Update JiRackTernaryPyTorch_236b.py

JiRackTernaryPyTorch_236b.py

@@ -1,165 +1,168 @@
-# ==============================================================================
-# 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.
-# 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.
-    Реализация весов {-1, 0, +1} с обучаемым скаляром Gamma.
-    """
-    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. Центрирование весов (STE Approximation)
-        w_centered = self.weight - self.weight.mean()
-        
-        # 2. Квантование в {-1, 0, 1}
-        # Используем detach() для реализации Straight-Through Estimator (STE)
-        w_quant = torch.sign(w_centered)
-        w_ternary = (w_quant - self.weight).detach() + self.weight
-        
-        # 3. Линейная операция с тернарными весами и масштабированием
-        return F.linear(x, w_ternary) * self.gamma
-
-class RMSNorm(nn.Module):
-    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.
-    Единый вычислительный блок для оптимизации HBM и снижения нагрева.
-    """
-    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])
-        
-        # 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 in same block execution (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: Konstantin Vladimirovich Grabko
-    """
-    def __init__(self, config=None):
-        super().__init__()
-        # CLAIM 2: Buffered Routing Embedding (BRE) base
-        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 = "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 Simulation via buffered embedding access
-        x = self.token_emb(idx)
-        
-        for layer in self.layers:
-            # SWA Block execution with residual routing
-            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):
-        return "".join([chr(c) for c in self.proof.tolist()])
-
-class JiRackTernaryConfig:
-    def __init__(self, num_hidden_layers=NUM_LAYERS):
+# ==============================================================================
+# 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