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

# DeepSpeed Team

import os
import torch
import tqdm
import deepspeed
import deepspeed.ops.transformer as transformer_inference
from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention
from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock
from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig
from deepspeed.accelerator import get_accelerator
from .replace_policy import replace_policies, generic_policies
from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading

from deepspeed import comm as dist
from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd

from .load_checkpoint import load_model_with_checkpoint
import time

from .utils import policy_to_ds_container
import gc


def get_transformer_name(replaced_module):
    from .containers import supported_models
    from torch.nn import ModuleList
    transformer_name = ''
    for n, c in replaced_module.named_children():
        if c.__class__ in supported_models:
            transformer_name += n + '.'
            for name, child in c.named_children():
                if child.__class__ is ModuleList:
                    transformer_name += name
                    break
            break
    return transformer_name


class GroupQuantizer:

    def __init__(self, q_int8=True, group_size=1, num_bits=8, num_groups=0):
        self.group_size = group_size
        self.num_bits = num_bits
        self.q_int8 = q_int8

        self.num_groups = num_groups

    def quantize(self, inputs, qkv=True, count=1, parallel_dim=0):
        if not self.q_int8 or not qkv:
            inputs = torch.nn.Parameter(inputs, requires_grad=False)
            inputs.scale = torch.empty(1)
            return inputs
        q_range = 2**self.num_bits
        num_groups = self.num_groups if self.num_groups > 0 else inputs.shape[0] // self.group_size
        inputs = inputs.to(get_accelerator().current_device_name())
        input_flat = inputs.reshape(num_groups, -1).contiguous()
        input_min = torch.min(input_flat, dim=1, keepdim=True)[0].float()
        input_max = torch.max(input_flat, dim=1, keepdim=True)[0].float()
        scale = torch.max(input_min.abs(), input_max.abs()) * 2.0 / (q_range)
        input_flat = (input_flat / scale).round().clamp(-q_range // 2, q_range // 2 - 1)
        inputs_q = input_flat.reshape(inputs.shape).to(torch.int8).contiguous()
        out = torch.nn.Parameter(inputs_q, requires_grad=False)
        inputs_split = inputs.split(inputs.shape[parallel_dim] // 2, dim=parallel_dim)
        input_flat = [inputs_split[i].reshape(num_groups, -1).contiguous() for i in range(2)]
        input_min = [torch.min(input_flat[i], dim=1, keepdim=True)[0].float() for i in range(2)]
        input_max = [torch.max(input_flat[i], dim=1, keepdim=True)[0].float() for i in range(2)]
        scale1 = [(torch.max(input_min[i].abs(), input_max[i].abs()) * 2.0 / (q_range)).squeeze().unsqueeze(0)
                  for i in range(2)]

        out.scale = torch.cat([scale.squeeze().unsqueeze(0), scale1[0], scale1[1]], dim=0).reshape(num_groups,
                                                                                                   -1).contiguous()
        return out


def _module_match(module):
    for policy in generic_policies:
        policy = policy()
        if policy.match(module):
            return policy
    return None


def generic_injection(module, dtype=None, enable_cuda_graph=True):

    def replace_attn(child, policy):
        policy_attn = policy.attention(child)
        if policy_attn is None:
            return child
        if len(policy_attn) == 5:
            qkvw, attn_ow, attn_ob, hidden_size, heads = policy_attn
        else:
            qw, kw, vw, attn_ow, attn_ob, hidden_size, heads = policy_attn

        config = transformer_inference.DeepSpeedInferenceConfig(
            hidden_size=hidden_size,
            heads=heads,
            dtype=dtype,
            triangular_masking=False,
            max_out_tokens=4096,
        )
        attn_module = DeepSpeedDiffusersAttention(config)

        def transpose(data):
            data = data.contiguous()
            data.reshape(-1).copy_(data.transpose(-1, -2).contiguous().reshape(-1))
            data = data.reshape(data.shape[-1], data.shape[-2])
            data.to(get_accelerator().current_device_name())
            return data

        if len(policy_attn) == 5:
            attn_module.attn_qkvw.data = transpose(qkvw.data)
        else:
            attn_module.attn_qkvw = None
            attn_module.attn_qw.data = transpose(qw.data)
            attn_module.attn_kw.data = transpose(kw.data)
            attn_module.attn_vw.data = transpose(vw.data)

        attn_module.attn_qkvb = None
        attn_module.attn_ow.data = transpose(attn_ow.data)
        attn_module.attn_ob.data.copy_(attn_ob.data.to(get_accelerator().current_device_name()))
        return attn_module

    def replace_attn_block(child, policy):
        config = Diffusers2DTransformerConfig()
        return DeepSpeedDiffusersTransformerBlock(child, config)

    if isinstance(module, torch.nn.Module):
        pass
    else:
        if dtype not in [torch.float16, torch.half]:
            raise ValueError("Generic injection only supported with FP16")

        try:
            import diffusers
            if hasattr(diffusers.models.attention, 'CrossAttention'):
                cross_attention = diffusers.models.attention.CrossAttention
            else:
                cross_attention = diffusers.models.attention_processor.Attention
            attention_block = diffusers.models.attention.BasicTransformerBlock
            new_policies = {
                cross_attention: replace_attn,
                attention_block: replace_attn_block,
            }
        except ImportError:
            new_policies = {}

        #replace_transformer_layer(None,
        #                          module.text_encoder,
        #                          training=False,
        #                          replace_with_kernel_inject=True,
        #                          triangular_masking=True,
        #                          max_out_tokens=8192)
        from ..model_implementations.transformers.clip_encoder import DSClipEncoder
        cg_encoder = DSClipEncoder(module.text_encoder, enable_cuda_graph=enable_cuda_graph)
        setattr(module, 'text_encoder', cg_encoder)
        for name in module.__dict__.keys():
            sub_module = getattr(module, name)
            policy = _module_match(sub_module)

            if policy is not None:

                def _replace_module(module, policy):
                    for name, child in module.named_children():
                        _replace_module(child, policy)
                        if child.__class__ in new_policies:
                            replaced_module = new_policies[child.__class__](child, policy)
                            setattr(module, name, replaced_module)

                _replace_module(sub_module, policy)
                new_module = policy.apply(sub_module, enable_cuda_graph=enable_cuda_graph)
                print(f"**** found and replaced {name} w. {type(new_module)}")
                setattr(module, name, new_module)


container_g = None


def replace_transformer_layer(orig_layer_impl, model, checkpoint_dict, config, model_config):
    """ Replace bert-style transformer layers with DeepSpeed's transformer layer
    Arguments:
        orig_layer_impl (torch.nn.Module): the original transformer layer implementation to look for,
            e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer
        model (torch.nn.Module): user's nn.module representing their model
        checkpoint_dict: Dictionary for checkpoint passed from the Inference Engine
        config: top-level DS Inference config defined in inference/config.py
        model_config: HuggingFace model config passed from the inference/engine.py
    Returns:
        Updated nn.module with replaced transformer layers
    """
    # defining globals as internally defined functions inherit these everywhere
    quantize = (config.dtype == torch.int8)
    # todo: Refactor later. In future, let's minimize the style used above and use config.** instead

    linear_layer_setting = None
    '''
        linear_layer_setting (tuple of modules) [Optional]: shows which two classes are used for linear layers and embedding layers
    '''
    micro_batch_size = -1
    seed = -1
    local_rank = -1

    mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group,
                                          mp_size=config.tensor_parallel.tp_size)  #, out_dim=0, in_dim=1)

    def replace_with_policy(child, policy_cls, triangular_masking, inference=False, layer_id=0):
        policy = policy_cls(child, inference=inference)
        if not policy.cuda_graph_supported:
            # policy says cuda graph is not supported raise an error if set
            assert not config.enable_cuda_graph, "cuda graph is not supported with this model, please disable"

        from deepspeed.moe.layer import MoE
        moe = False
        if hasattr(child, 'mlp') and isinstance(child.mlp, MoE):
            num_experts = child.mlp.num_experts
            moe = True

        # 1. Create a model-specific container object using the policy object.
        _container = policy_to_ds_container(policy=policy,
                                            config=config,
                                            model_config=model_config,
                                            layer_id=layer_id,
                                            child=child)
        _container.set_moe(moe)

        # 2. Set the tensor parallelism config
        _container.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group)

        # 3. Initialize tensors
        _container.initialize_tensors()

        # 4. deal with data types -- needs refactor to use dtype instead of fp16
        if config.dtype in [torch.float16, torch.bfloat16, torch.int8]:
            _container.convert_to_required_dtype()

        # 5. Set the quantization config
        quantizer = GroupQuantizer(q_int8=quantize)
        _container.set_quantization_config(quantizer)

        # 6. create a DS Inference config object
        _container.create_ds_model_config()

        # 7. use the config and create the module
        _container.create_module()

        # 8. transpose the weights and bias if needed
        _container.transpose()

        # 9. deal with tensor parallelism.
        _container.apply_tensor_parallelism(mp_replace)

        # 10. copy the tensors from the model-specific container to the new module
        _container.copy_data_to_new_module()

        # 11. set global for generic checkpoint loading
        global container_g

        if container_g is None:
            container_g = _container

        return _container.module

    def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None):
        #mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group)

        # 1. Create AutoTP object
        _autotp = AutoTP(module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl)

        # 2. Set the tensor parallelism config
        _autotp.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group)

        # 3. Try to get num_key_heads from model_config.num_key_value_heads
        num_kv_heads = _autotp.get_model_num_kv_heads(model_config)

        # 4. When we have num_kv_heads defined, uneven division is possible, otherwise enforce even division
        set_num_kv_heads(num_kv_heads)

        # 4.1 Get n_embd
        n_embd = None
        multi_query_n_embd_names = ['n_embd']
        for name in multi_query_n_embd_names:
            if hasattr(model_config, name):
                n_embd = getattr(model_config, name)
            if n_embd != None:
                break

        # 4.2 set n_embd
        set_n_embd(n_embd)

        # 5. Set linear policies
        _autotp.update_linear_policies()

        # 6. Replace modules
        if "lm_head" in all_reduce_linears or "embed_out" in all_reduce_linears:
            return _autotp._replace_last_linear_module(module)
        return _autotp._replace_module(module)

    def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None):
        training = False  # todo: refactor this part to go in the config
        if training:
            # copy relevant state from child -> new module
            new_module = replace_with_policy(child, _policy, config.triangular_masking)

        else:
            # copy relevant state from child -> new module
            if config.replace_with_kernel_inject:
                new_module = replace_with_policy(child,
                                                 _policy,
                                                 config.triangular_masking,
                                                 inference=True,
                                                 layer_id=layer_id)
            else:
                new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)

        return new_module

    def set_lm_head(module):
        embedding_weight = None
        for n, p in module.named_parameters():
            if "word_embeddings." in n or "embed_tokens." in n or "wte." in n:
                embedding_weight = p
        if embedding_weight is not None and hasattr(module, "lm_head") and hasattr(
                module.lm_head, "weight") and module.lm_head.weight.is_meta:
            module.lm_head.weight = embedding_weight
        # enable tensor parallel for the last linear
        if hasattr(module, "lm_head") and hasattr(module.lm_head,
                                                  "weight") and not module.lm_head.weight.is_meta and isinstance(
                                                      module.lm_head, torch.nn.Linear):
            module = replace_wo_policy(module, ("lm_head", ), 0, "lm_head")
        elif hasattr(module, "embed_out") and hasattr(module.embed_out,
                                                      "weight") and not module.embed_out.weight.is_meta and isinstance(
                                                          module.embed_out, torch.nn.Linear):
            module = replace_wo_policy(module, ("embed_out", ), 0, "embed_out")
        return module

    if checkpoint_dict is not None and not config.replace_with_kernel_inject:
        # AutoTP shard loading
        checkpoint = checkpoint_dict["checkpoints"]
        pbar = tqdm.tqdm(total=len(checkpoint), desc=f"Loading {len(checkpoint)} checkpoint shards")
        for i in range(len(checkpoint)):
            checkpoint_file = os.path.join(config.base_dir, checkpoint[i])
            replaced_module = replace_module(model=model,
                                             orig_class=orig_layer_impl,
                                             replace_fn=replace_fn,
                                             _replace_policy=config.injection_policy_tuple,
                                             checkpoint=checkpoint_file)
            pbar.update(1)
            gc.collect()
        replaced_module = set_lm_head(replaced_module)
    else:
        replaced_module = replace_module(model=model,
                                         orig_class=orig_layer_impl,
                                         replace_fn=replace_fn,
                                         _replace_policy=config.injection_policy_tuple)

    quantizer = GroupQuantizer(q_int8=quantize)
    world_size = dist.get_world_size() if dist.is_initialized() else 1
    rank = dist.get_rank() if dist.is_initialized() else 0
    if checkpoint_dict is not None and config.replace_with_kernel_inject:
        assert container_g.ckpt_load_enabled, \
               f"Meta Tensor checkpoint loading not supported in {container_g.__class__.__name__} container"
        start_time = time.time()
        checkpoint = checkpoint_dict['checkpoints']
        ckpt_list = checkpoint["tp"] if type(checkpoint) is dict else checkpoint
        ckpt_type = checkpoint_dict.get('parallelization', 'pp')
        ckpt_mp_size = checkpoint_dict.get('tp_size', len(ckpt_list))
        ckpt_mp_size = checkpoint_dict.get('mp_size', ckpt_mp_size)
        base_dir1 = checkpoint_dict.get('base_dir', config.base_dir)

        if ckpt_type == 'pp' and type(checkpoint) is list:
            pbar = tqdm.tqdm(total=len(checkpoint), desc=f"Loading {len(checkpoint)} checkpoint shards")

            for i in range(len(checkpoint)):
                sd = [torch.load(os.path.join(base_dir1, checkpoint[i]), map_location='cpu')]
                load_model_with_checkpoint(replaced_module,
                                           sd,
                                           mp_replace,
                                           ckpt_type,
                                           ckpt_mp_size,
                                           quantizer,
                                           container=container_g)
                pbar.update(1)
        else:
            num_checkpoints = len(ckpt_list) // ckpt_mp_size
            tp_split_size = (world_size / ckpt_mp_size)
            sd_offset = int(rank / tp_split_size)
            sd_count = int((rank + max(1, tp_split_size)) / tp_split_size) - sd_offset
            pbar = tqdm.tqdm(total=num_checkpoints, desc=f"Loading {num_checkpoints} checkpoint shards")
            for i in range(num_checkpoints):
                pbar.update(1)
                ckpt_index = i * ckpt_mp_size + sd_offset
                ckpt_files = [
                    os.path.join(base_dir1, ckpt_list[ckpt_index + j]) if base_dir1 else ckpt_list[ckpt_index + j]
                    for j in range(sd_count)
                ]
                sds = [torch.load(ckpt_file, map_location='cpu') for ckpt_file in ckpt_files]
                load_model_with_checkpoint(replaced_module,
                                           sds,
                                           mp_replace,
                                           ckpt_type,
                                           ckpt_mp_size,
                                           quantizer,
                                           int(rank % tp_split_size),
                                           container=container_g)
                sds = [None for _ in sds]
                gc.collect()

            if "non_tp" in checkpoint:
                pbar = tqdm.tqdm(total=len(checkpoint["non_tp"]),
                                 desc=f"Loading {len(checkpoint['non_tp'])} checkpoint shards")

                for i in range(len(checkpoint["non_tp"])):
                    pbar.update(1)
                    ckpt_file = os.path.join(base_dir1,
                                             checkpoint["non_tp"][i]) if base_dir1 else checkpoint["non_tp"][i]
                    sds = [torch.load(ckpt_file, map_location='cpu')]
                    load_model_with_checkpoint(replaced_module,
                                               sds,
                                               mp_replace,
                                               ckpt_type,
                                               ckpt_mp_size,
                                               quantizer,
                                               int(rank % tp_split_size),
                                               container=container_g)
                    sds = [None for _ in sds]
                    gc.collect()
        set_lm_head(replaced_module)
        print(f"checkpoint loading time at rank {rank}: {time.time()-start_time} sec")

    if config.save_mp_checkpoint_path is not None:
        from collections import OrderedDict
        import json
        num_partitions = 8

        if checkpoint_dict is None:
            ckpt_name = "ds_model"
            try:
                from transformers.models.bloom.modeling_bloom import BloomForCausalLM
                if isinstance(model, BloomForCausalLM):
                    ckpt_name = "bloom"
            except ImportError:
                ckpt_name = "ds_model"
        else:
            ckpt_name = checkpoint_dict['type']
        if dist.is_initialized():
            dist.barrier()
        transformer_name = get_transformer_name(replaced_module)
        non_tp_ckpt_name = f'non-tp.pt'
        ckpt_files = [non_tp_ckpt_name]
        os.makedirs(config.save_mp_checkpoint_path, exist_ok=True)

        if not dist.is_initialized() or dist.get_rank() == 0:
            print("Saving tp-sharded checkpoints")
            torch.save(
                OrderedDict({k: v
                             for k, v in dict(replaced_module.state_dict()).items()
                             if transformer_name not in k}), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}')

            dtype_reprs = {
                torch.float32: 'float32',
                torch.float16: 'float16',
                torch.int8: 'int8',
                torch.bfloat16: 'bfloat16'
            }

            ckpt_config = json.dumps({
                'type': ckpt_name,
                'base_dir': f'{config.save_mp_checkpoint_path}',
                'checkpoints': {
                    "non_tp": ckpt_files,
                    "tp": [f'tp_{r:0>2d}_{m:0>2d}.pt' for m in range(num_partitions) for r in range(world_size)]
                },
                'version': 1.0,
                'parallelization': 'tp',
                'tp_size': world_size,
                'dtype': dtype_reprs[config.dtype]
            })
            with open(f"{config.save_mp_checkpoint_path}/ds_inference_config.json", "w") as cfg:
                cfg.write(ckpt_config)

        rep_sd = replaced_module.state_dict()
        for n, p in replaced_module.named_parameters():
            if hasattr(p, 'scale'):
                rep_sd[n] = [p, p.scale]
        keys = list(rep_sd.keys())
        partition_size = (len(keys) // num_partitions + 1)
        for m in range(num_partitions):
            torch.save(
                OrderedDict({
                    k: [rep_sd[k], rep_sd[k].scale] if hasattr(rep_sd[k], 'scale') else rep_sd[k]
                    for k in keys[m * partition_size:(m + 1) * partition_size] if transformer_name in k
                }), f'{config.save_mp_checkpoint_path}/tp_{rank:0>2d}_{m:0>2d}.pt')

    return replaced_module


def revert_transformer_layer(orig_layer_impl, model, config, preln=False):
    """ Revert DeepSpeed's transformer layer back to original bert-style transformer layer
    Arguments:
        orig_layer_impl (torch.nn.Module): the original transformer layer implementation that was replaced,
            e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer
        model (torch.nn.Module): user's nn.module representing their model
        config (dict): model config containing hidden size, attention heads, etc.
    Returns:
        Updated nn.module with original bert-style transformer layers
    """

    def replace_fn(child, _replace_policy, layer_id):
        #from turing.nvidia_modelingpreln import BertLayer
        orig_module = orig_layer_impl(config)

        # copy relevant state from child -> original module
        qkvw = child.attn_qkvw.data
        qkvb = child.attn_qkvb.data

        qw, kw, vw = torch.chunk(qkvw, 3, axis=0)
        qb, kb, vb = torch.chunk(qkvb, 3, axis=0)

        orig_module.attention.self.query.weight.data = qw
        orig_module.attention.self.query.bias.data = qb
        orig_module.attention.self.key.weight.data = kw
        orig_module.attention.self.key.bias.data = kb
        orig_module.attention.self.value.weight.data = vw
        orig_module.attention.self.value.bias.data = vb

        orig_module.attention.output.dense.weight.data = child.attn_ow.data
        orig_module.attention.output.dense.bias.data = child.attn_ob.data

        attn_ln_w = child.attn_nw.data
        attn_ln_b = child.attn_nb.data
        if preln:
            orig_module.PostAttentionLayerNorm.weight.data = attn_ln_w
            orig_module.PostAttentionLayerNorm.bias.data = attn_ln_b
        else:
            orig_module.attention.output.LayerNorm.weight.data = attn_ln_w
            orig_module.attention.output.LayerNorm.bias.data = attn_ln_b

        inter_ff_w = child.inter_w.data
        inter_ff_b = child.inter_b.data
        if preln:
            orig_module.intermediate.dense_act.weight.data = inter_ff_w
            orig_module.intermediate.dense_act.bias.data = inter_ff_b
        else:
            orig_module.intermediate.dense.weight.data = inter_ff_w
            orig_module.intermediate.dense.bias.data = inter_ff_b

        orig_module.output.dense.weight.data = child.output_w.data
        orig_module.output.dense.bias.data = child.output_b.data

        transformer_ln_w = child.norm_w.data
        transformer_ln_b = child.norm_b.data
        if preln:
            orig_module.PreAttentionLayerNorm.weight.data = transformer_ln_w
            orig_module.PreAttentionLayerNorm.bias.data = transformer_ln_b
        else:
            orig_module.output.LayerNorm.weight.data = transformer_ln_w
            orig_module.output.LayerNorm.bias.data = transformer_ln_b
        return orig_module

    return replace_module(model=model,
                          orig_class=deepspeed.DeepSpeedTransformerLayer,
                          replace_fn=replace_fn,
                          _replace_policy=None)


def replace_module(model, orig_class, replace_fn, _replace_policy, checkpoint=None):
    """ Scan the model for instances of ``orig_clas:`` to replace using ``replace_fn``.
    Arguments:
        model (torch.nn.Module): the model to augment
        orig_class (torch.nn.Module): the module to search for
        replace_fn (method): a method to convert instances of ``orig_class`` to the
                             desired type and return a new instance.
    Returns:
        A modified ``model``.
    """
    sd = None
    if checkpoint is not None:
        if checkpoint.endswith(".safetensors"):
            from safetensors.torch import load_file
            sd = load_file(checkpoint)
        else:
            sd = torch.load(checkpoint, map_location='cpu')

    policy = {}
    if orig_class is not None:
        policy.update({orig_class: (replace_fn, _replace_policy)})
    else:
        for plcy in replace_policies:
            # instantiate a throw-away policy in order to populate the _orig_layer_class
            _ = plcy(None)
            if isinstance(plcy._orig_layer_class, list):
                for orig_layer_class in plcy._orig_layer_class:
                    policy.update({orig_layer_class: (replace_fn, plcy)})
            elif plcy._orig_layer_class is not None:
                policy.update({plcy._orig_layer_class: (replace_fn, plcy)})
    assert len(policy.items()) > 0,\
        "No default policy found! Please specify your policy injection_policy (like {BertLayer:HFBEertLayerPolicy})." +\
        "You can find some samples here: https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/module_inject/replace_policy.py"

    replaced_module, _ = _replace_module(model, policy, state_dict=sd)
    return replaced_module


from ..pipe import PipelineModule

import re


def skip_level_0_prefix(model, state_dict):
    model = str(model)
    key = re.search(r": (.*?)Model", model)
    if key is None:
        key = re.search(r": (.*?)Stack", model)
    if key is None:
        key = re.match(r"(.*?)Model", model)
    # if keys start with 'model.', don't skip level 0 prefix
    if state_dict is not None:
        for item in state_dict.keys():
            if re.match("^model[.]", item):
                return False
    if key is not None and key.group(1).lower() in ["bloom", "opt"]:
        return True
    return False


def _replace_module(model, policies, prefix='', layer_id=0, level_id=0, state_dict=None):
    """ Traverse model's children recursively and apply any transformations in ``policies``.
    Arguments:
        model (torch.nn.Module): model to augment
        policies (dict): Mapping of source class to replacement function.
    Returns:
        Modified ``model``.
    """
    for name, child in model.named_children():
        if child.__class__ in policies:
            replaced_module = policies[child.__class__][0](child,
                                                           policies[child.__class__][-1],
                                                           layer_id,
                                                           prefix=prefix + name,
                                                           state_dict=state_dict)
            setattr(model, name, replaced_module)
            if isinstance(model, PipelineModule):
                assert hasattr(model, 'forward_funcs'),\
                    "we require pipe-module to have the list of fwd_functions"
                model.forward_funcs[model.fwd_map[name]] = replaced_module
            layer_id += 1
        else:
            checking_key = prefix + name + '.'
            if Loading.is_load_module(child) and state_dict is not None:
                if any(checking_key in item for item in state_dict):
                    Loading.load(
                        child,
                        state_dict,
                        checking_key,
                    )
                else:
                    continue
            if len(child._buffers) != 0 and state_dict is not None:
                Loading.load_buffer(child, state_dict, checking_key)
            _, layer_id = _replace_module(child,
                                          policies,
                                          prefix if level_id == 0 and skip_level_0_prefix(model, state_dict) else \
                                          prefix + name + '.',
                                          layer_id=layer_id,
                                          level_id=level_id + 1,
                                          state_dict=state_dict)

    # Add the reset_cache func to the model, so that it can be called in the beginning of text-generation.
    model.reset_cache = transformer_inference.DeepSpeedTransformerInference.reset_cache
    return model, layer_id