Delete JiRackTernaryPyTorch_405b.py

JiRackTernaryPyTorch_405b.py

@@ -1,171 +0,0 @@
-# ==============================================================================
-# COPYRIGHT (C) 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 of this code requires compliance with 
-# the terms found in the LICENSE.md file in the root directory.
-#
-# 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
-# ==============================================================================
-# COPYRIGHT (C) 2025 KONSTANTIN VLADIMIROVICH GRABKO. ALL RIGHTS RESERVED.
-# PATENT PENDING | CMS MANHATTAN JIRACK TECHNOLOGY | MOE-VERSION 405B
-# ==============================================================================
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from typing import Optional, Tuple
-from transformers import PreTrainedModel, PretrainedConfig
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-class JiRackMoEConfig(PretrainedConfig):
-    model_type = "jirack_moe_transformer"
-    def __init__(
-        self,
-        vocab_size=128256,
-        hidden_size=12288,
-        num_hidden_layers=112,    # MoE allows reduced depth while increasing experts
-        num_attention_heads=96,
-        num_experts=16,           # Total of 16 experts per layer
-        num_experts_per_tok=2,    # Activate only the top 2 experts per token
-        intermediate_size=12288,  # Size of each expert
-        max_position_embeddings=32768,
-        rms_norm_eps=1e-5,
-        author="Author: Konstantin Vladimirovich Grabko (CMS Manhattan) 2025",
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_experts = num_experts
-        self.num_experts_per_tok = num_experts_per_tok
-        self.intermediate_size = intermediate_size
-        self.max_position_embeddings = max_position_embeddings
-        self.rms_norm_eps = rms_norm_eps
-        self.author = author
-
-# --- Ternary Linear Logic (Bit-Response Engine) ---
-
-class JiRackBitLinear(nn.Linear):
-    def __init__(self, in_features, out_features, bias=False):
-        super().__init__(in_features, out_features, bias)
-        nn.init.normal_(self.weight, std=0.02)
-    def forward(self, x):
-        w = self.weight
-        gamma = w.abs().mean() + 1e-9
-        w_quant = torch.clamp(torch.round(w / gamma), -1, 1)
-        w_final = w + (w_quant * gamma - w).detach()
-        x_norm = x - x.mean(dim=-1, keepdim=True)
-        x_quant = x_norm + (torch.clamp(x_norm, -1.2, 1.2) - x_norm).detach()
-        return F.linear(x_quant, w_final, self.bias)
-
-# --- Expert Block ---
-
-class JiRackExpert(nn.Module):
-    def __init__(self, config: JiRackMoEConfig):
-        super().__init__()
-        self.w1 = JiRackBitLinear(config.hidden_size, config.intermediate_size)
-        self.w2 = JiRackBitLinear(config.intermediate_size, config.hidden_size)
-        self.w3 = JiRackBitLinear(config.hidden_size, config.intermediate_size)
-    def forward(self, x):
-        return self.w2(F.silu(self.w1(x)) * self.w3(x))
-
-# --- Router (Gate) for Expert Selection ---
-
-class JiRackMoEGate(nn.Module):
-    def __init__(self, config: JiRackMoEConfig):
-        super().__init__()
-        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
-        self.top_k = config.num_experts_per_tok
-    def forward(self, x):
-        logits = self.gate(x)
-        weights, indices = torch.topk(logits, self.top_k, dim=-1)
-        weights = F.softmax(weights.float(), dim=-1).type_as(x)
-        return weights, indices
-
-# --- Signature and Phase CMS Layer ---
-
-class SignatureLayer(nn.Module):
-    def __init__(self, dim, author_name):
-        super().__init__()
-        self.gate = nn.Parameter(torch.ones(dim))
-        seed = sum(ord(c) for c in author_name)
-        torch.manual_seed(seed)
-        self.signage_cms = nn.Parameter(torch.randn(dim, dim) * 0.001)
-    def forward(self, x):
-        sig = torch.tanh(F.linear(x, self.signage_cms))
-        return x * torch.sigmoid(self.gate) + sig
-
-# --- Main MoE Transformer Block ---
-
-class JiRackMoEBlock(nn.Module):
-    def __init__(self, config: JiRackMoEConfig):
-        super().__init__()
-        self.ln1 = nn.LayerNorm(config.hidden_size)
-        self.attn = nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
-        self.ln2 = nn.LayerNorm(config.hidden_size)
-        
-        # MoE Integration
-        self.gate = JiRackMoEGate(config)
-        self.experts = nn.ModuleList([JiRackExpert(config) for _ in range(config.num_experts)])
-        
-        self.signature = SignatureLayer(config.hidden_size, config.author)
-
-    def forward(self, x):
-        # Attention
-        residual = x
-        x = self.ln1(x)
-        x, _ = self.attn(x, x, x, need_weights=False)
-        x = residual + x
-        
-        # MoE Routing
-        residual = x
-        x = self.ln2(x)
-        weights, indices = self.gate(x)
-        
-        final_output = torch.zeros_like(x)
-        # A loop is used here for simplicity; scatter/gather is used in production
-        for i, expert in enumerate(self.experts):
-            mask = (indices == i).any(dim=-1)
-            if mask.any():
-                expert_output = expert(x[mask])
-                # Applying expert contribution weights
-                w = weights[indices == i].unsqueeze(-1)
-                final_output[mask] += expert_output * w
-        
-        x = residual + self.signature(final_output)
-        return x
-
-# --- Main 405B MoE Model ---
-
-class JiRackTernaryMoE405B(PreTrainedModel):
-    config_class = JiRackMoEConfig
-    def __init__(self, config: JiRackMoEConfig):
-        super().__init__(config)
-        self.token_emb = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.blocks = nn.ModuleList([JiRackMoEBlock(config) for _ in range(config.num_hidden_layers)])
-        self.ln_f = nn.LayerNorm(config.hidden_size)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        
-        self.post_init()
-
-    def forward(self, input_ids, labels=None):
-        x = self.token_emb(input_ids)
-        for block in self.blocks:
-            if self.training:
-                x = torch.utils.checkpoint.checkpoint(block, x, use_reentrant=False)
-            else:
-                x = block(x)
-        
-        logits = self.lm_head(self.ln_f(x))
-        loss = None
-        if labels is not None:
-            loss = F.cross_entropy(logits.view(-1, self.config.vocab_size), labels.view(-1))
-        
-        return CausalLMOutputWithPast(loss=loss, logits=logits)