wehub-resource-sync
315 lines
10 KiB
Python
315 lines
10 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 config
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import numpy as np
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import utils
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import paddle
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@utils.place(config.DEVICES)
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@utils.parameterize(
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(utils.TEST_CASE_NAME, 'fun', 'args', 'dtype'),
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(
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('unary_float32', paddle.tanh, (np.random.rand(2, 3),), 'float32'),
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(
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'binary_float32',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float32',
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),
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('unary_float64', paddle.tanh, (np.random.rand(2, 3),), 'float64'),
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(
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'binary_float64',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float64',
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),
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),
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)
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class TestJacobianPrim(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.args = [arg.astype(cls.dtype) for arg in cls.args]
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cls._rtol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('rtol')
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)
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cls._atol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('atol')
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)
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def setUp(self):
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paddle.enable_static()
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paddle.incubate.autograd.enable_prim()
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def tearDown(self):
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paddle.incubate.autograd.disable_prim()
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paddle.disable_static()
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def test_jacobian_prim(self):
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def wrapper(fun, args):
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mp = paddle.static.Program()
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sp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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static_args = [
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paddle.static.data(f'arg{i}', arg.shape, self.dtype)
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for i, arg in enumerate(args)
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]
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for arg in static_args:
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arg.stop_gradient = False
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jac = paddle.incubate.autograd.Jacobian(fun, static_args)[:]
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if paddle.incubate.autograd.prim_enabled():
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paddle.incubate.autograd.prim2orig()
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exe = paddle.static.Executor()
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exe.run(sp)
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[jac] = exe.run(
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mp,
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feed={f'arg{i}': arg for i, arg in enumerate(args)},
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fetch_list=[jac],
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)
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return jac
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paddle.incubate.autograd.enable_prim()
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prim_jac = wrapper(self.fun, self.args)
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paddle.incubate.autograd.disable_prim()
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orig_jac = wrapper(self.fun, self.args)
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np.testing.assert_allclose(
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orig_jac, prim_jac, rtol=self._rtol, atol=self._atol
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)
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@utils.place(config.DEVICES)
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@utils.parameterize(
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(utils.TEST_CASE_NAME, 'fun', 'args', 'dtype'),
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(
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('unary_float32', paddle.tanh, (np.random.rand(1),), 'float32'),
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(
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'binary_float32',
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paddle.multiply,
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(np.random.rand(1), np.random.rand(1)),
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'float32',
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),
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('unary_float64', paddle.tanh, (np.random.rand(1),), 'float64'),
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(
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'binary_float64',
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paddle.multiply,
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(np.random.rand(1), np.random.rand(1)),
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'float64',
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),
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),
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)
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class TestHessianPrim(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.args = [arg.astype(cls.dtype) for arg in cls.args]
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cls._rtol = (
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config.TOLERANCE.get(cls.dtype).get('second_order_grad').get('rtol')
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)
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cls._atol = (
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config.TOLERANCE.get(cls.dtype).get('second_order_grad').get('atol')
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)
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def setUp(self):
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paddle.enable_static()
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paddle.incubate.autograd.enable_prim()
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def tearDown(self):
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paddle.incubate.autograd.disable_prim()
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paddle.disable_static()
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def test_jacobian_prim(self):
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def wrapper(fun, args):
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mp = paddle.static.Program()
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sp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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static_args = [
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paddle.static.data(f'arg{i}', arg.shape, self.dtype)
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for i, arg in enumerate(args)
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]
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for arg in static_args:
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arg.stop_gradient = False
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hessian = paddle.incubate.autograd.Hessian(fun, static_args)[:]
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if paddle.incubate.autograd.prim_enabled():
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paddle.incubate.autograd.prim2orig()
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exe = paddle.static.Executor()
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exe.run(sp)
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[hessian] = exe.run(
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mp,
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feed={f'arg{i}': arg for i, arg in enumerate(args)},
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fetch_list=[hessian],
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)
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return hessian
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paddle.incubate.autograd.enable_prim()
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prim_jac = wrapper(self.fun, self.args)
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paddle.incubate.autograd.disable_prim()
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orig_jac = wrapper(self.fun, self.args)
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np.testing.assert_allclose(
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orig_jac, prim_jac, rtol=self._rtol, atol=self._atol
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)
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@utils.place(config.DEVICES)
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@utils.parameterize(
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(utils.TEST_CASE_NAME, 'fun', 'args', 'dtype'),
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(
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('unary_float32', paddle.tanh, (np.random.rand(2, 3),), 'float32'),
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(
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'binary_float32',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float32',
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),
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('unary_float64', paddle.tanh, (np.random.rand(2, 3),), 'float64'),
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(
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'binary_float64',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float64',
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),
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),
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)
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class TestJvpPrim(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.args = [arg.astype(cls.dtype) for arg in cls.args]
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cls._rtol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('rtol')
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)
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cls._atol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('atol')
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)
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def setUp(self):
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paddle.enable_static()
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paddle.incubate.autograd.enable_prim()
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def tearDown(self):
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paddle.incubate.autograd.disable_prim()
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paddle.disable_static()
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def test_jacobian_prim(self):
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def wrapper(fun, args):
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mp = paddle.static.Program()
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sp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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static_args = [
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paddle.static.data(f'arg{i}', arg.shape, self.dtype)
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for i, arg in enumerate(args)
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]
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for arg in static_args:
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arg.stop_gradient = False
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_, jvp_res = paddle.incubate.autograd.jvp(fun, static_args)
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if paddle.incubate.autograd.prim_enabled():
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paddle.incubate.autograd.prim2orig()
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exe = paddle.static.Executor()
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exe.run(sp)
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jvp_res = exe.run(
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mp,
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feed={f'arg{i}': arg for i, arg in enumerate(args)},
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fetch_list=[jvp_res],
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)
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return jvp_res
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paddle.incubate.autograd.enable_prim()
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prim_jvp = wrapper(self.fun, self.args)
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paddle.incubate.autograd.disable_prim()
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orig_jvp = wrapper(self.fun, self.args)
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np.testing.assert_allclose(
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orig_jvp, prim_jvp, rtol=self._rtol, atol=self._atol
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)
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@utils.place(config.DEVICES)
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@utils.parameterize(
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(utils.TEST_CASE_NAME, 'fun', 'args', 'dtype'),
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(
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('unary_float32', paddle.tanh, (np.random.rand(2, 3),), 'float32'),
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(
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'binary_float32',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float32',
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),
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('unary_float64', paddle.tanh, (np.random.rand(2, 3),), 'float64'),
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(
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'binary_float64',
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paddle.matmul,
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(np.random.rand(2, 3), np.random.rand(3, 2)),
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'float64',
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),
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),
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)
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class TestVjpPrim(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.args = [arg.astype(cls.dtype) for arg in cls.args]
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cls._rtol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('rtol')
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)
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cls._atol = (
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config.TOLERANCE.get(cls.dtype).get('first_order_grad').get('atol')
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)
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def setUp(self):
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paddle.enable_static()
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paddle.incubate.autograd.enable_prim()
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def tearDown(self):
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paddle.incubate.autograd.disable_prim()
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paddle.disable_static()
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def test_jacobian_prim(self):
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def wrapper(fun, args):
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mp = paddle.static.Program()
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sp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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static_args = [
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paddle.static.data(f'arg{i}', arg.shape, self.dtype)
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for i, arg in enumerate(args)
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]
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for arg in static_args:
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arg.stop_gradient = False
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_, vjp_res = paddle.incubate.autograd.vjp(fun, static_args)
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if paddle.incubate.autograd.prim_enabled():
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paddle.incubate.autograd.prim2orig()
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exe = paddle.static.Executor()
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exe.run(sp)
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vjp_res = exe.run(
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mp,
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feed={f'arg{i}': arg for i, arg in enumerate(args)},
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fetch_list=[vjp_res],
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)
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return vjp_res
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paddle.incubate.autograd.enable_prim()
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prim_vjp = wrapper(self.fun, self.args)
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paddle.incubate.autograd.disable_prim()
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orig_vjp = wrapper(self.fun, self.args)
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for orig, prim in zip(orig_vjp, prim_vjp):
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np.testing.assert_allclose(
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orig, prim, rtol=self._rtol, atol=self._atol
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)
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if __name__ == "__main__":
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unittest.main()
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