matorus/replit-coder

1"""Converts Huggingface Causal LM to Prefix LM.2 3Conversion does lightweight surgery on a HuggingFace4Causal LM to convert it to a Prefix LM.5 6Prefix LMs accepts a `bidirectional_mask` input in `forward`7and treat the input prompt as the prefix in `generate`.8"""9import math10import warnings11from types import MethodType12from typing import Any, Dict, List, Optional, Tuple, Union13import torch14from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss15from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom16from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom17from transformers.models.bloom.modeling_bloom import logging18from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel19from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM20from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM21from transformers.models.gptj.modeling_gptj import GPTJForCausalLM22from transformers.models.opt.modeling_opt import OPTForCausalLM23from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt24from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt25logger = logging.get_logger(__name__)26_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)27CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]28 29def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:30    """Converts a GPT-style Causal LM to a Prefix LM.31 32    Supported HuggingFace model classes:33        - `GPT2LMHeadModel`34        - `GPTNeoForCausalLM`35        - `GPTNeoXForCausalLM`36        - `GPTJForCausalLM`37 38    See `convert_hf_causal_lm_to_prefix_lm` for more details.39    """40    if hasattr(model, '_prefix_lm_converted'):41        return model42    assert isinstance(model, _SUPPORTED_GPT_MODELS)43    assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'44 45    def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:46        """Helper that gets a list of the model's attention modules.47 48        Each module has a `bias` buffer used for causal masking. The Prefix LM49        conversion adds logic to dynamically manipulate these biases to support50        Prefix LM attention masking.51        """52        attn_modules = []53        if isinstance(model, GPTNeoXForCausalLM):54            blocks = model.gpt_neox.layers55        else:56            blocks = model.transformer.h57        for block in blocks:58            if isinstance(model, GPTNeoForCausalLM):59                if block.attn.attention_type != 'global':60                    continue61                attn_module = block.attn.attention62            elif isinstance(model, GPTNeoXForCausalLM):63                attn_module = block.attention64            else:65                attn_module = block.attn66            attn_modules.append(attn_module)67        return attn_modules68    setattr(model, '_original_forward', getattr(model, 'forward'))69    setattr(model, '_original_generate', getattr(model, 'generate'))70 71    def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):72        """Wraps original forward to enable PrefixLM attention."""73 74        def call_og_forward():75            if isinstance(self, GPTNeoXForCausalLM):76                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)77            else:78                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)79        if bidirectional_mask is None:80            return call_og_forward()81        assert isinstance(bidirectional_mask, torch.Tensor)82        attn_modules = _get_attn_modules(model)83        (b, s) = bidirectional_mask.shape84        max_length = attn_modules[0].bias.shape[-1]85        if s > max_length:86            raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')87        assert s <= max_length88        if s < max_length:89            pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)90            bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)91        bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)92        for attn_module in attn_modules:93            attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)94        output = call_og_forward()95        for attn_module in attn_modules:96            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]97        return output98 99    def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]):100        """Wraps original generate to enable PrefixLM attention."""101        attn_modules = _get_attn_modules(model)102        for attn_module in attn_modules:103            attn_module.bias.data[:] = 1104        output = self._original_generate(*args, **kwargs)105        for attn_module in attn_modules:106            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]107        return output108    setattr(model, 'forward', MethodType(forward, model))109    setattr(model, 'generate', MethodType(generate, model))110    setattr(model, '_prefix_lm_converted', True)111    return model112 113def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:114    """Converts a BLOOM Causal LM to a Prefix LM.115 116    Supported HuggingFace model classes:117        - `BloomForCausalLM`118 119    See `convert_hf_causal_lm_to_prefix_lm` for more details.120    """121    if hasattr(model, '_prefix_lm_converted'):122        return model123    assert isinstance(model, BloomForCausalLM)124    assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'125 126    def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor:127        combined_attention_mask = None128        device = attention_mask.device129        (_, src_length) = input_shape130        if src_length > 1:131            combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)132            if bidirectional_mask is not None:133                assert attention_mask.shape == bidirectional_mask.shape134                expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)135                combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)136        expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)137        combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask138        return combined_attention_mask139 140    def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:141        num_heads = self.config.n_head142        closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))143        base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)144        powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)145        slopes = torch.pow(base, powers)146        if closest_power_of_2 != num_heads:147            extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)148            num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)149            extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)150            slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)151        qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)152        ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)153        diffs = qa - ka + key_length - query_length154        diffs = -diffs.abs()155        alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)156        alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)157        return alibi.to(dtype)158    KeyValueT = Tuple[torch.Tensor, torch.Tensor]159 160    def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:161        if deprecated_arguments.pop('position_ids', False) is not False:162            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning)163        if len(deprecated_arguments) > 0:164            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')165        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions166        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states167        use_cache = use_cache if use_cache is not None else self.config.use_cache168        return_dict = return_dict if return_dict is not None else self.config.use_return_dict169        if input_ids is not None and inputs_embeds is not None:170            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')171        elif input_ids is not None:172            (batch_size, seq_length) = input_ids.shape173        elif inputs_embeds is not None:174            (batch_size, seq_length, _) = inputs_embeds.shape175        else:176            raise ValueError('You have to specify either input_ids or inputs_embeds')177        if past_key_values is None:178            past_key_values = tuple([None] * len(self.h))179        head_mask = self.get_head_mask(head_mask, self.config.n_layer)180        if inputs_embeds is None:181            inputs_embeds = self.word_embeddings(input_ids)182        hidden_states = self.word_embeddings_layernorm(inputs_embeds)183        presents = () if use_cache else None184        all_self_attentions = () if output_attentions else None185        all_hidden_states = () if output_hidden_states else None186        seq_length_with_past = seq_length187        past_key_values_length = 0188        if past_key_values[0] is not None:189            tmp = past_key_values[0][0]190            past_key_values_length = tmp.shape[2]191            seq_length_with_past = seq_length_with_past + past_key_values_length192        if attention_mask is None:193            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)194        else:195            attention_mask = attention_mask.to(hidden_states.device)196        alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device)197        causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)198        for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):199            if output_hidden_states:200                hst = (hidden_states,)201                all_hidden_states = all_hidden_states + hst202            if self.gradient_checkpointing and self.training:203                if use_cache:204                    logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')205                    use_cache = False206 207                def create_custom_forward(module):208 209                    def custom_forward(*inputs):210                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)211                    return custom_forward212                outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])213            else:214                outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi)215            hidden_states = outputs[0]216            if use_cache is True:217                presents = presents + (outputs[1],)218            if output_attentions:219                oa = (outputs[2 if use_cache else 1],)220                all_self_attentions = all_self_attentions + oa221        hidden_states = self.ln_f(hidden_states)222        if output_hidden_states:223            hst = (hidden_states,)224            all_hidden_states = all_hidden_states + hst225        if not return_dict:226            return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))227        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)228    setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))229    setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))230    setattr(model.transformer, 'forward', MethodType(forward, model.transformer))231    KeyValueT = Tuple[torch.Tensor, torch.Tensor]232 233    def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:234        """Replacement forward method for BloomCausalLM."""235        if deprecated_arguments.pop('position_ids', False) is not False:236            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning)237        if len(deprecated_arguments) > 0:238            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')239        return_dict = return_dict if return_dict is not None else self.config.use_return_dict240        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)241        hidden_states = transformer_outputs[0]242        lm_logits = self.lm_head(hidden_states)243        loss = None244        if labels is not None:245            shift_logits = lm_logits[..., :-1, :].contiguous()246            shift_labels = labels[..., 1:].contiguous()247            (batch_size, seq_length, vocab_size) = shift_logits.shape248            loss_fct = CrossEntropyLoss()249            loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))250        if not return_dict:251            output = (lm_logits,) + transformer_outputs[1:]252            return (loss,) + output if loss is not None else output253        return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)254 255    def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict:256        if past:257            input_ids = input_ids[:, -1].unsqueeze(-1)258            bidirectional_mask = None259            if past[0][0].shape[0] == input_ids.shape[0]:260                past = self._convert_to_bloom_cache(past)261        else:262            bidirectional_mask = torch.ones_like(input_ids)263        return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}264    setattr(model, 'forward', MethodType(forward, model))265    setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))266    setattr(model, '_prefix_lm_converted', True)267    return model268 269def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:270    """Converts an OPT Causal LM to a Prefix LM.271 272    Supported HuggingFace model classes:273        - `OPTForCausalLM`274 275    See `convert_hf_causal_lm_to_prefix_lm` for more details.276    """277    if hasattr(model, '_prefix_lm_converted'):278        return model279    assert isinstance(model, OPTForCausalLM)280    assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'281    setattr(model, '_original_forward', getattr(model, 'forward'))282    setattr(model, '_original_generate', getattr(model, 'generate'))283    model.model.decoder.bidirectional_mask = None284 285    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):286        combined_attention_mask = None287        if input_shape[-1] > 1:288            if self.bidirectional_mask == 'g':289                (bsz, src_length) = input_shape290                combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)291            else:292                combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)293                if self.bidirectional_mask is not None:294                    assert attention_mask.shape == self.bidirectional_mask.shape295                    expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)296                    combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)297        if attention_mask is not None:298            expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)299            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask300        return combined_attention_mask301    setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))302 303    def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):304 305        def call_og_forward():306            return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)307        if bidirectional_mask is None:308            return call_og_forward()309        self.model.decoder.bidirectional_mask = bidirectional_mask310        try:311            outputs = call_og_forward()312        except:313            self.model.decoder.bidirectional_mask = None314            raise315        self.model.decoder.bidirectional_mask = None316        return outputs317 318    def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]):319        """Wraps original generate to enable PrefixLM-style attention."""320        self.model.decoder.bidirectional_mask = 'g'321        try:322            output = self._original_generate(*args, **kwargs)323        except:324            self.model.decoder.bidirectional_mask = None325            raise326        self.model.decoder.bidirectional_mask = None327        return output328    setattr(model, 'forward', MethodType(forward, model))329    setattr(model, 'generate', MethodType(generate, model))330    setattr(model, '_prefix_lm_converted', True)331    return model332_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)333CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]334 335def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:336    """Converts a HuggingFace Causal LM to a Prefix LM.337 338    Supported HuggingFace model classes:339        - `GPT2LMHeadModel`340        - `GPTNeoForCausalLM`341        - `GPTNeoXForCausalLM`342        - `GPTJForCausalLM`343        - `BloomForCausalLM`344        - `OPTForCausalLM`345 346    Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the347    `generate` method and/or select underlying methods depending on the model class.348 349    These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".350 351    Notes on training:352        To actually train the converted model as a Prefix LM, training batches will need to indicate353        the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.354 355        **This is not a standard input and requires custom layers either within or after your dataloader.**356 357        In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`358        such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.359        That is, the prefix portion of the sequence should not generate any loss. Loss should only be360        generated by the target portion of the sequence.361 362    Notes on `GPTNeoForCausalLM`:363        To simplify the implementation, "global" and "local" attention layers are handled differently.364        For "global" layers, we handle conversion as described above. For "local" layers, which use a365        causal attention mask within a restricted local window, we do not alter the masking.366 367    Notes on `forward` method conversion:368        After conversion, the `forward` method will handle a new input, `bidirectional_mask`,369        which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions370        belonging to the prefix (prefix tokens can attend to one another bidirectionally), and371        0 indicates token positions belonging to the target.372 373        The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing374        causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset375        the causal masks before returning the result.376 377    Notes on `generate` method conversion:378        After conversion, the `generate` method will have the same signature but will internally379        convert all causal masks to be purely bidirectional, call the original `generate` method, and380        (where appropriate) reset the causal masks before returning the result.381 382        This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token383        "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates384        each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one385        another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and386        previously-generated tokens (also as expected in a Prefix LM).387 388    To preserve the API, the original methods are renamed to `_original_forward` and389    `_original_generate`, and replaced with new `forward` and `generate` methods that wrap390    them, respectively. Although implementation details vary by model class.391    """392    if isinstance(model, _SUPPORTED_GPT_MODELS):393        return _convert_gpt_causal_lm_to_prefix_lm(model)394    elif isinstance(model, BloomForCausalLM):395        return _convert_bloom_causal_lm_to_prefix_lm(model)396    elif isinstance(model, OPTForCausalLM):397        return _convert_opt_causal_lm_to_prefix_lm(model)398    else:399        raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')400 401def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):402    """Attempts to add bidirectional_mask to batch if missing.403 404    Raises:405        KeyError if bidirectional_mask is missing and can't be inferred406    """407    if 'bidirectional_mask' not in batch:408        if batch.get('mode', None) == 'icl_task':409            batch['bidirectional_mask'] = batch['attention_mask'].clone()410            for (i, continuation_indices) in enumerate(batch['continuation_indices']):411                batch['bidirectional_mask'][i, continuation_indices] = 0412        elif 'labels' in batch and 'attention_mask' in batch:413            batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])414        else:415            raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')