mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-08-10 17:51:43 +00:00
394 lines
14 KiB
Python
394 lines
14 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 os
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import urllib.request
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from pathlib import Path
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import torch
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import torch.nn as nn
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# 0.6B model
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QWEN_CONFIG_06_B = {
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"vocab_size": 151_936, # Vocabulary size
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"context_length": 40_960, # Context length that was used to train the model
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"emb_dim": 1024, # Embedding dimension
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"n_heads": 16, # Number of attention heads
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"n_layers": 28, # Number of layers
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"hidden_dim": 3072, # Size of the intermediate dimension in FeedForward
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"head_dim": 128, # Size of the heads in GQA
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"qk_norm": True, # Whether to normalize queries and values in GQA
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"n_kv_groups": 8, # Key-Value groups for grouped-query attention
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"rope_base": 1_000_000.0, # The base in RoPE's "theta"
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"dtype": torch.bfloat16, # Lower-precision dtype to reduce memory usage
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}
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class Qwen3Model(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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# Main model parameters
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self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"], dtype=cfg["dtype"])
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self.trf_blocks = nn.ModuleList( # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`
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[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
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)
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self.final_norm = RMSNorm(cfg["emb_dim"])
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self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False, dtype=cfg["dtype"])
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# Reusuable utilities
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if cfg["head_dim"] is None:
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head_dim = cfg["emb_dim"] // cfg["n_heads"]
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else:
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head_dim = cfg["head_dim"]
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cos, sin = compute_rope_params(
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head_dim=head_dim,
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theta_base=cfg["rope_base"],
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context_length=cfg["context_length"]
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)
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self.register_buffer("cos", cos, persistent=False)
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self.register_buffer("sin", sin, persistent=False)
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self.cfg = cfg
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def forward(self, in_idx):
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# Forward pass
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tok_embeds = self.tok_emb(in_idx)
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x = tok_embeds
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num_tokens = x.shape[1]
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mask = torch.triu(torch.ones(num_tokens, num_tokens, device=x.device, dtype=torch.bool), diagonal=1)
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for block in self.trf_blocks:
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x = block(x, mask, self.cos, self.sin)
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x = self.final_norm(x)
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logits = self.out_head(x.to(self.cfg["dtype"]))
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return logits
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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 = GroupedQueryAttention(
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d_in=cfg["emb_dim"],
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num_heads=cfg["n_heads"],
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head_dim=cfg["head_dim"],
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num_kv_groups=cfg["n_kv_groups"],
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qk_norm=cfg["qk_norm"],
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dtype=cfg["dtype"]
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)
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self.ff = FeedForward(cfg)
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self.norm1 = RMSNorm(cfg["emb_dim"], eps=1e-6)
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self.norm2 = RMSNorm(cfg["emb_dim"], eps=1e-6)
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def forward(self, x, mask, cos, sin):
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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, mask, cos, sin) # Shape [batch_size, num_tokens, emb_size]
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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 = x + shortcut # Add the original input back
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return x
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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.fc1 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
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self.fc2 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
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self.fc3 = nn.Linear(cfg["hidden_dim"], cfg["emb_dim"], dtype=cfg["dtype"], bias=False)
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def forward(self, x):
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x_fc1 = self.fc1(x)
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x_fc2 = self.fc2(x)
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x = nn.functional.silu(x_fc1) * x_fc2
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return self.fc3(x)
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class GroupedQueryAttention(nn.Module):
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def __init__(
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self, d_in, num_heads, num_kv_groups, head_dim=None, qk_norm=False, dtype=None
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):
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super().__init__()
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assert num_heads % num_kv_groups == 0, "num_heads must be divisible by num_kv_groups"
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self.num_heads = num_heads
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self.num_kv_groups = num_kv_groups
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self.group_size = num_heads // num_kv_groups
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if head_dim is None:
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assert d_in % num_heads == 0, "`d_in` must be divisible by `num_heads` if `head_dim` is not set"
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head_dim = d_in // num_heads
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self.head_dim = head_dim
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self.d_out = num_heads * head_dim
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self.W_query = nn.Linear(d_in, self.d_out, bias=False, dtype=dtype)
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self.W_key = nn.Linear(d_in, num_kv_groups * head_dim, bias=False, dtype=dtype)
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self.W_value = nn.Linear(d_in, num_kv_groups * head_dim, bias=False, dtype=dtype)
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self.out_proj = nn.Linear(self.d_out, d_in, bias=False, dtype=dtype)
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if qk_norm:
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self.q_norm = RMSNorm(head_dim, eps=1e-6)
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self.k_norm = RMSNorm(head_dim, eps=1e-6)
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else:
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self.q_norm = self.k_norm = None
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def forward(self, x, mask, cos, sin):
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b, num_tokens, _ = x.shape
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# Apply projections
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queries = self.W_query(x) # (b, num_tokens, num_heads * head_dim)
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keys = self.W_key(x) # (b, num_tokens, num_kv_groups * head_dim)
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values = self.W_value(x) # (b, num_tokens, num_kv_groups * head_dim)
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# Reshape
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queries = queries.view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2)
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keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim).transpose(1, 2)
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values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim).transpose(1, 2)
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# Optional normalization
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if self.q_norm:
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queries = self.q_norm(queries)
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if self.k_norm:
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keys = self.k_norm(keys)
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# Apply RoPE
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queries = apply_rope(queries, cos, sin)
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keys = apply_rope(keys, cos, sin)
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# Expand K and V to match number of heads
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keys = keys.repeat_interleave(self.group_size, dim=1)
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values = values.repeat_interleave(self.group_size, dim=1)
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# Attention
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attn_scores = queries @ keys.transpose(2, 3)
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attn_scores = attn_scores.masked_fill(mask, -torch.inf)
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attn_weights = torch.softmax(attn_scores / self.head_dim**0.5, dim=-1)
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context = (attn_weights @ values).transpose(1, 2).reshape(b, num_tokens, self.d_out)
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return self.out_proj(context)
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def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, dtype=torch.float32):
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assert head_dim % 2 == 0, "Embedding dimension must be even"
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# Compute the inverse frequencies
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inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))
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# Generate position indices
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positions = torch.arange(context_length, dtype=dtype)
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# Compute the angles
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angles = positions[:, None] * inv_freq[None, :] # Shape: (context_length, head_dim // 2)
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# Expand angles to match the head_dim
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angles = torch.cat([angles, angles], dim=1) # Shape: (context_length, head_dim)
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# Precompute sine and cosine
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cos = torch.cos(angles)
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sin = torch.sin(angles)
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return cos, sin
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def apply_rope(x, cos, sin):
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# x: (batch_size, num_heads, seq_len, head_dim)
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batch_size, num_heads, seq_len, head_dim = x.shape
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assert head_dim % 2 == 0, "Head dimension must be even"
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# Split x into first half and second half
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x1 = x[..., : head_dim // 2] # First half
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x2 = x[..., head_dim // 2:] # Second half
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# Adjust sin and cos shapes
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cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0) # Shape: (1, 1, seq_len, head_dim)
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sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)
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# Apply the rotary transformation
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rotated = torch.cat((-x2, x1), dim=-1)
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x_rotated = (x * cos) + (rotated * sin)
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# It's ok to use lower-precision after applying cos and sin rotation
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return x_rotated.to(dtype=x.dtype)
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class RMSNorm(nn.Module):
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def __init__(self, emb_dim, eps=1e-6, bias=False, qwen3_compatible=True):
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super().__init__()
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self.eps = eps
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self.qwen3_compatible = qwen3_compatible
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self.scale = nn.Parameter(torch.ones(emb_dim))
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self.shift = nn.Parameter(torch.zeros(emb_dim)) if bias else None
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def forward(self, x):
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input_dtype = x.dtype
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if self.qwen3_compatible:
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x = x.to(torch.float32)
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variance = x.pow(2).mean(dim=-1, keepdim=True)
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norm_x = x * torch.rsqrt(variance + self.eps)
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norm_x = norm_x * self.scale
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if self.shift is not None:
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norm_x = norm_x + self.shift
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return norm_x.to(input_dtype)
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def load_weights_into_qwen(model, param_config, params):
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def assign(left, right, tensor_name="unknown"):
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if left.shape != right.shape:
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raise ValueError(f"Shape mismatch in tensor '{tensor_name}'. Left: {left.shape}, Right: {right.shape}")
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return torch.nn.Parameter(right.clone().detach() if isinstance(right, torch.Tensor) else torch.tensor(right))
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model.tok_emb.weight = assign(model.tok_emb.weight, params["model.embed_tokens.weight"], "model.embed_tokens.weight")
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for l in range(param_config["n_layers"]):
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block = model.trf_blocks[l]
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att = block.att
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# Q, K, V projections
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att.W_query.weight = assign(
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att.W_query.weight,
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params[f"model.layers.{l}.self_attn.q_proj.weight"],
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f"model.layers.{l}.self_attn.q_proj.weight"
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)
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att.W_key.weight = assign(
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att.W_key.weight,
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params[f"model.layers.{l}.self_attn.k_proj.weight"],
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f"model.layers.{l}.self_attn.k_proj.weight"
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)
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att.W_value.weight = assign(
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att.W_value.weight,
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params[f"model.layers.{l}.self_attn.v_proj.weight"],
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f"model.layers.{l}.self_attn.v_proj.weight"
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)
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# Output projection
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att.out_proj.weight = assign(
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att.out_proj.weight,
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params[f"model.layers.{l}.self_attn.o_proj.weight"],
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f"model.layers.{l}.self_attn.o_proj.weight"
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)
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# QK norms
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if hasattr(att, "q_norm") and att.q_norm is not None:
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att.q_norm.scale = assign(
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att.q_norm.scale,
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params[f"model.layers.{l}.self_attn.q_norm.weight"],
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f"model.layers.{l}.self_attn.q_norm.weight"
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)
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if hasattr(att, "k_norm") and att.k_norm is not None:
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att.k_norm.scale = assign(
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att.k_norm.scale,
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params[f"model.layers.{l}.self_attn.k_norm.weight"],
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f"model.layers.{l}.self_attn.k_norm.weight"
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)
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# Attention layernorm
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block.norm1.scale = assign(
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block.norm1.scale,
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params[f"model.layers.{l}.input_layernorm.weight"],
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f"model.layers.{l}.input_layernorm.weight"
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)
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# Feedforward weights
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block.ff.fc1.weight = assign(
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block.ff.fc1.weight,
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params[f"model.layers.{l}.mlp.gate_proj.weight"],
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f"model.layers.{l}.mlp.gate_proj.weight"
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)
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block.ff.fc2.weight = assign(
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block.ff.fc2.weight,
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params[f"model.layers.{l}.mlp.up_proj.weight"],
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f"model.layers.{l}.mlp.up_proj.weight"
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)
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block.ff.fc3.weight = assign(
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block.ff.fc3.weight,
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params[f"model.layers.{l}.mlp.down_proj.weight"],
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f"model.layers.{l}.mlp.down_proj.weight"
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)
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block.norm2.scale = assign(
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block.norm2.scale,
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params[f"model.layers.{l}.post_attention_layernorm.weight"],
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f"model.layers.{l}.post_attention_layernorm.weight"
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)
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# Final normalization and output head
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model.final_norm.scale = assign(model.final_norm.scale, params["model.norm.weight"], "model.norm.weight")
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# Model uses weight tying, hence we reuse the embedding layer weights here
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model.out_head.weight = assign(model.out_head.weight, params["model.embed_tokens.weight"], "model.embed_tokens.weight")
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class Qwen3Tokenizer():
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def __init__(self, tokenizer_file_path="tokenizer.json",
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repo_id=None, add_generation_prompt=False, add_thinking=False):
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from tokenizers import Tokenizer
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self.tokenizer_file_path = tokenizer_file_path
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if add_generation_prompt != add_thinking:
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raise ValueError(
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"Only add_generation_prompt==add_thinking settings are currently supported"
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)
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self.add_generation_prompt = add_generation_prompt
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self.add_thinking = add_thinking
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tokenizer_file_path_obj = Path(tokenizer_file_path)
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if not tokenizer_file_path_obj.is_file() and repo_id is not None:
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_ = download_from_huggingface(
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repo_id=repo_id,
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filename=str(tokenizer_file_path_obj.name),
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local_dir=str(tokenizer_file_path_obj.parent.name)
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)
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self.tokenizer = Tokenizer.from_file(tokenizer_file_path)
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def encode(self, prompt):
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messages = [
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{"role": "user", "content": prompt}
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]
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formatted_prompt = self.format_qwen_chat(
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messages,
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add_generation_prompt=self.add_generation_prompt,
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add_thinking=self.add_thinking
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)
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return self.tokenizer.encode(formatted_prompt).ids
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def decode(self, token_ids):
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return self.tokenizer.decode(token_ids, skip_special_tokens=False)
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@staticmethod
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def format_qwen_chat(messages, add_generation_prompt=False, add_thinking=False):
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prompt = ""
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for msg in messages:
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prompt += f"<|im_start|>{msg['role']}\n{msg['content']}<|im_end|>\n"
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if add_generation_prompt:
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prompt += "<|im_start|>assistant"
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if not add_thinking:
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prompt += "<|think>\n\n<|/think>\n\n"
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else:
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prompt += "\n"
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return prompt
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def download_from_huggingface(repo_id, filename, local_dir, revision="main"):
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base_url = "https://huggingface.co"
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url = f"{base_url}/{repo_id}/resolve/{revision}/{filename}"
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Path(local_dir).mkdir(parents=True, exist_ok=True)
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dest_path = os.path.join(local_dir, filename)
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print(f"Downloading {url} to {dest_path}...")
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urllib.request.urlretrieve(url, dest_path)
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return dest_path
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