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288 lines
11 KiB
Python
288 lines
11 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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import torch
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import torch.nn as nn
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#####################################
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# Chapter 3
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#####################################
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class MultiHeadAttention(nn.Module):
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def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False, max_seq_len=None, window_size=None):
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super().__init__()
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assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
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self.d_out = d_out
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self.num_heads = num_heads
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self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
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self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
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self.dropout = nn.Dropout(dropout)
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####################################################
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# NEW
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self.max_seq_len = max_seq_len or context_length
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self.window_size = window_size or self.max_seq_len
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self.register_buffer("cache_k", None, persistent=False)
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self.register_buffer("cache_v", None, persistent=False)
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####################################################
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def forward(self, x, use_cache=False):
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b, num_tokens, d_in = x.shape
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keys_new = self.W_key(x) # Shape: (b, num_tokens, d_out)
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values_new = self.W_value(x)
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queries = self.W_query(x)
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# We implicitly split the matrix by adding a `num_heads` dimension
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# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
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keys_new = keys_new.view(b, num_tokens, self.num_heads, self.head_dim)
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values_new = values_new.view(b, num_tokens, self.num_heads, self.head_dim)
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queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
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# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
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keys_new = keys_new.transpose(1, 2)
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values_new = values_new.transpose(1, 2)
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queries = queries.transpose(1, 2)
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####################################################
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# NEW
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if use_cache:
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if self.cache_k is None or self.cache_k.size(0) != b:
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self.cache_k = torch.zeros(b, self.num_heads,
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self.window_size, self.head_dim,
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device=x.device)
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self.cache_v = torch.zeros_like(self.cache_k)
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self.ptr_cur = 0 # pointer to next free slot
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# if incoming chunk would overflow discard oldest tokens
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if self.ptr_cur + num_tokens > self.window_size:
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overflow = self.ptr_cur + num_tokens - self.window_size
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# shift everything left by `overflow` (cheap view-copy)
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self.cache_k[:, :, :-overflow, :] = self.cache_k[:, :, overflow:, :].clone()
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self.cache_v[:, :, :-overflow, :] = self.cache_v[:, :, overflow:, :].clone()
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self.ptr_cur -= overflow # pointer after shift
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self.cache_k[:, :, self.ptr_cur:self.ptr_cur + num_tokens, :] = keys_new
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self.cache_v[:, :, self.ptr_cur:self.ptr_cur + num_tokens, :] = values_new
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self.ptr_cur += num_tokens
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keys = self.cache_k[:, :, :self.ptr_cur, :]
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values = self.cache_v[:, :, :self.ptr_cur, :]
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else:
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keys, values = keys_new, values_new
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self.ptr_cur = 0 # keep pointer sane if you interleave modes
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####################################################
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# Compute scaled dot-product attention (aka self-attention) with a causal mask
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attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
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####################################################
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# NEW
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K = attn_scores.size(-1)
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if num_tokens == K:
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# No cache → use the pre‑baked triangular mask slice
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causal_mask = torch.triu(torch.ones(num_tokens, K, device=x.device, dtype=torch.bool), diagonal=1)
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else:
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# Cached: need to offset the diagonal by (K − num_tokens)
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offset = K - num_tokens # number of tokens already in cache before this chunk
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row_idx = torch.arange(num_tokens, device=x.device).unsqueeze(1) # (num_tokens, 1)
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col_idx = torch.arange(K, device=x.device).unsqueeze(0) # (1, K)
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causal_mask = row_idx + offset < col_idx # True where j > i+offset
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####################################################
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# Use the mask to fill attention scores
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attn_scores.masked_fill_(causal_mask.unsqueeze(0).unsqueeze(0), -torch.inf)
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attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
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attn_weights = self.dropout(attn_weights)
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# Shape: (b, num_tokens, num_heads, head_dim)
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context_vec = (attn_weights @ values).transpose(1, 2)
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# Combine heads, where self.d_out = self.num_heads * self.head_dim
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context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
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context_vec = self.out_proj(context_vec) # optional projection
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return context_vec
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####################################################
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# NEW
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def reset_cache(self):
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self.cache_k, self.cache_v = None, None
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####################################################
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#####################################
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# Chapter 4
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#####################################
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class LayerNorm(nn.Module):
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def __init__(self, emb_dim):
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super().__init__()
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self.eps = 1e-5
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self.scale = nn.Parameter(torch.ones(emb_dim))
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self.shift = nn.Parameter(torch.zeros(emb_dim))
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def forward(self, x):
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mean = x.mean(dim=-1, keepdim=True)
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var = x.var(dim=-1, keepdim=True, unbiased=False)
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norm_x = (x - mean) / torch.sqrt(var + self.eps)
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return self.scale * norm_x + self.shift
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class GELU(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return 0.5 * x * (1 + torch.tanh(
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torch.sqrt(torch.tensor(2.0 / torch.pi)) *
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(x + 0.044715 * torch.pow(x, 3))
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))
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class FeedForward(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.layers = nn.Sequential(
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nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
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GELU(),
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nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
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)
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def forward(self, x):
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return self.layers(x)
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class TransformerBlock(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.att = MultiHeadAttention(
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d_in=cfg["emb_dim"],
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d_out=cfg["emb_dim"],
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context_length=cfg["context_length"],
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num_heads=cfg["n_heads"],
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dropout=cfg["drop_rate"],
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qkv_bias=cfg["qkv_bias"],
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window_size=cfg["kv_window_size"] if "kv_window_size" in cfg else cfg["context_length"] # NEW
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)
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self.ff = FeedForward(cfg)
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self.norm1 = LayerNorm(cfg["emb_dim"])
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self.norm2 = LayerNorm(cfg["emb_dim"])
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self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
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def forward(self, x, use_cache=False):
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# Shortcut connection for attention block
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shortcut = x
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x = self.norm1(x)
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# x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
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####################################################
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# NEW
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x = self.att(x, use_cache=use_cache)
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####################################################
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x = self.drop_shortcut(x)
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x = x + shortcut # Add the original input back
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# Shortcut connection for feed-forward block
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shortcut = x
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x = self.norm2(x)
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x = self.ff(x)
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x = self.drop_shortcut(x)
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x = x + shortcut # Add the original input back
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return x
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class GPTModel(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
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self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
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self.drop_emb = nn.Dropout(cfg["drop_rate"])
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# self.trf_blocks = nn.Sequential(
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# *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
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####################################################
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# NEW
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self.trf_blocks = nn.ModuleList(
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[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
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self.ptr_current_pos = 0
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####################################################
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self.final_norm = LayerNorm(cfg["emb_dim"])
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self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
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def forward(self, in_idx, use_cache=False):
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batch_size, seq_len = in_idx.shape
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tok_embeds = self.tok_emb(in_idx)
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# pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
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####################################################
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# NEW
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if use_cache:
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pos_ids = torch.arange(self.ptr_current_pos, self.ptr_current_pos + seq_len, device=in_idx.device, dtype=torch.long)
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self.ptr_current_pos += seq_len
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else:
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pos_ids = torch.arange(0, seq_len, device=in_idx.device, dtype=torch.long)
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pos_embeds = self.pos_emb(pos_ids).unsqueeze(0)
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####################################################
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x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
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x = self.drop_emb(x)
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# x = self.trf_blocks(x)
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####################################################
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# NEW
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for blk in self.trf_blocks:
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x = blk(x, use_cache=use_cache)
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####################################################
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x = self.final_norm(x)
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logits = self.out_head(x)
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return logits
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####################################################
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# NEW
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def reset_kv_cache(self):
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for blk in self.trf_blocks:
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blk.att.reset_cache()
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self.ptr_current_pos = 0
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####################################################
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def generate_text_simple(model, idx, max_new_tokens, context_size):
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# idx is (B, T) array of indices in the current context
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for _ in range(max_new_tokens):
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# Crop current context if it exceeds the supported context size
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# E.g., if LLM supports only 5 tokens, and the context size is 10
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# then only the last 5 tokens are used as context
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idx_cond = idx[:, -context_size:]
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# Get the predictions
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with torch.no_grad():
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logits = model(idx_cond)
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# Focus only on the last time step
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# (batch, n_token, vocab_size) becomes (batch, vocab_size)
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logits = logits[:, -1, :]
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# Get the idx of the vocab entry with the highest logits value
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idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
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# Append sampled index to the running sequence
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idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
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return idx
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