wehub-resource-sync
121 lines
4.4 KiB
Python
121 lines
4.4 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 paddle
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from paddle.base.data_feeder import check_type
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from paddle.base.framework import Variable, in_pir_mode
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def check_input_type(input, name, op_name):
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r"""Check whether the input is tensor or variable."""
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if paddle.in_dynamic_mode():
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if not isinstance(input, paddle.Tensor):
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raise ValueError(f"The input: {input} must be tensor.")
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else:
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check_type(input, name, (Variable, paddle.pir.Value), op_name)
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def check_initial_inverse_hessian_estimate(H0):
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r"""Check whether the specified initial_inverse_hessian_estimate is symmetric and positive definite.
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Raise errors when precondition not met.
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Note:
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In static graph can not raise error directly, so use py_func make raise_func as a op,
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and use paddle.static.nn.cond to decide if put the op in net.
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cholesky is the fast way to check positive definition, but in static graph can not catch
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exception to raise value error, so use eigvals rather than cholesky in static graph.
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"""
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is_symmetric = paddle.all(paddle.equal(H0, H0.t()))
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def raise_func():
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raise ValueError(
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"The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not."
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)
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if paddle.in_dynamic_mode():
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if not is_symmetric:
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raise_func()
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try:
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paddle.linalg.cholesky(H0)
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except RuntimeError as error:
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raise_func()
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elif in_pir_mode():
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paddle.static.nn.control_flow.Assert(
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is_symmetric,
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None,
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10,
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name="The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not.",
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)
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eigvals = paddle.linalg.eigvals(H0)
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is_positive = paddle.bitwise_and(
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paddle.all(eigvals.real() > 0.0), paddle.all(eigvals.imag() == 0.0)
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)
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paddle.static.nn.control_flow.Assert(
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is_positive,
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None,
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10,
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name="The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not.",
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)
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else:
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def create_tmp_var(program, name, dtype, shape):
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return program.current_block().create_var(
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name=name, dtype=dtype, shape=shape
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)
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out_var = create_tmp_var(
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paddle.static.default_main_program(),
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name='output',
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dtype='float32',
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shape=[-1],
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)
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def false_fn():
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paddle.static.nn.py_func(
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func=raise_func, x=is_symmetric, out=out_var
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)
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paddle.static.nn.cond(is_symmetric, None, false_fn)
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# eigvals only support cpu
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paddle.set_device("cpu")
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eigvals = paddle.linalg.eigvals(H0)
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is_positive = paddle.all(eigvals.real() > 0.0) and paddle.all(
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eigvals.imag() == 0.0
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)
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paddle.static.nn.cond(is_positive, None, false_fn)
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def _value_and_gradient(f, x, v=None):
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r"""Compute function value and gradient of f at x.
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Args:
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f (Callable): the objective function.
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x (Tensor): the input tensor.
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Returns:
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value: a tensor that holds the function value.
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gradient: a tensor that holds the function gradients.
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"""
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# use detach to cut off relation between x and original graph
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x = x.detach()
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x.stop_gradient = False
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value = f(x)
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if paddle.in_dynamic_mode():
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# only need to compute first order derivative, and some op dont support high order derivative.
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gradient = paddle.grad([value], [x], create_graph=False)[0]
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else:
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gradient = paddle.static.gradients([value], [x])[0]
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# use detach to make results real number without grad to avoid assign error
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return value.detach(), gradient.detach()
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