import torch import torch.nn as nn from torch.nn import functional as F import json import logging block_size = 128 vocab_size = 500 n_embed = 384 dropout = 0.2 n_head = 6 n_layer = 6 kv_heads = 3 max_position_embeddings = 128 class Head(nn.Module): def __init__(self, head_size=16): super().__init__() self.query = nn.Linear(n_embed, head_size, bias=False) self.key = nn.Linear(n_embed, head_size, bias=False) self.value = nn.Linear(n_embed, head_size, bias=False) self.register_buffer('tril',torch.tril(torch.ones(block_size,block_size))) self.dropout = nn.Dropout(dropout) def forward(self,x): B,T,C = x.shape q = self.query(x) k = self.key(x) wei = (q @ k.transpose(-2,-1)) * (k.shape[-1]**(-0.5)) wei = wei.masked_fill(self.tril[:T,:T]==0, float('-inf')) wei = F.softmax(wei, dim=-1) wei = self.dropout(wei) v = self.value(x) out = wei @ v ## (B,T,HS) return out # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class MultiHeadAttention(nn.Module): def __init__(self,num_heads, head_dim) : super().__init__() assert num_heads%kv_heads == 0 self.n_embed = n_embed self.num_attn_heads = num_heads self.head_dim = head_dim self.kv_heads = kv_heads # self.kv_out_proj = head_dim * self.kv_heads #Doubt self.num_kv_groups = self.num_attn_heads // self.kv_heads self.heads = nn.ModuleList(Head(head_size=head_dim) for _ in range(num_heads)) ##Only self attention #For num_attn_heads number of heads self.Wq = nn.Linear(self.n_embed, self.num_attn_heads*self.head_dim) #For kv_heads number of heads self.Wk = nn.Linear(self.n_embed, self.kv_heads * self.head_dim) self.Wv = nn.Linear(self.n_embed, self.kv_heads * self.head_dim) self.o_proj = nn.Linear(self.head_dim * self.num_attn_heads, self.n_embed) self.dropout = nn.Dropout(dropout) # self.attention_mask = torch.zeros((bsz, self.num_attn_heads, qlen, qlen)) # self.attention_mask[:, :, :, qlen:] = float('-inf') # Mask out positions beyond the key sequence length def forward(self, x, attn_mask= None): """ Parameters: x (bsz, qlen, embed) : input """ # out = torch.cat([h(x) for h in self.heads], dim=-1) # attn_output = self.dropout(self.o_proj(out)) # ################ Experiment bsz, qlen, embed = x.size() # print("input size", x.size()) q = self.Wq(x) ##(B,T,head_dim * num_heads) k = self.Wk(x) ##(B,T,head_dim * kv_heads) v = self.Wv(x) ##(B,T,head_dim * kv_heads) q = q.view(bsz, qlen, self.num_attn_heads, self.head_dim).transpose(2,1) ##(B,T,head_dim * num_heads) k = k.view(bsz, qlen, self.kv_heads, self.head_dim).transpose(2,1) ##(B,T,head_dim * kv_heads) v = v.view(bsz, qlen, self.kv_heads, self.head_dim).transpose(2,1) ##(B,T,head_dim * kv_heads) # print("k-shape before ",k.shape) k = repeat_kv(k, self.num_kv_groups) ##(B, n_kvheads * nrep, qlen, head_dim) v = repeat_kv(v, self.num_kv_groups) attn_scores = q @ k.transpose(-1,-2)/torch.sqrt(torch.tensor(self.n_embed)) ##(B, T, block_size) ################ # print("Q-shape ", q.shape) # print("k-shape ",k.shape) # print(k.shape[-2]) # print(attn_scores.shape) if attn_mask is not None: # causal_mask = attn_mask[:, :, :, : k.shape[-2]] # attn_scores = attn_scores + causal_mask attn_scores = attn_scores.masked_fill( attn_mask[None, None, :qlen, :qlen]==0 , float("-inf") ) attn_scores = F.softmax(attn_scores, dim=-1) attn_scores = F.dropout(attn_scores) ##Why this dropout is required?? attn_output = torch.matmul(attn_scores, v) ##(B, n_heads, qlen, hidden_size) attn_output = attn_output.transpose(1,2).contiguous() attn_output = attn_output.view(bsz, qlen, self.n_embed) attn_output = self.o_proj(attn_output) return attn_output class FeedForward(nn.Module): def __init__(self,n_embed) -> None: super().__init__() self.net = nn.Sequential( nn.Linear(n_embed,4* n_embed), nn.ReLU(), nn.Linear(4 * n_embed, n_embed), nn.Dropout(dropout), ) def forward(self, x): x = self.net(x) return x class decoder_block(nn.Module): def __init__(self, n_embed, n_heads, attn_mask=None): super().__init__() # Assume 0 for allowed positions and -inf for masked positions self.sa = MultiHeadAttention(n_heads,n_embed//n_head) self.ln1 = nn.LayerNorm(n_embed) self.ln2 = nn.LayerNorm(n_embed) self.ffwd = FeedForward(n_embed) # self.causal_mask = torch.tril(torch.ones(block_size,block_size)) self.register_buffer('causal_mask',torch.tril(torch.ones(block_size,block_size))) def forward(self, x): x = x + self.sa(self.ln1(x), attn_mask = self.causal_mask) x = x + self.ffwd(self.ln2(x)) return x class my_gpt(nn.Module): def __init__(self, device='cpu', block_size = 128): super().__init__() self.device = device self.block_size = block_size ##context window size self.token_embed = nn.Embedding(vocab_size, n_embed) self.pos_embed = nn.Embedding(max_position_embeddings, n_embed) self.lm_head = nn.Linear(n_embed, vocab_size) self.sa_head = Head(vocab_size) self.d_blocks = nn.Sequential(*[decoder_block(n_embed=n_embed,n_heads=n_head) for _ in range(n_layer)]) self.ln_f = nn.LayerNorm(n_embed) # final layer norm self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Linear): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) 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, x, targets = None): """ Args: x: int(B,T) Token ids targets : Returns: logits """ # print("idx ", idx) B, T = x.size() ## tok_emd = self.token_embed(x) ##(B,T,C) position_ids = torch.arange(T, device = self.device ) pos_emd = self.pos_embed(position_ids) x = tok_emd + pos_emd # print("x1 ", x.shape) x = self.d_blocks(x) # x = self.ln_f(x) # (B,T,C) logits = self.lm_head(x) ##(B,T,vocab_size) if targets is None: loss = None else: B, T, C = logits.shape # print("logits ", logits.shape) logits = logits.view(B*T,C) targets = targets.view(B*T) loss = F.cross_entropy(logits, targets) # print("Logits", logits.shape) return logits, loss def generate(self, context : torch.tensor, max_new_tokens: int = 46, use_cache = False): """ Generates the next "max_new_tokens" number of tokens. Args: context (B,T): max_new_tokens (int): Returns: [token] : List of generated tokens. """ # print("Context:" , context) for _ in range(max_new_tokens): ##Take only last allowed number of tokens idx_tokens = context[:, -self.block_size:] # print(f"idx tokens {idx_tokens.shape}") ##generate the next token logits, loss = self(idx_tokens) ##Take only last allowed number of tokens logits = logits[:,-1,:] ##(B,vocab_size) # print("logits:" , logits.shape) probs = F.softmax(logits, dim= -1) idx_next = torch.multinomial(probs,num_samples=1) ##(B,1) context = torch.concatenate([context, idx_next], dim=1) return context def save_pretrained(self, path): torch.save(self.state_dict(),path) print("Saved pretrained Successfully") @classmethod def load_pretrained(cls, path): print("Loading pretrained model...") model = cls() model.load_state_dict(torch.load(path)) return model