precious3_gpt_multi_modal.py

from typing import Optional, Tuple, Union, List

from transformers.models.mpt.modeling_mpt import MptBlock, build_mpt_alibi_tensor
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, LayerNorm
from transformers.models.mpt.modeling_mpt import MptBlock, build_mpt_alibi_tensor
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, \
BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPast
# from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, MptForCausalLM, MptModel
from transformers import PreTrainedTokenizerFast
import os
import torch.nn.functional as F

from mpt_7b.modeling_mpt import MPTModel, MPTForCausalLM, gen_attention_mask_in_length
from mpt_7b.configuration_mpt import MPTConfig
from mpt_7b.blocks import MPTBlock
from mpt_7b.norm import NORM_CLASS_REGISTRY
from mpt_7b.custom_embedding import SharedEmbedding
from mpt_7b.attention import ATTN_CLASS_REGISTRY, attn_bias_shape, build_attn_bias, gen_slopes

import logging
log = logging.getLogger(__name__)


class Custom_MPTConfig(MPTConfig):
    def __init__(self):
        super().__init__()


class Custom_MptModel(MPTModel): # MptModel
    def __init__(self, config: MPTConfig, modality0_dim=128, modality2_dim=1536):
        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.learned_pos_emb = config.learned_pos_emb
        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()]
        self.embedding_fraction = config.embedding_fraction
        self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
        if self.learned_pos_emb:
            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)
        
        
         ### Added for P3GPT - START
        # Freeze all parameters except the projection layer
        for param in self.wte.parameters():
            param.requires_grad = False

        for param in self.blocks.parameters():
            param.requires_grad = False
        
        # Add a projection layer for the custom embedding
        # torch.set_default_dtype(torch.bfloat16)
        self.modality0_embedding_projection = nn.ModuleList([nn.Linear(modality0_dim, config.d_model), 
                                                             # nn.BatchNorm1d(config.d_model),
                                                             nn.ReLU(),
                                                             nn.Linear(config.d_model, config.d_model),
                                                             # nn.BatchNorm1d(config.d_model),
                                                             nn.ReLU(),
                                                             nn.Linear(config.d_model, config.d_model)])# nn.Linear(modality0_dim, self.hidden_size)
        

        self.modality2_embedding_projection = nn.ModuleList([nn.Linear(modality2_dim, config.d_model), 
                                                             # nn.BatchNorm1d(config.d_model),
                                                             nn.ReLU(),
                                                             nn.Linear(config.d_model, config.d_model),
                                                             # nn.BatchNorm1d(config.d_model),
                                                             nn.ReLU(),
                                                             nn.Linear(config.d_model, config.d_model)])# nn.Linear(modality0_dim, self.hidden_size)
        
        
        ### Added for P3GPT - FINISH
        
        self.rope = config.attn_config['rope']
        self.rope_impl = None
        if self.rope:
            self.rope_impl = config.attn_config['rope_impl']
            self.rotary_embedding = gen_rotary_embedding(rope_head_dim=config.d_model // config.n_heads, rope_impl=self.rope_impl, rope_theta=config.attn_config['rope_theta'], rope_dail_config=config.attn_config['rope_dail_config'], rope_hf_config=config.attn_config['rope_hf_config'], max_seq_len=self.config.max_seq_len)
        if config.init_device != 'meta':
            log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster 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):
                    log.info(f'Removing bias from module={module!r}.')
                    module.register_parameter('bias', None)
                if hasattr(module, 'use_bias'):
                    log.info(f'Setting use_bias=False for module={module!r}.')
                    module.use_bias = False
        log.debug(self)
        log.debug(f"Using {self.config.init_config['name']} initialization.")

        # Initialize weights and apply final processing
#        self.post_init()
        
        
    def get_input_embeddings(self):
        return self.wte


    def set_input_embeddings(self, new_embeddings):
        # self.wte = new_embeddings
        self.wte.weight = new_embeddings

        
    def forward(self, input_ids: Optional[torch.LongTensor]=None, 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, modality0_emb: Optional[bool] = None,
                modality0_token_id: Optional[bool] = None, modality1_emb: Optional[bool] = None, modality1_token_id: Optional[bool] = None,
                modality2_emb: Optional[bool] = None, modality2_token_id: Optional[bool] = None, modality3_emb: Optional[bool] = None,
                modality3_token_id: Optional[bool] = None,) -> BaseModelOutputWithPast:
        
        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:
            if self.attn_impl != 'torch':
                raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
        if self.training and attention_mask is not None and (attention_mask[:, 0].sum() != attention_mask.shape[0]):
            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.')
        
        ### ADDED FOR P3 - START
        
        if modality0_emb is not None:
            modality0_emb = torch.tensor(modality0_emb, dtype=torch.bfloat16)
            hidden_states = self.wte.weight.detach() 

            for layer in self.modality0_embedding_projection:
                modality0_emb = layer(modality0_emb)
            proj_modality0_emb = modality0_emb

            # Replace the original embedding for the custom token with the custom embedding
            hidden_states[modality0_token_id, :] = torch.mean(torch.squeeze(proj_modality0_emb, 1), dim=0)
            self.set_input_embeddings(torch.nn.Parameter(hidden_states))
            
        if modality1_emb is not None:
            modality1_emb = torch.tensor(modality1_emb, dtype=torch.bfloat16)
            hidden_states = self.wte.weight.detach() 

            for layer in self.modality0_embedding_projection:
                modality1_emb = layer(modality1_emb)
            proj_modality1_emb = modality1_emb

            # Replace the original embedding for the custom token with the custom embedding
            hidden_states[modality1_token_id, :] = torch.mean(torch.squeeze(proj_modality1_emb, 1), dim=0)
            self.set_input_embeddings(torch.nn.Parameter(hidden_states))
        
        if modality2_emb is not None:
            modality2_emb = torch.tensor(modality2_emb, dtype=torch.bfloat16)
            hidden_states = self.wte.weight.detach() 

            for layer in self.modality2_embedding_projection:
                modality2_emb = layer(modality2_emb)
            proj_modality2_emb = modality2_emb

            # Replace the original embedding for the custom token with the custom embedding
            hidden_states[modality2_token_id, :] = torch.mean(torch.squeeze(proj_modality2_emb, 1), dim=0)
            self.set_input_embeddings(torch.nn.Parameter(hidden_states))
            
        if modality3_emb is not None:
            modality3_emb = torch.tensor(modality3_emb, dtype=torch.bfloat16)
            hidden_states = self.wte.weight.detach() 

            for layer in self.modality2_embedding_projection:
                modality3_emb = layer(modality3_emb)
            proj_modality3_emb = modality3_emb

            # Replace the original embedding for the custom token with the custom embedding
            hidden_states[modality3_token_id, :] = torch.mean(torch.squeeze(proj_modality3_emb, 1), dim=0)
            self.set_input_embeddings(torch.nn.Parameter(hidden_states))
        
        ### ADDED FOR P3 - END
        
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError('You cannot specify both input_ids and inputs_embeds.')
        elif input_ids is not None:
            bsz = input_ids.size(0)
            S = input_ids.size(1)
            x = self.wte(input_ids)
            input_device = input_ids.device
        elif inputs_embeds is not None:
            bsz = inputs_embeds.size(0)
            S = inputs_embeds.size(1)
            x = inputs_embeds
            input_device = inputs_embeds.device
        else:
            raise ValueError('You must specify input_ids or inputs_embeds')
        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}'
        rotary_emb_w_meta_info = None
        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 self.attn_impl == 'torch':
                past_position = past_key_values[0][0].size(3)
        if self.learned_pos_emb or self.rope:
            if self.learned_pos_emb and 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}.')
            if self.learned_pos_emb or (self.rope and self.rope_impl == 'hf'):
                pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_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)
                if self.learned_pos_emb:
                    x = x + self.wpe(pos)
                elif self.rope and self.rope_impl == 'hf':
                    rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': pos, 'seq_len': S + past_position}
            elif self.rope and self.rope_impl == 'dail':
                rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': past_position, 'seq_len': S + past_position}
        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=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
        attention_mask_in_length = gen_attention_mask_in_length(sequence_id=sequence_id, S=S, attn_uses_sequence_id=self.attn_uses_sequence_id, attn_impl=self.attn_impl, attention_mask=attention_mask)
        alibi_slopes = None
        if self.alibi and self.attn_impl == 'flash':
            alibi_slopes = gen_slopes(n_heads=self.config.n_heads, alibi_bias_max=self.alibi_bias_max, device=x.device, return_1d=True)
            
        presents = () if use_cache else None
        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
        all_self_attns = () if output_attentions else None
        flash_attn_padding_info = {}
        if self.attn_impl == 'flash':
            flash_attn_padding_info = gen_flash_attn_padding_info(bsz, S, past_position, x.device, attention_mask_in_length, attention_mask)
        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, attn_weights, present) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions), alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info)
            if presents is not None:
                presents += (present,)
            if output_attentions:
                assert all_self_attns is not None
                all_self_attns = all_self_attns + (attn_weights,)
        x = self.norm_f(x)
        if output_hidden_states:
            assert all_hidden_states is not None
            all_hidden_states = all_hidden_states + (x,)
        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns)


class Custom_MPTForCausalLM(MPTForCausalLM):

    def __init__(self, config: MPTConfig):
        super().__init__(config)
        # log.info(f'Instantiating an MPTForCausalLM model from {__file__}')
        self.transformer: MPTModel = Custom_MptModel(config)
        self.lm_head = None
        if not config.tie_word_embeddings:
            self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False, device=config.init_device)
            self.lm_head._fsdp_wrap = True
        for child in self.transformer.children():
            if isinstance(child, torch.nn.ModuleList):
                continue
            if isinstance(child, torch.nn.Module):
                child._fsdp_wrap = True
        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 forward(self, input_ids: Optional[torch.LongTensor]=None, 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,
                modality0_emb: Optional[bool] = None, modality0_token_id: Optional[bool] = None,
                modality1_emb: Optional[bool] = None, modality1_token_id: Optional[bool] = None, 
                modality2_emb: Optional[bool] = None, modality2_token_id: Optional[bool] = None,
                modality3_emb: Optional[bool] = None, modality3_token_id: Optional[bool] = None) -> CausalLMOutputWithPast:
        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, inputs_embeds=inputs_embeds,
            modality0_emb=modality0_emb,
            modality0_token_id=modality0_token_id,
            modality1_emb=modality1_emb,
            modality1_token_id=modality1_token_id,
            modality2_emb=modality2_emb,
            modality2_token_id=modality2_token_id,
            modality3_emb=modality3_emb,
            modality3_token_id=modality3_token_id
        )
        if self.lm_head is not None:
            logits = self.lm_head(outputs.last_hidden_state)
        else:
            out = outputs.last_hidden_state
            out = out.to(self.transformer.wte.weight.device)
            logits = self.transformer.wte(out, True)
        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, attentions=outputs.attentions)