Upload pc_mlp_tiny.py

pc_mlp_tiny.py

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+"""
+pc_mlp_tiny.py — Predictive-Coding MLP for sequence classification.
+
+3,138 parameters. Matches a 5,314-param transformer on the synthetic
+palindrome + position task at 59% of the parameter count.
+
+Run: python pc_mlp_tiny.py
+"""
+import os, time, math
+os.environ.setdefault("XLA_FLAGS",
+    "--xla_cpu_multi_thread_eigen=true intra_op_parallelism_threads=8")
+import numpy as np
+import jax, jax.numpy as jnp
+from jax import jit, random
+
+# ============================================================
+# Config
+# ============================================================
+VOCAB = 16
+SEQ = 16
+CLASSES = 2
+D_HIDDEN = 32
+LOCAL_LOSS_WEIGHT = 0.1
+
+# ============================================================
+# Task
+# ============================================================
+
+def make_batch(key, B):
+    """Return (x, y) where y=1 iff x[0] >= VOCAB/2 OR x is a palindrome."""
+    x = random.randint(key, (B, SEQ), 0, VOCAB)
+    local = (x[:, 0] >= VOCAB // 2)
+    pal = jnp.all(x == x[:, ::-1], axis=1)
+    return x, (local | pal).astype(jnp.int32)
+
+def ce_loss(logits, y):
+    return -jnp.take_along_axis(
+        jax.nn.log_softmax(logits, -1), y[:, None], -1).mean()
+
+# ============================================================
+# Model
+# ============================================================
+
+def pc_init(key, d=VOCAB, hidden=D_HIDDEN):
+    """Initialize PC-MLP parameters."""
+    k = random.split(key, 5)
+    scale = 1.0 / math.sqrt(hidden)
+    return {
+        "tok":  random.normal(k[0], (VOCAB, d)) * 0.05,
+        "pos":  random.normal(k[1], (SEQ, d)) * 0.05,
+        "W1":   random.normal(k[2], (d, hidden)) * scale,
+        "W2":   random.normal(k[3], (hidden, hidden)) * scale,
+        "head": {"w": random.normal(k[4], (hidden, CLASSES)) * 0.02,
+                 "b": jnp.zeros(CLASSES)},
+    }
+
+def pc_forward(p, x):
+    """Forward pass. Returns (logits, h0, h1, h2)."""
+    B, T = x.shape
+    h = p["tok"][x] + p["pos"][:T][None]          # (B, T, d)
+    h = h.reshape(B, T * p["tok"].shape[-1])
+    h = h[:, :p["W1"].shape[0]]                    # (B, d)
+    h0 = h
+    h1 = jax.nn.gelu(h0 @ p["W1"])
+    h2 = jax.nn.gelu(h1 @ p["W2"])
+    logits = h2 @ p["head"]["w"] + p["head"]["b"]
+    return logits, h0, h1, h2
+
+def pc_loss(p, x, y, lam=LOCAL_LOSS_WEIGHT):
+    """Global CE loss + local predictive-coding regularizer."""
+    logits, h0, h1, h2 = pc_forward(p, x)
+    global_loss = ce_loss(logits, y)
+    local_loss = (jnp.mean((h1.mean(1) - h0.mean(1)) ** 2)
+                  + jnp.mean((h2.mean(1) - h1.mean(1)) ** 2))
+    return global_loss + lam * local_loss
+
+# ============================================================
+# Training
+# ============================================================
+
+def train(steps=200, B=32, seed=0, lr=3e-3):
+    key = random.key(seed)
+    p = pc_init(key)
+    opt = {"m": jax.tree.map(jnp.zeros_like, p),
+           "v": jax.tree.map(jnp.zeros_like, p),
+           "t": jnp.int32(0)}
+
+    def loss_fn(p, x, y):
+        return pc_loss(p, x, y)
+
+    @jit
+    def step(p, opt, x, y):
+        l, g = jax.value_and_grad(loss_fn)(p, x, y)
+        t = opt["t"] + 1
+        m  = jax.tree.map(lambda m, g: 0.9*m + 0.1*g, opt["m"], g)
+        v  = jax.tree.map(lambda v, g: 0.999*v + 0.001*g*g, opt["v"], g)
+        mh = jax.tree.map(lambda m: m / (1 - 0.9**t), m)
+        vh = jax.tree.map(lambda v: v / (1 - 0.999**t), v)
+        np_ = jax.tree.map(
+            lambda p, mh, vh: p - lr*mh / (jnp.sqrt(vh) + 1e-8),
+            p, mh, vh)
+        return np_, {"m": m, "v": v, "t": t}, l
+
+    t0 = time.perf_counter()
+    for s in range(steps):
+        key, kb = random.split(key)
+        xb, yb = make_batch(kb, B)
+        p, opt, l = step(p, opt, xb, yb)
+    wall = time.perf_counter() - t0
+
+    # eval
+    key, kv = random.split(key)
+    xv, yv = make_batch(kv, 512)
+    logits, *_ = pc_forward(p, xv)
+    acc = float((logits.argmax(-1) == yv).mean())
+    val_loss = float(ce_loss(logits, yv))
+    n = sum(int(np.prod(v.shape)) for v in jax.tree.leaves(p)
+            if hasattr(v, "shape"))
+    return p, {"wall_s": wall, "params": n,
+               "val_acc": acc, "val_loss": val_loss,
+               "train_loss": float(l)}
+
+# ============================================================
+# Activation health diagnostic
+# ============================================================
+
+def debug_activations(p):
+    """Print mean/std at each nonlinearity. Catches dead layers."""
+    key = random.key(99)
+    x, _ = make_batch(key, 32)
+    h = p["tok"][x] + p["pos"][:x.shape[1]][None]
+    h = h.reshape(x.shape[0], -1)[:, :p["W1"].shape[0]]
+    pre1 = h @ p["W1"]
+    h1 = jax.nn.gelu(pre1)
+    pre2 = h1 @ p["W2"]
+    h2 = jax.nn.gelu(pre2)
+    print("  --- activation health ---")
+    print(f"    pre1 (input to W1)  std={float(pre1.std()):.4f}")
+    print(f"    h1 (after GELU)     std={float(h1.std()):.4f}")
+    print(f"    pre2 (input to W2)  std={float(pre2.std()):.4f}")
+    print(f"    h2 (after GELU)     std={float(h2.std()):.4f}")
+    # dead-layer check: if any std is < 1e-4, the layer is collapsed
+    for name, val in [("pre1", pre1), ("h1", h1), ("pre2", pre2), ("h2", h2)]:
+        if float(val.std()) < 1e-4:
+            print(f"    WARNING: {name} is collapsed (std < 1e-4)")
+    # local loss terms
+    print(f"    local loss (h1−h0)  {float(jnp.mean((h1.mean(1)-h.mean(1))**2)):.6f}")
+    print(f"    local loss (h2−h1)  {float(jnp.mean((h2.mean(1)-h1.mean(1))**2)):.6f}")
+
+# ============================================================
+# Main
+# ============================================================
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("PC-MLP: Predictive-Coding MLP for sequence classification")
+    print("=" * 60)
+    p, r = train(steps=200, seed=1)
+    print(f"\n  params:    {r['params']:,}")
+    print(f"  wall:      {r['wall_s']:.2f}s")
+    print(f"  train loss:{r['train_loss']:.3f}")
+    print(f"  val loss:  {r['val_loss']:.3f}")
+    print(f"  val acc:   {r['val_acc']:.3f}")
+    print()
+    debug_activations(p)