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import math
import torch
import torch.nn as nn
from torch.nn import functional as F
class CosmicConfig:
"""Configuration class for CosmicFish."""
def __init__(self,
vocab_size=50257,
block_size=512,
n_layer=10,
n_head=16,
n_embd=640,
bias=True,
dropout=0.0, # Always 0 for inference
n_query_groups=4,
eps=1e-6,
use_rotary=True,
use_swiglu=True,
use_qk_norm=False,
use_gqa=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.bias = bias
self.dropout = dropout
self.eps = eps
self.use_rotary = use_rotary
self.use_swiglu = use_swiglu
self.use_qk_norm = use_qk_norm
self.use_gqa = use_gqa
self.n_query_groups = n_query_groups if use_gqa else n_head
# Ensure n_head is divisible by n_query_groups
assert n_head % self.n_query_groups == 0, "n_head must be divisible by n_query_groups"
class RMSNorm(nn.Module):
"""Root Mean Square Normalization"""
def __init__(self, dim, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
return self.weight * (x / rms)
def precompute_freqs_cis(dim, end, theta=10000.0):
"""Precompute the frequency tensor for complex exponentials (cis)"""
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
return freqs_cis
def apply_rotary_emb(xq, xk, freqs_cis):
"""Apply rotary embeddings to input tensors"""
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
seq_len = xq_.size(2)
if freqs_cis.size(0) < seq_len:
raise ValueError(f"freqs_cis has only {freqs_cis.size(0)} values but sequence length is {seq_len}")
freqs_cis_seq = freqs_cis[:seq_len]
xq_out = torch.view_as_real(xq_ * freqs_cis_seq.unsqueeze(0)).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis_seq.unsqueeze(0)).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class GroupedQueryAttention(nn.Module):
"""Grouped Query Attention (GQA) implementation"""
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
head_dim = config.n_embd // config.n_head
self.head_dim = head_dim
self.n_head = config.n_head
self.n_embd = config.n_embd
self.n_query_groups = config.n_query_groups
self.kv_heads = config.n_head // config.n_query_groups if config.use_gqa else config.n_head
qkv_proj_size = (config.n_head + 2 * self.kv_heads) * head_dim
self.c_attn = nn.Linear(config.n_embd, qkv_proj_size, bias=config.bias)
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# Flash attention support
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.flash:
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
# Query-key normalization
self.qk_norm = getattr(config, 'use_qk_norm', False)
if self.qk_norm:
self.q_norm = RMSNorm(head_dim, eps=getattr(config, 'eps', 1e-6))
self.k_norm = RMSNorm(head_dim, eps=getattr(config, 'eps', 1e-6))
def forward(self, x, freqs_cis=None):
B, T, C = x.size()
qkv = self.c_attn(x)
head_dim = C // self.n_head
q_size = self.n_head * head_dim
k_size = self.kv_heads * head_dim
v_size = self.kv_heads * head_dim
q, k, v = qkv.split([q_size, k_size, v_size], dim=2)
q = q.view(B, T, self.n_head, head_dim).transpose(1, 2)
k = k.view(B, T, self.kv_heads, head_dim).transpose(1, 2)
v = v.view(B, T, self.kv_heads, head_dim).transpose(1, 2)
# Repeat k and v if needed for GQA
if self.kv_heads < self.n_head:
repeats = self.n_head // self.kv_heads
k = k.repeat_interleave(repeats, dim=1)
v = v.repeat_interleave(repeats, dim=1)
# Apply rotary embeddings
if freqs_cis is not None:
q, k = apply_rotary_emb(q, k, freqs_cis)
# Apply query-key normalization
if self.qk_norm:
q = self.q_norm(q)
k = self.k_norm(k)
# Compute attention
if self.flash:
y = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True
)
else:
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)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.c_proj(y)
return y
class Block(nn.Module):
"""Transformer block"""
def __init__(self, config):
super().__init__()
self.ln_1 = RMSNorm(config.n_embd, eps=config.eps)
self.ln_2 = RMSNorm(config.n_embd, eps=config.eps)
self.attn = GroupedQueryAttention(config)
# MLP implementation based on configuration
if config.use_swiglu:
# SwiGLU MLP
self.mlp = nn.ModuleDict(dict(
gate=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
up=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
down=nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias),
act=nn.SiLU(),
))
m = self.mlp
self.mlpf = lambda x: m.down(m.act(m.up(x)) * m.gate(x))
else:
# Traditional MLP
self.mlp = nn.ModuleDict(dict(
c_fc=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
c_proj=nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias),
act=nn.GELU(),
))
m = self.mlp
self.mlpf = lambda x: m.c_proj(m.act(m.c_fc(x)))
def forward(self, x, freqs_cis=None):
x = x + self.attn(self.ln_1(x), freqs_cis)
x = x + self.mlpf(self.ln_2(x))
return x
class CosmicFish(nn.Module):
"""
CosmicFish model for inference only.
Features: Rotary Positional Embeddings, Grouped-Query Attention, SwiGLU, RMSNorm
"""
def __init__(self, config):
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(dict(
wte=nn.Embedding(config.vocab_size, config.n_embd),
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f=RMSNorm(config.n_embd, eps=config.eps),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Share weights between embedding and output
self.transformer.wte.weight = self.lm_head.weight
# Precompute rotary embedding frequencies
if config.use_rotary:
head_dim = config.n_embd // config.n_head
self.freqs_cis = precompute_freqs_cis(head_dim, config.block_size)
else:
self.freqs_cis = None
self.transformer.wpe = nn.Embedding(config.block_size, config.n_embd)
def get_num_params(self, non_embedding=True):
"""Return the number of parameters in the model."""
n_params = sum(p.numel() for p in self.parameters())
if non_embedding and hasattr(self.transformer, 'wpe'):
n_params -= self.transformer.wpe.weight.numel()
return n_params
def forward(self, idx, targets=None):
"""Forward pass through the model."""
device = idx.device
b, t = idx.size()
assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
# Get token embeddings
tok_emb = self.transformer.wte(idx)
# Handle positional embeddings
if self.config.use_rotary:
x = tok_emb
freqs_cis = self.freqs_cis.to(device) if self.freqs_cis is not None else None
else:
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)
pos_emb = self.transformer.wpe(pos)
x = tok_emb + pos_emb
freqs_cis = None
# Apply transformer blocks
for block in self.transformer.h:
x = block(x, freqs_cis)
# Apply final normalization
x = self.transformer.ln_f(x)
# Calculate outputs
if targets is not None:
logits = self.lm_head(x)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
# For inference, only compute logits for the last token
logits = self.lm_head(x[:, [-1], :])
loss = None
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""
Generate text by sampling from the model, token by token.
"""
for _ in range(max_new_tokens):
# Crop sequence to block size if needed
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
# Forward pass
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
# Apply top-k sampling
if top_k is not None:
v, _ = torch.topk(logits, top_k)
logits[logits < v[:, [-1]]] = -float('Inf')
# Sample next token
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
# Append to sequence
idx = torch.cat((idx, idx_next), dim=1)
return idx
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