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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
def __init__(self, ndim, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, x):
return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
class MultiHeadAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.n_head = config.n_head
self.head_dim = config.n_embd // config.n_head
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
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):
B, T, C = x.size() # batch, sequence length, embedding dim
# calculate query, key, values
q, k, v = self.c_attn(x).split(self.config.n_embd, dim=2)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
# causal self-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
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.resid_dropout(self.c_proj(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 Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = MultiHeadAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
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 ModelConfig:
def __init__(self, vocab_size=50257, block_size=1024, n_layer=24, n_head=16,
n_embd=1024, dropout=0.1, bias=True):
self.vocab_size = vocab_size
self.block_size = block_size
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.dropout = dropout
self.bias = bias
def count_parameters(model):
"""Count number of trainable parameters in the model"""
total = sum(p.numel() for p in model.parameters() if p.requires_grad)
# Calculate parameters for each component
embedding_params = model.transformer.wte.weight.numel() + model.transformer.wpe.weight.numel()
attention_params = 0
mlp_params = 0
layer_norm_params = 0
for block in model.transformer.h:
# Attention parameters
attention_params += (
block.attn.c_attn.weight.numel() +
(block.attn.c_attn.bias.numel() if block.attn.c_attn.bias is not None else 0) +
block.attn.c_proj.weight.numel() +
(block.attn.c_proj.bias.numel() if block.attn.c_proj.bias is not None else 0)
)
# MLP parameters
mlp_params += (
block.mlp.c_fc.weight.numel() +
(block.mlp.c_fc.bias.numel() if block.mlp.c_fc.bias is not None else 0) +
block.mlp.c_proj.weight.numel() +
(block.mlp.c_proj.bias.numel() if block.mlp.c_proj.bias is not None else 0)
)
# Layer norm parameters
layer_norm_params += (
block.ln_1.weight.numel() +
(block.ln_1.bias.numel() if block.ln_1.bias is not None else 0) +
block.ln_2.weight.numel() +
(block.ln_2.bias.numel() if block.ln_2.bias is not None else 0)
)
# Final layer norm
layer_norm_params += (
model.transformer.ln_f.weight.numel() +
(model.transformer.ln_f.bias.numel() if model.transformer.ln_f.bias is not None else 0)
)
# Print detailed breakdown
print(f"\nParameter Count Breakdown:")
print(f"Embeddings: {embedding_params:,} parameters")
print(f"Attention Layers: {attention_params:,} parameters")
print(f"MLP Layers: {mlp_params:,} parameters")
print(f"Layer Normalization: {layer_norm_params:,} parameters")
print(f"Total: {total:,} parameters")
return total
class SmallLanguageModel(nn.Module):
def __init__(self, config):
super().__init__()
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([Block(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)
self.transformer.wte.weight = self.lm_head.weight
# Initialize weights
self.apply(self._init_weights)
print("\nModel Configuration:")
print(f"Layers: {config.n_layer}")
print(f"Heads: {config.n_head}")
print(f"Embedding Dimension: {config.n_embd}")
print(f"Context Window: {config.block_size}")
count_parameters(self)
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, input_ids, targets=None):
device = input_ids.device
b, t = input_ids.size()
pos = torch.arange(0, t, dtype=torch.long, device=device)
# forward the model
tok_emb = self.transformer.wte(input_ids)
pos_emb = self.transformer.wpe(pos)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
if targets is not None:
# Reshape logits and targets for loss calculation
logits = logits.reshape(-1, logits.size(-1))
targets = targets.reshape(-1)
loss = F.cross_entropy(logits, targets)
return logits, loss
return logits
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