modeling_mpt.py

'A simple, flexible implementation of a GPT model.\n\nInspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py\n'
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 transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from .attention import attn_bias_shape, build_attn_bias
from .blocks import MPTBlock
from .norm import NORM_CLASS_REGISTRY
from .configuration_mpt import MPTConfig
from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
from .meta_init_context import init_empty_weights
from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
try:
    from .flash_attn_triton import flash_attn_func
except:
    pass
Tokenizer = Union[(PreTrainedTokenizer, PreTrainedTokenizerFast)]

class MPTPreTrainedModel(PreTrainedModel):
    config_class = MPTConfig
    base_model_prefix = 'model'

class MPTModel(MPTPreTrainedModel):

    def __init__(self, config: MPTConfig):
        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']
        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()]
        self.embedding_fraction = config.embedding_fraction
        self.wte = nn.Embedding(config.vocab_size, config.d_model, device=config.init_device)
        if (not self.alibi):
            self.wpe = 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={config.init_device!r}, 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)
        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)
        if config.no_bias:
            for module in self.modules():
                if (hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter)):
                    if config.verbose:
                        warnings.warn(f'Removing bias ({module.bias}) from {module}.')
                    module.register_parameter('bias', None)
        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):
        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):
        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.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)
            self._attn_bias_initialized = True
        if (self.attn_impl == 'flash'):
            return (self.attn_bias, attention_mask)
        if (self.attn_bias is not None):
            self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
        attn_bias = self.attn_bias
        if self.prefix_lm:
            assert isinstance(attn_bias, torch.Tensor)
            assert isinstance(prefix_mask, torch.Tensor)
            attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
        if (self.attn_uses_sequence_id and (sequence_id is not None)):
            assert isinstance(attn_bias, torch.Tensor)
            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
        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:
                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, 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}')
        attn_bias = attn_bias[..., :seq_len, :seq_len]
        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}')
        attn_bias = attn_bias[..., :seq_len, :seq_len]
        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):
        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)
        if (attention_mask is not None):
            attention_mask = attention_mask.bool()
        if (prefix_mask is not None):
            prefix_mask = prefix_mask.bool()
        if (not return_dict):
            raise NotImplementedError('return_dict False is not implemented yet for MPT')
        if output_attentions:
            raise NotImplementedError('output_attentions is not implemented yet for MPT')
        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.')
        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)
        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)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).'))
                past_position = past_key_values[0][0].size(1)
            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 {(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):
                pos = torch.clamp((pos - torch.cumsum((~ attention_mask).to(torch.int32), dim=1)[:, past_position:]), min=0)
            pos_emb = self.wpe(pos)
            x = (tok_emb + pos_emb)
        if (self.embedding_fraction == 1):
            x = self.emb_drop(x)
        else:
            x_shrunk = ((x * self.embedding_fraction) + (x.detach() * (1 - self.embedding_fraction)))
            assert isinstance(self.emb_drop, nn.Module)
            x = self.emb_drop(x_shrunk)
        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=x.dtype, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
        if (use_cache and (past_key_values is None)):
            past_key_values = [() for _ in range(self.config.n_layers)]
        all_hidden_states = (() if output_hidden_states else None)
        for (b_idx, block) in enumerate(self.blocks):
            if output_hidden_states:
                assert (all_hidden_states is not None)
                all_hidden_states = (all_hidden_states + (x,))
            past_key_value = (past_key_values[b_idx] if (past_key_values is not None) else None)
            (x, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
            if (past_key_values is not None):
                past_key_values[b_idx] = past_key_value
        x = self.norm_f(x)
        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states)

    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)

    def fsdp_wrap_fn(self, module):
        return isinstance(module, MPTBlock)

    def activation_checkpointing_fn(self, module):
        return isinstance(module, MPTBlock)

class MPTForCausalLM(MPTPreTrainedModel):

    def __init__(self, config: MPTConfig):
        super().__init__(config)
        if (not config.tie_word_embeddings):
            raise ValueError('MPTForCausalLM only supports tied word embeddings')
        self.transformer = MPTModel(config)
        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={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
            self.logit_scale = logit_scale

    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):
        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)
        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)
        logits = F.linear(outputs.last_hidden_state, self.transformer.wte.weight)
        if (self.logit_scale is not None):
            if (self.logit_scale == 0):
                warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. 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)

    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)

    def fsdp_wrap_fn(self, module):
        return isinstance(module, MPTBlock)

    def activation_checkpointing_fn(self, module):
        return isinstance(module, MPTBlock)

    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:
            prefix_mask = torch.ones_like(attention_mask)
            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)}

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        'Used by HuggingFace generate when using beam search with kv-caching.\n\n        See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133\n        for an example in transformers.\n        '
        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