replit/replit-code-v1_5-3b

1"""A simple, flexible implementation of a GPT model.2 3Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py4"""5import math6import warnings7from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union8import torch9import torch.nn as nn10import torch.nn.functional as F11from transformers import PreTrainedModel, PreTrainedTokenizerBase12from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast13from .attention import attn_bias_shape, build_attn_bias14from .blocks import MPTBlock15from .custom_embedding import SharedEmbedding16from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY17from .ffn import FFN_CLASS_REGISTRY as FFN_CLASS_REGISTRY18from .ffn import MPTMLP as MPTMLP19from .ffn import build_ffn as build_ffn20from .norm import NORM_CLASS_REGISTRY21from .configuration_mpt import MPTConfig22from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising23from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm24from .meta_init_context import init_empty_weights25from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY26try:27    from .flash_attn_triton import flash_attn_func as flash_attn_func28except:29    pass30import logging31log = logging.getLogger(__name__)32 33class MPTPreTrainedModel(PreTrainedModel):34    config_class = MPTConfig35    base_model_prefix = 'model'36    _no_split_modules = ['MPTBlock']37 38class MPTModel(MPTPreTrainedModel):39 40    def __init__(self, config: MPTConfig):41        config._validate_config()42        super().__init__(config)43        self.attn_impl = config.attn_config['attn_impl']44        self.prefix_lm = config.attn_config['prefix_lm']45        self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']46        self.alibi = config.attn_config['alibi']47        self.alibi_bias_max = config.attn_config['alibi_bias_max']48        self.learned_pos_emb = config.learned_pos_emb49        if config.init_device == 'mixed':50            if dist.get_local_rank() == 0:51                config.init_device = 'cpu'52            else:53                config.init_device = 'meta'54        if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():55            norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())56            raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')57        norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]58        self.embedding_fraction = config.embedding_fraction59        self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)60        if self.learned_pos_emb:61            self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)62        self.emb_drop = nn.Dropout(config.emb_pdrop)63        self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])64        self.norm_f = norm_class(config.d_model, device=config.init_device)65        if config.init_device != 'meta':66            log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.')67            self.apply(self.param_init_fn)68        self.is_causal = not self.prefix_lm69        self._attn_bias_initialized = False70        self.attn_bias = None71        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)72        if config.no_bias:73            for module in self.modules():74                if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):75                    log.info(f'Removing bias ({module.bias}) from {module}.')76                    module.register_parameter('bias', None)77        log.debug(self)78        log.debug(f"Using {self.config.init_config['name']} initialization.")79 80    def get_input_embeddings(self) -> nn.Embedding:81        return self.wte82 83    def set_input_embeddings(self, value: nn.Embedding) -> None:84        self.wte = value85 86    @torch.no_grad()87    def _attn_bias(self, device: torch.device, dtype: torch.dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None) -> Tuple[Optional[torch.Tensor], Optional[torch.ByteTensor]]:88        if not self._attn_bias_initialized:89            if self.attn_bias_shape:90                self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)91                self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)92            self._attn_bias_initialized = True93        if self.attn_impl == 'flash':94            return (self.attn_bias, attention_mask)95        if self.attn_bias is not None:96            self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)97        attn_bias = self.attn_bias98        if self.prefix_lm:99            assert isinstance(attn_bias, torch.Tensor)100            assert isinstance(prefix_mask, torch.Tensor)101            attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)102        if self.attn_uses_sequence_id and sequence_id is not None:103            assert isinstance(attn_bias, torch.Tensor)104            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)105        if attention_mask is not None:106            s_k = attention_mask.shape[-1]107            if attn_bias is None:108                attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)109            else:110                _s_k = max(0, attn_bias.size(-1) - s_k)111                attn_bias = attn_bias[:, :, :, _s_k:]112            if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:113                raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')114            min_val = torch.finfo(attn_bias.dtype).min115            attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)116        return (attn_bias, None)117 118    def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor) -> torch.Tensor:119        (s_k, s_q) = attn_bias.shape[-2:]120        if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:121            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}.')122        seq_len = prefix_mask.shape[-1]123        if seq_len > self.config.max_seq_len:124            raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')125        attn_bias = attn_bias[..., :seq_len, :seq_len]126        causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)127        prefix = prefix_mask.view(-1, 1, 1, seq_len)128        cannot_attend = ~torch.logical_or(causal, prefix.bool())129        min_val = torch.finfo(attn_bias.dtype).min130        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)131        return attn_bias132 133    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor) -> torch.Tensor:134        seq_len = sequence_id.shape[-1]135        if seq_len > self.config.max_seq_len:136            raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')137        attn_bias = attn_bias[..., :seq_len, :seq_len]138        cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)139        min_val = torch.finfo(attn_bias.dtype).min140        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)141        return attn_bias142 143    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) -> BaseModelOutputWithPast:144        return_dict = return_dict if return_dict is not None else self.config.return_dict145        use_cache = use_cache if use_cache is not None else self.config.use_cache146        if attention_mask is not None:147            attention_mask = attention_mask.bool()148        if prefix_mask is not None:149            prefix_mask = prefix_mask.bool()150        if not return_dict:151            raise NotImplementedError('return_dict False is not implemented yet for MPT')152        if output_attentions:153            if self.attn_impl != 'torch':154                raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')155        if self.training and attention_mask is not None and (attention_mask[:, 0].sum() != attention_mask.shape[0]):156            raise NotImplementedError('MPT does not support training with left padding.')157        if self.prefix_lm and prefix_mask is None:158            raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')159        if inputs_embeds is not None:160            raise NotImplementedError('inputs_embeds is not implemented for MPT.')161        if self.training:162            if self.attn_uses_sequence_id and sequence_id is None:163                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.')164            elif self.attn_uses_sequence_id is False and sequence_id is not None:165                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.')166        S = input_ids.size(1)167        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}'168        tok_emb = self.wte(input_ids)169        if self.learned_pos_emb:170            past_position = 0171            if past_key_values is not None:172                if len(past_key_values) != self.config.n_layers:173                    raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')174                past_position = past_key_values[0][0].size(1)175                if self.attn_impl == 'torch':176                    past_position = past_key_values[0][0].size(3)177            if S + past_position > self.config.max_seq_len:178                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}.')179            pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)180            if attention_mask is not None:181                pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)182            pos_emb = self.wpe(pos)183            x = tok_emb + pos_emb184        else:185            x = tok_emb186        if self.embedding_fraction == 1:187            x = self.emb_drop(x)188        else:189            x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)190            assert isinstance(self.emb_drop, nn.Module)191            x = self.emb_drop(x_shrunk)192        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)193        if use_cache and past_key_values is None:194            past_key_values = [() for _ in range(self.config.n_layers)]195        all_hidden_states = () if output_hidden_states else None196        all_self_attns = () if output_attentions else None197        for (b_idx, block) in enumerate(self.blocks):198            if output_hidden_states:199                assert all_hidden_states is not None200                all_hidden_states = all_hidden_states + (x,)201            past_key_value = past_key_values[b_idx] if past_key_values is not None else None202            (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions))203            if past_key_values is not None:204                past_key_values[b_idx] = past_key_value205            if output_attentions:206                assert all_self_attns is not None207                all_self_attns = all_self_attns + (attn_weights,)208        x = self.norm_f(x)209        if output_hidden_states:210            assert all_hidden_states is not None211            all_hidden_states = all_hidden_states + (x,)212        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)213 214    def param_init_fn(self, module: nn.Module) -> None:215        init_fn_name = self.config.init_config['name']216        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)217 218    def fsdp_wrap_fn(self, module: nn.Module) -> bool:219        return isinstance(module, MPTBlock)220 221    def activation_checkpointing_fn(self, module: nn.Module) -> bool:222        return isinstance(module, MPTBlock)223 224class MPTForCausalLM(MPTPreTrainedModel):225 226    def __init__(self, config: MPTConfig):227        super().__init__(config)228        if not config.tie_word_embeddings:229            raise ValueError('MPTForCausalLM only supports tied word embeddings')230        log.info(f'Instantiating an MPTForCausalLM model from {__file__}')231        self.transformer: MPTModel = MPTModel(config)232        for child in self.transformer.children():233            if isinstance(child, torch.nn.ModuleList):234                continue235            if isinstance(child, torch.nn.Module):236                child._fsdp_wrap = True237        self.logit_scale = None238        if config.logit_scale is not None:239            logit_scale = config.logit_scale240            if isinstance(logit_scale, str):241                if logit_scale == 'inv_sqrt_d_model':242                    logit_scale = 1 / math.sqrt(config.d_model)243                else:244                    raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")245            self.logit_scale = logit_scale246 247    def get_input_embeddings(self) -> nn.Embedding:248        return self.transformer.wte249 250    def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:251        self.transformer.wte = value252 253    def get_output_embeddings(self) -> nn.Embedding:254        return self.transformer.wte255 256    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding]) -> None:257        self.transformer.wte = new_embeddings258 259    def set_decoder(self, decoder: MPTModel) -> None:260        self.transformer = decoder261 262    def get_decoder(self) -> MPTModel:263        return self.transformer264 265    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) -> CausalLMOutputWithPast:266        return_dict = return_dict if return_dict is not None else self.config.return_dict267        use_cache = use_cache if use_cache is not None else self.config.use_cache268        if inputs_embeds is not None:269            raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')270        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, 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)271        logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)272        if self.logit_scale is not None:273            if self.logit_scale == 0:274                warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')275            logits *= self.logit_scale276        loss = None277        if labels is not None:278            _labels = torch.roll(labels, shifts=-1)279            _labels[:, -1] = -100280            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), _labels.to(logits.device).view(-1))281        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)282 283    def param_init_fn(self, module: nn.Module) -> None:284        init_fn_name = self.config.init_config['name']285        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)286 287    def fsdp_wrap_fn(self, module: nn.Module) -> bool:288        return isinstance(module, MPTBlock)289 290    def activation_checkpointing_fn(self, module: nn.Module) -> bool:291        return isinstance(module, MPTBlock)292 293    def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]=None, inputs_embeds: Optional[torch.Tensor]=None, **kwargs: Any) -> Dict[str, Any]:294        if inputs_embeds is not None:295            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')296        attention_mask = kwargs['attention_mask'].bool()297        if attention_mask[:, -1].sum() != attention_mask.shape[0]:298            raise NotImplementedError('MPT does not support generation with right padding.')299        if self.transformer.attn_uses_sequence_id and self.training:300            sequence_id = torch.zeros_like(input_ids[:1])301        else:302            sequence_id = None303        if past_key_values is not None:304            input_ids = input_ids[:, -1].unsqueeze(-1)305        if self.transformer.prefix_lm:306            prefix_mask = torch.ones_like(attention_mask)307            if kwargs.get('use_cache') == False:308                raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')309        else:310            prefix_mask = None311        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)}312 313    @staticmethod314    def _reorder_cache(past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor) -> List[Tuple[torch.Tensor, ...]]:315        """Used by HuggingFace generate when using beam search with kv-caching.316 317        See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133318        for an example in transformers.319        """320        reordered_past = []321        for layer_past in past_key_values:322            reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]323        return reordered_past