submodule/sapiens/engine/mmengine/optim/optimizer/amp_optimizer_wrapper.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from contextlib import contextmanager
from typing import Union

import torch
import torch.nn as nn

from mmengine.device import (is_cuda_available, is_mlu_available,
                             is_npu_available)
from mmengine.registry import OPTIM_WRAPPERS
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
from .optimizer_wrapper import OptimWrapper

if is_npu_available():
    from torch.npu.amp import GradScaler
elif is_mlu_available():
    from torch.mlu.amp import GradScaler
else:
    from torch.cuda.amp import GradScaler


@OPTIM_WRAPPERS.register_module()
class AmpOptimWrapper(OptimWrapper):
    """A subclass of :class:`OptimWrapper` that supports automatic mixed
    precision training based on torch.cuda.amp.

    ``AmpOptimWrapper`` provides a unified interface with
    ``OptimWrapper``, so ``AmpOptimWrapper`` can be used in the same way
    as ``OptimWrapper``.

    Warnings:
        ``AmpOptimWrapper`` requires PyTorch >= 1.6.

    Args:
        loss_scale (float or str or dict): The initial configuration of
            `torch.cuda.amp.GradScaler`. See more specific arguments
            introduction at `PyTorch AMP <https://pytorch.org/docs/stable/amp.html?highlight=gradscalertorch.cuda.amp.GradScaler>`_ # noqa: E501
            Defaults to ``dynamic``.

            - "dynamic": Initialize GradScale without any arguments.
            - float: Initialize GradScaler with ``init_scale``.
            - dict: Initialize GradScaler with more detail configuration.

        dtype (str or torch.dtype, optional): The data type to autocast in amp.
            If a ``str`` is given, it will be converted to ``torch.dtype``.
            Valid ``str`` format are `'float16'`, `'bfloat16'`, `'float32'` and
            `'float64'`. If set to ``None``, the default data type will be used.
            Defaults to None.
            `New in version 0.6.1.`
        use_fsdp (bool): Using ``ShardedGradScaler`` when it is True. It should
            be enabled when using ``FullyShardedDataParallel``.
            Defaults to False.
            `New in version 0.8.0.`
        **kwargs: Keyword arguments passed to OptimWrapper.

    Warnings:
        ``dtype`` argument is only available with PyTorch version >= 1.10.0. If
        you use PyTorch of an older version, it will be ignored.

    Note:
        If you use ``IterBasedRunner`` and enable gradient accumulation,
        the original `max_iters` should be multiplied by
        ``accumulative_counts``.
    """

    valid_dtypes = ('float16', 'bfloat16', 'float32', 'float64')

    def __init__(self,
                 loss_scale: str = 'dynamic',
                 dtype: Union[str, torch.dtype] = None,
                 use_fsdp: bool = False,
                 **kwargs):
        assert digit_version(TORCH_VERSION) >= digit_version('1.6.0'), (
            '`torch.cuda.amp` is only available when pytorch version >= 1.6')
        assert is_cuda_available() or is_npu_available() or is_mlu_available(
        ), ('``AmpOptimizerWrapper`` is only available training '
            'on gpu, npu or mlu')
        super().__init__(**kwargs)
        self._scale_update_param = None

        if use_fsdp:
            if digit_version(torch.__version__) >= digit_version('2.0.0'):
                from torch.distributed.fsdp.sharded_grad_scaler import \
                    ShardedGradScaler
                scaler_type = ShardedGradScaler
            else:
                raise RuntimeError(
                    'PyTorch>=2.0.0 is required when sets `use_fsdp=True`')
        else:
            scaler_type = GradScaler

        if loss_scale == 'dynamic':
            #  If loss_scale is a string, it must be 'dynamic', then dynamic
            #  loss scaling will be used.
            self.loss_scaler = scaler_type()
        elif isinstance(loss_scale, float):
            # Static loss scaling
            self._scale_update_param = loss_scale
            self.loss_scaler = scaler_type(init_scale=loss_scale)
        elif isinstance(loss_scale, dict):
            # More specific configuration.
            self.loss_scaler = scaler_type(**loss_scale)
        else:
            raise TypeError('loss_scale must be of type float, dict, or '
                            f'"dynamic", but got {loss_scale}')

        # convert string value to torch.dtype
        if isinstance(dtype, str):
            assert dtype in self.valid_dtypes, (
                f'dtype should be any of {self.valid_dtypes}, got {dtype}')
            dtype = getattr(torch, dtype)

        assert dtype is None or isinstance(dtype, torch.dtype), (
            f'dtype should be None or instance of torch.dtype, got {dtype}')
        self.cast_dtype = dtype

    def backward(self, loss: torch.Tensor, **kwargs):
        """Perform gradient back propagation with :attr:`loss_scaler`.

        Args:
            loss (torch.Tensor): The loss of current iteration.
            kwargs: Keyword arguments passed to :meth:`torch.Tensor.backward`
        """
        self.loss_scaler.scale(loss).backward(**kwargs)
        self._inner_count += 1

    def step(self, **kwargs):
        """Update parameters with :attr:`loss_scaler`.

        Args:
            kwargs: Keyword arguments passed to
                :meth:`torch.optim.Optimizer.step`.
        """
        ##-------------zero out nan--------------
        params = [p for pg in self.optimizer.param_groups for p in pg["params"]]
        for p in params:
            if hasattr(p, "grad") and p.grad is not None:
                p.grad.data[torch.isnan(p.grad.data)] = 0
                p.grad.data[torch.isinf(p.grad.data)] = 0
        
        ##----------------------------------------
        if self.clip_grad_kwargs:
            self.loss_scaler.unscale_(self.optimizer)
            self._clip_grad()
        self.loss_scaler.step(self.optimizer, **kwargs)
        self.loss_scaler.update(self._scale_update_param)

    def state_dict(self) -> dict:
        """Get the state dictionary of :attr:`optimizer` and
        :attr:`loss_scaler`.

        Based on the state dictionary of the optimizer, the returned state
        dictionary will add a key named "loss_scaler".

        Returns:
            dict: The merged state dict of :attr:`loss_scaler` and
            :attr:`optimizer`.
        """
        # save state_dict of loss_scaler
        state_dict = super().state_dict()
        state_dict['loss_scaler'] = self.loss_scaler.state_dict()
        return state_dict

    def load_state_dict(self, state_dict: dict):
        """Load and parse the state dictionary of :attr:`optimizer` and
        :attr:`loss_scaler`.

        If state_dict contains "loss_scaler.", the :attr:`loss_scaler` will
        load the corresponding keys. Otherwise, only the :attr:`optimizer`
        will load the state dictionary.

        Args:
            state_dict (dict): The state dict of :attr:`optimizer` and
                :attr:`loss_scaler`
        """
        if 'loss_scaler' in state_dict:
            self.loss_scaler.load_state_dict(state_dict.pop('loss_scaler'))

        if 'base_param_settings' in state_dict:
            self.base_param_settings = state_dict.pop('base_param_settings')

        # load state_dict of optimizer
        self.optimizer.load_state_dict(state_dict)

    @contextmanager
    def optim_context(self, model: nn.Module):
        """Enables the context for mixed precision training, and enables the
        context for disabling gradient synchronization during gradient
        accumulation context.

        Args:
            model (nn.Module): The training model.
        """
        from mmengine.runner.amp import autocast
        with super().optim_context(model), autocast(dtype=self.cast_dtype):
            yield