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# MAP-NEO Mini Model Architecture
# Scaled-down version of MAP-NEO (300M parameters) with RMSNorm, RoPE, and Flash Attention
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
import json
class RMSNorm(nn.Module):
"""Root Mean Square Layer Normalization (same as MAP-NEO)"""
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
# RMS normalization
norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return x * norm * self.weight
class RotaryPositionalEmbedding(nn.Module):
"""Rotary Position Embedding (RoPE) - same as MAP-NEO"""
def __init__(self, dim: int, max_len: int = 8192, theta: float = 10000.0):
super().__init__()
self.dim = dim
self.max_len = max_len
self.theta = theta
# Precompute frequencies
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("freqs", freqs, persistent=False)
def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
# x shape: [batch, seq_len, n_heads, head_dim]
device = x.device
positions = torch.arange(seq_len, device=device).float()
# Compute angles
angles = positions.unsqueeze(1) * self.freqs.unsqueeze(0) # [seq_len, dim//2]
cos = torch.cos(angles) # [seq_len, dim//2]
sin = torch.sin(angles) # [seq_len, dim//2]
return cos, sin
def apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
"""Apply rotary embedding to query/key tensors"""
# x: [batch, seq_len, n_heads, head_dim]
# Split into real and imaginary parts
x1, x2 = x[..., ::2], x[..., 1::2] # Even and odd indices
# Apply rotation
rotated = torch.cat([
x1 * cos.unsqueeze(0).unsqueeze(-2) - x2 * sin.unsqueeze(0).unsqueeze(-2),
x1 * sin.unsqueeze(0).unsqueeze(-2) + x2 * cos.unsqueeze(0).unsqueeze(-2)
], dim=-1)
return rotated
class MultiHeadAttention(nn.Module):
"""Multi-head attention with RoPE and optional Flash Attention"""
def __init__(self, dim: int, n_heads: int, dropout: float = 0.0):
super().__init__()
assert dim % n_heads == 0
self.dim = dim
self.n_heads = n_heads
self.head_dim = dim // n_heads
self.scale = self.head_dim ** -0.5
# Linear projections
self.q_proj = nn.Linear(dim, dim, bias=False)
self.k_proj = nn.Linear(dim, dim, bias=False)
self.v_proj = nn.Linear(dim, dim, bias=False)
self.o_proj = nn.Linear(dim, dim, bias=False)
self.dropout = nn.Dropout(dropout)
# RoPE
self.rotary_emb = RotaryPositionalEmbedding(self.head_dim)
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
batch_size, seq_len, dim = x.shape
# Project to Q, K, V
q = self.q_proj(x) # [batch, seq_len, dim]
k = self.k_proj(x) # [batch, seq_len, dim]
v = self.v_proj(x) # [batch, seq_len, dim]
# Reshape for multi-head attention
q = q.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
# Apply RoPE to Q and K
cos, sin = self.rotary_emb(q, seq_len)
q = apply_rotary_emb(q.transpose(1, 2), cos, sin).transpose(1, 2)
k = apply_rotary_emb(k.transpose(1, 2), cos, sin).transpose(1, 2)
# Try Flash Attention 2 if available
try:
# Flash Attention 2 format: [batch, seq_len, n_heads, head_dim]
q_flash = q.transpose(1, 2) # [batch, seq_len, n_heads, head_dim]
k_flash = k.transpose(1, 2) # [batch, seq_len, n_heads, head_dim]
v_flash = v.transpose(1, 2) # [batch, seq_len, n_heads, head_dim]
# Use Flash Attention (causal mask built-in)
out = F.scaled_dot_product_attention(
q_flash.transpose(1, 2), k_flash.transpose(1, 2), v_flash.transpose(1, 2),
attn_mask=None, # Causal mask applied automatically
dropout_p=self.dropout.p if self.training else 0.0,
is_causal=True
)
out = out.transpose(1, 2) # Back to [batch, seq_len, n_heads, head_dim]
except:
# Fallback to manual attention
scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale
# Apply causal mask
causal_mask = torch.triu(torch.ones(seq_len, seq_len, device=x.device), diagonal=1).bool()
scores = scores.masked_fill(causal_mask, float('-inf'))
# Apply attention mask if provided
if attention_mask is not None:
scores = scores.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(1), float('-inf'))
attn_weights = F.softmax(scores, dim=-1)
attn_weights = self.dropout(attn_weights)
out = torch.matmul(attn_weights, v) # [batch, n_heads, seq_len, head_dim]
out = out.transpose(1, 2) # [batch, seq_len, n_heads, head_dim]
# Concat heads and project
out = out.contiguous().view(batch_size, seq_len, dim)
out = self.o_proj(out)
return out
class FeedForward(nn.Module):
"""SwiGLU Feed-Forward Network (same as MAP-NEO)"""
def __init__(self, dim: int, hidden_dim: int, dropout: float = 0.0):
super().__init__()
self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# SwiGLU activation: swish(gate) * up
gate = F.silu(self.gate_proj(x)) # SiLU = Swish
up = self.up_proj(x)
hidden = gate * up
hidden = self.dropout(hidden)
return self.down_proj(hidden)
class TransformerBlock(nn.Module):
"""Transformer block with pre-norm (RMSNorm)"""
def __init__(self, dim: int, n_heads: int, hidden_dim: int, dropout: float = 0.0):
super().__init__()
self.attention_norm = RMSNorm(dim)
self.attention = MultiHeadAttention(dim, n_heads, dropout)
self.ffn_norm = RMSNorm(dim)
self.ffn = FeedForward(dim, hidden_dim, dropout)
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
# Pre-norm attention
h = x + self.attention(self.attention_norm(x), attention_mask)
# Pre-norm FFN
h = h + self.ffn(self.ffn_norm(h))
return h
class NeoMiniConfig:
"""Configuration for MAP-NEO Mini (300M parameters)"""
def __init__(self):
# Model architecture
self.vocab_size = 50257 # GPT-2 tokenizer vocab size (will update for MAP-NEO tokenizer)
self.max_seq_len = 2048
self.dim = 1024 # Hidden dimension
self.n_layers = 16 # Number of transformer layers
self.n_heads = 16 # Number of attention heads
self.hidden_dim = 2736 # FFN hidden dimension (2.67x of dim)
# Training
self.dropout = 0.0 # No dropout for pretraining
# Approximated parameter count: ~300M
def to_dict(self):
return {k: v for k, v in self.__dict__.items() if not k.startswith('_')}
@classmethod
def from_dict(cls, config_dict):
config = cls()
for k, v in config_dict.items():
setattr(config, k, v)
return config
class NeoMini(nn.Module):
"""MAP-NEO Mini Language Model (300M parameters)"""
def __init__(self, config: NeoMiniConfig):
super().__init__()
self.config = config
# Embeddings
self.token_embedding = nn.Embedding(config.vocab_size, config.dim)
# Transformer blocks
self.blocks = nn.ModuleList([
TransformerBlock(
dim=config.dim,
n_heads=config.n_heads,
hidden_dim=config.hidden_dim,
dropout=config.dropout
)
for _ in range(config.n_layers)
])
# Output
self.ln_f = RMSNorm(config.dim)
self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False)
# Tie weights (common in modern LLMs)
self.lm_head.weight = self.token_embedding.weight
# Initialize weights
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
# Token embeddings
x = self.token_embedding(input_ids)
# Apply transformer blocks
for block in self.blocks:
x = block(x, attention_mask)
# Final layer norm and projection
x = self.ln_f(x)
logits = self.lm_head(x)
return logits
def get_num_params(self):
"""Count model parameters"""
return sum(p.numel() for p in self.parameters())
def save_config(self, path: str):
"""Save model configuration"""
with open(path, 'w') as f:
json.dump(self.config.to_dict(), f, indent=2)
@classmethod
def from_config(cls, config_path: str):
"""Load model from configuration"""
with open(config_path, 'r') as f:
config_dict = json.load(f)
config = NeoMiniConfig.from_dict(config_dict)
return cls(config)
def create_model():
"""Create a MAP-NEO Mini model"""
config = NeoMiniConfig()
model = NeoMini(config)
print(f"Created MAP-NEO Mini with {model.get_num_params():,} parameters")
print(f"Config: {config.n_layers} layers, {config.dim} dim, {config.n_heads} heads")
return model, config
if __name__ == "__main__":
# Test model creation
model, config = create_model()
# Test forward pass
batch_size, seq_len = 2, 512
input_ids = torch.randint(0, config.vocab_size, (batch_size, seq_len))
with torch.no_grad():
logits = model(input_ids)
print(f"Input shape: {input_ids.shape}")
print(f"Output shape: {logits.shape}")
print("Model test passed!") |