KERNEL(randperm, kernel, kernel); +REGISTER_OP_CPU_KERNEL(randperm, kernel, kernel, kernel, + kernel); diff --git a/paddle/fluid/operators/randperm_op.cu b/paddle/fluid/operators/randperm_op.cu index 21ae1a4968a..7ed52a8fd25 100644 --- a/paddle/fluid/operators/randperm_op.cu +++ b/paddle/fluid/operators/randperm_op.cu @@ -20,4 +20,5 @@ template using kernel = paddle::operators::RandpermKernel; -REGISTER_OP_CUDA_KERNEL(randperm, kernel, kernel); +REGISTER_OP_CUDA_KERNEL(randperm, kernel, kernel, kernel, + kernel); diff --git a/python/paddle/fluid/tests/unittests/test_randperm_op.py b/python/paddle/fluid/tests/unittests/test_randperm_op.py index 2fbdc83f3ab..6938b8ef1e0 100644 --- a/python/paddle/fluid/tests/unittests/test_randperm_op.py +++ b/python/paddle/fluid/tests/unittests/test_randperm_op.py @@ -16,10 +16,8 @@ import unittest import numpy as np from op_test import OpTest import paddle -import paddle.fluid as fluid import paddle.fluid.core as core -from paddle.fluid.op import Operator -from paddle.fluid import Program, program_guard +from paddle import Program, program_guard def check_randperm_out(n, data_np): @@ -36,8 +34,11 @@ def error_msg(data_np): def convert_dtype(dtype_str): - dtype_str_list = ["int32", "int64"] - dtype_num_list = [2, 3] + dtype_str_list = ["int32", "int64", "float32", "float64"] + dtype_num_list = [ + core.VarDesc.VarType.INT32, core.VarDesc.VarType.INT64, + core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP64 + ] assert dtype_str in dtype_str_list, dtype_str + \ " should in " + str(dtype_str_list) return dtype_num_list[dtype_str_list.index(dtype_str)] @@ -50,8 +51,6 @@ class TestRandpermOp(OpTest): self.op_type = "randperm" self.n = 200 self.dtype = "int64" - self.device = None - self.seed = 0 self.inputs = {} self.outputs = {"Out": np.zeros((self.n)).astype(self.dtype)} @@ -59,8 +58,6 @@ class TestRandpermOp(OpTest): self.attrs = { "n": self.n, "dtype": convert_dtype(self.dtype), - "device": self.device, - "seed": self.seed, } def init_attrs(self): @@ -75,100 +72,60 @@ class TestRandpermOp(OpTest): check_randperm_out(self.n, out_np), msg=error_msg(out_np)) -class TestRandpermOp_attr_n(TestRandpermOp): - """ Test randperm op for attr n. """ - +class TestRandpermOpN(TestRandpermOp): def init_attrs(self): self.n = 10000 -class TestRandpermOp_attr_int32(TestRandpermOp): - """ Test randperm op for attr int32 dtype. """ - +class TestRandpermOpInt32(TestRandpermOp): def init_attrs(self): self.dtype = "int32" -class TestRandpermOp_attr_device_cpu(TestRandpermOp): - """ Test randperm op for cpu device. """ - +class TestRandpermOpFloat32(TestRandpermOp): def init_attrs(self): - self.device = "cpu" + self.dtype = "float32" -class TestRandpermOp_attr_device_gpu(TestRandpermOp): - """ Test randperm op for gpu device. """ - +class TestRandpermOpFloat64(TestRandpermOp): def init_attrs(self): - self.device = "gpu" - - -class TestRandpermOp_attr_seed(TestRandpermOp): - """ Test randperm op for attr seed. """ - - def init_attrs(self): - self.seed = 10 + self.dtype = "float64" class TestRandpermOpError(unittest.TestCase): - """ Test randperm op for raise error. """ - def test_errors(self): - main_prog = Program() - start_prog = Program() - with program_guard(main_prog, start_prog): + with program_guard(Program(), Program()): + self.assertRaises(ValueError, paddle.randperm, -3) + self.assertRaises(TypeError, paddle.randperm, 10, 'int8') - def test_Variable(): - out = np.arange(10) - paddle.randperm(n=10, out=out) - self.assertRaises(TypeError, test_Variable) +class TestRandpermAPI(unittest.TestCase): + def test_out(self): + n = 10 + place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda( + ) else paddle.CPUPlace() + with program_guard(Program(), Program()): + x1 = paddle.randperm(n) + x2 = paddle.randperm(n, 'float32') - def test_value(): - paddle.randperm(n=-3) + exe = paddle.Executor(place) + res = exe.run(fetch_list=[x1, x2]) - self.assertRaises(ValueError, test_value) + self.assertEqual(res[0].dtype, np.int64) + self.assertEqual(res[1].dtype, np.float32) + self.assertTrue(check_randperm_out(n, res[0])) + self.assertTrue(check_randperm_out(n, res[1])) -class TestRandpermOp_attr_out(unittest.TestCase): - """ Test randperm op for attr out. """ - - def test_attr_tensor_API(self): - startup_program = fluid.Program() - train_program = fluid.Program() - with fluid.program_guard(train_program, startup_program): - n = 10 - data_1 = fluid.layers.fill_constant([n], "int64", 3) - paddle.randperm(n=n, out=data_1) - - data_2 = paddle.randperm(n=n, dtype="int32", device="cpu") - - place = fluid.CPUPlace() - if fluid.core.is_compiled_with_cuda(): - place = fluid.CUDAPlace(0) - exe = fluid.Executor(place) - - exe.run(startup_program) - outs = exe.run(train_program, fetch_list=[data_1, data_2]) - - out_np = np.array(outs[0]) - self.assertTrue( - check_randperm_out(n, out_np), msg=error_msg(out_np)) - - -class TestRandpermDygraphMode(unittest.TestCase): - def test_check_output(self): - with fluid.dygraph.guard(): +class TestRandpermImperative(unittest.TestCase): + def test_out(self): + with paddle.imperative.guard(): n = 10 - data_1 = paddle.randperm(n, dtype="int64") - data_1_np = data_1.numpy() - self.assertTrue( - check_randperm_out(n, data_1_np), msg=error_msg(data_1_np)) - - data_2 = paddle.randperm(n, dtype="int32", device="cpu") - data_2_np = data_2.numpy() - self.assertTrue( - check_randperm_out(n, data_2_np), msg=error_msg(data_2_np)) + for dtype in ['int32', np.int64, 'float32', 'float64']: + data_p = paddle.randperm(n, dtype) + data_np = data_p.numpy() + self.assertTrue( + check_randperm_out(n, data_np), msg=error_msg(data_np)) if __name__ == "__main__": diff --git a/python/paddle/tensor/random.py b/python/paddle/tensor/random.py index 16bbb09b4f3..7b105cc01e1 100644 --- a/python/paddle/tensor/random.py +++ b/python/paddle/tensor/random.py @@ -317,12 +317,7 @@ def randn(shape, @templatedoc() -def randperm(n, - out=None, - dtype="int64", - device=None, - stop_gradient=True, - seed=0): +def randperm(n, dtype="int64", name=None): """ :alias_main: paddle.randperm :alias: paddle.randperm,paddle.tensor.randperm,paddle.tensor.random.randperm @@ -330,23 +325,13 @@ def randperm(n, ${comment} Args: - n (int): The upper bound (exclusive), and it should be greater than 0. - out (Variable, optional): Optional output which can be any created - Variable that meets the requirements to store the result of operation. - If out is None, a new Varibale will be create to store the result. - Default: None. - dtype (np.dtype|core.VarDesc.VarType|str, optional): The type of the - output Tensor. Supported data types: int64, int32. Default: int32. - device (str, optional): Specific the output variable to be saved in cpu - or gpu memory. Supported None, 'cpu', 'gpu'. If it is None, the output - variable will be automatically assigned devices. - Default: None. - stop_gradient (bool, optional): Whether grad should record operations - on the returned tensor. Default: True. - seed (int, optional): Random seed used for permute samples. If seed is - equal to 0, it means use a seed generated by the system. Note that - if seed is not 0, this operator will always generate the same random - permutation every time. Default: 0. + n(int): The upper bound (exclusive), and it should be greater than 0. + dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the + output Tensor. Supported data types: int32, int64, float32, float64. + Default: int32. + name(str, optional): Normally there is no need for user to set this property. + For more information, please refer to :ref:`api_guide_Name` . + Default is None. Returns: ${out_comment}. @@ -357,52 +342,33 @@ def randperm(n, Examples: .. code-block:: python - import paddle - import paddle.fluid as fluid - - num = 6 - is_use_gpu = False - - data_1 = paddle.randperm(num) - fluid.layers.Print(data_1) - - data_2 = paddle.randperm(num, dtype="int32", seed=1) - fluid.layers.Print(data_2) + import paddle - data_3 = paddle.randperm(num, stop_gradient=False, device="cpu") - fluid.layers.Print(data_3) + paddle.enable_imperative() - paddle.randperm(num, out=data_3) - fluid.layers.Print(data_3) + result_1 = paddle.randperm(5) + # [4 1 2 3 0] - place = fluid.CUDAPlace(0) if is_use_gpu else fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - exe.run() + result_2 = paddle.randperm(7, 'int32') + # [1 6 2 0 4 3 5] """ + if not isinstance(dtype, core.VarDesc.VarType): + dtype = convert_np_dtype_to_dtype_(dtype) + + if in_dygraph_mode(): + return core.ops.randperm('n', n, 'seed', 0, 'dtype', dtype) if n < 1: raise ValueError("The input n should be greater than 0 in randperm op.") - check_dtype(dtype, 'dtype', ['int64', 'int32'], 'randperm') - dtype = convert_dtype(dtype) - if device not in [None, 'cpu', 'gpu']: - raise ValueError("The input device should in [None, 'cpu', 'gpu'].") - check_type(stop_gradient, 'stop_gradient', bool, 'randperm') + check_dtype(dtype, 'dtype', ['int64', 'int32', 'float32', 'float64'], + 'randperm') helper = LayerHelper("randperm", **locals()) - if out is None: - out = helper.create_variable_for_type_inference(dtype=dtype) - else: - check_variable_and_dtype(out, 'out', [dtype], 'randperm') - if stop_gradient: - out.stop_gradient = True - inputs = dict() - outputs = {'Out': [out]} - attrs = {'n': n, 'dtype': out.dtype, 'seed': seed} - with device_guard(device): - helper.append_op( - type='randperm', inputs=inputs, outputs=outputs, attrs=attrs) + out = helper.create_variable_for_type_inference(dtype) + attrs = {'n': n, 'dtype': dtype, 'seed': 0} + helper.append_op( + type='randperm', inputs={}, outputs={'Out': out}, attrs=attrs) return out -- GitLab