VTimeLLM/flash-attention/training/src/utils/ema.py

# Copied from https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py
from __future__ import division
from __future__ import unicode_literals

from typing import Iterable, Optional
import weakref
import copy
import contextlib

import torch


def to_float_maybe(x):
    return x.float() if x.dtype in [torch.float16, torch.bfloat16] else x


# Partially based on:
# https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
class ExponentialMovingAverage:
    """
    Maintains (exponential) moving average of a set of parameters.
    Args:
        parameters: Iterable of `torch.nn.Parameter` (typically from
            `model.parameters()`).
        decay: The exponential decay.
        use_num_updates: Whether to use number of updates when computing
            averages.
    """
    def __init__(
        self,
        parameters: Iterable[torch.nn.Parameter],
        decay: float,
        use_num_updates: bool = True
    ):
        if decay < 0.0 or decay > 1.0:
            raise ValueError('Decay must be between 0 and 1')
        self.decay = decay
        self.num_updates = 0 if use_num_updates else None
        parameters = list(parameters)
        self.shadow_params = [to_float_maybe(p.clone().detach())
                              for p in parameters if p.requires_grad]
        self.collected_params = None
        # By maintaining only a weakref to each parameter,
        # we maintain the old GC behaviour of ExponentialMovingAverage:
        # if the model goes out of scope but the ExponentialMovingAverage
        # is kept, no references to the model or its parameters will be
        # maintained, and the model will be cleaned up.
        self._params_refs = [weakref.ref(p) for p in parameters]

    def _get_parameters(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]]
    ) -> Iterable[torch.nn.Parameter]:
        if parameters is None:
            parameters = [p() for p in self._params_refs]
            if any(p is None for p in parameters):
                raise ValueError(
                    "(One of) the parameters with which this "
                    "ExponentialMovingAverage "
                    "was initialized no longer exists (was garbage collected);"
                    " please either provide `parameters` explicitly or keep "
                    "the model to which they belong from being garbage "
                    "collected."
                )
            return parameters
        else:
            parameters = list(parameters)
            if len(parameters) != len(self.shadow_params):
                raise ValueError(
                    "Number of parameters passed as argument is different "
                    "from number of shadow parameters maintained by this "
                    "ExponentialMovingAverage"
                )
            return parameters

    def update(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Update currently maintained parameters.
        Call this every time the parameters are updated, such as the result of
        the `optimizer.step()` call.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; usually the same set of
                parameters used to initialize this object. If `None`, the
                parameters with which this `ExponentialMovingAverage` was
                initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        decay = self.decay
        if self.num_updates is not None:
            self.num_updates += 1
            decay = min(
                decay,
                (1 + self.num_updates) / (10 + self.num_updates)
            )
        one_minus_decay = 1.0 - decay
        if parameters[0].device != self.shadow_params[0].device:
            self.to(device=parameters[0].device)
        with torch.no_grad():
            parameters = [p for p in parameters if p.requires_grad]
            for s_param, param in zip(self.shadow_params, parameters):
                torch.lerp(s_param, param.to(dtype=s_param.dtype), one_minus_decay, out=s_param)

    def copy_to(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Copy current averaged parameters into given collection of parameters.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                updated with the stored moving averages. If `None`, the
                parameters with which this `ExponentialMovingAverage` was
                initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        for s_param, param in zip(self.shadow_params, parameters):
            if param.requires_grad:
                param.data.copy_(s_param.data)

    def store(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Save the current parameters for restoring later.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                temporarily stored. If `None`, the parameters of with which this
                `ExponentialMovingAverage` was initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        self.collected_params = [
            param.clone()
            for param in parameters
            if param.requires_grad
        ]

    def restore(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Restore the parameters stored with the `store` method.
        Useful to validate the model with EMA parameters without affecting the
        original optimization process. Store the parameters before the
        `copy_to` method. After validation (or model saving), use this to
        restore the former parameters.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                updated with the stored parameters. If `None`, the
                parameters with which this `ExponentialMovingAverage` was
                initialized will be used.
        """
        if self.collected_params is None:
            raise RuntimeError(
                "This ExponentialMovingAverage has no `store()`ed weights "
                "to `restore()`"
            )
        parameters = self._get_parameters(parameters)
        for c_param, param in zip(self.collected_params, parameters):
            if param.requires_grad:
                param.data.copy_(c_param.data)

    @contextlib.contextmanager
    def average_parameters(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ):
        r"""
        Context manager for validation/inference with averaged parameters.
        Equivalent to:
            ema.store()
            ema.copy_to()
            try:
                ...
            finally:
                ema.restore()
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                updated with the stored parameters. If `None`, the
                parameters with which this `ExponentialMovingAverage` was
                initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        self.store(parameters)
        self.copy_to(parameters)
        try:
            yield
        finally:
            self.restore(parameters)

    def to(self, device=None, dtype=None) -> None:
        r"""Move internal buffers of the ExponentialMovingAverage to `device`.
        Args:
            device: like `device` argument to `torch.Tensor.to`
        """
        # .to() on the tensors handles None correctly
        self.shadow_params = [
            p.to(device=device, dtype=dtype)
            if p.is_floating_point()
            else p.to(device=device)
            for p in self.shadow_params
        ]
        if self.collected_params is not None:
            self.collected_params = [
                p.to(device=device, dtype=dtype)
                if p.is_floating_point()
                else p.to(device=device)
                for p in self.collected_params
            ]
        return

    def state_dict(self) -> dict:
        r"""Returns the state of the ExponentialMovingAverage as a dict."""
        # Following PyTorch conventions, references to tensors are returned:
        # "returns a reference to the state and not its copy!" -
        # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
        return {
            "decay": self.decay,
            "num_updates": self.num_updates,
            "shadow_params": self.shadow_params,
            "collected_params": self.collected_params
        }

    def load_state_dict(self, state_dict: dict) -> None:
        r"""Loads the ExponentialMovingAverage state.
        Args:
            state_dict (dict): EMA state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        # deepcopy, to be consistent with module API
        state_dict = copy.deepcopy(state_dict)
        self.decay = state_dict["decay"]
        if self.decay < 0.0 or self.decay > 1.0:
            raise ValueError('Decay must be between 0 and 1')
        self.num_updates = state_dict["num_updates"]
        assert self.num_updates is None or isinstance(self.num_updates, int), \
            "Invalid num_updates"

        self.shadow_params = state_dict["shadow_params"]
        assert isinstance(self.shadow_params, list), \
            "shadow_params must be a list"
        assert all(
            isinstance(p, torch.Tensor) for p in self.shadow_params
        ), "shadow_params must all be Tensors"

        self.collected_params = state_dict["collected_params"]
        if self.collected_params is not None:
            assert isinstance(self.collected_params, list), \
                "collected_params must be a list"
            assert all(
                isinstance(p, torch.Tensor) for p in self.collected_params
            ), "collected_params must all be Tensors"
            assert len(self.collected_params) == len(self.shadow_params), \
                "collected_params and shadow_params had different lengths"

        if len(self.shadow_params) == len(self._params_refs):
            # Consistent with torch.optim.Optimizer, cast things to consistent
            # device and dtype with the parameters
            params = [p() for p in self._params_refs]
            # If parameters have been garbage collected, just load the state
            # we were given without change.
            if not any(p is None for p in params):
                # ^ parameter references are still good
                for i, p in enumerate(params):
                    self.shadow_params[i] = to_float_maybe(self.shadow_params[i].to(
                        device=p.device, dtype=p.dtype
                    ))
                    if self.collected_params is not None:
                        self.collected_params[i] = self.collected_params[i].to(
                            device=p.device, dtype=p.dtype
                        )
        else:
            raise ValueError(
                "Tried to `load_state_dict()` with the wrong number of "
                "parameters in the saved state."
            )