5. LLM Architecture

LLM Architecture

Lengo la awamu hii ya tano ni rahisi sana: Kuunda usanifu wa LLM kamili. Panga kila kitu pamoja, tumia tabaka zote na uunde kazi zote za kuzalisha maandiko au kubadilisha maandiko kuwa IDs na kinyume chake.

Usanifu huu utatumika kwa mafunzo na kutabiri maandiko baada ya kufundishwa.

Mfano wa usanifu wa LLM kutoka https://github.com/rasbt/LLMs-from-scratch/blob/main/ch04/01_main-chapter-code/ch04.ipynb:

Mwakilishi wa kiwango cha juu unaweza kuonekana katika:

https://camo.githubusercontent.com/6c8c392f72d5b9e86c94aeb9470beab435b888d24135926f1746eb88e0cc18fb/68747470733a2f2f73656261737469616e72617363686b612e636f6d2f696d616765732f4c4c4d732d66726f6d2d736372617463682d696d616765732f636830345f636f6d707265737365642f31332e776562703f31

1. Input (Tokenized Text): Mchakato huanza na maandiko yaliyotolewa token, ambayo yanabadilishwa kuwa uwakilishi wa nambari.

2. Token Embedding and Positional Embedding Layer: Maandiko yaliyotolewa token yanapita kupitia tabaka la token embedding na tabaka la positional embedding, ambayo yanakamata nafasi ya tokens katika mfuatano, muhimu kwa kuelewa mpangilio wa maneno.

3. Transformer Blocks: Mfano una blocks 12 za transformer, kila moja ikiwa na tabaka nyingi. Blocks hizi hurudia mfuatano ufuatao:

• Masked Multi-Head Attention: Inaruhusu mfano kuzingatia sehemu tofauti za maandiko ya ingizo kwa wakati mmoja.

• Layer Normalization: Hatua ya kawaida ili kuimarisha na kuboresha mafunzo.

• Feed Forward Layer: Inawajibika kwa kuchakata habari kutoka kwa tabaka la umakini na kufanya utabiri kuhusu token inayofuata.

• Dropout Layers: Tabaka hizi zinazuia overfitting kwa kuacha vitengo kwa bahati nasibu wakati wa mafunzo.

1. Final Output Layer: Mfano unatoa tensor ya 4x50,257-dimensional, ambapo 50,257 inawakilisha ukubwa wa msamiati. Kila safu katika tensor hii inahusiana na vector ambayo mfano hutumia kutabiri neno linalofuata katika mfuatano.

2. Goal: Lengo ni kuchukua embeddings hizi na kuzibadilisha tena kuwa maandiko. Kwa hakika, safu ya mwisho ya pato inatumika kuzalisha neno linalofuata, inawakilishwa kama "forward" katika mchoro huu.

Code representation

import torch
import torch.nn as nn
import tiktoken

class GELU(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))

class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)

def forward(self, x):
return self.layers(x)

class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by num_heads"

self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim

self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out)  # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))

def forward(self, x):
b, num_tokens, d_in = x.shape

keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)

# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)

# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)

# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head

# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]

# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)

attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)

# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)

# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection

return context_vec

class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))

def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift

class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])

def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x)  # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut  # Add the original input back

# Shortcut connection for feed forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut  # Add the original input back

return x


class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])

self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])

self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(
cfg["emb_dim"], cfg["vocab_size"], bias=False
)

def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits

GPT_CONFIG_124M = {
"vocab_size": 50257,    # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768,         # Embedding dimension
"n_heads": 12,          # Number of attention heads
"n_layers": 12,         # Number of layers
"drop_rate": 0.1,       # Dropout rate
"qkv_bias": False       # Query-Key-Value bias
}

torch.manual_seed(123)
model = GPTModel(GPT_CONFIG_124M)
out = model(batch)
print("Input batch:\n", batch)
print("\nOutput shape:", out.shape)
print(out)

Kazi ya Kuamsha ya GELU

# From https://github.com/rasbt/LLMs-from-scratch/tree/main/ch04
class GELU(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))

Madhumuni na Ufanisi

• GELU (Gaussian Error Linear Unit): Kazi ya kuamsha ambayo inaingiza kutokuwa na mstari ndani ya mfano.

• Kuamsha Kunyumbulika: Tofauti na ReLU, ambayo inafanya kuwa sifuri kwa pembejeo hasi, GELU inachora kwa laini pembejeo hadi matokeo, ikiruhusu thamani ndogo, zisizo sifuri kwa pembejeo hasi.

• Mwelekeo wa Kihesabu:

Lengo la matumizi ya kazi hii baada ya tabaka za mstari ndani ya tabaka la FeedForward ni kubadilisha data ya mstari kuwa isiyo ya mstari ili kuruhusu mfano kujifunza uhusiano tata, usio wa mstari.

Mtandao wa Neva wa FeedForward

Mifano imeongezwa kama maoni ili kuelewa vyema mifano ya matrix:

# From https://github.com/rasbt/LLMs-from-scratch/tree/main/ch04
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)

def forward(self, x):
# x shape: (batch_size, seq_len, emb_dim)

x = self.layers[0](x)# x shape: (batch_size, seq_len, 4 * emb_dim)
x = self.layers[1](x) # x shape remains: (batch_size, seq_len, 4 * emb_dim)
x = self.layers[2](x) # x shape: (batch_size, seq_len, emb_dim)
return x  # Output shape: (batch_size, seq_len, emb_dim)

Madhumuni na Ufanisi

• Mtandao wa Kila Nafasi wa FeedForward: Inatumia mtandao wa viwango viwili uliounganishwa kikamilifu kwa kila nafasi tofauti na kwa njia sawa.

• Maelezo ya Kiwango:

• Kiwango cha Kwanza cha Mstari: Kinapanua ukubwa kutoka emb_dim hadi 4 * emb_dim.

• Kazi ya GELU: Inatumia kutokuwa na mstari.

• Kiwango cha Pili cha Mstari: Kinapunguza ukubwa kurudi kwenye emb_dim.

Kama unavyoona, mtandao wa Feed Forward unatumia viwango 3. Kiwango cha kwanza ni kiwango cha mstari ambacho kitazidisha ukubwa kwa 4 kwa kutumia uzito wa mstari (vigezo vya kufundisha ndani ya mfano). Kisha, kazi ya GELU inatumika katika ukubwa wote ili kuleta mabadiliko yasiyo ya mstari ili kupata uwakilishi mzuri na hatimaye kiwango kingine cha mstari kinatumika kurudi kwenye ukubwa wa awali wa ukubwa.

Mekanismu ya Umakini wa Vichwa Vingi

Hii tayari imeelezwa katika sehemu ya awali.

Madhumuni na Ufanisi

• Umakini wa Kujitazama wa Vichwa Vingi: Inaruhusu mfano kuzingatia nafasi tofauti ndani ya mfuatano wa ingizo wakati wa kuandika token.

• Vipengele Muhimu:

• Maswali, Funguo, Thamani: Mipango ya mstari ya ingizo, inayotumika kuhesabu alama za umakini.

• Vichwa: Mekanismu nyingi za umakini zinazoendesha kwa sambamba (num_heads), kila moja ikiwa na ukubwa mdogo (head_dim).

• Alama za Umakini: Zinahesabiwa kama bidhaa ya dot ya maswali na funguo, zimepimwa na kufichwa.

• Kuficha: Mask ya sababu inatumika kuzuia mfano kuzingatia token za baadaye (muhimu kwa mifano ya autoregressive kama GPT).

• Uzito wa Umakini: Softmax ya alama za umakini zilizofichwa na kupimwa.

• Vector ya Muktadha: Jumla yenye uzito ya thamani, kulingana na uzito wa umakini.

• Mipango ya Matokeo: Kiwango cha mstari cha kuunganisha matokeo ya vichwa vyote.

Lengo la mtandao huu ni kupata uhusiano kati ya token katika muktadha sawa. Aidha, token zimegawanywa katika vichwa tofauti ili kuzuia overfitting ingawa uhusiano wa mwisho uliofanywa kwa kila kichwa unachanganywa mwishoni mwa mtandao huu.

Zaidi ya hayo, wakati wa mafunzo mask ya sababu inatumika ili token za baadaye zisihesabiwe wakati wa kutafuta uhusiano maalum kwa token na dropout pia inatumika ili kuzuia overfitting.

Kiwango Kurekebisha

# From https://github.com/rasbt/LLMs-from-scratch/tree/main/ch04
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5 # Prevent division by zero during normalization.
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))

def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift

Madhumuni na Ufanisi

• Layer Normalization: Mbinu inayotumika kurekebisha ingizo kati ya vipengele (embedding dimensions) kwa kila mfano binafsi katika kundi.

• Vipengele:

• eps: Kiwango kidogo (1e-5) kinachoongezwa kwenye variance ili kuzuia kugawanya kwa sifuri wakati wa normalization.

• scale na shift: Vigezo vinavyoweza kujifunza (nn.Parameter) vinavyomruhusu modeli kupima na kuhamasisha matokeo yaliyorekebishwa. Vimeanzishwa kuwa moja na sifuri, mtawalia.

• Mchakato wa Kurekebisha:

• Hesabu Mean (mean): Hesabu mean ya ingizo x kati ya dimension ya embedding (dim=-1), ikihifadhi dimension kwa ajili ya broadcasting (keepdim=True).

• Hesabu Variance (var): Hesabu variance ya x kati ya dimension ya embedding, pia ikihifadhi dimension. Kigezo unbiased=False kinahakikisha kuwa variance inahesabiwa kwa kutumia mhesabu wa biased (kugawanya kwa N badala ya N-1), ambayo ni sahihi wakati wa kurekebisha juu ya vipengele badala ya sampuli.

• Normalize (norm_x): Inapunguza mean kutoka x na kugawanya kwa mzizi wa variance pamoja na eps.

• Scale na Shift: Inatumia vigezo vinavyoweza kujifunza scale na shift kwa matokeo yaliyorekebishwa.

Lengo ni kuhakikisha mean ya 0 na variance ya 1 kati ya dimensions zote za token sawa. Lengo la hili ni kuimarisha mafunzo ya mitandao ya neva ya kina kwa kupunguza mabadiliko ya ndani ya covariate, ambayo inahusisha mabadiliko katika usambazaji wa uhamasishaji wa mtandao kutokana na kubadilishwa kwa vigezo wakati wa mafunzo.

Transformer Block

Mifano imeongezwa kama maoni ili kuelewa vyema sura za matrices:

# From https://github.com/rasbt/LLMs-from-scratch/tree/main/ch04

class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"]
)
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])

def forward(self, x):
# x shape: (batch_size, seq_len, emb_dim)

# Shortcut connection for attention block
shortcut = x  # shape: (batch_size, seq_len, emb_dim)
x = self.norm1(x)  # shape remains (batch_size, seq_len, emb_dim)
x = self.att(x)    # shape: (batch_size, seq_len, emb_dim)
x = self.drop_shortcut(x)  # shape remains (batch_size, seq_len, emb_dim)
x = x + shortcut   # shape: (batch_size, seq_len, emb_dim)

# Shortcut connection for feedforward block
shortcut = x       # shape: (batch_size, seq_len, emb_dim)
x = self.norm2(x)  # shape remains (batch_size, seq_len, emb_dim)
x = self.ff(x)     # shape: (batch_size, seq_len, emb_dim)
x = self.drop_shortcut(x)  # shape remains (batch_size, seq_len, emb_dim)
x = x + shortcut   # shape: (batch_size, seq_len, emb_dim)

return x  # Output shape: (batch_size, seq_len, emb_dim)

Madhumuni na Ufanisi

• Muundo wa Tabaka: Inachanganya umakini wa vichwa vingi, mtandao wa feedforward, urekebishaji wa tabaka, na muunganisho wa ziada.

• Urekebishaji wa Tabaka: Unatumika kabla ya tabaka za umakini na feedforward kwa mafunzo thabiti.

• Muunganisho wa Ziada (Mifupisho): Ongeza ingizo la tabaka kwa matokeo yake ili kuboresha mtiririko wa gradient na kuwezesha mafunzo ya mitandao yenye kina.

• Dropout: Unatumika baada ya tabaka za umakini na feedforward kwa ajili ya urekebishaji.

Ufanisi wa Hatua kwa Hatua

1. Njia ya Kwanza ya Ziada (Umakini wa Kibinafsi):

• Ingizo (shortcut): Hifadhi ingizo la awali kwa muunganisho wa ziada.

• Urekebishaji wa Tabaka (norm1): Rekebisha ingizo.

• Umakini wa Vichwa Vingi (att): Tumia umakini wa kibinafsi.

• Dropout (drop_shortcut): Tumia dropout kwa urekebishaji.

• Ongeza Ziada (x + shortcut): Changanya na ingizo la awali.

1. Njia ya Pili ya Ziada (FeedForward):

• Ingizo (shortcut): Hifadhi ingizo lililosasishwa kwa muunganisho wa ziada unaofuata.

• Urekebishaji wa Tabaka (norm2): Rekebisha ingizo.

• Mtandao wa FeedForward (ff): Tumia mabadiliko ya feedforward.

• Dropout (drop_shortcut): Tumia dropout.

• Ongeza Ziada (x + shortcut): Changanya na ingizo kutoka kwa njia ya kwanza ya ziada.

Block ya transformer inakusanya mitandao yote pamoja na kutumia urekebishaji na dropouts kuboresha utulivu wa mafunzo na matokeo. Kumbuka jinsi dropouts zinavyofanywa baada ya matumizi ya kila mtandao wakati urekebishaji unatumika kabla.

Zaidi ya hayo, inatumia mifupisho ambayo inajumuisha kuongeza matokeo ya mtandao na ingizo lake. Hii husaidia kuzuia tatizo la gradient inayopotea kwa kuhakikisha kuwa tabaka za mwanzo zinachangia "kiasi" sawa na zile za mwisho.

GPTModel

Umbo umeongezwa kama maelezo ili kuelewa vyema umbo la matrices:

# From https://github.com/rasbt/LLMs-from-scratch/tree/main/ch04
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
# shape: (vocab_size, emb_dim)

self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
# shape: (context_length, emb_dim)

self.drop_emb = nn.Dropout(cfg["drop_rate"])

self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
)
# Stack of TransformerBlocks

self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
# shape: (emb_dim, vocab_size)

def forward(self, in_idx):
# in_idx shape: (batch_size, seq_len)
batch_size, seq_len = in_idx.shape

# Token embeddings
tok_embeds = self.tok_emb(in_idx)
# shape: (batch_size, seq_len, emb_dim)

# Positional embeddings
pos_indices = torch.arange(seq_len, device=in_idx.device)
# shape: (seq_len,)
pos_embeds = self.pos_emb(pos_indices)
# shape: (seq_len, emb_dim)

# Add token and positional embeddings
x = tok_embeds + pos_embeds  # Broadcasting over batch dimension
# x shape: (batch_size, seq_len, emb_dim)

x = self.drop_emb(x)  # Dropout applied
# x shape remains: (batch_size, seq_len, emb_dim)

x = self.trf_blocks(x)  # Pass through Transformer blocks
# x shape remains: (batch_size, seq_len, emb_dim)

x = self.final_norm(x)  # Final LayerNorm
# x shape remains: (batch_size, seq_len, emb_dim)

logits = self.out_head(x)  # Project to vocabulary size
# logits shape: (batch_size, seq_len, vocab_size)

return logits  # Output shape: (batch_size, seq_len, vocab_size)

Madhumuni na Ufanisi

• Tabaka za Kuunganisha:

• Token Embeddings (tok_emb): Hubadilisha viashiria vya token kuwa embeddings. Kama ukumbusho, hizi ni uzito zinazotolewa kwa kila kipimo cha kila token katika msamiati.

• Positional Embeddings (pos_emb): Inaongeza taarifa za nafasi kwa embeddings ili kukamata mpangilio wa token. Kama ukumbusho, hizi ni uzito zinazotolewa kwa token kulingana na nafasi yake katika maandiko.

• Dropout (drop_emb): Inatumika kwa embeddings kwa ajili ya udhibiti.

• Transformer Blocks (trf_blocks): Mkusanyiko wa n_layers transformer blocks ili kushughulikia embeddings.

• Normalisasi ya Mwisho (final_norm): Normalisasi ya tabaka kabla ya tabaka la matokeo.

• Tabaka la Matokeo (out_head): Inatoa hali za siri za mwisho kwa ukubwa wa msamiati ili kutoa logits kwa ajili ya utabiri.

Lengo la darasa hili ni kutumia mitandao mingine yote iliyotajwa ili kutabiri token inayofuata katika mfuatano, ambayo ni muhimu kwa kazi kama vile uzalishaji wa maandiko.

Kumbuka jinsi itakavy tumia blocks za transformer nyingi kama ilivyoonyeshwa na kwamba kila block ya transformer inatumia mtandao mmoja wa multi-head attestation, mtandao mmoja wa feed forward na normalizations kadhaa. Hivyo, ikiwa blocks 12 za transformer zinatumika, ongeza hii kwa 12.

Zaidi ya hayo, tabaka la normalisasi linaongezwa kabla ya matokeo na tabaka la mwisho la lineari linawekwa mwishoni ili kupata matokeo yenye vipimo sahihi. Kumbuka jinsi kila vector ya mwisho ina ukubwa wa msamiati ulio tumika. Hii ni kwa sababu inajaribu kupata uwezekano kwa kila token inayowezekana ndani ya msamiati.

Idadi ya Paramita za kufundisha

Baada ya muundo wa GPT kufafanuliwa, inawezekana kugundua idadi ya paramita za kufundisha:

GPT_CONFIG_124M = {
"vocab_size": 50257,    # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768,         # Embedding dimension
"n_heads": 12,          # Number of attention heads
"n_layers": 12,         # Number of layers
"drop_rate": 0.1,       # Dropout rate
"qkv_bias": False       # Query-Key-Value bias
}

model = GPTModel(GPT_CONFIG_124M)
total_params = sum(p.numel() for p in model.parameters())
print(f"Total number of parameters: {total_params:,}")
# Total number of parameters: 163,009,536

Hatua kwa Hatua Hesabu

1. Tabaka za Kuunganisha: Token Embedding & Position Embedding

• Tabaka: nn.Embedding(vocab_size, emb_dim)

• Vigezo: vocab_size * emb_dim

token_embedding_params = 50257 * 768 = 38,597,376

• Tabaka: nn.Embedding(context_length, emb_dim)

• Vigezo: context_length * emb_dim

position_embedding_params = 1024 * 768 = 786,432

Jumla ya Vigezo vya Embedding

embedding_params = token_embedding_params + position_embedding_params
embedding_params = 38,597,376 + 786,432 = 39,383,808

2. Transformer Blocks

Kuna vizuizi 12 vya transformer, hivyo tutahesabu vigezo vya kizuizi kimoja kisha kuzidisha kwa 12.

Parameters per Transformer Block

a. Multi-Head Attention

• Components:

• Query Linear Layer (W_query): nn.Linear(emb_dim, emb_dim, bias=False)

• Key Linear Layer (W_key): nn.Linear(emb_dim, emb_dim, bias=False)

• Value Linear Layer (W_value): nn.Linear(emb_dim, emb_dim, bias=False)

• Output Projection (out_proj): nn.Linear(emb_dim, emb_dim)

• Calculations:

• Kila moja ya W_query, W_key, W_value:

qkv_params = emb_dim * emb_dim = 768 * 768 = 589,824

Kwa kuwa kuna tabaka tatu kama hizo:

total_qkv_params = 3 * qkv_params = 3 * 589,824 = 1,769,472

• Output Projection (out_proj):

out_proj_params = (emb_dim * emb_dim) + emb_dim = (768 * 768) + 768 = 589,824 + 768 = 590,592

• Jumla ya Vigezo vya Multi-Head Attention:

mha_params = total_qkv_params + out_proj_params
mha_params = 1,769,472 + 590,592 = 2,360,064

b. FeedForward Network

• Components:

• Kiwango cha Kwanza: nn.Linear(emb_dim, 4 * emb_dim)

• Kiwango cha Pili: nn.Linear(4 * emb_dim, emb_dim)

• Calculations:

• Kiwango cha Kwanza:

ff_first_layer_params = (emb_dim * 4 * emb_dim) + (4 * emb_dim)
ff_first_layer_params = (768 * 3072) + 3072 = 2,359,296 + 3,072 = 2,362,368

• Kiwango cha Pili:

ff_second_layer_params = (4 * emb_dim * emb_dim) + emb_dim
ff_second_layer_params = (3072 * 768) + 768 = 2,359,296 + 768 = 2,360,064

• Jumla ya Vigezo vya FeedForward:

ff_params = ff_first_layer_params + ff_second_layer_params
ff_params = 2,362,368 + 2,360,064 = 4,722,432

c. Layer Normalizations

• Components:

• Vitu viwili vya LayerNorm kwa kila kizuizi.

• Kila LayerNorm ina vigezo 2 * emb_dim (kuyeyusha na kuhamasisha).

• Calculations:

layer_norm_params_per_block = 2 * (2 * emb_dim) = 2 * 768 * 2 = 3,072

d. Jumla ya Vigezo kwa Kizuizi cha Transformer

pythonCopy codeparams_per_block = mha_params + ff_params + layer_norm_params_per_block
params_per_block = 2,360,064 + 4,722,432 + 3,072 = 7,085,568

Jumla ya Vigezo kwa ajili ya Vizui vya Transformer Vyote

pythonCopy codetotal_transformer_blocks_params = params_per_block * n_layers
total_transformer_blocks_params = 7,085,568 * 12 = 85,026,816

3. Tabaka la Mwisho

a. Kurekebisha Tabaka la Mwisho

• Parameta: 2 * emb_dim (kubwa na kuhamasisha)

pythonCopy codefinal_layer_norm_params = 2 * 768 = 1,536

b. Safu ya Matokeo (out_head)

• Safu: nn.Linear(emb_dim, vocab_size, bias=False)

• Parameta: emb_dim * vocab_size

pythonCopy codeoutput_projection_params = 768 * 50257 = 38,597,376

4. Kuweka Pamoja Vigezo Vyote

pythonCopy codetotal_params = (
embedding_params +
total_transformer_blocks_params +
final_layer_norm_params +
output_projection_params
)
total_params = (
39,383,808 +
85,026,816 +
1,536 +
38,597,376
)
total_params = 163,009,536

Generate Text

Kuwa na mfano unaotabiri token inayofuata kama ile ya awali, inahitajika tu kuchukua thamani za token za mwisho kutoka kwa matokeo (kama zitakuwa zile za token inayotabiriwa), ambayo itakuwa thamani kwa kila kipengee katika msamiati na kisha tumia kazi ya softmax kuimarisha vipimo kuwa uwezekano vinavyos suma 1 na kisha pata index ya kipengee kikubwa zaidi, ambacho kitakuwa index ya neno ndani ya msamiati.

Code from https://github.com/rasbt/LLMs-from-scratch/blob/main/ch04/01_main-chapter-code/ch04.ipynb:

def generate_text_simple(model, idx, max_new_tokens, context_size):
# idx is (batch, n_tokens) array of indices in the current context
for _ in range(max_new_tokens):

# Crop current context if it exceeds the supported context size
# E.g., if LLM supports only 5 tokens, and the context size is 10
# then only the last 5 tokens are used as context
idx_cond = idx[:, -context_size:]

# Get the predictions
with torch.no_grad():
logits = model(idx_cond)

# Focus only on the last time step
# (batch, n_tokens, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]

# Apply softmax to get probabilities
probas = torch.softmax(logits, dim=-1)  # (batch, vocab_size)

# Get the idx of the vocab entry with the highest probability value
idx_next = torch.argmax(probas, dim=-1, keepdim=True)  # (batch, 1)

# Append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1)  # (batch, n_tokens+1)

return idx


start_context = "Hello, I am"

encoded = tokenizer.encode(start_context)
print("encoded:", encoded)

encoded_tensor = torch.tensor(encoded).unsqueeze(0)
print("encoded_tensor.shape:", encoded_tensor.shape)

model.eval() # disable dropout

out = generate_text_simple(
model=model,
idx=encoded_tensor,
max_new_tokens=6,
context_size=GPT_CONFIG_124M["context_length"]
)

print("Output:", out)
print("Output length:", len(out[0]))

Marejeleo

• https://www.manning.com/books/build-a-large-language-model-from-scratch