# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import sys
import torch
from collections import OrderedDict
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtime.zero.partition_parameters import _init_external_params
from deepspeed.runtime.zero.partition_parameters import *
from deepspeed.runtime.zero.partitioned_param_coordinator import PartitionedParameterCoordinator, iter_params
from deepspeed import comm as dist
from deepspeed.accelerator import get_accelerator

FWD_MODULE_STACK = list()


def is_builtin_type(obj):
    # https://stackoverflow.com/a/17795199
    return obj.__class__.__module__ == '__builtin__' or obj.__class__.__module__ == "builtins"


def isinstance_namedtuple(obj: object) -> bool:
    """
    Is this an instance of namedtuple/NamedTuple?
    From: https://stackoverflow.com/a/62692640

    Args:
        obj (object): An object.

    Returns:
        bool: True if namedtuple/NamedTuple else False.
    """
    return isinstance(obj, tuple) and hasattr(obj, '_asdict') and hasattr(obj, '_fields')


# ensure we only warn once, otherwise every iteration will trigger a warning
warned = False


def _apply_to_tensors_only(module, functional, backward_function, outputs):
    """
    Apply a torch.autograd.Function that calls a `backward_function` to every Tensor in `outputs`.

    Args:
        module (torch.nn.Module):  A torch module
        functional (Type[torch.autograd.Function]): The function class to apply.
        backward_function (Callable[[torch.nn.Module], None]): A backward_function to pass to
            `functional.apply`.
        outputs (Any): The output of `module`.

    Returns:
        Any: The output of `module`.
    """
    if isinstance(outputs, (tuple, list)):
        touched_outputs = []
        for output in outputs:
            touched_output = _apply_to_tensors_only(module, functional, backward_function, output)
            touched_outputs.append(touched_output)

        if isinstance_namedtuple(outputs):
            # namedtuples require a slightly different syntax.
            return outputs.__class__(*touched_outputs)

        return outputs.__class__(touched_outputs)
    elif isinstance(outputs, dict):
        # apply inplace to avoid recreating dict inherited objects
        for key in outputs.keys():
            outputs[key] = _apply_to_tensors_only(module, functional, backward_function, outputs[key])
        return outputs

    elif isinstance(outputs, torch.Tensor):
        # this also applies to torch.Tensor's subclasses like torch.nn.parameter.Parameter
        touched_outputs = functional.apply(module, backward_function, outputs)

        # restore zero param attributes if those get stripped by `backward_function`
        if not is_zero_param(touched_outputs) and is_zero_param(outputs):
            touched_outputs.ds_param_alias = outputs
        return touched_outputs
    else:
        if not is_builtin_type(outputs):
            global warned
            if not warned and dist.get_rank() == 0:
                logger.warning(
                    f"A module has unknown inputs or outputs type ({type(outputs)}) and the tensors embedded in it cannot be detected. "
                    "The ZeRO-3 hooks designed to trigger before or after backward pass of the module relies on knowing the input and "
                    "output tensors and therefore may not get triggered properly.")
                warned = True
        return outputs


#for each tensor in outputs run the forward_function and register backward_function as hook
def _apply_forward_and_backward_to_tensors_only(module, forward_function, backward_function, outputs):
    if type(outputs) is tuple:
        touched_outputs = []
        for output in outputs:
            touched_output = _apply_forward_and_backward_to_tensors_only(module, forward_function, backward_function,
                                                                         output)
            touched_outputs.append(touched_output)
        return tuple(touched_outputs)
    elif type(outputs) is torch.Tensor:
        forward_function(outputs)
        if outputs.requires_grad:
            outputs.register_hook(backward_function)
        return outputs
    else:
        return outputs


class ZeROOrderedDict(OrderedDict):

    def __init__(self, parent_module, *args, **kwargs):
        """A replacement for ``collections.OrderedDict`` to detect external ZeRO params.

        Args:
            parent_module (``collections.OrderedDict``): the collection to replace
        """

        super().__init__(*args, **kwargs)
        self._parent_module = parent_module
        self._in_forward = False

    def __getitem__(self, key):
        param = super().__getitem__(key)

        # Params can be registered as None (e.g., bias)
        if param is None:
            return param

        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
            if self._parent_module._parameters._in_forward:
                register_external_parameter(FWD_MODULE_STACK[-1], param)
                param.all_gather()
                print_rank_0(f'Registering external parameter from getter {key} ds_id = {param.ds_id}', force=False)

        return param


def _inject_parameters(module, cls):
    for module in module.modules():
        if cls == ZeROOrderedDict:
            new_param = cls(parent_module=module)
        else:
            new_param = cls()

        for key, param in module._parameters.items():
            new_param[key] = param
        module._parameters = new_param


class PreBackwardFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, module, pre_backward_function, outputs):
        ctx.module = module
        ctx.pre_backward_function = pre_backward_function
        if not hasattr(module, "applied_pre_backward_ref_cnt"):
            module.applied_pre_backward_ref_cnt = 0
        module.applied_pre_backward_ref_cnt += 1
        #print(f"After Forward: {ctx.module.__class__.__name__}")
        outputs = outputs.detach()
        return outputs

    @staticmethod
    def backward(ctx, *args):
        #print(f"Before Backward: {ctx.module.__class__.__name__}")
        ctx.pre_backward_function(ctx.module)
        return (None, None) + args


class PostBackwardFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, module, pre_backward_function, output):
        ctx.module = module
        if output.requires_grad:
            #TODO SOME TIMES post backward does not seem to be triggered debug in detail
            #Should only cause increase in memory not correctness issue
            #if output.grad_fn.__class__.__name__ == 'ViewBackward':
            #    ctx.view=True
            #    print(f"Warning view tensor for input to module : {module.__class__.__name__}. Backward hooks may not trigger properly")
            #assert len(module.parameters(recurse=False)), "The input tensor to the module is a view, and autograd Function or register_hook is not triggered with view tensors."
            #if module.ds_grads_remaining == 0:
            #    print(f"Before Forward: {ctx.module.__class__.__name__}")
            module.ds_grads_remaining += 1
            ctx.pre_backward_function = pre_backward_function
        output = output.detach()
        return output

    @staticmethod
    def backward(ctx, *args):
        ctx.module.ds_grads_remaining = ctx.module.ds_grads_remaining - 1
        if ctx.module.ds_grads_remaining == 0:
            ctx.pre_backward_function(ctx.module)
            #print(f"After Backward: {ctx.module.__class__.__name__}")
        return (None, None) + args


class DeepSpeedZeRoOffload(object):

    def __init__(self,
                 module,
                 timers,
                 ds_config,
                 overlap_comm=True,
                 prefetch_bucket_size=50000000,
                 max_reuse_distance=1000000000,
                 max_live_parameters=1000000000,
                 param_persistence_threshold=100000,
                 model_persistence_threshold=sys.maxsize,
                 offload_param_config=None,
                 mpu=None):

        see_memory_usage("DeepSpeedZeRoOffload initialize [begin]", force=True)

        print_rank_0(f"initialized {__class__.__name__} with args: {locals()}", force=False)

        self.module = module
        self.dtype = list(module.parameters())[0].dtype
        self.offload_device = None
        self.offload_param_pin_memory = False

        if offload_param_config is not None and offload_param_config.device != OffloadDeviceEnum.none:
            self.offload_device = offload_param_config.device
            self.offload_param_pin_memory = offload_param_config.pin_memory

        self._convert_to_zero_parameters(ds_config, module, mpu)

        for m in module.modules():
            _init_external_params(m)

        _inject_parameters(module, ZeROOrderedDict)

        self.param_numel_persistence_threshold = int(param_persistence_threshold)
        self.model_persistence_threshold = int(model_persistence_threshold)
        self.persistent_parameters = self.mark_persistent_parameters(self.param_numel_persistence_threshold,
                                                                     self.model_persistence_threshold)

        self.param_coordinators = {}
        self._prefetch_bucket_sz = int(prefetch_bucket_size)
        self._max_reuse_distance_in_numel = int(max_reuse_distance)
        self._max_available_parameters_in_numel = int(max_live_parameters)
        self.__allgather_stream = get_accelerator().Stream() if overlap_comm else get_accelerator().default_stream()

        self.forward_hooks = []
        self.backward_hooks = []
        self.setup_zero_stage3_hooks()
        print_rank_0(
            f'Created module hooks: forward = {len(self.forward_hooks)}, backward = {len(self.backward_hooks)}',
            force=False)

        see_memory_usage("DeepSpeedZeRoOffload initialize [end]", force=True)

    @instrument_w_nvtx
    def partition_all_parameters(self):
        """Partitioning Parameters that were not partitioned usually if parameters
        of modules whose input parameters do not require grad computation do not
        trigger post call and will therefore will remain unpartitioned"""
        self.get_param_coordinator(training=self.module.training).release_and_reset_all(self.module)
        for param in iter_params(self.module, recurse=True):
            if param.ds_status != ZeroParamStatus.NOT_AVAILABLE:
                raise RuntimeError(f"{param.ds_summary()} expected to be released")

    def get_param_coordinator(self, training):
        if not training in self.param_coordinators:
            self.param_coordinators[training] = PartitionedParameterCoordinator(
                prefetch_bucket_sz=self._prefetch_bucket_sz,
                max_reuse_distance_in_numel=self._max_reuse_distance_in_numel,
                max_available_parameters_in_numel=self._max_available_parameters_in_numel,
                allgather_stream=self.__allgather_stream,
                prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme,
            )

        return self.param_coordinators[training]

    def empty_partition_cache(self):
        self.partition_all_parameters()

    def _convert_to_zero_parameters(self, ds_config, module, mpu):
        non_zero_params = [p for p in module.parameters() if not is_zero_param(p)]
        if non_zero_params:
            zero_params = [p for p in module.parameters() if is_zero_param(p)]
            if zero_params:
                zero_params[0].convert_to_zero_parameters(param_list=non_zero_params)
            else:
                group = None
                if mpu:
                    group = mpu.get_data_parallel_group()

                Init(module=module,
                     data_parallel_group=group,
                     dtype=self.dtype,
                     config_dict_or_path=ds_config,
                     remote_device=self.offload_device,
                     pin_memory=self.offload_param_pin_memory,
                     mpu=mpu)

    def destroy(self):
        self._remove_module_hooks()

    def _remove_module_hooks(self):
        num_forward_hooks = len(self.forward_hooks)
        num_backward_hooks = len(self.backward_hooks)

        for hook in self.forward_hooks:
            hook.remove()

        for hook in self.backward_hooks:
            hook.remove()

        print_rank_0(f'Deleted module hooks: forward = {num_forward_hooks}, backward = {num_backward_hooks}',
                     force=False)

    def setup_zero_stage3_hooks(self):
        self.hierarchy = 0

        #reset step if in inference mode
        @instrument_w_nvtx
        def _end_of_forward_hook(module, *args):

            if not torch._C.is_grad_enabled():
                self.get_param_coordinator(training=False).reset_step()

        #likely one of them should be enough but just to be safe
        self._register_hooks_recursively(self.module)
        self.module.register_forward_hook(_end_of_forward_hook)

        # Add top module to stack trace
        global FWD_MODULE_STACK
        FWD_MODULE_STACK.append(self.module)

    def mark_persistent_parameters(self, param_threshold, model_threshold):
        persistent_params = []
        total_persistent_parameters = 0
        params_count = 0
        for _, param in self.module.named_parameters(recurse=True):
            if param.ds_numel + total_persistent_parameters > model_threshold:
                continue

            if param.ds_numel <= param_threshold:
                params_count += 1
                param.ds_persist = True
                persistent_params.append(param)
                total_persistent_parameters += param.ds_numel

        print_rank_0(
            f"Parameter Offload: Total persistent parameters: {total_persistent_parameters} in {params_count} params",
            force=True)

        return persistent_params

    def _register_hooks_recursively(self, module, count=[0]):
        my_count = count[0]
        module.id = my_count

        #print(f"{module.__class__} : {module.id}")

        for child in module.children():
            count[0] = count[0] + 1
            self._register_hooks_recursively(child, count=count)

        @instrument_w_nvtx
        def _pre_forward_module_hook(module, *args):
            self.pre_sub_module_forward_function(module)

        @instrument_w_nvtx
        def _post_forward_module_hook(module, input, output):
            global FWD_MODULE_STACK
            FWD_MODULE_STACK.pop()
            if output is None:
                output = []
            elif not isinstance(output, (list, tuple)):
                if torch.is_tensor(output):
                    output = [output]
                else:
                    #print(f'got UNKNOWN type {type(output)}')
                    outputs = []
                    output = output if isinstance(output, dict) else vars(output)
                    for name, val in output.items():
                        if not name.startswith('__') and torch.is_tensor(val):
                            outputs.append(val)
                    output = outputs

            for item in filter(lambda item: is_zero_param(item) or hasattr(item, 'ds_param_alias'), output):
                key = id(item) if hasattr(item, 'ds_id') else id(item.ds_param_alias)
                actual_external_param = item if hasattr(item, 'ds_id') else item.ds_param_alias

                if not any(key in m._external_params for m in FWD_MODULE_STACK):
                    actual_external_param.is_external_param = True
                    module_to_register = FWD_MODULE_STACK[-1]
                    register_external_parameter(module_to_register, actual_external_param)
                    print_rank_0(
                        f'Registering dangling parameter for module {module_to_register.__class__.__name__}, ds_id = {actual_external_param.ds_id}.',
                        force=False)

                    # It's possible that the parameter was already external to the completed module. If so, remove it the
                    # registration as it will be covered by the outer module instead.
                    if key in module._external_params:
                        print_rank_0(
                            f'  Unregistering nested dangling parameter from module {module.__class__.__name__}, ds_id = {actual_external_param.ds_id}',
                            force=False)
                        unregister_external_parameter(module, actual_external_param)

                    actual_external_param.all_gather()

            self.post_sub_module_forward_function(module)

        def _pre_backward_module_hook(module, inputs, output):

            @instrument_w_nvtx
            def _run_before_backward_function(sub_module):
                # some models (e.g. Albert) may run multiple forwards on the same layer in a loop
                # before doing backwards, so each backward will need a pre-fetch - using reference
                # counting to support this scenario
                #print(f"COUNTER before: {sub_module.applied_pre_backward_ref_cnt}")
                if sub_module.applied_pre_backward_ref_cnt > 0:
                    self.pre_sub_module_backward_function(sub_module)
                    sub_module.applied_pre_backward_ref_cnt -= 1
                #print(f"COUNTER after: {sub_module.applied_pre_backward_ref_cnt}")

            return _apply_to_tensors_only(module, PreBackwardFunction, _run_before_backward_function, output)

        #This is an alternate to doing _post_backward_module_hook
        #it uses tensor.register_hook instead of using torch.autograd.Function
        def _alternate_post_backward_module_hook(module, inputs):
            module.ds_grads_remaining = 0

            #print(f"Before Forward {module.__class__.__name__}")

            def _run_after_backward_hook(*unused):
                module.ds_grads_remaining = module.ds_grads_remaining - 1
                if module.ds_grads_remaining == 0:
                    #print(f"After backward {module.__class__.__name__}")
                    self.post_sub_module_backward_function(module)

            def _run_before_forward_function(input):
                if input.requires_grad:
                    module.ds_grads_remaining += 1

            return _apply_forward_and_backward_to_tensors_only(module, _run_before_forward_function,
                                                               _run_after_backward_hook, inputs)

        def _post_backward_module_hook(module, inputs):
            module.ds_grads_remaining = 0

            @instrument_w_nvtx
            def _run_after_backward_function(sub_module):
                if sub_module.ds_grads_remaining == 0:
                    self.post_sub_module_backward_function(sub_module)

            return _apply_to_tensors_only(module, PostBackwardFunction, _run_after_backward_function, inputs)

        # Pre forward hook
        self.forward_hooks.append(module.register_forward_pre_hook(_pre_forward_module_hook))

        # Post forward hook
        self.forward_hooks.append(module.register_forward_hook(_post_forward_module_hook))

        # Pre backward hook
        self.backward_hooks.append(module.register_forward_hook(_pre_backward_module_hook))

        # post backward hook
        self.backward_hooks.append(module.register_forward_pre_hook(_post_backward_module_hook))

    @torch.no_grad()
    def pre_sub_module_forward_function(self, sub_module):
        see_memory_usage(f"Before sub module function {sub_module.__class__.__name__}", force=False)

        global FWD_MODULE_STACK
        FWD_MODULE_STACK.append(sub_module)

        param_coordinator = self.get_param_coordinator(training=sub_module.training)
        param_coordinator.trace_prologue(sub_module)
        if param_coordinator.is_record_trace():
            param_coordinator.record_module(sub_module)
        param_coordinator.fetch_sub_module(sub_module)

        see_memory_usage(f"Before sub module function {sub_module.__class__.__name__} after fetch", force=False)

    @torch.no_grad()
    def post_sub_module_forward_function(self, sub_module):
        see_memory_usage(f"After sub module function {sub_module.__class__.__name__} {sub_module.id} before release",
                         force=False)

        param_coordinator = self.get_param_coordinator(training=sub_module.training)
        param_coordinator.release_sub_module(sub_module)

        see_memory_usage(f"After sub module function {sub_module.__class__.__name__}  {sub_module.id} after release",
                         force=False)

    @torch.no_grad()
    def pre_sub_module_backward_function(self, sub_module):
        assert sub_module.training, "backward pass is invalid for module in evaluation mode"
        param_coordinator = self.get_param_coordinator(training=True)
        param_coordinator.trace_prologue(sub_module)
        if param_coordinator.is_record_trace():
            param_coordinator.record_module(sub_module)
        param_coordinator.fetch_sub_module(sub_module)

    @torch.no_grad()
    def post_sub_module_backward_function(self, sub_module):
        assert sub_module.training, "backward pass is invalid for module in evaluation mode"
        see_memory_usage(
            f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} before release",
            force=False)

        self.get_param_coordinator(training=True).release_sub_module(sub_module)

        see_memory_usage(
            f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} after release",
            force=False)
