Delete mpt_7b/precious_multi_modal.py

mpt_7b/precious_multi_modal.py

@@ -1,354 +0,0 @@
-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 modeling_mpt import MPTModel, MPTForCausalLM, gen_attention_mask_in_length
-from configuration_mpt import MPTConfig
-from blocks import MPTBlock
-from norm import NORM_CLASS_REGISTRY
-from custom_embedding import SharedEmbedding
-from 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 CustomTokenizer(PreTrainedTokenizerFast):
-    def __init__(self, **kwargs):
-        super().__init__( tokenizer_file="../tokenizer.json",
-        unk_token="[UNK]",
-        pad_token="[PAD]",
-        eos_token="[EOS]",
-        bos_token="[BOS]", **kwargs)
-
-
-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)