paddlepaddle--paddle/python/paddle/incubate/optimizer/lookahead.py

# 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.
from __future__ import annotations

from typing import TYPE_CHECKING

import paddle
from paddle.base import framework, unique_name
from paddle.base.dygraph import base as imperative_base
from paddle.base.framework import Variable
from paddle.base.layer_helper import LayerHelper
from paddle.framework import in_pir_mode
from paddle.optimizer import Optimizer
from paddle.pir.core import create_parameter

if TYPE_CHECKING:
    from paddle import Tensor
    from paddle.base.framework import Operator
    from paddle.static import Program


__all__ = []


class LookAhead(Optimizer):
    r"""
    This implements the Lookahead optimizer of the
    paper : https://arxiv.org/abs/1907.08610.

    Lookahead keeps two sets of params: the fast_params and
    the slow_params. inner_optimizer update fast_params every
    training step. Lookahead updates the slow_params and fast_params
    every k training steps as follows:

    .. math::

        slow\_param_t &= slow\_param_{t-1} + \\alpha * (fast\_param_{t-1} - slow\_param_{t-1})

        fast\_param_t &=  slow\_param_t

    Args:
        inner_optimizer (Optimizer): The optimizer that update fast params step by step.
        alpha (float, optional): The learning rate of Lookahead. The default value is 0.5.
        k (int, optional): The slow params is updated every k steps. The default value is 5.
        name (str, optional): Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name`.
            The default value is None.

    Examples:

        .. code-block:: pycon

            >>> import numpy as np
            >>> import paddle
            >>> import paddle.nn as nn

            >>> BATCH_SIZE = 16
            >>> BATCH_NUM = 4
            >>> EPOCH_NUM = 4

            >>> IMAGE_SIZE = 784
            >>> CLASS_NUM = 10
            >>> # define a random dataset
            >>> class RandomDataset(paddle.io.Dataset):  # type: ignore[type-arg]
            ...     def __init__(self, num_samples):
            ...         self.num_samples = num_samples
            ...
            ...     def __getitem__(self, idx):
            ...         image = np.random.random([IMAGE_SIZE]).astype('float32')
            ...         label = np.random.randint(0, CLASS_NUM - 1, (1,)).astype('int64')
            ...         return image, label
            ...
            ...     def __len__(self):
            ...         return self.num_samples

            >>> class LinearNet(nn.Layer):
            ...     def __init__(self):
            ...         super().__init__()
            ...         self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
            ...         self.bias = self._linear.bias
            ...
            ...     @paddle.jit.to_static
            ...     def forward(self, x):
            ...         return self._linear(x)

            >>> def train(layer, loader, loss_fn, opt):
            ...     for epoch_id in range(EPOCH_NUM):
            ...         for batch_id, (image, label) in enumerate(loader()):
            ...             out = layer(image)
            ...             loss = loss_fn(out, label)
            ...             loss.backward()
            ...             opt.step()
            ...             opt.clear_grad()
            ...             print("Train Epoch {} batch {}: loss = {}".format(epoch_id, batch_id, np.mean(loss.numpy())))
            >>> layer = LinearNet()
            >>> loss_fn = nn.CrossEntropyLoss()
            >>> optimizer = paddle.optimizer.SGD(learning_rate=0.1, parameters=layer.parameters())
            >>> lookahead = paddle.incubate.LookAhead(optimizer, alpha=0.2, k=5)

            >>> # create data loader
            >>> dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
            >>> loader = paddle.io.DataLoader(
            ...     dataset,
            ...     batch_size=BATCH_SIZE,
            ...     shuffle=True,
            ...     drop_last=True,
            ...     num_workers=2,
            ... )

            >>> # doctest: +SKIP('The run time is too long to pass the CI check.')
            >>> train(layer, loader, loss_fn, lookahead)

    """

    inner_optimizer: Optimizer
    alpha: float
    k: int
    type: str
    helper: LayerHelper

    _slow_str = "slow"

    def __init__(
        self,
        inner_optimizer: Optimizer,
        alpha: float = 0.5,
        k: int = 5,
        name: str | None = None,
    ) -> None:
        assert inner_optimizer is not None, "inner optimizer can not be None"
        assert 0.0 <= alpha <= 1.0, (
            "alpha should be larger or equal to 0.0, and less or equal than 1.0"
        )
        assert isinstance(k, int) and k > 0, "k should be a positive integer"

        self.inner_optimizer = inner_optimizer
        if self.inner_optimizer._parameter_list is None:
            parameters = (
                paddle.static.default_main_program()
                .global_block()
                .all_parameters()
            )
        else:
            parameters = self.inner_optimizer._parameter_list

        super().__init__(
            learning_rate=alpha,
            parameters=parameters,
            weight_decay=None,
            grad_clip=None,
            name=name,
        )

        self.alpha = alpha
        self.k = k
        self.type = "lookahead"
        self.helper = LayerHelper(self.__class__.__name__)
        self._global_step_var = None
        self._k_var = None

    def _set_auxiliary_var(self, key, val):
        super()._set_auxiliary_var(key, val)
        self.inner_optimizer._set_auxiliary_var(key, val)

    @framework.dygraph_only
    @imperative_base.no_grad
    def step(self) -> None:
        """
        Execute the optimizer and update parameters once.

        Returns:
            None

        Examples:

            .. code-block:: pycon

                >>> import paddle
                >>> inp = paddle.rand([1, 10], dtype="float32")
                >>> linear = paddle.nn.Linear(10, 1)
                >>> out = linear(inp)
                >>> loss = paddle.mean(out)
                >>> sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
                >>> lookahead = paddle.incubate.LookAhead(sgd, alpha=0.2, k=5)
                >>> loss.backward()
                >>> lookahead.step()
                >>> lookahead.clear_grad()

        """
        self.inner_optimizer.step()

        self._increment_global_var()
        params_grads = []
        for param in self._parameter_list:
            if not param.trainable:
                continue
            if param._grad_ivar() is not None:
                grad_var = param._grad_ivar()
                params_grads.append((param, grad_var))

        self._apply_optimize(
            loss=None, startup_program=None, params_grads=params_grads
        )

    def _create_accumulators(self, block, parameters):
        assert isinstance(block, (framework.Block, paddle.pir.Block))

        for p in parameters:
            self._add_accumulator(self._slow_str, p)

    def _increment_global_var(self):
        if in_pir_mode():
            if self._global_step_var is None:
                self._global_step_var = create_parameter(
                    dtype='int32',
                    shape=[1],
                    name=unique_name.generate("lookahead_step"),
                    trainable=False,
                    initializer=paddle.nn.initializer.ConstantInitializer(
                        value=0.0, force_cpu=False
                    ),
                )
            self._global_step_var = paddle.increment(self._global_step_var, 1.0)
        else:
            if self._global_step_var is None:
                self._global_step_var = paddle.static.create_global_var(
                    name=unique_name.generate("lookahead_step"),
                    shape=[1],
                    value=0,
                    dtype='int32',
                    persistable=True,
                )

            self.helper.append_op(
                type='increment',
                inputs={'X': [self._global_step_var]},
                outputs={'Out': [self._global_step_var]},
                attrs={'step': 1.0},
            )

    def _append_optimize_op(self, block, param_and_grad):
        one_var = paddle.ones(shape=[1], dtype='int32', name='lookahead_ones')
        zero_var = paddle.zeros(
            shape=[1], dtype='int32', name='lookahead_zeros'
        )
        if in_pir_mode():
            k_var = create_parameter(
                dtype='int32',
                shape=[1],
                name=unique_name.generate("lookahead_k"),
                trainable=False,
                initializer=paddle.nn.initializer.ConstantInitializer(
                    value=float(self.k), force_cpu=False
                ),
            )
        else:
            k_var = paddle.static.create_global_var(
                name=unique_name.generate("lookahead_k"),
                shape=[1],
                value=self.k,
                dtype='int32',
                persistable=True,
            )

        mod = paddle.remainder(self._global_step_var, k_var)

        cond_1 = paddle.equal(self._global_step_var, one_var)
        cond_1 = paddle.cast(cond_1, dtype='float32')

        cond_2 = paddle.equal(mod, zero_var)
        cond_2 = paddle.cast(cond_2, dtype='float32')

        slow_var = self._get_accumulator(self._slow_str, param_and_grad[0])

        tmp_var = cond_1 * param_and_grad[0] + (1 - cond_1) * slow_var
        paddle.assign(tmp_var, slow_var)

        tmp_var = self.alpha * param_and_grad[0] + (1.0 - self.alpha) * slow_var
        tmp_var_1 = cond_2 * tmp_var + (1 - cond_2) * param_and_grad[0]
        paddle.assign(tmp_var_1, param_and_grad[0])

        tmp_var_1 = cond_2 * tmp_var + (1 - cond_2) * slow_var
        paddle.assign(tmp_var_1, slow_var)

    @imperative_base.no_grad
    def minimize(
        self,
        loss: Tensor,
        startup_program: Program | None = None,
        parameters: list[Tensor] | list[str] | None = None,
        no_grad_set: set[Tensor] | set[str] | None = None,
    ) -> tuple[list[Operator], list[tuple[Tensor, Tensor]]]:
        """
        Add operations to minimize ``loss`` by updating ``parameters``.

        Args:
            loss (Tensor): A ``Tensor`` containing the value to minimize.
            startup_program (Program, optional): :ref:`api_paddle_static_Program` for
                initializing parameters in ``parameters``. The default value
                is None, at this time :ref:`api_paddle_static_default_startup_program` will be used.
            parameters (list, optional): List of ``Tensor`` or ``Tensor.name`` to update
                to minimize ``loss``. The default value is None, at this time all parameters
                will be updated.
            no_grad_set (set, optional): Set of ``Tensor``  or ``Tensor.name`` that don't need
                to be updated. The default value is None.

        Returns:
            tuple: tuple (optimize_ops, params_grads), A list of operators appended
            by minimize and a list of (param, grad) tensor pairs, param is
            ``Parameter``, grad is the gradient value corresponding to the parameter.
            In static graph mode, the returned tuple can be passed to ``fetch_list`` in ``Executor.run()`` to
            indicate program pruning. If so, the program will be pruned by ``feed`` and
            ``fetch_list`` before run, see details in ``Executor``.

        Examples:

            .. code-block:: pycon

                >>> import paddle

                >>> inp = paddle.rand([1, 10], dtype="float32")
                >>> linear = paddle.nn.Linear(10, 1)
                >>> out = linear(inp)
                >>> loss = paddle.mean(out)
                >>> sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
                >>> lookahead = paddle.incubate.LookAhead(sgd, alpha=0.2, k=5)
                >>> loss.backward()
                >>> lookahead.minimize(loss)
                >>> lookahead.clear_grad()

        """
        assert isinstance(loss, (Variable, paddle.pir.Value)), (
            "The loss should be an Tensor."
        )

        # Apply inner optimizer to the main_program
        optimize_ops, params_grads = self.inner_optimizer.minimize(
            loss,
            startup_program=startup_program,
            parameters=parameters,
            no_grad_set=no_grad_set,
        )

        self._increment_global_var()

        _ = self._apply_optimize(
            loss, startup_program=startup_program, params_grads=params_grads
        )

        return optimize_ops, params_grads