modeling_mpt.py

# Adapted from https://github.com/mosaicml/llm-foundry
# Classes changed: MPTModel, MPTForCausalLM
# SPDX-License-Identifier: Apache-2.0

"""A simple, flexible implementation of a GPT model.

Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""

import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.linalg import vector_norm
import faiss
from einops import rearrange
from composer.utils import dist
from omegaconf import DictConfig

from transformers import (PreTrainedModel, PreTrainedTokenizer,
                          PreTrainedTokenizerFast)
from transformers.modeling_outputs import (BaseModelOutputWithPast,
                                           CausalLMOutputWithPast)
from llmfoundry.models.layers.custom_embedding import SharedEmbedding
from llmfoundry.models.layers.norm import NORM_CLASS_REGISTRY
from llmfoundry.models.utils.param_init_fns import MODEL_INIT_REGISTRY

from .configuration import ExtendedMPTConfig
from .attention import attn_bias_shape, build_attn_bias
from .blocks import MPTBlock
from .utils import instantiate_from_config

Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]

class MPTPreTrainedModel(PreTrainedModel):
    config_class = ExtendedMPTConfig
    base_model_prefix = 'model'
    _no_split_modules = ['MPTBlock']

class ExtendedMPTModel(MPTPreTrainedModel):

    def __init__(self, config: ExtendedMPTConfig):
        config._validate_config()
        super().__init__(config)

        self.attn_impl = config.attn_config['attn_impl']
        self.prefix_lm = config.attn_config['prefix_lm']
        self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
        self.alibi = config.attn_config['alibi']
        self.alibi_bias_max = config.attn_config['alibi_bias_max']

        self.mask_by_sim = config.attn_config['mask_by_sim']
        self.sim_threshold = config.attn_config['sim_threshold']
        self.topk = config.attn_config['topk']
        self.use_active_externalism = config.attn_config['use_active_externalism']

        self.use_active_externalism_by_layer = config.use_active_externalism_by_layer
        
        if config.init_device == 'mixed':
            if dist.get_local_rank() == 0:
                config.init_device = 'cpu'
            else:
                config.init_device = 'meta'

        if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
            norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
            raise NotImplementedError(
                f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).'
            )
        norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]

        # CogView (https://arxiv.org/abs/2105.13290) and GLM-130B (https://arxiv.org/abs/2210.02414)
        # both report this helping with stabilizing training
        self.embedding_fraction = config.embedding_fraction

        self.wte = SharedEmbedding(config.vocab_size,
                                   config.d_model,
                                   device=config.init_device)
        if not self.alibi:
            self.wpe = torch.nn.Embedding(config.max_seq_len,
                                          config.d_model,
                                          device=config.init_device)
        self.emb_drop = nn.Dropout(config.emb_pdrop)
        self.blocks = nn.ModuleList([
            MPTBlock(
                device=config.init_device,
                **config.to_dict(),
            ) for _ in range(config.n_layers)
        ])
        self.norm_f = norm_class(config.d_model, device=config.init_device)

        if config.init_device != 'meta':
            print(
                f'You are using {config.init_device=}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.'
            )
            self.apply(self.param_init_fn)

        self.is_causal = not self.prefix_lm

        # define attn mask
        self._attn_bias_initialized = False
        self.attn_bias = None
        self.attn_bias_shape = attn_bias_shape(
            self.attn_impl,
            config.n_heads,
            config.max_seq_len,
            self.alibi,
            prefix_lm=self.prefix_lm,
            causal=self.is_causal,
            use_sequence_id=self.attn_uses_sequence_id,
        )
        self._attn_bias_ae_initialized = False #for active externalism
        self.attn_bias_ae = None

        if self.config.no_bias:
            for module in self.modules():
                if hasattr(module, 'bias') and isinstance(
                        module.bias, nn.Parameter):
                    if self.config.verbose:
                        warnings.warn(
                            f'Removing bias ({module.bias}) from {module}.')
                    module.register_parameter('bias', None)

        # Print verbose info
        if config.verbose and config.verbose > 2:
            print(self)
        if 'verbose' not in self.config.init_config:
            self.config.init_config['verbose'] = self.config.verbose
        if self.config.init_config['verbose'] > 1:
            init_fn_name = self.config.init_config['name']
            warnings.warn(f'Using {init_fn_name} initialization.')

    def get_input_embeddings(self):
        return self.wte
    
    def set_input_embeddings(self, value: nn.Embedding):
        self.wte = value

    @torch.no_grad()
    def _attn_bias(
        self,
        device,
        dtype,
        attention_mask: Optional[torch.ByteTensor] = None,
        prefix_mask: Optional[torch.ByteTensor] = None,
        sequence_id: Optional[torch.LongTensor] = None,
        seq_len: Optional[int] = None,
        use_active_externalism:bool=None,
        topk=None,
    ):
        if not self._attn_bias_initialized:
            if self.attn_bias_shape:
                self.attn_bias = torch.zeros(self.attn_bias_shape,
                                             device=device,
                                             dtype=dtype)
                self.attn_bias = build_attn_bias(
                    self.attn_impl,
                    self.config.n_heads,
                    self.config.max_seq_len,
                    device=device,
                    dtype=dtype,
                    attn_bias = self.attn_bias,
                    causal=self.is_causal,
                    alibi=self.alibi,
                    alibi_bias_max=self.alibi_bias_max
                )
            self._attn_bias_initialized = True

        if use_active_externalism: #for active externalism, init every time since seq_len changes
            self.attn_bias_ae = build_attn_bias(
                self.attn_impl,
                self.config.n_heads,
                seq_len,
                device=device,
                dtype=dtype,
                causal=self.is_causal,
                alibi=self.alibi,
                alibi_bias_max=self.alibi_bias_max,
                for_ae=use_active_externalism,
                topk=topk
            )
    
            self._attn_bias_ae_initialized = True

        # flash does not support prefix_lm and will incorporate any
        # attention_mask inside the attention module
        if self.attn_impl == 'flash':
            return self.attn_bias, attention_mask

        if self.attn_bias is not None:
            # .to(*args, **kwargs) is a no-op if tensor is already on
            # specified device or of specificed dtype
            self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)

        attn_bias = self.attn_bias

        if self.attn_bias_ae is not None: #for active externalism
            self.attn_bias_ae = self.attn_bias_ae.to(dtype=dtype, device=device)
        attn_bias_ae = self.attn_bias_ae

        # If using torch or triton, we incorporate the prefix_mask (if appropriate)
        if self.prefix_lm:
            assert isinstance(attn_bias, torch.Tensor)  # pyright
            assert isinstance(prefix_mask, torch.Tensor)  # pyright
            attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)

        # If using torch or triton, we incorporate sequence_id (if appropriate)
        if self.attn_uses_sequence_id and sequence_id is not None:
            assert isinstance(attn_bias, torch.Tensor)  # pyright
            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)

        # If using torch or triton, we incorporate attention_mask. This will output
        # None in place of attention_mask since it will not be further needed in the
        # attention modules.
        if attention_mask is not None:
            s_k = attention_mask.shape[-1]
            if attn_bias is None:
                attn_bias = torch.zeros((1, 1, 1, s_k),
                                        device=device,
                                        dtype=dtype)
            else:
                # clamp to 0 necessary for torch 2.0 compile()
                _s_k = max(0, attn_bias.size(-1) - s_k)
                attn_bias = attn_bias[:, :, :, _s_k:]
            if prefix_mask is not None and (attention_mask.shape !=
                                            prefix_mask.shape):
                raise ValueError(
                    f'attention_mask shape={attention_mask.shape} ' +
                    f'and prefix_mask shape={prefix_mask.shape} are not equal.')
            min_val = torch.finfo(attn_bias.dtype).min
            attn_bias = attn_bias.masked_fill(
                ~attention_mask.view(-1, 1, 1, s_k), min_val)

        return attn_bias, attn_bias_ae, None

    def _apply_prefix_mask(self, attn_bias: torch.Tensor,
                           prefix_mask: torch.Tensor):
        s_k, s_q = attn_bias.shape[-2:]
        if (s_k != self.config.max_seq_len) or (s_q != self.config.max_seq_len):
            raise ValueError(
                'attn_bias does not match the expected shape. ' +
                f'The last two dimensions should both be {self.config.max_length} '
                + f'but are {s_k} and {s_q}.')
        seq_len = prefix_mask.shape[-1]
        if seq_len > self.config.max_seq_len:
            raise ValueError(
                f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}'
            )

        # select seq_len subset of attn mask
        attn_bias = attn_bias[..., :seq_len, :seq_len]

        # Mix the causal max and the bidirectional mask to get the full
        # allowable attention (i.e. full = not accounting for padding yet)
        causal = torch.tril(
            torch.ones((seq_len, seq_len),
                       dtype=torch.bool,
                       device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
        prefix = prefix_mask.view(-1, 1, 1, seq_len)
        cannot_attend = ~torch.logical_or(causal, prefix.bool())

        min_val = torch.finfo(attn_bias.dtype).min
        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)

        return attn_bias

    def _apply_sequence_id(self, attn_bias: torch.Tensor,
                           sequence_id: torch.LongTensor):
        seq_len = sequence_id.shape[-1]
        if seq_len > self.config.max_seq_len:
            raise ValueError(
                f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}'
            )

        # select seq_len subset of attn mask
        attn_bias = attn_bias[..., :seq_len, :seq_len]

        # Restrict attention to tokens that share the same value
        # in sequence_id
        cannot_attend = torch.logical_not(
            torch.eq(
                sequence_id.view(-1, seq_len, 1),
                sequence_id.view(-1, 1, seq_len),
            )).unsqueeze(1)
        min_val = torch.finfo(attn_bias.dtype).min
        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)

        return attn_bias

    def forward(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        prefix_mask: Optional[torch.ByteTensor] = None,
        sequence_id: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        use_cache: Optional[bool] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_active_externalism:Optional[bool]=None,
        long_range_past_key_values:Optional[List[Tuple[torch.FloatTensor]]] = None,
        faiss_indexes:Tuple=None,
        topk:int=None,
    ):
        return_dict = (return_dict
                       if return_dict is not None else self.config.return_dict)
        use_cache = (use_cache
                     if use_cache is not None else self.config.use_cache)
        use_active_externalism = (use_active_externalism
                     if use_active_externalism is not None else self.use_active_externalism)
        topk = (topk if topk is not None else self.topk)

        if attention_mask is not None:
            attention_mask = attention_mask.bool()

        if prefix_mask is not None:
            prefix_mask = prefix_mask.bool()

        # These args are passed in by keyword in huggingface's generate function
        # https://github.com/huggingface/transformers/blob/68287689f2f0d8b7063c400230b3766987abf18d/src/transformers/generation/utils.py#L2201-L2206
        # but have not yet been fully implemented in MPTModel
        if not return_dict:
            raise NotImplementedError(
                'return_dict False is not implemented yet for MPT')
        if output_attentions:
            if self.attn_impl != 'torch':
                raise NotImplementedError(
                    'output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.'
                )

        if (attention_mask is not None and
                attention_mask[:, 0].sum() != attention_mask.shape[0] and
                self.training):
            raise NotImplementedError(
                'MPT does not support training with left padding.')

        if self.prefix_lm and prefix_mask is None:
            raise ValueError(
                'prefix_mask is a required argument when MPT is configured with prefix_lm=True.'
            )

        # Raise a not implemented error if input_embeds is not None (this is an arg in huggingface transformers and we need to support it for PEFT)
        if inputs_embeds is not None:
            raise NotImplementedError(
                'inputs_embeds is not implemented for MPT.')

        if self.training:
            if self.attn_uses_sequence_id and sequence_id is None:
                raise ValueError(
                    'sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True '
                    + 'and the model is in train mode.')
            elif (self.attn_uses_sequence_id is False) and (sequence_id
                                                            is not None):
                warnings.warn(
                    'MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. '
                    +
                    'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.'
                )

        S = input_ids.size(1)

        assert (
            S <= self.config.max_seq_len
        ), f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'

        tok_emb = self.wte(input_ids)  # type: ignore
        if self.alibi:
            x = tok_emb
        else:
            past_position = 0
            if past_key_values is not None:
                if len(past_key_values) != self.config.n_layers:
                    raise ValueError(
                        f'past_key_values must provide a past_key_value for each attention '
                        +
                        f'layer in the network ({len(past_key_values)=}; {self.config.n_layers=}).'
                    )
                # For attn_impl: triton and flash the past key tensor spec is (batch, seq, dim).
                # For attn_impl: torch the past key tensor spec is (batch, heads, head_dim, seq).
                # Here we shift position embedding using the `seq` dim of the past key
                past_position = past_key_values[0][0].size(1)
                if self.attn_impl == 'torch':
                    past_position = past_key_values[0][0].size(3)

            if S + past_position > self.config.max_seq_len:
                raise ValueError(
                    f'Cannot forward input with past sequence length {past_position} and current sequence length '
                    f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.'
                )
            pos = torch.arange(
                past_position,
                S + past_position,
                dtype=torch.long,
                device=input_ids.device,
            ).unsqueeze(0)
            if attention_mask is not None:
                # adjust the position indices to account for padding tokens
                pos = torch.clamp(
                    pos - torch.cumsum((~attention_mask).to(torch.int32),
                                       dim=1)[:, past_position:],
                    min=0,
                )

            pos_emb = self.wpe(pos)  # type: ignore
            x = tok_emb + pos_emb

        if self.embedding_fraction == 1:
            x = self.emb_drop(x)  # type: ignore
        else:
            # this implementation is proposed on page 7 of the GLM-130B paper https://arxiv.org/abs/2210.02414
            x_shrunk = (x * self.embedding_fraction) + (
                x.detach() * (1 - self.embedding_fraction))
            assert isinstance(self.emb_drop, nn.Module)  # pyright
            x = self.emb_drop(x_shrunk)

        seq_len = S #for active externalism
        if past_key_values is not None:
            past_position = past_key_values[0][0].size(-1)
            seq_len += past_position

        attn_bias, attn_bias_ae, attention_mask = self._attn_bias(
            device=x.device,
            dtype=torch.float32,
            attention_mask=attention_mask,
            prefix_mask=prefix_mask,
            sequence_id=sequence_id,
            seq_len = seq_len,
            use_active_externalism=use_active_externalism,
            topk=topk
        )

        # initialize the past key values cache if it should be used
        if use_cache and past_key_values is None:
            past_key_values = [() for _ in range(self.config.n_layers)
                              ]  # type: ignore

        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_idx = () if output_attentions else None
        for b_idx, block in enumerate(self.blocks):  # type: ignore
            if output_hidden_states:
                assert all_hidden_states is not None  # pyright
                all_hidden_states = all_hidden_states + (x,)
            past_key_value = (past_key_values[b_idx]
                              if past_key_values is not None else None)
            long_range_past_key_value = (long_range_past_key_values[b_idx]
                                          if (long_range_past_key_values is not None and self.use_active_externalism_by_layer[b_idx] and use_active_externalism is True) else None)

            if long_range_past_key_value is not None and faiss_indexes is not None:
                raise NotImplementedError(
                    'Using faiss and passing key value pairs manually are mutually exclusive right now.')
            
            x, attn_weights, past_key_value, reshaped_idx = block(
                x,
                past_key_value=past_key_value,
                long_range_past_key_value=long_range_past_key_value,
                attn_bias=attn_bias,
                attention_mask=attention_mask,
                attn_bias_ae=attn_bias_ae,
                is_causal=self.is_causal,
                topk=topk,
                needs_weights=output_attentions,
                faiss_indexes=faiss_indexes,
                n_layers=self.config.n_layers,
                current_layer=b_idx,
                mask_by_sim=self.mask_by_sim,
                sim_threshold=self.sim_threshold,
            )
            if past_key_values is not None:
                past_key_values[b_idx] = past_key_value

            if output_attentions:
                assert all_self_attns is not None  # pyright
                all_self_attns = all_self_attns + (attn_weights,)

                assert all_idx is not None
                all_idx = all_idx + (reshaped_idx,)

        x = self.norm_f(x)  # type: ignore

        # add hidden states from the last decoder layer
        if output_hidden_states:
            assert all_hidden_states is not None  # pyright
            all_hidden_states = all_hidden_states + (x,)

        return BaseModelOutputWithPast(
            last_hidden_state=x,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=(all_self_attns, all_idx), #return reshaped_idx for active externalism
        )

    # Param Initialization, needed for device='meta' fast initialization
    def param_init_fn(self, module):
        init_fn_name = self.config.init_config['name']
        MODEL_INIT_REGISTRY[init_fn_name](
            module=module,
            n_layers=self.config.n_layers,
            d_model=self.config.d_model,
            **self.config.init_config,
        )

    # FSDP Wrap function
    def fsdp_wrap_fn(self, module):
        return isinstance(module, MPTBlock)

    # Activation Checkpointing
    def activation_checkpointing_fn(self, module):
        return isinstance(module, MPTBlock)

class ExtendedMPTForCausalLM(MPTPreTrainedModel):

    def __init__(self, config:ExtendedMPTConfig, external_memories=None):
        if isinstance(config, DictConfig):
            config = instantiate_from_config(config)

        super().__init__(config)
        if not config.tie_word_embeddings:
            raise ValueError(
                'MPTForCausalLM only supports tied word embeddings')

        print(f'Instantiating an MPTForCausalLM model from {__file__}')

        self.transformer: ExtendedMPTModel = ExtendedMPTModel(config)

        self.use_active_externalism = config.attn_config['use_active_externalism']
        self.memory_type = config.attn_config['memory_type']
        self._memories = None
        self.memory_device = config.memory_device

        for child in self.transformer.children():
            if isinstance(child, torch.nn.ModuleList):
                continue
            if isinstance(child, torch.nn.Module):
                child._fsdp_wrap = True

        # enables scaling output logits; similar to a softmax "temperature"
        # PaLM paper uses scale 1/sqrt(config.d_model)
        self.logit_scale = None
        if config.logit_scale is not None:
            logit_scale = config.logit_scale
            if isinstance(logit_scale, str):
                if logit_scale == 'inv_sqrt_d_model':
                    logit_scale = 1 / math.sqrt(config.d_model)
                else:
                    raise ValueError(
                        f"{logit_scale=} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'."
                    )
            self.logit_scale = logit_scale
        
        if external_memories is not None:
            self._memories = external_memories
            self.memories = None
    
    def set_memories(self, memories):
        self.memories = memories
    
    def empty_memories(self):
        self.memories = None

    def get_input_embeddings(self):
        return self.transformer.wte

    def set_input_embeddings(self, value):
        self.transformer.wte = value

    def get_output_embeddings(self):
        return self.transformer.wte

    def set_output_embeddings(self, new_embeddings):
        self.transformer.wte = new_embeddings

    def set_decoder(self, decoder):
        self.transformer = decoder

    def get_decoder(self):
        return self.transformer

    def forward(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        prefix_mask: Optional[torch.ByteTensor] = None,
        sequence_id: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        use_cache: Optional[bool] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_active_externalism: Optional[bool]=None,
        topk:int=None
    ):
        if self._memories is not None and self.memories is None: #init memories once on first call 
            self.memories = self.generate_cache(self._memories, cache_type=self.memory_type)

        return_dict = (return_dict
                       if return_dict is not None else self.config.return_dict)
        use_cache = (use_cache
                     if use_cache is not None else self.config.use_cache)
        use_active_externalism = (use_active_externalism
                     if use_active_externalism is not None else self.use_active_externalism)
        
        topk = topk if topk is not None else None

        # if input_embeds is not none, raise a not implemented error
        if inputs_embeds is not None:
            raise NotImplementedError(
                'inputs_embeds has to be None (for hf/peft support).')
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)

        if hasattr(self, "memories") and type(self.memories)==list:
            long_range_past_key_values = self.memories
            faiss_indexes = None
        elif hasattr(self, "memories"):
            long_range_past_key_values = None
            faiss_indexes = self.memories
        else:
            long_range_past_key_values = None
            faiss_indexes = None

        outputs = self.transformer(
            input_ids=input_ids,
            past_key_values=past_key_values,
            long_range_past_key_values=long_range_past_key_values,
            faiss_indexes=faiss_indexes,
            attention_mask=attention_mask,
            prefix_mask=prefix_mask,
            sequence_id=sequence_id,
            return_dict=return_dict,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            use_active_externalism=use_active_externalism,
            topk=topk
        )

        # move outputs to same device as weights for token embedding
        # needed to support HF `device_map`
        logits = self.transformer.wte(
            outputs.last_hidden_state.to(self.transformer.wte.weight.device),
            True,
        )

        if self.logit_scale is not None:
            if self.logit_scale == 0:
                warnings.warn(
                    f'Multiplying logits by {self.logit_scale=}. This will produce uniform (uninformative) outputs.'
                )
            logits *= self.logit_scale

        loss = None
        if labels is not None:
            _labels = torch.roll(labels, shifts=-1)
            _labels[:, -1] = -100
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)),
                _labels.to(logits.device).view(-1),
            )

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    # Param Initialization, needed for device='meta' fast initialization
    def param_init_fn(self, module):
        init_fn_name = self.config.init_config['name']
        MODEL_INIT_REGISTRY[init_fn_name](
            module=module,
            n_layers=self.config.n_layers,
            d_model=self.config.d_model,
            **self.config.init_config,
        )

    # FSDP Wrap function
    def fsdp_wrap_fn(self, module):
        return isinstance(module, MPTBlock)

    # Activation Checkpointing
    def activation_checkpointing_fn(self, module):
        return isinstance(module, MPTBlock)
    
    def generate_cache(self, 
                       input_ids:torch.LongTensor, 
                       stride:int=512,
                       max_len:int=2048,
                       cache_type:str='manual'):
        if cache_type not in ['manual', 'faiss']:
            raise NotImplementedError(f"Cache type {cache_type} not implemented.")
        
        prev_end_loc=0
        long_range_past_key_values = None
        faiss_indexes= None
        for b_idx in range(0, input_ids.size(-1), stride): #generate kv-pairs using stride 
            end_loc = min(b_idx + max_len, input_ids.size(-1))
            trg_len = end_loc - prev_end_loc 
            subseq = input_ids[:, b_idx:end_loc].to(self.device)
            with torch.no_grad():
                outputs = self.transformer(subseq, use_cache=True, use_active_externalism=False)
            to_cache = [(
                        kv[0][:,:,:,-trg_len:],
                        kv[1][:,:,-trg_len:])
                        for kv in outputs.past_key_values
                        ]
            long_range_past_key_values, faiss_indexes = self.cache(to_cache, cache_type, long_range_past_key_values=long_range_past_key_values, faiss_indexes=faiss_indexes)
            
            prev_end_loc = end_loc
            if end_loc == input_ids.size(-1):
                break
        if long_range_past_key_values is not None:
            return long_range_past_key_values
        else:
            return faiss_indexes
    
    def cache(self, 
              to_cache:List, 
              cache_type:str='manual', 
              long_range_past_key_values:List=None, 
              faiss_indexes:faiss.IndexFlatIP=None, 
              max_length_cache=100000,
              verbose=False):
        if long_range_past_key_values is not None and faiss_indexes is not None:
            raise NotImplementedError("Using faiss and passing key value pairs manually are mutually exclusive right now.")
    
        if cache_type=='faiss': #add one-hot encoding to match layer, head indices
            one_hot_encodings = F.one_hot(torch.arange(0, self.config.n_heads*self.config.n_layers))*10
            if faiss_indexes is None:
                faiss_indexes = (faiss.IndexFlatIP(to_cache[0][0].size(-2)+one_hot_encodings.size(-1)), faiss.IndexFlatIP(to_cache[0][1].size(-1)*2))
            kn_index, kv_index = faiss_indexes
            for b_idx, (k, v) in enumerate(to_cache):
                k_n = (k/vector_norm(k, ord=2, dim=-2, keepdim=True)).to('cpu')
                k_n = torch.concat([rearrange(k_n, 'b h d s -> b (h s) d', h=self.config.n_heads), one_hot_encodings[self.config.n_heads*b_idx:self.config.n_heads*(b_idx+1)].unsqueeze(0).repeat_interleave(repeats=k.size(-1), dim=-2)], dim=-1)
                kn_index.add(k_n.squeeze().numpy())

                k= rearrange(k, 'b h d s -> b (h s) d', h=self.config.n_heads)
                v= rearrange(v, 'b h s d -> b (h s) d', h=self.config.n_heads)
                kv_index.add(torch.concat([v.squeeze(), k.squeeze()], dim=1).to('cpu').numpy())
        else: 
            if long_range_past_key_values is None:
                long_range_past_key_values = [(k.to(self.memory_device),v.to(self.memory_device)) for k,v in to_cache]
            else:
                long_range_past_key_values = [
                    (
                    torch.concat([kv[0], to_cache[ind][0].to(self.memory_device)], dim=3),
                    torch.concat([kv[1], to_cache[ind][1].to(self.memory_device)], dim=2)
                    )
                    for ind, kv in enumerate(long_range_past_key_values)
                ]
        if long_range_past_key_values is not None: #set a limit on manual memory length 
            if long_range_past_key_values[0][0].size(-1) > max_length_cache: 
                long_range_past_key_values = [
                    (
                        kv[0][:, :, :, -max_length_cache:],
                        kv[1][:, :, -max_length_cache:]
                    )
                    for kv in long_range_past_key_values]
        if verbose:
            if cache_type == 'faiss':
                print(f"{kn_index.ntotal} keys in faiss index")
            else:
                print(f"{long_range_past_key_values[0][0].size(-1)} cached kvs")
            
        return long_range_past_key_values, (kn_index, kv_index) if cache_type == 'faiss' else None
    
    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        **kwargs,
    ):
        if inputs_embeds is not None:
            raise NotImplementedError(
                'inputs_embeds is not implemented for MPT yet')

        attention_mask = kwargs['attention_mask'].bool()
        if attention_mask[:, -1].sum() != attention_mask.shape[0]:
            raise NotImplementedError(
                'MPT does not support generation with right padding.')

        if self.transformer.attn_uses_sequence_id and self.training:
            sequence_id = torch.zeros_like(input_ids[:1])
        else:
            sequence_id = None

        if past_key_values is not None:
            input_ids = input_ids[:, -1].unsqueeze(-1)

        if self.transformer.prefix_lm:
            # Leverage a convenience of sequential generation!
            prefix_mask = torch.ones_like(attention_mask)
            # This requires that we're using the cache
            if kwargs.get('use_cache') == False:
                raise NotImplementedError(
                    'MPT with prefix_lm=True does not support use_cache=False.')
        else:
            prefix_mask = None

        return {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'prefix_mask': prefix_mask,
            'sequence_id': sequence_id,
            'past_key_values': past_key_values,
            'use_cache': kwargs.get('use_cache', True),
            'use_active_externalism': kwargs.get('use_active_externalism'), #add a few more kwargs for active externalism
            'topk': kwargs.get('topk', None),
        }

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        """Used by HuggingFace generate when using beam search with kv-caching.

        See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
        for an example in transformers.
        """
        reordered_past = []
        for layer_past in past_key_values:
            reordered_past += [
                tuple(
                    past_state.index_select(0, beam_idx)
                    for past_state in layer_past)
            ]
        return reordered_past