wehub-resource-sync
467 lines
17 KiB
Python
467 lines
17 KiB
Python
# Copyright (c) 2020 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.
|
|
|
|
import unittest
|
|
from unittest import mock
|
|
|
|
import numpy as np
|
|
from op_test import OpTest, get_device, get_device_place, is_custom_device
|
|
|
|
import paddle
|
|
import paddle.tensor.random as paddle_tensor_random
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
def output_hist(out):
|
|
hist, _ = np.histogram(out, range=(-10, 10))
|
|
hist = hist.astype("float32")
|
|
hist /= float(out.size)
|
|
prob = 0.1 * np.ones(10)
|
|
return hist, prob
|
|
|
|
|
|
class TestRandintOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "randint"
|
|
self.python_api = paddle.randint
|
|
self.inputs = {}
|
|
self.init_attrs()
|
|
self.outputs = {"Out": np.zeros((10000, 784)).astype("float32")}
|
|
|
|
def init_attrs(self):
|
|
self.attrs = {"shape": [10000, 784], "low": -10, "high": 10, "seed": 10}
|
|
self.output_hist = output_hist
|
|
|
|
def test_check_output(self):
|
|
self.check_output_customized(self.verify_output, check_pir=True)
|
|
|
|
def verify_output(self, outs):
|
|
hist, prob = self.output_hist(np.array(outs[0]))
|
|
np.testing.assert_allclose(hist, prob, rtol=0, atol=0.001)
|
|
|
|
|
|
class TestRandintOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
self.assertRaises(TypeError, paddle.randint, 5, shape=np.array([2]))
|
|
self.assertRaises(TypeError, paddle.randint, 5, dtype='float32')
|
|
self.assertRaises(ValueError, paddle.randint, 5, 5)
|
|
self.assertRaises(ValueError, paddle.randint, -5)
|
|
self.assertRaises(TypeError, paddle.randint, 5, shape=['2'])
|
|
shape_tensor = paddle.static.data('X', [1])
|
|
self.assertRaises(TypeError, paddle.randint, 5, shape=shape_tensor)
|
|
self.assertRaises(
|
|
TypeError, paddle.randint, 5, shape=[shape_tensor]
|
|
)
|
|
|
|
|
|
class TestRandintOp_attr_tensorlist(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "randint"
|
|
self.python_api = paddle.randint
|
|
self.new_shape = (10000, 784)
|
|
shape_tensor = []
|
|
for index, ele in enumerate(self.new_shape):
|
|
shape_tensor.append(
|
|
("x" + str(index), np.ones(1).astype("int64") * ele)
|
|
)
|
|
self.inputs = {'ShapeTensorList': shape_tensor}
|
|
self.init_attrs()
|
|
self.outputs = {"Out": np.zeros((10000, 784)).astype("int32")}
|
|
|
|
def init_attrs(self):
|
|
self.attrs = {"low": -10, "high": 10, "seed": 10}
|
|
self.output_hist = output_hist
|
|
|
|
def test_check_output(self):
|
|
self.check_output_customized(self.verify_output, check_pir=True)
|
|
|
|
def verify_output(self, outs):
|
|
hist, prob = self.output_hist(np.array(outs[0]))
|
|
np.testing.assert_allclose(hist, prob, rtol=0, atol=0.001)
|
|
|
|
|
|
class TestRandint_attr_tensor(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "randint"
|
|
self.python_api = paddle.randint
|
|
self.inputs = {"ShapeTensor": np.array([10000, 784]).astype("int64")}
|
|
self.init_attrs()
|
|
self.outputs = {"Out": np.zeros((10000, 784)).astype("int64")}
|
|
|
|
def init_attrs(self):
|
|
self.attrs = {"low": -10, "high": 10, "seed": 10}
|
|
self.output_hist = output_hist
|
|
|
|
def test_check_output(self):
|
|
self.check_output_customized(self.verify_output, check_pir=True)
|
|
|
|
def verify_output(self, outs):
|
|
hist, prob = self.output_hist(np.array(outs[0]))
|
|
np.testing.assert_allclose(hist, prob, rtol=0, atol=0.001)
|
|
|
|
|
|
# Test python API
|
|
class TestRandintAPI(unittest.TestCase):
|
|
def test_api(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
# results are from [0, 5).
|
|
out1 = paddle.randint(5)
|
|
# shape is a list and dtype is 'int32'
|
|
out2 = paddle.randint(
|
|
low=-100, high=100, shape=[64, 64], dtype='int32'
|
|
)
|
|
# shape is a tuple and dtype is 'int64'
|
|
out3 = paddle.randint(
|
|
low=-100, high=100, shape=(32, 32, 3), dtype='int64'
|
|
)
|
|
# shape is a tensorlist and dtype is 'float32'
|
|
dim_1 = paddle.tensor.fill_constant([1], "int64", 32)
|
|
dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
|
|
out4 = paddle.randint(
|
|
low=-100, high=100, shape=[dim_1, 5, dim_2], dtype='int32'
|
|
)
|
|
# shape is a tensor and dtype is 'float64'
|
|
var_shape = paddle.static.data(
|
|
name='var_shape', shape=[2], dtype="int64"
|
|
)
|
|
out5 = paddle.randint(
|
|
low=1, high=1000, shape=var_shape, dtype='int64'
|
|
)
|
|
|
|
place = get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
outs = exe.run(
|
|
feed={'var_shape': np.array([100, 100]).astype('int64')},
|
|
fetch_list=[out1, out2, out3, out4, out5],
|
|
)
|
|
|
|
|
|
class TestRandintImperative(unittest.TestCase):
|
|
def test_case(self):
|
|
paddle.disable_static()
|
|
n = 10
|
|
x1 = paddle.randint(n, shape=[10], dtype="int32")
|
|
x2 = paddle.tensor.randint(n)
|
|
x3 = paddle.tensor.random.randint(n)
|
|
for i in [x1, x2, x3]:
|
|
for j in i.numpy().tolist():
|
|
self.assertTrue(j >= 0 and j < n)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestRandomValue(unittest.TestCase):
|
|
def test_fixed_random_number(self):
|
|
# Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
|
|
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
|
|
# Different GPU generatte different random value. Only test V100 here.
|
|
if "V100" not in paddle.device.get_device_name():
|
|
return
|
|
|
|
print("Test Fixed Random number on GPU------>")
|
|
paddle.disable_static()
|
|
|
|
self.run_test_case()
|
|
|
|
paddle.enable_static()
|
|
|
|
def run_test_case(self):
|
|
paddle.set_device(get_device())
|
|
paddle.seed(100)
|
|
|
|
x = paddle.randint(
|
|
-10000, 10000, [32, 3, 1024, 1024], dtype='int32'
|
|
).numpy()
|
|
self.assertTrue(x.mean(), -0.7517569760481516)
|
|
self.assertTrue(x.std(), 5773.696619107639)
|
|
expect = [2535, 2109, 5916, -5011, -261]
|
|
np.testing.assert_array_equal(x[10, 0, 100, 100:105], expect)
|
|
expect = [3465, 7206, -8660, -9628, -6574]
|
|
np.testing.assert_array_equal(x[20, 1, 600, 600:605], expect)
|
|
expect = [881, 1560, 1100, 9664, 1669]
|
|
np.testing.assert_array_equal(x[30, 2, 1000, 1000:1005], expect)
|
|
|
|
x = paddle.randint(
|
|
-10000, 10000, [32, 3, 1024, 1024], dtype='int64'
|
|
).numpy()
|
|
self.assertTrue(x.mean(), -1.461287518342336)
|
|
self.assertTrue(x.std(), 5773.023477548159)
|
|
expect = [7213, -9597, 754, 8129, -1158]
|
|
np.testing.assert_array_equal(x[10, 0, 100, 100:105], expect)
|
|
expect = [-7159, 8054, 7675, 6980, 8506]
|
|
np.testing.assert_array_equal(x[20, 1, 600, 600:605], expect)
|
|
expect = [3581, 3420, -8027, -5237, -2436]
|
|
np.testing.assert_array_equal(x[30, 2, 1000, 1000:1005], expect)
|
|
|
|
|
|
# Test API shape
|
|
class TestRandintAPI_ZeroDim(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(0, 2, [])
|
|
self.assertEqual(x.shape, [])
|
|
paddle.enable_static()
|
|
|
|
def test_static(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.randint(-10, 10, [])
|
|
|
|
# Test compile shape
|
|
self.assertEqual(tuple(x.shape), ())
|
|
|
|
# Test runtime shape
|
|
exe = paddle.static.Executor()
|
|
result = exe.run(fetch_list=[x])
|
|
self.assertEqual(tuple(result[0].shape), ())
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestRandintAliasAndOut(unittest.TestCase):
|
|
def test_alias_and_out(self):
|
|
paddle.disable_static()
|
|
|
|
# Test size alias (param_one_alias decorator: shape -> size)
|
|
result_1 = paddle.randint(5, size=[3, 4])
|
|
result_2 = paddle.randint(5, size=paddle.to_tensor([3, 4]))
|
|
self.assertEqual(result_1.shape, [3, 4])
|
|
self.assertEqual(result_2.shape, [3, 4])
|
|
|
|
# Test out parameter with int32 dtype
|
|
result_3 = paddle.randint(high=5, shape=[3, 4], dtype='int32')
|
|
out = paddle.zeros([3, 4], dtype='int32')
|
|
result_4 = paddle.randint(high=5, shape=[3, 4], dtype='int32', out=out)
|
|
self.assertTrue(paddle.equal_all(result_4, out))
|
|
self.assertEqual(result_4.dtype, paddle.int32)
|
|
|
|
# Test out parameter with int64 dtype
|
|
out_int64 = paddle.zeros([2, 5], dtype='int64')
|
|
result_5 = paddle.randint(
|
|
high=10, shape=[2, 5], dtype='int64', out=out_int64
|
|
)
|
|
self.assertTrue(paddle.equal_all(result_5, out_int64))
|
|
self.assertEqual(result_5.dtype, paddle.int64)
|
|
|
|
# Test WITHOUT out parameter (out=None, triggers 'if out is None' branch)
|
|
result_6 = paddle.randint(high=5, shape=[3, 4], dtype='int32')
|
|
self.assertEqual(result_6.shape, [3, 4])
|
|
self.assertEqual(result_6.dtype, paddle.int32)
|
|
|
|
result_7 = paddle.randint(high=5, shape=[2, 3], dtype='int64')
|
|
self.assertEqual(result_7.shape, [2, 3])
|
|
self.assertEqual(result_7.dtype, paddle.int64)
|
|
|
|
paddle.enable_static()
|
|
|
|
def test_out_static_mode(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
# In static mode (PIR), out parameter is not supported (as shown by warning)
|
|
# Test creates new tensor (out=None), triggering 'if out is None' branch
|
|
result1 = paddle.randint(high=5, shape=[3, 4], dtype='int32')
|
|
self.assertEqual(result1.shape, (3, 4))
|
|
|
|
result2 = paddle.randint(high=10, shape=[2, 5], dtype='int64')
|
|
self.assertEqual(result2.shape, (2, 5))
|
|
|
|
def test_size_alias_static_mode(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
# Test size parameter as an alias for shape in static mode
|
|
result = paddle.randint(high=5, size=[3, 4], dtype='int32')
|
|
self.assertEqual(result.shape, (3, 4))
|
|
|
|
|
|
class TestRandintHighAsList(unittest.TestCase):
|
|
"""Test randint when high is a list/tuple (positional args compatibility).
|
|
|
|
When called as paddle.randint(10, [3, 4]), the second positional arg
|
|
binds to `high` as a list. The code detects this and treats it as
|
|
shape=high, high=low, low=0.
|
|
"""
|
|
|
|
def test_high_is_list(self):
|
|
paddle.disable_static()
|
|
# paddle.randint(10, [3, 4]) means low=0, high=10, shape=[3, 4]
|
|
x = paddle.randint(10, [3, 4])
|
|
self.assertEqual(x.shape, [3, 4])
|
|
self.assertTrue(np.all(x.numpy() >= 0) and np.all(x.numpy() < 10))
|
|
paddle.enable_static()
|
|
|
|
def test_high_is_tuple(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(5, (2, 3))
|
|
self.assertEqual(x.shape, [2, 3])
|
|
self.assertTrue(np.all(x.numpy() >= 0) and np.all(x.numpy() < 5))
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestRandintOldStaticMode(unittest.TestCase):
|
|
"""Test randint in old static graph mode (non-PIR mode).
|
|
|
|
This test specifically covers the else branch in randint:
|
|
if out is None:
|
|
out = helper.create_variable_for_type_inference(dtype=dtype)
|
|
|
|
This branch is only executed when:
|
|
1. Not in dynamic mode (in_dynamic_mode() returns False)
|
|
2. Not in PIR mode (in_pir_mode() returns False)
|
|
"""
|
|
|
|
def test_out_none_old_static_mode(self):
|
|
"""Test that 'if out is None' branch is covered in old static mode."""
|
|
from paddle.pir_utils import OldIrGuard
|
|
|
|
with OldIrGuard():
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
# This should go through the else branch (old static mode)
|
|
# and trigger 'if out is None: out = helper.create_variable_for_type_inference(dtype=dtype)'
|
|
result1 = paddle.randint(high=5, shape=[3, 4], dtype='int32')
|
|
result2 = paddle.randint(high=10, shape=[2, 5], dtype='int64')
|
|
|
|
# Verify shapes are correct
|
|
self.assertEqual(result1.shape, (3, 4))
|
|
self.assertEqual(result2.shape, (2, 5))
|
|
|
|
# Execute the program to verify it works
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_program)
|
|
outs = exe.run(main_program, fetch_list=[result1, result2])
|
|
|
|
# Verify the outputs
|
|
self.assertEqual(outs[0].shape, (3, 4))
|
|
self.assertEqual(outs[1].shape, (2, 5))
|
|
# Verify values are in expected range
|
|
self.assertTrue(np.all(outs[0] >= 0) and np.all(outs[0] < 5))
|
|
self.assertTrue(np.all(outs[1] >= 0) and np.all(outs[1] < 10))
|
|
|
|
def test_size_alias_old_static_mode(self):
|
|
"""Test size alias in old static mode."""
|
|
from paddle.pir_utils import OldIrGuard
|
|
|
|
with OldIrGuard():
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
# Test using 'size' parameter alias
|
|
result = paddle.randint(high=5, size=[4, 5], dtype='int32')
|
|
self.assertEqual(result.shape, (4, 5))
|
|
|
|
# Execute the program
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_program)
|
|
outs = exe.run(main_program, fetch_list=[result])
|
|
|
|
self.assertEqual(outs[0].shape, (4, 5))
|
|
self.assertTrue(np.all(outs[0] >= 0) and np.all(outs[0] < 5))
|
|
|
|
|
|
class TestRandintDeviceRequiresGradPinMemory(unittest.TestCase):
|
|
def test_device_cpu(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(high=10, shape=[3, 4], device='cpu')
|
|
self.assertEqual(x.shape, [3, 4])
|
|
self.assertTrue(x.place.is_cpu_place())
|
|
paddle.enable_static()
|
|
|
|
def test_requires_grad(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(high=10, shape=[2, 3], requires_grad=True)
|
|
self.assertEqual(x.shape, [2, 3])
|
|
self.assertFalse(x.stop_gradient)
|
|
paddle.enable_static()
|
|
|
|
def test_requires_grad_false(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(high=10, shape=[2, 3], requires_grad=False)
|
|
self.assertTrue(x.stop_gradient)
|
|
paddle.enable_static()
|
|
|
|
def test_device_and_requires_grad(self):
|
|
paddle.disable_static()
|
|
x = paddle.randint(
|
|
high=10, shape=[2, 3], device='cpu', requires_grad=True
|
|
)
|
|
self.assertEqual(x.shape, [2, 3])
|
|
self.assertTrue(x.place.is_cpu_place())
|
|
self.assertFalse(x.stop_gradient)
|
|
paddle.enable_static()
|
|
|
|
def test_pin_memory_cpu(self):
|
|
if not (
|
|
paddle.device.is_compiled_with_cuda()
|
|
or paddle.device.is_compiled_with_xpu()
|
|
):
|
|
return
|
|
paddle.disable_static()
|
|
x = paddle.randint(high=10, shape=[2, 3], device='cpu', pin_memory=True)
|
|
self.assertEqual(x.shape, [2, 3])
|
|
self.assertTrue("pinned" in str(x.place))
|
|
paddle.enable_static()
|
|
|
|
def test_pin_memory_cuda(self):
|
|
if not paddle.device.is_compiled_with_cuda():
|
|
return
|
|
paddle.disable_static()
|
|
x = paddle.randint(high=10, shape=[2, 3], device='gpu', pin_memory=True)
|
|
self.assertTrue("pinned" in str(x.place))
|
|
paddle.enable_static()
|
|
|
|
def test_pin_memory_cpu_xpu_branch(self):
|
|
# Cover the cpu+pin_memory XPU branch (line where
|
|
# ``place = core.XPUPinnedPlace()`` runs when
|
|
# ``is_compiled_with_xpu()`` is True). XPUPinnedPlace can't be
|
|
# instantiated on a CUDA-only build, so route it to CUDAPinnedPlace.
|
|
if not paddle.device.is_compiled_with_cuda():
|
|
return
|
|
paddle.disable_static()
|
|
with (
|
|
mock.patch.object(
|
|
paddle.device, 'is_compiled_with_xpu', return_value=True
|
|
),
|
|
mock.patch.object(
|
|
paddle_tensor_random.core,
|
|
'XPUPinnedPlace',
|
|
paddle_tensor_random.core.CUDAPinnedPlace,
|
|
),
|
|
):
|
|
x = paddle.randint(
|
|
high=10, shape=[2, 3], device='cpu', pin_memory=True
|
|
)
|
|
self.assertTrue("pinned" in str(x.place))
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|