# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import abc as container_abcs
from collections import defaultdict
from copy import deepcopy
from itertools import chain

import torch

import bitsandbytes.functional as F


class MockArgs:
    def __init__(self, initial_data):
        for key in initial_data:
            setattr(self, key, initial_data[key])


class GlobalOptimManager:
    _instance = None

    def __init__(self):
        raise RuntimeError("Call get_instance() instead")

    def initialize(self):
        self.pid2config = {}
        self.index2config = {}
        self.optimizer = None
        self.uses_config_override = False
        self.module_weight_config_triple = []

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = cls.__new__(cls)
            cls._instance.initialize()
        return cls._instance

    def register_parameters(self, params):
        param_groups = list(params)
        if not isinstance(param_groups[0], dict):
            param_groups = [{"params": param_groups}]

        for group_index, group in enumerate(param_groups):
            for p_index, p in enumerate(group["params"]):
                if id(p) in self.pid2config:
                    self.index2config[(group_index, p_index)] = self.pid2config[
                        id(p)
                    ]

    def override_config(
        self, parameters, key=None, value=None, key_value_dict=None
    ):
        """
        Overrides initial optimizer config for specific parameters.

        The key-values of the optimizer config for the input parameters are overridden
        This can be both, optimizer parameters like "betas", or "lr" or it can be
        8-bit specific parameters like "optim_bits", "percentile_clipping".

        Parameters
        ----------
        parameters : torch.Tensor or list(torch.Tensors)
            The input parameters.
        key : str
            The hyperparamter to override.
        value : object
            The value for the hyperparamters.
        key_value_dict : dict
            A dictionary with multiple key-values to override.
        """
        self.uses_config_override = True
        if isinstance(parameters, torch.nn.Parameter):
            parameters = [parameters]
        if isinstance(parameters, torch.Tensor):
            parameters = [parameters]
        if key is not None and value is not None:
            assert key_value_dict is None
            key_value_dict = {key: value}

        if key_value_dict is not None:
            for p in parameters:
                if id(p) in self.pid2config:
                    self.pid2config[id(p)].update(key_value_dict)
                else:
                    self.pid2config[id(p)] = key_value_dict

    def register_module_override(self, module, param_name, config):
        self.module_weight_config_triple.append((module, param_name, config))


class Optimizer8bit(torch.optim.Optimizer):
    def __init__(self, params, defaults, optim_bits=32):
        super().__init__(params, defaults)
        self.initialized = False
        self.name2qmap = {}

        self.mng = GlobalOptimManager.get_instance()
        self.non_castable_tensor_keys = {
                "qmap1",
                "qmap2",
                "max1",
                "max2",
                "new_max1",
                "new_max2",
                "state1",
                "state2",
                "gnorm_vec",
                "absmax1",
                "absmax2",
                "unorm_vec",
        }

        if optim_bits == 8:
            self.fill_qmap()

    def fill_qmap(self):
        self.name2qmap["dynamic"] = F.create_dynamic_map(signed=True)
        self.name2qmap["udynamic"] = F.create_dynamic_map(signed=False)

    def __setstate__(self, state):
        super().__setstate__(state)

    def load_state_dict(self, state_dict):
        r"""Loads the optimizer state.

        Args:
            state_dict (dict): optimizer state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        # deepcopy, to be consistent with module API
        state_dict = deepcopy(state_dict)
        # Validate the state_dict
        groups = self.param_groups
        saved_groups = state_dict["param_groups"]

        if len(groups) != len(saved_groups):
            raise ValueError(
                "loaded state dict has a different number of "
                "parameter groups"
            )
        param_lens = (len(g["params"]) for g in groups)
        saved_lens = (len(g["params"]) for g in saved_groups)
        if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
            raise ValueError(
                "loaded state dict contains a parameter group "
                "that doesn't match the size of optimizer's group"
            )

        # Update the state
        id_map = {
            old_id: p
            for old_id, p in zip(
                chain.from_iterable(g["params"] for g in saved_groups),
                chain.from_iterable(g["params"] for g in groups),
            )
        }

        def cast(param, value):
            r"""Make a deep copy of value, casting all tensors to device of param."""
            if isinstance(value, torch.Tensor):
                # Floating-point types are a bit special here. They are the only ones
                # that are assumed to always match the type of params.
                if param.is_floating_point() and value.dtype != torch.uint8:
                    value = value.to(param.dtype)
                return value
            elif isinstance(value, dict):
                for k, v in value.items():
                    if k in self.non_castable_tensor_keys:
                        value[k] = v.to(param.device)
                    else:
                        value[k] = cast(param, v)

                return value
            elif isinstance(value, container_abcs.Iterable):
                return type(value)(cast(param, v) for v in value)
            else:
                return value

        # Copy state assigned to params (and cast tensors to appropriate types).
        # State that is not assigned to params is copied as is (needed for
        # backward compatibility).
        state = defaultdict(dict)
        for k, v in state_dict["state"].items():
            if k in id_map:
                param = id_map[k]
                state[param] = cast(param, v)
            else:
                state[k] = v

        # Update parameter groups, setting their 'params' value
        def update_group(group, new_group):
            new_group["params"] = group["params"]
            return new_group

        param_groups = [
            update_group(g, ng) for g, ng in zip(groups, saved_groups)
        ]
        self.__setstate__({"state": state, "param_groups": param_groups})

    def to_gpu(self):
        for gindex, group in enumerate(self.param_groups):
            for pindex, p in enumerate(group["params"]):
                if p in self.state:
                    values = self.state[p]
                    for k, v in values.items():
                        if isinstance(v, torch.Tensor):
                            self.state[p][k] = v.to(p.device)

    def check_overrides(self):
        for module, attr, config in self.mng.module_weight_config_triple:
            pmodule = getattr(module, attr)
            assert pmodule is not None
            assert isinstance(pmodule, torch.Tensor) or isinstance(
                pmodule, torch.Parameter
            )
            found = False
            for gindex, group in enumerate(self.param_groups):
                if found:
                    break
                for pindex, p in enumerate(group["params"]):
                    if found:
                        break
                    if id(p) == id(pmodule):
                        # found the matching parameter
                        # init override
                        self.mng.pid2config[id(p)] = config
                        self.mng.index2config[
                            (gindex, pindex)
                        ] = self.mng.pid2config[id(p)]
                        found = True

    @torch.no_grad()
    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        overflows = []

        if not self.initialized:
            self.check_overrides()
            self.to_gpu()  # needed for fairseq pure fp16 training
            self.initialized = True

        for gindex, group in enumerate(self.param_groups):
            for pindex, p in enumerate(group["params"]):
                if p.grad is None:
                    continue
                state = self.state[p]
                if len(state) == 0:
                    self.init_state(group, p, gindex, pindex)

                self.update_step(group, p, gindex, pindex)

        return loss

    def get_config(self, gindex, pindex, group):
        config = {}
        config["betas"] = group["betas"]
        config["eps"] = group["eps"]
        config["weight_decay"] = group["weight_decay"]
        config["lr"] = group["lr"]
        config["optim_bits"] = self.args.optim_bits
        config["min_8bit_size"] = self.args.min_8bit_size
        config["percentile_clipping"] = self.args.percentile_clipping
        config["block_wise"] = self.args.block_wise
        config["max_unorm"] = self.args.max_unorm
        config["skip_zeros"] = self.args.skip_zeros

        if (gindex, pindex) in self.mng.index2config:
            config.update(self.mng.index2config[(gindex, pindex)])
        return config

    def init_state(self, group, p, gindex, pindex):
        raise NotImplementedError("init_state method needs to be overridden")

    def update_step(self, group, p, gindex, pindex):
        raise NotImplementedError(
            "The update_step method needs to be overridden"
        )


class Optimizer2State(Optimizer8bit):
    def __init__(
        self,
        optimizer_name,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0.0,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        max_unorm=0.0,
        skip_zeros=False,
    ):
        if not 0.0 <= lr:
            raise ValueError(f"Invalid learning rate: {lr}")
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps}")
        if isinstance(betas, str):
            # format: '(beta1, beta2)'
            betas = betas.replace("(", "").replace(")", "").strip().split(",")
            betas = [float(b) for b in betas]
        for i in range(len(betas)):
            if not 0.0 <= betas[i] < 1.0:
                raise ValueError(
                    f"Invalid beta parameter at index {i}: {betas[i]}"
                )
        if not 0.0 <= weight_decay:
            raise ValueError(
                f"Invalid weight_decay value: {weight_decay}"
            )
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults, optim_bits)

        if args is None:
            args = {}
            args["optim_bits"] = optim_bits
            args["percentile_clipping"] = 100
            args["min_8bit_size"] = min_8bit_size
            args["percentile_clipping"] = percentile_clipping
            args["block_wise"] = block_wise
            args["max_unorm"] = max_unorm
            args["skip_zeros"] = skip_zeros

            self.args = MockArgs(args)
        else:
            self.args = args

        self.optimizer_name = optimizer_name

    @torch.no_grad()
    def init_state(self, group, p, gindex, pindex):
        config = self.get_config(gindex, pindex, group)

        if config["optim_bits"] == 32:
            dtype = torch.float32
        elif config["optim_bits"] == 8:
            dtype = torch.uint8
        else:
            raise NotImplementedError(
                f'Amount of optimizer bits not supported: {config["optim_bits"]}'
            )

        if p.numel() < config["min_8bit_size"]:
            dtype = torch.float32

        state = self.state[p]
        state["step"] = 0

        if dtype == torch.float32 or (
            dtype == torch.uint8 and p.numel() < 4096
        ):
            state["state1"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.float32,
                device=p.device,
            )
            state["state2"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.float32,
                device=p.device,
            )
        elif dtype == torch.uint8:
            if state["step"] == 0:
                if "dynamic" not in self.name2qmap:
                    self.fill_qmap()
                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(
                    p.device
                )
                self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to(
                    p.device
                )

            state["state1"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.uint8,
                device=p.device,
            )
            state["qmap1"] = self.name2qmap["dynamic"]

            state["state2"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.uint8,
                device=p.device,
            )
            state["qmap2"] = self.name2qmap["udynamic"]

            if config["block_wise"]:
                n = p.numel()
                blocks = n // 2048
                blocks += 1 if n % 2048 > 0 else 0

                state["absmax1"] = torch.zeros(
                    (blocks,), dtype=torch.float32, device=p.device
                )
                state["absmax2"] = torch.zeros(
                    (blocks,), dtype=torch.float32, device=p.device
                )
            else:
                state["max1"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )
                state["new_max1"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )
                state["max2"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )
                state["new_max2"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )

        if config["percentile_clipping"] < 100:
            state["gnorm_vec"] = torch.zeros((100,), device=p.device)

        if config["max_unorm"] > 0.0:
            state["unorm_vec"] = torch.zeros((1,), device=p.device)

    @torch.no_grad()
    def update_step(self, group, p, gindex, pindex):
        state = self.state[p]
        grad = p.grad

        config = self.get_config(gindex, pindex, group)

        state["step"] += 1
        step = state["step"]

        if config["percentile_clipping"] < 100:
            current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(
                grad, state["gnorm_vec"], step, config["percentile_clipping"]
            )
        else:
            gnorm_scale = 1.0

        if state["state1"].dtype == torch.float:
            F.optimizer_update_32bit(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                config["betas"][0],
                config["eps"],
                step,
                config["lr"],
                state["state2"],
                config["betas"][1],
                config["weight_decay"],
                gnorm_scale,
                state["unorm_vec"] if config["max_unorm"] > 0.0 else None,
                max_unorm=config["max_unorm"],
                skip_zeros=config["skip_zeros"],
            )

        elif state["state1"].dtype == torch.uint8 and not config["block_wise"]:
            F.optimizer_update_8bit(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                state["state2"],
                config["betas"][0],
                config["betas"][1],
                config["eps"],
                step,
                config["lr"],
                state["qmap1"],
                state["qmap2"],
                state["max1"],
                state["max2"],
                state["new_max1"],
                state["new_max2"],
                config["weight_decay"],
                gnorm_scale=gnorm_scale,
                unorm_vec=state["unorm_vec"]
                if config["max_unorm"] > 0.0
                else None,
                max_unorm=config["max_unorm"],
            )

            # swap maxes
            state["max1"], state["new_max1"] = state["new_max1"], state["max1"]
            state["max2"], state["new_max2"] = state["new_max2"], state["max2"]
        elif state["state1"].dtype == torch.uint8 and config["block_wise"]:
            F.optimizer_update_8bit_blockwise(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                state["state2"],
                config["betas"][0],
                config["betas"][1],
                config["eps"],
                step,
                config["lr"],
                state["qmap1"],
                state["qmap2"],
                state["absmax1"],
                state["absmax2"],
                config["weight_decay"],
                gnorm_scale=gnorm_scale,
                skip_zeros=config["skip_zeros"],
            )


class Optimizer1State(Optimizer8bit):
    def __init__(
        self,
        optimizer_name,
        params,
        lr=1e-3,
        betas=(0.9, 0.0),
        eps=1e-8,
        weight_decay=0.0,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        max_unorm=0.0,
        skip_zeros=False,
    ):
        if not 0.0 <= lr:
            raise ValueError(f"Invalid learning rate: {lr}")
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps}")
        for i in range(len(betas)):
            if not 0.0 <= betas[i] < 1.0:
                raise ValueError(
                    f"Invalid beta parameter at index {i}: {betas[i]}"
                )
        if not 0.0 <= weight_decay:
            raise ValueError(
                f"Invalid weight_decay value: {weight_decay}"
            )
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults, optim_bits)

        if args is None:
            args = {}
            args["optim_bits"] = optim_bits
            args["percentile_clipping"] = 100
            args["min_8bit_size"] = min_8bit_size
            args["percentile_clipping"] = percentile_clipping
            args["block_wise"] = block_wise
            args["max_unorm"] = max_unorm
            args["skip_zeros"] = skip_zeros

            self.args = MockArgs(args)
        else:
            self.args = args

        self.optimizer_name = optimizer_name

    @torch.no_grad()
    def init_state(self, group, p, gindex, pindex):
        config = self.get_config(gindex, pindex, group)

        if config["optim_bits"] == 32:
            dtype = torch.float32
        elif config["optim_bits"] == 8:
            dtype = torch.uint8
        else:
            raise NotImplementedError(
                f'Amount of optimizer bits not supported: {config["optim_bits"]}'
            )

        if p.numel() < config["min_8bit_size"]:
            dtype = torch.float32

        state = self.state[p]
        state["step"] = 0

        if dtype == torch.float32 or (
            dtype == torch.uint8 and p.numel() < 4096
        ):
            state["state1"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.float32,
                device=p.device,
            )
        elif dtype == torch.uint8:
            if state["step"] == 0:
                if "dynamic" not in self.name2qmap:
                    self.fill_qmap()
                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(
                    p.device
                )

            state["state1"] = torch.zeros_like(
                p,
                memory_format=torch.preserve_format,
                dtype=torch.uint8,
                device=p.device,
            )
            state["qmap1"] = self.name2qmap["dynamic"]

            if config["block_wise"]:
                n = p.numel()
                blocks = n // 2048
                blocks += 1 if n % 2048 > 0 else 0

                state["absmax1"] = torch.zeros(
                    (blocks,), dtype=torch.float32, device=p.device
                )
            else:
                state["max1"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )
                state["new_max1"] = torch.zeros(
                    (1,), dtype=torch.float32, device=p.device
                )

        if config["percentile_clipping"] < 100:
            state["gnorm_vec"] = torch.zeros((100,), device=p.device)

        if config["max_unorm"] > 0.0:
            state["unorm_vec"] = torch.zeros((1,), device=p.device)

    @torch.no_grad()
    def update_step(self, group, p, gindex, pindex):
        state = self.state[p]
        grad = p.grad

        config = self.get_config(gindex, pindex, group)

        state["step"] += 1
        step = state["step"]

        if config["percentile_clipping"] < 100:
            current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(
                grad, state["gnorm_vec"], step, config["percentile_clipping"]
            )
        else:
            gnorm_scale = 1.0

        if state["state1"].dtype == torch.float:
            F.optimizer_update_32bit(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                config["betas"][0],
                config["eps"],
                step,
                config["lr"],
                None,
                0.0,
                config["weight_decay"],
                gnorm_scale,
                state["unorm_vec"] if config["max_unorm"] > 0.0 else None,
                max_unorm=config["max_unorm"],
                skip_zeros=config["skip_zeros"],
            )

        elif state["state1"].dtype == torch.uint8 and not config["block_wise"]:
            F.optimizer_update_8bit(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                None,
                config["betas"][0],
                config["betas"][1],
                config["eps"],
                step,
                config["lr"],
                state["qmap1"],
                None,
                state["max1"],
                None,
                state["new_max1"],
                None,
                config["weight_decay"],
                gnorm_scale,
                state["unorm_vec"] if config["max_unorm"] > 0.0 else None,
                max_unorm=config["max_unorm"],
            )

            state["max1"], state["new_max1"] = state["new_max1"], state["max1"]
        elif state["state1"].dtype == torch.uint8 and config["block_wise"]:
            F.optimizer_update_8bit_blockwise(
                self.optimizer_name,
                grad,
                p,
                state["state1"],
                None,
                config["betas"][0],
                config["betas"][1],
                config["eps"],
                step,
                config["lr"],
                state["qmap1"],
                None,
                state["absmax1"],
                None,
                config["weight_decay"],
                gnorm_scale=gnorm_scale,
                skip_zeros=config["skip_zeros"],
            )