hf_modeling.py

from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
import math
import inspect
import os
from hellaswag import render_example, iterate_examples
from tqdm import tqdm
from hf_configuration import ExGPTConfig
from transformers import PreTrainedModel

# ==================================================

class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projection for all heads
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1 # a flag
        # regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        # not really a 'bias', more of a  mask
        self.register_buffer('bias', torch.tril(torch.ones(config.block_size, config.block_size))
                                                .view(1, 1, config.block_size, config.block_size)) # Batch, head, the table x2 รึ

    def forward(self, x):
        B, T, C = x.size() # batch, seq len, embed dim
        qkv = self.c_attn(x) # project first, reshape later for each heads
        q, k, v = qkv.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)

        # begin the fk huge quadratic table
        # 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)
        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)

        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
        # output projection
        out = self.c_proj(y)
        return out

class MLP(nn.Module):
    "change it to SwiGLU"
    def __init__(self, config):
        super().__init__()
        self.gate   = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.c_fc   = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.silu   = nn.SiLU()
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1 # a flag

    def forward(self, x):
        # x = self.c_fc(x)
        # x = self.gelu(x)
        # x = self.c_proj(x)
        x = self.c_proj(self.silu(self.c_fc(x) * self.gate(x)))
        return x

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = nn.RMSNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.RMSNorm(config.n_embd)
        self.mlp = MLP(config)
    
    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

class GPT(PreTrainedModel):
    
    def __init__(self, config):
        super().__init__(config)
        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), # Learned positional embedding
            h = nn.ModuleList(Block(config) for _ in range(config.n_layer)),
            ln_f = nn.RMSNorm(config.n_embd),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        # Weight sharing scheme
        self.transformer.wte.weight = self.lm_head.weight # GPT2/transformers is all you need's style
        # Worse trainging loss though. From my observation

        # init params
        # Apply fn recursively to every submodule (as returned by .children()) as well as self.
        self.apply(self._init_weights)
    
    def _init_weights(self, module): # iterate over each module เลยสินะ
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, 'NANOGPT_SCALE_INIT'): # if there is the flag
                std *= (2 * self.config.n_layer) ** -0.5
            torch.nn.init.normal_(module.weight, mean=0.0, std=std) # typicall, std is 1/sqrt(feature)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
    
    def forward(self, idx, target=None):
        # idx is of shape (B, T)
        B, T = idx.size()
        assert T <= self.config.block_size, f"Cannot forward a sequence of length {T}, blocksize is only {self.config.block_size}"
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
        tok_emb = self.transformer.wte(idx)

        # with torch.autocast(device_type=device, enabled=False):
        pos_emb = self.transformer.wpe(pos)
        x = tok_emb + pos_emb
        # forward the block of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        loss = None
        logits = self.lm_head(x) # (B, T, vocab_size)
        if target is not None:
            loss = F.cross_entropy(logits.view(-1,logits.size(-1)), target.view(-1)) # view -1 to flatten B,T dim to B*T for target, and logits.view(-1,logits.size(-1)) to get logit into shape B*T, vocab
        return logits, loss
        # Typo แดกโลก
    
    @classmethod
    def from_pretrained(cls, model_type):
        """Loads pretrained GPT-2 model weights from huggingface"""
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
        }[model_type]
        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
        # create a from-scratch initialized minGPT model
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # this means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model
    
    def configure_optimizers(self, weight_decay, learning_rate, device):
        # start wit all of the candidate parameters (that require grad)
        param_dict = {pn: p for pn, p in self.named_parameters()}
        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorm don't.
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0}
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(f"num decayed parameter tensor: {len(decay_params)}, with {num_decay_params:,} paramters")
        print(f"num non-decayed parameter tensor: {len(nodecay_params)}, with {num_nodecay_params:,} paramters")
        # Create AdamW optimizer and use fused version if it is available
        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and 'cuda' in device
        print(f"using fused AdamW: {use_fused}")
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
        return optimizer

# ===============================================================================================
num_return_sequences = 5
max_length = 30

# =================================================================================================
import tiktoken
import numpy as np

def load_tokens(filename):
    npt = np.load(filename)
    ptt = torch.tensor(npt, dtype=torch.long)
    return ptt

class DataLoaderLite:
    def __init__(self, B, T, process_rank, num_processes, split):
        self.B = B
        self.T = T
        self.process_rank = process_rank
        self.num_processes = num_processes
        assert split in {'train', 'val'}

        # get the shard filename
        data_root = "edu_fineweb10B"
        shards = os.listdir(data_root)
        shards = [s for s in shards if split in s]
        shards = sorted(shards)
        shards = [os.path.join(data_root, s) for s in shards]
        self.shards = shards
        assert len(shards) > 0, f"no shards found in the split {split}"
        if master_process:
            print(f"found {len(shards)} shards for split {split}")

        # state
        # self.current_position = 0
        # We wanna stride out dall the processes
        # self.current_shard = 0
        # self.tokens = load_tokens(self.shards[self.current_shard])
        # self.current_position = self.B * self.T * self.process_rank
        self.reset() # reset take care of the trouble
    
    def reset(self):
        # state, init at shard zero
        self.current_shard = 0
        self.tokens = load_tokens(self.shards[self.current_shard])
        self.current_position = self.B * self.T * self.process_rank
    
    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position:self.current_position+B*T+1]
        x = (buf[:-1]).view(B, T) # input
        y = (buf[1:]).view(B, T) # target

        # advance the position in the tensor
        # self.current_position += B*T
        self.current_position += B * T * self.num_processes
        if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens): # When we run out of token in a chard, we advance to the next shard
            self.current_shard = (self.current_shard + 1) % len(self.shards)
            self.tokens = load_tokens(self.shards[self.current_shard])
            self.current_position = B * T * self.process_rank
        return x, y

# -----------------------------------------------------------------------------
# helper function for HellaSwag eval
# takes tokens, mask, and logits, returns the index of the completion with the lowest loss

def get_most_likely_row(tokens, mask, logits):
    # evaluate the autoregressive loss at all positions
    shift_logits = (logits[..., :-1, :]).contiguous()
    shift_tokens = (tokens[..., 1:]).contiguous()
    flat_shift_logits = shift_logits.view(-1, shift_logits.size(-1))
    flat_shift_tokens = shift_tokens.view(-1)
    shift_losses = F.cross_entropy(flat_shift_logits, flat_shift_tokens, reduction='none')
    shift_losses = shift_losses.view(tokens.size(0), -1)
    # now get the average loss just for the completion region (where mask == 1), in each row
    shift_mask = (mask[..., 1:]).contiguous() # we must shift mask, so we start at the last prompt token
    masked_shift_losses = shift_losses * shift_mask
    # sum and divide by the number of 1s in the mask
    sum_loss = masked_shift_losses.sum(dim=1)
    avg_loss = sum_loss / shift_mask.sum(dim=1)
    # now we have a loss for each of the 4 completions
    # the one with the lowest loss should be the most likely
    pred_norm = avg_loss.argmin().item()
    return pred_norm