pretrained_model.py

from transformers import AutoConfig, PreTrainedModel, AutoModelForCausalLM
from typing import List, Optional
from torch import nn
# from model import LayerNorm, BlockJ
from transformers.modeling_outputs import CausalLMOutputWithPast
import torch
import math
from torch.nn import functional as F
from transformers import AutoConfig, AutoModel
from .pretrained_config import *

   
   
class LayerNorm(nn.Module):
    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """

    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

    def forward(self, input):
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
        # regularization
        self.attn_dropout = nn.Dropout(config.dropout)
        self.resid_dropout = nn.Dropout(config.dropout)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.dropout = config.dropout
        # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        # if not self.flash:
        #     print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
            # causal mask to ensure that attention is only applied to the left in the input sequence
        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
                                        .view(1, 1, config.block_size, config.block_size))

    def forward(self, x, attn_mask=None):
        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        if self.flash:
            if attn_mask is not None:
            # efficient attention using Flash Attention CUDA kernels
                attn_mask = attn_mask.to(torch.bool)
                y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=self.dropout if self.training else 0)
            else:
                y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
        else:
            # manual implementation of attention
            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
            att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
            att = F.softmax(att, dim=-1)
            att = self.attn_dropout(att)
            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y

class MLP(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.gelu    = nn.GELU()
        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x

class BlockJ(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.j = LayerNorm(config.n_embd, config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

    def forward(self, x, attn_mask=None):
        h = x
        x = self.ln_1(x)
        x = h + self.attn(x, attn_mask) + self.j(x)
        x = x + self.mlp(self.ln_2(x))
        return x
        
        
class GPTJXForCausalLM(PreTrainedModel):
    config_class = GPTJXConfig
    base_model_prefix = "transformer"
    is_parallelizable = True
    supports_gradient_checkpointing = True
    _no_split_modules = ["BlockJ"]
    # _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _tied_weights_keys = ["lm_head.weight"]
    
    
    def __init__(self, config):
        super().__init__(config)
        assert config.vocab_size is not None
        assert config.block_size is not None
        self.config = config

        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            drop = nn.Dropout(config.dropout),
            h = nn.ModuleList([BlockJ(config) for _ in range(config.n_layer)]),
            ln_f = LayerNorm(config.n_embd, bias=config.bias),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        # with weight tying when using torch.compile() some warnings get generated:
        # "UserWarning: functional_call was passed multiple values for tied weights.
        # This behavior is deprecated and will be an error in future versions"
        # not 100% sure what this is, so far seems to be harmless. TODO investigate
        self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying

        # init all weights
        self.apply(self._init_weights)
        # apply special scaled init to the residual projections, per GPT-2 paper
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))

        # report number of parameters
        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))

    def get_num_params(self, non_embedding=True):
        """
        Return the number of parameters in the model.
        For non-embedding count (default), the position embeddings get subtracted.
        The token embeddings would too, except due to the parameter sharing these
        params are actually used as weights in the final layer, so we include them.
        """
        n_params = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n_params -= self.transformer.wpe.weight.numel()
        return n_params


    def get_input_embeddings(self):
        return self.wte

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

    def forward(self, idx, targets=None, attn_mask= None,  output_hidden_states: Optional[bool] = None, **kwargs):
        device = idx.device
        b, t = idx.size()
        
        # attn_mask = _prepare_mask_(idx, b, eval)
        # print("attention mask: ", attn_mask)
        
        assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
        pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)

        # forward the GPT model itself
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
        x = self.transformer.drop(tok_emb + pos_emb)
        for block in self.transformer.h:
            x = block(x, attn_mask=attn_mask)
        x = self.transformer.ln_f(x)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.lm_head(x)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-100)
        else:
            # inference-time mini-optimization: only forward the lm_head on the very last position
            logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
            loss = None

        # return {"logits": logits, "loss": loss}
        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            hidden_states=x if output_hidden_states else None,
            attentions= None,
        )

    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
        # Default model inputs
        model_inputs = {"idx": input_ids}

        # Add attention mask if provided
        if attention_mask is not None:
            model_inputs["attn_mask"] = attention_mask
            
        return model_inputs
        

    # @torch.no_grad()
    # def stream(self, idx, max_new_tokens, temperature=1.0, top_k=None,gen_mode="greedy"):
    #     """
    #     Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
    #     the sequence max_new_tokens times, feeding the predictions back into the model each time.
    #     Most likely you'll want to make sure to be in model.eval() mode of operation for this.
    #     """
    #     for _ in range(max_new_tokens):
    #         # if the sequence context is growing too long we must crop it at block_size
    #         idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
    #         # forward the model to get the logits for the index in the sequence
    #         logits, _ = self(idx_cond, eval=True)
    #         # pluck the logits at the final step and scale by desired temperature
    #         logits = logits[:, -1, :] / temperature
    #         # optionally crop the logits to only the top k options
    #         if top_k is not None:
    #             v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
    #             logits[logits < v[:, [-1]]] = -float('Inf')
    #         # apply softmax to convert logits to (normalized) probabilities
    #         probs = F.softmax(logits, dim=-1)
    #         # sample from the distribution
    #         if gen_mode == 'greedy':
    #             idx_next = torch.argmax(probs, dim=-1).unsqueeze(0)
                
    #         else:
    #             idx_next = torch.multinomial(probs, num_samples=1)
    #         # print(idx_next.shape, idx.shape)
    #         idx = torch.cat((idx, idx_next), dim=1)
    #         # append sampled index to the running sequence and continue
    #         yield idx_next
            

    def crop_block_size(self, block_size):
        # model surgery to decrease the block size if necessary
        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
        # but want to use a smaller block size for some smaller, simpler model
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
        for block in self.transformer.h:
            if hasattr(block.attn, 'bias'):
                block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
                

AutoConfig.register("nanogpt-j", GPTJXConfig)
AutoModel.register(GPTJXConfig,GPTJXForCausalLM)
AutoModelForCausalLM.register(GPTJXConfig, GPTJXForCausalLM)               
                
        
# if __name__ == '__main__':
#     from transformers import AutoTokenizer

#     tokenizer = AutoTokenizer.from_pretrained("BeardedMonster/SabiYarn")
#     input_ids = tokenizer("Awọn eeyan Cairo, ni Egypt ti bẹrẹ si n to lawọn ileesẹ to n ṣe burẹdi bayii.", return_tensors="pt")["input_ids"] 
#     targets = input_ids
    
#     # config  = GPTJConfig()
#     # config.save_pretrained("gptj-config")
#     # new_config = GPTJ.from_pretrained("gptj-config")
#     # model = GPTJ(config)
#     # state_dict = torch.load('model.pt', map_location="cpu")
#     # model.load_state_dict(state_dict)
#     model = GPTJXForCausalLM.from_pretrained("/pretrainedmodel")
#     # model.save_pretrained("/pretrainedmodel")
#     # outputs = model(input_ids, targets)
#     # print(outputs)
#     output = model.generate(input_ids, max_new_tokens=50)
#     print(tokenizer.decode(output[0]))
    # print(new_config)