paddlepaddle--paddle
wehub-resource-sync
608 行
22 KiB
Python
608 行
22 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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def Heaviside_grad(x, y, dout, astype="float16", is_bfloat16=False):
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tmp = np.zeros(x.shape).astype(astype)
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dx = np.multiply(tmp, dout)
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dy = np.multiply(np.equal(x, 0), dout).astype(astype)
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if is_bfloat16:
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dx = convert_float_to_uint16(dx)
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dy = convert_float_to_uint16(dy)
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return dx, dy
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class TestElementwiseOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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x = np.random.random((13, 17)).astype("float64")
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y = np.random.random((13, 17)).astype("float64")
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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self.check_output(
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check_pir=True, check_prim_pir=True, check_symbol_infer=False
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)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_pir=True,
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check_prim_pir=True,
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)
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class TestHeavisideBroadcast(unittest.TestCase):
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def setUp(self):
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self.input_1 = np.random.rand(2, 100, 13, 17).astype("float32")
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self.input_2 = np.random.rand(100, 13, 17).astype("float32")
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self.input_3 = np.random.rand(100, 13, 1).astype("float32")
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self.input_4 = np.random.rand(13, 17).astype("float32")
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self.input_5 = np.random.rand(1).astype("float32")
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self.np_expected1 = np.heaviside(self.input_1, self.input_2)
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self.np_expected2 = np.heaviside(self.input_2, self.input_3)
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self.np_expected3 = np.heaviside(self.input_2, self.input_4)
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self.np_expected4 = np.heaviside(self.input_4, self.input_5)
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def test_broadcast(self):
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paddle.disable_static()
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self.tensor_1 = paddle.to_tensor(self.input_1)
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self.tensor_2 = paddle.to_tensor(self.input_2)
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self.tensor_3 = paddle.to_tensor(self.input_3)
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self.tensor_4 = paddle.to_tensor(self.input_4)
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self.tensor_5 = paddle.to_tensor(self.input_5)
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res = paddle.heaviside(self.tensor_1, self.tensor_2)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
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res = paddle.heaviside(self.tensor_2, self.tensor_3)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
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res = paddle.heaviside(self.tensor_2, self.tensor_4)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
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res = paddle.heaviside(self.tensor_4, self.tensor_5)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
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class TestHeavisideAPI_float64(unittest.TestCase):
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def setUp(self):
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self.x_np = np.random.random((13, 17)).astype("float64")
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self.y_np = np.random.random((13, 17)).astype("float64")
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self.out_np = np.heaviside(self.x_np, self.y_np)
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self.dtype = "float64"
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def test_static(self):
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for use_cuda in (
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[False, True]
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if (paddle.device.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.enable_static()
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.static.data(
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name=f"x_{self.dtype}", shape=[13, 17], dtype=self.dtype
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)
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y = paddle.static.data(
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name=f"y_{self.dtype}", shape=[13, 17], dtype=self.dtype
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)
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out = paddle.heaviside(x, y)
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exe = paddle.static.Executor(place=place)
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(res,) = exe.run(
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prog,
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feed={
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f"x_{self.dtype}": self.x_np,
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f"y_{self.dtype}": self.y_np,
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},
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fetch_list=out,
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use_prune=True,
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)
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np.testing.assert_allclose(res, self.out_np, rtol=1e-05)
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def test_dygraph(self):
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for use_cuda in (
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[False, True]
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if (paddle.device.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.disable_static(place=place)
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result = paddle.heaviside(
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paddle.to_tensor(self.x_np), paddle.to_tensor(self.y_np)
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)
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np.testing.assert_allclose(result.numpy(), self.out_np, rtol=1e-05)
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class TestHeavisideAPI_float32(TestHeavisideAPI_float64):
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def setUp(self):
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self.x_np = np.random.random((13, 17)).astype("float32")
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self.y_np = np.random.random((13, 17)).astype("float32")
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self.out_np = np.heaviside(self.x_np, self.y_np)
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self.dtype = "float32"
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class TestHeavisideAPI_int64(TestHeavisideAPI_float64):
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def setUp(self):
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self.x_np = np.random.random((13, 17)).astype("int64")
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self.y_np = np.random.random((13, 17)).astype("int64")
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self.out_np = np.heaviside(self.x_np, self.y_np)
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self.dtype = "int64"
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class TestHeavisideAPI_int32(TestHeavisideAPI_float64):
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def setUp(self):
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self.x_np = np.random.random((13, 17)).astype("int32")
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self.y_np = np.random.random((13, 17)).astype("int32")
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self.out_np = np.heaviside(self.x_np, self.y_np)
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self.dtype = "int32"
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class TestElementwiseOp1(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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x = np.random.random(100).astype("float64")
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y = np.random.random((13, 100)).astype("float64")
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseOp2(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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x = np.random.random((13, 100)).astype("float64")
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y = np.random.random(100).astype("float64")
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseOp3(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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x = np.random.uniform(-10, 10, [100]).astype("float64")
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y = np.random.uniform(-10, 10, [3, 100]).astype("float64")
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseOp4(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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x = np.random.uniform(0, 10, []).astype("float64")
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y = np.random.uniform(-10, 0, [2, 3, 20]).astype("float64")
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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class TestHeavisideFP16Op(OpTest):
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def setUp(self):
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self.dtype = np.float16
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self.op_type = "elementwise_heaviside"
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {
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'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
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'Y': np.random.uniform(1, 2, [20, 5]).astype("float16"),
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}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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self.check_output(
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check_pir=True, check_prim_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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user_defined_grads=Heaviside_grad(
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self.inputs['X'], self.inputs['Y'], 1 / self.inputs['X'].size
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),
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check_pir=True,
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check_prim_pir=True,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestHeavisideBF16Op(OpTest):
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def setUp(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.op_type = "elementwise_heaviside"
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self.python_api = paddle.heaviside
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self.prim_op_type = "comp"
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self.public_python_api = paddle.heaviside
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self.inputs = {
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'X': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
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'Y': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
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}
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self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
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self.place = get_device_place()
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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def test_check_output(self):
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self.check_output_with_place(
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self.place,
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check_pir=True,
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check_prim_pir=True,
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check_symbol_infer=False,
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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['X', 'Y'],
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'Out',
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user_defined_grads=Heaviside_grad(
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self.inputs['X'],
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self.inputs['Y'],
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1 / self.inputs['X'].size,
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self.np_dtype,
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True,
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),
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check_pir=True,
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check_prim_pir=True,
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)
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class TestHeavisideError(unittest.TestCase):
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def test_input(self):
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paddle.disable_static()
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def test_input_x():
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paddle.heaviside(1, paddle.randn([100]))
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self.assertRaises(ValueError, test_input_x)
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def test_input_y():
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paddle.heaviside(paddle.randn([100]), 1)
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self.assertRaises(ValueError, test_input_y)
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def test_input_xy():
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paddle.heaviside(
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paddle.randn([100], 'float32'), paddle.randn([100], 'float64')
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)
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self.assertRaises(ValueError, test_input_xy)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseHeavisideOp_Stride(OpTest):
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "elementwise_heaviside"
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self.python_api = paddle.heaviside
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self.public_python_api = paddle.heaviside
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self.transpose_api = paddle.transpose
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self.as_stride_api = paddle.as_strided
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self.init_dtype()
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self.init_input_output()
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self.inputs_stride = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
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}
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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def init_dtype(self):
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self.dtype = np.float64
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self.val_dtype = np.float64
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def test_check_output(self):
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place = get_device_place()
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self.check_strided_forward = True
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self.check_output(
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place,
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)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def test_check_gradient(self):
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pass
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class TestElementwiseHeavisideOp_Stride1(TestElementwiseHeavisideOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseHeavisideOp_Stride2(TestElementwiseHeavisideOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseHeavisideOp_Stride3(TestElementwiseHeavisideOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseHeavisideOp_Stride4(TestElementwiseHeavisideOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseHeavisideOp_Stride5(TestElementwiseHeavisideOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = np.heaviside(self.x, self.y)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseHeavisideOp_Stride_ZeroDim1(
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TestElementwiseHeavisideOp_Stride
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):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.heaviside(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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|
|
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class TestElementwiseHeavisideOp_Stride_ZeroSize1(
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TestElementwiseHeavisideOp_Stride
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):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = np.heaviside(self.x, self.y)
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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|
|
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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|
"core is not compiled with CUDA",
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|
)
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class TestHeavisideZeroSizeTensor(unittest.TestCase):
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|
"""Regression test for 0-size tensor in paddle.heaviside forward and backward.
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|
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|
When input has a dimension of 0, the broadcast backward kernels
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(ElemwiseGradBroadcast1CUDA / ElemwiseGradBroadcast2CUDA) were incorrectly
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launched with block_size=0 or grid_size=0, causing CUDA error(9)
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|
(cudaErrorInvalidConfiguration).
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|
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|
Fix: add early-return guards in ElemwiseGradBroadcast1CUDA (h==0 || w==0)
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and ElemwiseGradBroadcast2CUDA (pre==0 || n==0 || post==0).
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"""
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|
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def setUp(self):
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self.place = get_device_place()
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paddle.disable_static(place=self.place)
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|
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def _check_forward_backward(self, x_shape, y_shape, dtype='float32'):
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"""Run forward + backward and assert output shape and no CUDA error."""
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x = paddle.zeros(x_shape, dtype=dtype)
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y = paddle.ones(y_shape, dtype=dtype)
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x.stop_gradient = False
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y.stop_gradient = False
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out = paddle.heaviside(x, y)
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expected_shape = list(
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np.broadcast_shapes(tuple(x_shape), tuple(y_shape))
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|
)
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|
self.assertEqual(list(out.shape), expected_shape)
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|
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|
out_grad = paddle.ones_like(out)
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|
grads = paddle.grad(
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|
[out],
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|
[x, y],
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grad_outputs=[out_grad],
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|
allow_unused=True,
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|
)
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self.assertEqual(list(grads[0].shape), x_shape)
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self.assertEqual(list(grads[1].shape), y_shape)
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|
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# Verify no sticky CUDA error was left by any kernel launch
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core.eager._for_test_check_cuda_error()
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|
return out
|
|
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|
def _check_forward_only(self, x_shape, y_shape, dtype='int32'):
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|
"""Run forward-only for non-float dtypes and assert shape + no CUDA error."""
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|
x = paddle.zeros(x_shape, dtype=dtype)
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|
y = paddle.ones(y_shape, dtype=dtype)
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|
out = paddle.heaviside(x, y)
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|
|
|
expected_shape = list(
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|
np.broadcast_shapes(tuple(x_shape), tuple(y_shape))
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|
)
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|
self.assertEqual(list(out.shape), expected_shape)
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core.eager._for_test_check_cuda_error()
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|
return out
|
|
|
|
# ---------------------------------------------------------------
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|
# Same-shape 0-size (no broadcast) — ElemwiseGradComputeNoBroadcast
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|
# ---------------------------------------------------------------
|
|
|
|
def test_same_shape_zero_leading_dim_float32(self):
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|
"""[0, 2048] x [0, 2048] – same shape, no broadcast."""
|
|
self._check_forward_backward([0, 2048], [0, 2048])
|
|
|
|
def test_same_shape_zero_leading_dim_float64(self):
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|
"""[0, 17] x [0, 17]."""
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|
self._check_forward_backward([0, 17], [0, 17], 'float64')
|
|
|
|
def test_same_shape_zero_trailing_dim_float64(self):
|
|
"""[13, 0] x [13, 0]."""
|
|
self._check_forward_backward([13, 0], [13, 0], 'float64')
|
|
|
|
def test_same_shape_zero_trailing_dim_int32(self):
|
|
"""[13, 0] x [13, 0] – int32, forward only."""
|
|
self._check_forward_only([13, 0], [13, 0], 'int32')
|
|
|
|
def test_same_shape_zero_trailing_dim_int64(self):
|
|
"""[13, 0] x [13, 0] – int64, forward only."""
|
|
self._check_forward_only([13, 0], [13, 0], 'int64')
|
|
|
|
def test_same_shape_zero_leading_dim_int32(self):
|
|
"""[0, 17] x [0, 17] – int32, forward only."""
|
|
self._check_forward_only([0, 17], [0, 17], 'int32')
|
|
|
|
def test_same_shape_zero_leading_dim_int64(self):
|
|
"""[0, 17] x [0, 17] – int64, forward only."""
|
|
self._check_forward_only([0, 17], [0, 17], 'int64')
|
|
|
|
# ---------------------------------------------------------------
|
|
# ElemwiseGradBroadcast1CUDA — h=0 (block_size would be 0)
|
|
# ---------------------------------------------------------------
|
|
|
|
def test_broadcast1_zero_trailing_dim_scalar(self):
|
|
"""[300, 0] x [1] → Broadcast1CUDA(h=pre=0, w=n=1), block_size=0."""
|
|
self._check_forward_backward([300, 0], [1])
|
|
|
|
def test_broadcast1_zero_leading_dim_scalar(self):
|
|
"""[0, 2048] x [1] → Broadcast1CUDA(h=pre=0, w=n=1), block_size=0."""
|
|
self._check_forward_backward([0, 2048], [1])
|
|
|
|
def test_broadcast1_zero_leading_dim_last_dim(self):
|
|
"""[0, 2048] x [2048] → Broadcast1CUDA(h=pre=0, w=n=2048), block_size=0."""
|
|
self._check_forward_backward([0, 2048], [2048])
|
|
|
|
def test_broadcast1_scalar_zero_trailing_dim(self):
|
|
"""[1] x [300, 0] – symmetric of test_broadcast1_zero_trailing_dim_scalar."""
|
|
self._check_forward_backward([1], [300, 0])
|
|
|
|
def test_broadcast1_scalar_zero_leading_dim(self):
|
|
"""[1] x [0, 2048] – symmetric of test_broadcast1_zero_leading_dim_scalar."""
|
|
self._check_forward_backward([1], [0, 2048])
|
|
|
|
def test_broadcast1_last_dim_zero_leading_dim(self):
|
|
"""[2048] x [0, 2048] – symmetric of test_broadcast1_zero_leading_dim_last_dim."""
|
|
self._check_forward_backward([2048], [0, 2048])
|
|
|
|
# ---------------------------------------------------------------
|
|
# ElemwiseGradBroadcast1CUDA — w=0 (grid_size would be 0)
|
|
# ---------------------------------------------------------------
|
|
|
|
def test_broadcast1_zero_mid_dim_w_zero(self):
|
|
"""[2, 0, 3] x [0, 3] → Broadcast1CUDA(h=2, w=0), grid_size=0."""
|
|
self._check_forward_backward([2, 0, 3], [0, 3])
|
|
|
|
# ---------------------------------------------------------------
|
|
# ElemwiseGradBroadcast2CUDA — post=0 (block_size would be 0)
|
|
# ---------------------------------------------------------------
|
|
|
|
def test_broadcast2_zero_post_dim(self):
|
|
"""[2, 3, 0] x [3, 1] → Broadcast2CUDA(pre=2, n=3, post=0), block_size=0."""
|
|
self._check_forward_backward([2, 3, 0], [3, 1])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|