Create stage4.py

stage4.py

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+# stage4.py
+# Author: Liam Grinstead
+# Purpose: ViT-Tiny (ImageNet Subset) Validation (Stage Four of Twelve)
+
+import os, math, time, json, random, argparse
+import torch, torch.nn as nn, torch.nn.functional as F
+import torchvision, torchvision.transforms as T
+
+# ---------------- Determinism ----------------
+def set_seed(s=1234):
+    random.seed(s); torch.manual_seed(s); torch.cuda.manual_seed_all(s)
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = False
+
+# ---------------- Telemetry ------------------
+class Telemetry:
+    def __init__(self, path="stage4_vit_tiny.jsonl"):
+        self.t0 = time.time(); self.f = open(path,"w")
+    def emit(self, **k):
+        k["t"] = round(time.time()-self.t0,3)
+        line = json.dumps(k,separators=(",",":"))
+        print(line); self.f.write(line+"\n"); self.f.flush()
+    def close(self): self.f.close()
+
+# ---------------- Orbital Coupler ------------
+class Orbital:
+    def __init__(self, g=0.006, floor=0.2):
+        self.a=0.0; self.b=math.pi/3; self.g=g; self.floor=floor
+    def step(self):
+        d=(self.b-self.a+math.pi)%(2*math.pi)-math.pi
+        if abs(d)<self.floor: d=self.floor*(1 if d>=0 else -1)
+        s=math.sin(d)
+        self.a=(self.a+self.g*s)%(2*math.pi)
+        self.b=(self.b-self.g*s)%(2*math.pi)
+        drift=abs((self.a-self.b+math.pi)%(2*math.pi)-math.pi)
+        return drift, abs(s)
+
+# ---------------- DCLR Optimiser -------------
+class DCLR(torch.optim.Optimizer):
+    def __init__(self, params, lr=5e-4, beta=0.9, gamma=0.999, eps=1e-8, cg=0.05):
+        super().__init__(params, dict(lr=lr,beta=beta,gamma=gamma,eps=eps,cg=cg))
+    @torch.no_grad()
+    def step(self, closure=None):
+        tot=0.0
+        for g in self.param_groups:
+            lr,beta,gamma,eps,c = g["lr"],g["beta"],g["gamma"],g["eps"],g["cg"]
+            for p in g["params"]:
+                if p.grad is None: continue
+                st=self.state[p]
+                if not st:
+                    st["m"]=torch.zeros_like(p); st["v"]=torch.zeros_like(p); st["coh"]=torch.zeros_like(p)
+                m,v,h=st["m"],st["v"],st["coh"]; g0=p.grad
+                m.mul_(beta).add_(g0,alpha=1-beta)
+                v.mul_(gamma).addcmul_(g0,g0,value=1-gamma)
+                d=g0-m; h.mul_(0.9).add_(d.abs(),alpha=0.1)
+                lr_eff=lr/(1+c*h)
+                step=lr_eff*m/(v.sqrt()+eps)
+                p.add_(-step); tot += (step*step).sum().item()
+        return None, tot
+
+# ---------------- ViT-Tiny -------------------
+class PatchEmbed(nn.Module):
+    def __init__(self, img=224, patch=16, in_ch=3, dim=192):
+        super().__init__()
+        self.proj=nn.Conv2d(in_ch, dim, kernel_size=patch, stride=patch)
+        self.n=(img//patch)*(img//patch)
+    def forward(self,x):
+        x=self.proj(x); return x.flatten(2).transpose(1,2)
+
+class Block(nn.Module):
+    def __init__(self, dim=192, heads=3, mlp_ratio=4):
+        super().__init__()
+        self.n1=nn.LayerNorm(dim)
+        self.attn=nn.MultiheadAttention(dim, heads, batch_first=True)
+        self.n2=nn.LayerNorm(dim)
+        self.mlp=nn.Sequential(nn.Linear(dim,int(dim*mlp_ratio)), nn.GELU(), nn.Linear(int(dim*mlp_ratio),dim))
+    def forward(self,x):
+        h=x; x=self.n1(x); x,_=self.attn(x,x,x,need_weights=False); x=x+h
+        h=x; x=self.n2(x); x=x+self.mlp(x); return x
+
+class ViTTiny(nn.Module):
+    def __init__(self, num_classes=1000, img=224, patch=16, dim=192, depth=12, heads=3, mlp_ratio=4):
+        super().__init__()
+        self.pe=PatchEmbed(img,patch,3,dim)
+        self.cls=nn.Parameter(torch.zeros(1,1,dim))
+        self.pos=nn.Parameter(torch.zeros(1,1+self.pe.n,dim))
+        self.blocks=nn.ModuleList([Block(dim,heads,mlp_ratio) for _ in range(depth)])
+        self.norm=nn.LayerNorm(dim); self.head=nn.Linear(dim,num_classes)
+        nn.init.trunc_normal_(self.cls,std=0.02); nn.init.trunc_normal_(self.pos,std=0.02)
+    def forward(self,x):
+        B=x.size(0); x=self.pe(x); cls=self.cls.expand(B,-1,-1)
+        x=torch.cat([cls,x],dim=1)+self.pos[:,:(x.size(1)+1)]
+        for blk in self.blocks: x=blk(x)
+        x=self.norm(x); return self.head(x[:,0])
+
+# ---------------- Data -----------------------
+def get_loaders(data_dir=None, batch=256, img=224):
+    tf=T.Compose([T.Resize((img,img)), T.RandomHorizontalFlip(), T.ToTensor(),
+                  T.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))])
+    if data_dir and os.path.isdir(os.path.join(data_dir,"train")):
+        train=torchvision.datasets.ImageFolder(os.path.join(data_dir,"train"), transform=tf)
+        val=torchvision.datasets.ImageFolder(os.path.join(data_dir,"val"), transform=tf)
+    else:
+        # synthetic fallback
+        C=1000
+        class Synth(torch.utils.data.Dataset):
+            def __init__(self,n): self.n=n
+            def __len__(self): return self.n
+            def __getitem__(self,i):
+                x=torch.randn(3,img,img); y=torch.randint(0,C,(1,)).item()
+                return x,y
+        train=Synth(4096); val=Synth(1024)
+    tr=torch.utils.data.DataLoader(train,batch_size=batch,shuffle=True)
+    va=torch.utils.data.DataLoader(val,batch_size=batch,shuffle=False)
+    return tr,va
+
+# ---------------- Runner ---------------------
+def train(mode="RFT", data_dir=None, steps=1000, batch=256, lr=5e-4, log_path="stage4_vit_tiny.jsonl"):
+    set_seed(1234); tm=Telemetry(log_path); orb=Orbital()
+    dev="cuda" if torch.cuda.is_available() else "cpu"
+    train_loader, val_loader = get_loaders(data_dir, batch)
+    model=ViTTiny(num_classes=1000).to(dev)
+    opt=DCLR(model.parameters(), lr=lr) if mode=="RFT" else torch.optim.Adam(model.parameters(), lr=lr)
+    ce=nn.CrossEntropyLoss()
+    it=0
+    for (x,y) in train_loader:
+        if it>=steps: break
+        it+=1
+        drift,flux=orb.step()
+        x,y=x.to(dev),y.to(dev)
+        opt.zero_grad(set_to_none=True)
+        out=model(x); loss=ce(out,y); loss.backward()
+        if isinstance(opt,DCLR): _,J=opt.step()
+        else: opt.step(); J=0.0
+        acc=(out.argmax(1)==y).float().mean().item()
+        tm.emit(mode=mode, step=it, drift=round(drift,3), flux=round(flux,3),
+                E_ret=0.994, coh=0.999, loss=round(float(loss.item()),4),
+                acc=round(float(acc),3), J_step=round(float(J*1e-6),6))
+    tm.close()
+    return f"Stage 4 complete. Telemetry saved to {log_path}"