JiRackTernaryPyTorch_236b.py

# ==============================================================================
# COPYRIGHT (C) Dec 22 2025 KONSTANTIN VLADIMIROVICH GRABKO. ALL RIGHTS RESERVED.
# PATENT PENDING | CMS MANHATTAN JIRACK TECHNOLOGY
#
# This software is licensed under the Commercial License Agreement V.1.2.
# ANY USE, MODIFICATION, OR DISTRIBUTION REQUIRES COMPLIANCE WITH LICENSE TERMS.
# NO PATENTING RIGHTS: Users are strictly prohibited from filing patent claims
# based on the BRE or SWA architectures disclosed herein.
# Contact: grabko@cmsmanhattan.com | +1 (516) 777-0945
# ==============================================================================
# Version: 236B Ternary Extreme | Optimized for AMD ROCm & Tesla M10
# Architecture: 160 Layers | SWA Fusion | BRE Routing | Ternary Engine
# ==============================================================================

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# --- CONFIGURATION 236B TERNARY ---
VOCAB_SIZE = 128256      # Llama-3 Compatible Vocabulary
MODEL_DIM = 12288        
NUM_HEADS = 96           
NUM_KV_HEADS = 8         # Grouped-Query Attention (GQA)
NUM_LAYERS = 160         # Extreme Depth for JiRack 236B
MAX_SEQ_LEN = 2048       
FFN_HIDDEN_DIM = 32768   
HEAD_DIM = MODEL_DIM // NUM_HEADS
EPSILON = 1e-5

class JiRackTernaryLinear(nn.Module):
    """
    CLAIM 1: Ternary-Quantized Optimization.
    Implementation of weights restricted to {-1, 0, +1} with learnable Gamma scaling.
    """
    def __init__(self, in_features, out_features, bias=False):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.randn(out_features, in_features))
        self.gamma = nn.Parameter(torch.ones(1)) # Learnable scaling factor (Claim 1.1)

    def forward(self, x):
        # 1. Weight Centering for STE Approximation
        w_centered = self.weight - self.weight.mean()
        
        # 2. Quantization to {-1, 0, 1}
        # Using detach() to implement the Straight-Through Estimator (STE)
        w_quant = torch.sign(w_centered)
        w_ternary = (w_quant - self.weight).detach() + self.weight
        
        # 3. Linear operation with ternary weights and scaling
        return F.linear(x, w_ternary) * self.gamma

class RMSNorm(nn.Module):
    """Stable normalization for ultra-deep networks (100+ layers)"""
    def __init__(self, dim, eps=EPSILON):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
    def forward(self, x):
        return (x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)) * self.weight

def precompute_freqs_cis(dim, seq_len, theta=500000.0):
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(seq_len)
    freqs = torch.outer(t, freqs).float()
    return torch.polar(torch.ones_like(freqs), freqs)

def apply_rotary_emb(xq, xk, freqs_cis):
    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))
    freqs_cis = freqs_cis.view(1, xq_.size(1), 1, xq_.size(3))
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)

class SWA_Fusion_Block(nn.Module):
    """
    CLAIM 3: SwiGLU-Attention (SWA) Fusion.
    Unified compute block to optimize HBM throughput and reduce thermal throttling.
    """
    def __init__(self):
        super().__init__()
        self.n_rep = NUM_HEADS // NUM_KV_HEADS
        
        # Ternary Projections
        self.wq = JiRackTernaryLinear(MODEL_DIM, NUM_HEADS * HEAD_DIM)
        self.wk = JiRackTernaryLinear(MODEL_DIM, NUM_KV_HEADS * HEAD_DIM)
        self.wv = JiRackTernaryLinear(MODEL_DIM, NUM_KV_HEADS * HEAD_DIM)
        self.wo = JiRackTernaryLinear(NUM_HEADS * HEAD_DIM, MODEL_DIM)
        
        # SwiGLU FFN (Ternary)
        self.w1 = JiRackTernaryLinear(MODEL_DIM, FFN_HIDDEN_DIM)
        self.w2 = JiRackTernaryLinear(FFN_HIDDEN_DIM, MODEL_DIM)
        self.w3 = JiRackTernaryLinear(MODEL_DIM, FFN_HIDDEN_DIM)

    def forward(self, x, freqs_cis):
        b, t, _ = x.shape
        
        # 1. Attention Pipeline
        q, k, v = self.wq(x), self.wk(x), self.wv(x)
        q, k = apply_rotary_emb(q.view(b, t, NUM_HEADS, HEAD_DIM), 
                                k.view(b, t, NUM_KV_HEADS, HEAD_DIM), 
                                freqs_cis[:t])
        
        # Grouped-Query Attention (GQA) logic
        k = k[:, :, :, None, :].expand(b, t, NUM_KV_HEADS, self.n_rep, HEAD_DIM).reshape(b, t, NUM_HEADS, HEAD_DIM)
        v = v[:, :, :, None, :].expand(b, t, NUM_KV_HEADS, self.n_rep, HEAD_DIM).reshape(b, t, NUM_HEADS, HEAD_DIM)
        
        attn_out = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True)
        attn_out = self.wo(attn_out.transpose(1, 2).contiguous().view(b, t, MODEL_DIM))
        
        # 2. SwiGLU Path (FFN) - Fused execution within the same block (Claim 3.2)
        ffn_out = self.w2(F.silu(self.w1(x)) * self.w3(x))
        
        return attn_out + ffn_out

class JiRackTernary236B(nn.Module):
    """
    Main Engine: JiRack 236B (Ternary Extreme Edition)
    Inventor/Architect: Konstantin Vladimirovich Grabko
    """
    def __init__(self, config=None):
        super().__init__()
        # CLAIM 2: Buffered Routing Embedding (BRE) base implementation
        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
        
        self.layers = nn.ModuleList([
            nn.ModuleDict({
                'norm1': RMSNorm(MODEL_DIM),
                'swa': SWA_Fusion_Block(),
                'norm2': RMSNorm(MODEL_DIM)
            }) for _ in range(NUM_LAYERS)
        ])
        
        self.norm_f = RMSNorm(MODEL_DIM)
        self.head = JiRackTernaryLinear(MODEL_DIM, VOCAB_SIZE)
        
        self.register_buffer("freqs_cis", precompute_freqs_cis(HEAD_DIM, MAX_SEQ_LEN))
        
        # Digital Proof of Authorship Signature
        signature = "AUTHOR: KONSTANTIN VLADIMIROVICH GRABKO | CMS MANHATTAN 2025"
        self.register_buffer("proof", torch.tensor([ord(c) for c in signature], dtype=torch.uint8))

    def forward(self, idx, targets=None):
        # BRE Routing Emulation via buffered data access
        x = self.token_emb(idx)
        
        for layer in self.layers:
            # SWA Block execution with residual routing and normalization
            x = x + layer['swa'](layer['norm1'](x), self.freqs_cis)
            
        x = self.norm_f(x)
        logits = self.head(x)
        
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, VOCAB_SIZE), targets.view(-1))
            return type('Outputs', (object,), {'logits': logits, 'loss': loss})
        return logits

    def get_author_info(self):
        """Extracts the proof of authorship signature from model buffers."""
        return "".join([chr(c) for c in self.proof.tolist()])

class JiRackTernaryConfig:
    def __init__(self, num_hidden_layers=NUM_LAYERS):
        self.num_hidden_layers = num_hidden_layers