# Copyright The Lightning AI team.
#
# 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.
import functools
import logging
import math
import os
import warnings
from contextlib import ExitStack
from functools import partial
from types import ModuleType
from typing import Any, Callable, ContextManager, Literal, Optional, OrderedDict, Set, Tuple, Type, cast

import torch
from lightning_utilities import apply_to_collection
from lightning_utilities.core.imports import RequirementCache
from torch import Tensor
from torch.nn import init
from torch.nn.modules.module import _IncompatibleKeys
from typing_extensions import Self, override

from lightning_fabric.plugins.precision.precision import Precision
from lightning_fabric.plugins.precision.utils import (
    _ClassReplacementContextManager,
    _convert_fp_tensor,
    _DtypeContextManager,
)
from lightning_fabric.utilities.types import _DEVICE

log = logging.getLogger(__name__)

_BITSANDBYTES_AVAILABLE = RequirementCache("bitsandbytes>=0.42.0")


class BitsandbytesPrecision(Precision):
    """Plugin for quantizing weights with `bitsandbytes <https://github.com/TimDettmers/bitsandbytes>`__.

    .. warning::  This is an :ref:`experimental <versioning:Experimental API>` feature.

    .. note::
        The optimizer is not automatically replaced with ``bitsandbytes.optim.Adam8bit`` or equivalent 8-bit optimizers.

    Args:
        mode: The quantization mode to use.
        dtype: The compute dtype to use.
        ignore_modules: The submodules whose Linear layers should not be replaced, for example. ``{"lm_head"}``.
            This might be desirable for numerical stability. The string will be checked in as a prefix, so a value like
            "transformer.blocks" will ignore all linear layers in all of the transformer blocks.
    """

    # Note: you'll notice that the `precision` str class attribute is not defined. This is on purpose because there are
    # many configuration options so `precision="bitsandbytes"` would be ambiguous about which one to use. Additionally,
    # it would create backwards compatibility challenges if better modes or dtypes are added in the future

    # TODO: we could implement optimizer replacement with
    # - Fabric: Add `Precision.convert_optimizer` from `Strategy.setup_optimizer`
    # - Trainer: Use `Precision.connect`

    def __init__(
        self,
        mode: Literal["nf4", "nf4-dq", "fp4", "fp4-dq", "int8", "int8-training"],
        dtype: Optional[torch.dtype] = None,
        ignore_modules: Optional[Set[str]] = None,
    ) -> None:
        _import_bitsandbytes()

        if dtype is None:
            # try to be smart about the default selection
            if mode.startswith("int8"):
                dtype = torch.float16
            else:
                dtype = (
                    torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16
                )
        if mode.startswith("int8") and dtype is not torch.float16:
            # this limitation is mentioned in https://huggingface.co/blog/hf-bitsandbytes-integration#usage
            raise ValueError(f"{mode!r} only works with `dtype=torch.float16`, but you chose `{dtype}`")

        globals_ = globals()
        mode_to_cls = {
            "nf4": globals_["_NF4Linear"],
            "nf4-dq": globals_["_NF4DQLinear"],
            "fp4": globals_["_FP4Linear"],
            "fp4-dq": globals_["_FP4DQLinear"],
            "int8-training": globals_["_Linear8bitLt"],
            "int8": globals_["_Int8LinearInference"],
        }
        self._linear_cls = mode_to_cls[mode]
        self.dtype = dtype
        self.ignore_modules = ignore_modules or set()

    @override
    def convert_module(self, module: torch.nn.Module) -> torch.nn.Module:
        # avoid naive users thinking they quantized their model
        if not any(isinstance(m, torch.nn.Linear) for m in module.modules()):
            raise TypeError(
                "You are using the bitsandbytes precision plugin, but your model has no Linear layers. This plugin"
                " won't work for your model."
            )

        # convert modules if they haven't been converted already
        bnb = _import_bitsandbytes()
        if not any(isinstance(m, (bnb.nn.Linear8bitLt, bnb.nn.Linear4bit)) for m in module.modules()):
            # this will not quantize the model but only replace the layer classes
            _convert_layers(module, self._linear_cls, self.ignore_modules)

        # set the compute dtype if necessary
        for m in module.modules():
            if isinstance(m, bnb.nn.Linear4bit):
                m.compute_dtype = self.dtype
                m.compute_type_is_set = False
        return module

    @override
    def tensor_init_context(self) -> ContextManager:
        return _DtypeContextManager(self.dtype)

    @override
    def module_init_context(self) -> ContextManager:
        if self.ignore_modules:
            # cannot patch the Linear class if the user wants to skip some submodules
            raise RuntimeError(
                "Instantiating your model under the `init_module` context manager is not supported when used with"
                f" `BitsandbytesPrecision(..., ignore_modules={self.ignore_modules})` as this"
                " may initialize the layers on-device, defeating the purpose of quantization. You can remove"
                " `ignore_modules` or remove the `init_module` context manager."
            )
        dtype_ctx = self.tensor_init_context()
        # TODO: this could also support replacing `Embedding` and `Conv1D`
        context_manager = _ClassReplacementContextManager({"torch.nn.Linear": self._linear_cls})
        stack = ExitStack()
        stack.enter_context(dtype_ctx)
        stack.enter_context(context_manager)
        return stack

    @override
    def forward_context(self) -> ContextManager:
        return _DtypeContextManager(self.dtype)

    @override
    def convert_input(self, data: Any) -> Any:
        return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=self.dtype)

    @override
    def convert_output(self, data: Any) -> Any:
        return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=torch.get_default_dtype())


def _quantize_on_load_hook(quantize_fn: Callable[[torch.Tensor], None], state_dict: OrderedDict, *_: Any) -> None:
    # There is only one key that ends with `*.weight`, the other one is the bias
    weight_key = next((name for name in state_dict if name.endswith("weight")), None)
    if weight_key is None:
        return
    # Load the weight from the state dict and re-quantize it
    weight = state_dict.pop(weight_key)
    quantize_fn(weight)


def _ignore_missing_weights_hook(module: torch.nn.Module, incompatible_keys: _IncompatibleKeys) -> None:
    # since we manually loaded the weight in the `_quantize_on_load_hook` hook, we need to avoid this missing key false
    # positive
    for key in reversed(incompatible_keys.missing_keys):
        if key.endswith("weight"):
            incompatible_keys.missing_keys.remove(key)


def _replace_param(
    param: torch.nn.Parameter, data: torch.Tensor, quant_state: Optional[Tuple] = None
) -> torch.nn.Parameter:
    bnb = _import_bitsandbytes()

    # doing `param.data = weight` raises a RuntimeError if param.data was on meta-device, so
    # we need to re-create the parameters instead of overwriting the data
    if param.device.type == "meta":
        if isinstance(param, bnb.nn.Params4bit):
            return bnb.nn.Params4bit(
                data,
                requires_grad=data.requires_grad,
                quant_state=quant_state,
                compress_statistics=param.compress_statistics,
                quant_type=param.quant_type,
            )
        return torch.nn.Parameter(data, requires_grad=data.requires_grad)
    param.data = data
    if isinstance(param, bnb.nn.Params4bit):
        param.quant_state = quant_state
    return param


@functools.lru_cache(maxsize=1)
def _import_bitsandbytes() -> ModuleType:
    if not _BITSANDBYTES_AVAILABLE:
        raise ModuleNotFoundError(str(_BITSANDBYTES_AVAILABLE))
    # configuration for bitsandbytes before import
    nowelcome_set = "BITSANDBYTES_NOWELCOME" in os.environ
    if not nowelcome_set:
        os.environ["BITSANDBYTES_NOWELCOME"] = "1"
    warnings.filterwarnings("ignore", message=r".*bitsandbytes was compiled without GPU support.*")
    warnings.filterwarnings(
        "ignore", message=r"MatMul8bitLt: inputs will be cast from .* to float16 during quantization"
    )
    import bitsandbytes as bnb

    if not nowelcome_set:
        del os.environ["BITSANDBYTES_NOWELCOME"]

    class _Linear8bitLt(bnb.nn.Linear8bitLt):
        """Wraps `bnb.nn.Linear8bitLt` and enables instantiation directly on the device and re-quantizaton when loading
        the state dict."""

        def __init__(self, *args: Any, device: Optional[_DEVICE] = None, threshold: float = 6.0, **kwargs: Any) -> None:
            super().__init__(*args, device=device, threshold=threshold, **kwargs)
            self.weight = cast(bnb.nn.Int8Params, self.weight)  # type: ignore[has-type]
            self.bias = cast(Optional[torch.nn.Parameter], self.bias)  # type: ignore[has-type]
            # if the device is CUDA or we are under a CUDA context manager, quantize the weight here, so we don't end up
            # filling the device memory with float32 weights which could lead to OOM
            if torch.tensor(0, device=device).device.type == "cuda":
                self.quantize_()
            self._register_load_state_dict_pre_hook(partial(_quantize_on_load_hook, self.quantize_))
            self.register_load_state_dict_post_hook(_ignore_missing_weights_hook)

        def quantize_(self, weight: Optional[torch.Tensor] = None, device: Optional[torch.device] = None) -> None:
            """Inplace quantize."""
            if weight is None:
                weight = self.weight.data
            if weight.data.dtype == torch.int8:
                # already quantized
                return
            assert isinstance(self.weight, bnb.nn.Int8Params)
            self.weight = self.quantize(self.weight, weight, device)

        @staticmethod
        def quantize(
            int8params: bnb.nn.Int8Params, weight: torch.Tensor, device: Optional[torch.device]
        ) -> bnb.nn.Int8Params:
            device = device or torch.device("cuda")
            if device.type != "cuda":
                raise RuntimeError(f"Unexpected device type: {device.type}")
            # https://github.com/TimDettmers/bitsandbytes/blob/0.41.0/bitsandbytes/nn/modules.py#L291-L302
            B = weight.contiguous().to(device=device, dtype=torch.float16)
            if int8params.has_fp16_weights:
                int8params.data = B
            else:
                CB, CBt, SCB, SCBt, _ = bnb.functional.double_quant(B)
                del CBt
                del SCBt
                int8params.data = CB
                setattr(int8params, "CB", CB)
                setattr(int8params, "SCB", SCB)
            return int8params

        def to_empty(self, *, device: _DEVICE, recurse: bool = True) -> Self:
            if self.weight.device.type == "meta":
                # need custom logic if int8params is on meta device
                raise NotImplementedError
            if self.weight.dtype == torch.uint8:  # was quantized
                # need the original shape here
                raise NotImplementedError
            device = torch.device(device)
            weight = torch.empty_like(self.weight.data, device=device)
            if device.type == "cuda":  # re-quantize
                self.quantize_(weight, device)
            else:
                self.weight = _replace_param(self.weight, weight)
            if self.bias is not None:
                self.bias = _replace_param(self.bias, torch.empty_like(self.bias, device=device))
            return self

        def reset_parameters(self) -> None:
            # from `torch.nn.Linear.reset_parameters`
            if self.bias is not None:
                fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
                bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
                init.uniform_(self.bias, -bound, bound)

            linear_init_finished = isinstance(self.weight, bnb.nn.Params4bit)
            if linear_init_finished and self.weight.dtype == torch.uint8:  # was quantized
                # need the original shape here
                raise NotImplementedError
            weight = self.weight.data
            torch.nn.init.kaiming_uniform_(weight, a=math.sqrt(5))
            if linear_init_finished:
                if self.weight.device.type == "meta":
                    # need custom logic if int8params is on meta device
                    raise NotImplementedError
                if self.weight.device.type == "cuda":  # re-quantize
                    self.quantize_(weight)
                else:
                    self.weight = _replace_param(self.weight, weight)

    class _Linear4bit(bnb.nn.Linear4bit):
        """Wraps `bnb.nn.Linear4bit` to enable: instantiation directly on the device, re-quantizaton when loading the
        state dict, meta-device initialization, and materialization."""

        def __init__(self, *args: Any, device: Optional[_DEVICE] = None, **kwargs: Any) -> None:
            super().__init__(*args, device=device, **kwargs)
            self.weight = cast(bnb.nn.Params4bit, self.weight)  # type: ignore[has-type]
            self.bias = cast(Optional[torch.nn.Parameter], self.bias)  # type: ignore[has-type]
            # if the device is CUDA or we are under a CUDA context manager, quantize the weight here, so we don't end up
            # filling the device memory with float32 weights which could lead to OOM
            if torch.tensor(0, device=device).device.type == "cuda":
                self.quantize_()
            self._register_load_state_dict_pre_hook(partial(_quantize_on_load_hook, self.quantize_))
            self.register_load_state_dict_post_hook(_ignore_missing_weights_hook)

        def quantize_(self, weight: Optional[torch.Tensor] = None, device: Optional[torch.device] = None) -> None:
            """Inplace quantize."""
            if weight is None:
                weight = self.weight.data
            if weight.data.dtype == torch.uint8:
                # already quantized
                return
            assert isinstance(self.weight, bnb.nn.Params4bit)
            self.weight = self.quantize(self.weight, weight, device)

        @staticmethod
        def quantize(
            params4bit: bnb.nn.Params4bit, weight: torch.Tensor, device: Optional[torch.device]
        ) -> bnb.nn.Params4bit:
            device = device or torch.device("cuda")
            if device.type != "cuda":
                raise RuntimeError(f"Unexpected device type: {device.type}")
            # https://github.com/TimDettmers/bitsandbytes/blob/0.41.0/bitsandbytes/nn/modules.py#L156-L159
            w = weight.contiguous().to(device=device, dtype=torch.half)
            w_4bit, quant_state = bnb.functional.quantize_4bit(
                w,
                blocksize=params4bit.blocksize,
                compress_statistics=params4bit.compress_statistics,
                quant_type=params4bit.quant_type,
            )
            return _replace_param(params4bit, w_4bit, quant_state)

        def to_empty(self, *, device: _DEVICE, recurse: bool = True) -> Self:
            if self.weight.dtype == torch.uint8:  # was quantized
                # cannot init the quantized params directly
                weight = torch.empty(self.weight.quant_state.shape, device=device, dtype=torch.half)
            else:
                weight = torch.empty_like(self.weight.data, device=device)
            device = torch.device(device)
            if device.type == "cuda":  # re-quantize
                self.quantize_(weight, device)
            else:
                self.weight = _replace_param(self.weight, weight)
            if self.bias is not None:
                self.bias = _replace_param(self.bias, torch.empty_like(self.bias, device=device))
            return self

        def reset_parameters(self) -> None:
            # from `torch.nn.Linear.reset_parameters`
            if self.bias is not None:
                fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
                bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
                init.uniform_(self.bias, -bound, bound)

            linear_init_finished = isinstance(self.weight, bnb.nn.Params4bit)
            if linear_init_finished and self.weight.dtype == torch.uint8:  # was quantized
                # cannot init the quantized params directly
                weight = torch.empty(self.weight.quant_state.shape, device=self.weight.device, dtype=torch.half)
            else:
                weight = self.weight.data
            torch.nn.init.kaiming_uniform_(weight, a=math.sqrt(5))
            if linear_init_finished:
                if self.weight.device.type == "cuda":  # re-quantize
                    self.quantize_(weight)
                else:
                    self.weight = _replace_param(self.weight, weight)

    # Use a class instead `functools.partial` to respect `isinstance` checks and attribute accesses
    class _Int8LinearInference(_Linear8bitLt):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, has_fp16_weights=False, **kwargs)

    class _FP4Linear(_Linear4bit):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, quant_type="fp4", compress_statistics=False, **kwargs)

    class _FP4DQLinear(_Linear4bit):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, quant_type="fp4", compress_statistics=True, **kwargs)

    class _NF4Linear(_Linear4bit):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, quant_type="nf4", compress_statistics=False, **kwargs)

    class _NF4DQLinear(_Linear4bit):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, quant_type="nf4", compress_statistics=True, **kwargs)

    # these classes are defined programatically like this to avoid importing bitsandbytes in environments that have
    # it available but will not use it
    classes = {
        "_Linear8bitLt": _Linear8bitLt,
        "_Linear4bit": _Linear4bit,
        "_Int8LinearInference": _Int8LinearInference,
        "_FP4Linear": _FP4Linear,
        "_FP4DQLinear": _FP4DQLinear,
        "_NF4Linear": _NF4Linear,
        "_NF4DQLinear": _NF4DQLinear,
    }
    globals().update(classes)

    return bnb


def _convert_layers(module: torch.nn.Module, linear_cls: Type, ignore_modules: Set[str], prefix: str = "") -> None:
    for name, child in module.named_children():
        fullname = f"{prefix}.{name}" if prefix else name
        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
            log.debug(f"Replacing layer {fullname!r} with bitsandbytes equivalent")
            has_bias = child.bias is not None
            # since we are going to copy over the child's data, the device doesn't matter. I chose CPU
            # to avoid spiking CUDA memory even though initialization is slower
            # 4bit layers support quantizing from meta-device params so this is only relevant for 8-bit
            _Linear4bit = globals()["_Linear4bit"]
            device = torch.device("meta" if issubclass(linear_cls, _Linear4bit) else "cpu")
            replacement = linear_cls(
                child.in_features,
                child.out_features,
                bias=has_bias,
                device=device,
            )
            if has_bias:
                replacement.bias = _replace_param(replacement.bias, child.bias.data.clone())
            state = {"quant_state": replacement.weight.quant_state if issubclass(linear_cls, _Linear4bit) else None}
            replacement.weight = _replace_param(replacement.weight, child.weight.data.clone(), **state)
            module.__setattr__(name, replacement)
        else:
            _convert_layers(child, linear_cls, ignore_modules, prefix=fullname)
