Generate functionally correct weights from Neuron

weights.py

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+#!/usr/bin/env python3
+"""
+Generate FUNCTIONALLY CORRECT MLP weights for Gemma 4 31B (SwiGLU) integration.
+Uses Sign-Symmetric Aligned Pairs to eliminate mean-shift without destroying alignment.
+
+Gemma 4 31B architecture:
+  - 60 transformer layers (10 global full-context + 50 sliding-window attention)
+  - Interleaved attention: 5 SWA (sliding-window, 1024-token context) + 1 global full-context (period=6)
+  - Global attention layers (5, 11, 17, 23, 29, 35, 41, 47, 53, 59) use double-wide MLP
+  - Activation: gelu_pytorch_tanh
+"""
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+from safetensors.torch import load_file, save_file
+from scipy.special import expit as _sigmoid
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+NEURON_SOURCE = "single"
+SINGLE_FILE = "test_mlp_hf/model.safetensors"
+MULTI_DIR = "generated_neurons/gaussian"
+
+SINGLE_BOUNDARY_MODE = True
+
+# Gemma 4 31B defaults
+N_LAYERS = 60
+HIDDEN_SIZE = 3840
+INTERMEDIATE_SIZE = 15360
+
+# Gemma 4 31B interleaved attention: 5 SWA layers then 1 global, repeating.
+# Global layers use double-wide MLP intermediate size.
+INTERLEAVE_PERIOD = 6          # one global every 6 layers
+GLOBAL_LAYER_OFFSET = 5        # first global is at index 5 (0-based)
+DEFAULT_ACTIVATION = "gelu_pytorch_tanh"
+
+OUTPUT_DIR = "generated_weights_gemma4_31b"
+RANDOM_SEED = 42
+
+
+def is_global_attention_layer(layer_idx: int, period: int = INTERLEAVE_PERIOD,
+                               offset: int = GLOBAL_LAYER_OFFSET) -> bool:
+    """Return True if this layer uses global full-context attention (and thus double-wide MLP)."""
+    return (layer_idx - offset) % period == 0 and layer_idx >= offset
+
+
+def get_gating_function(name):
+    if name == "silu":
+        return _sigmoid
+    elif name == "gelu_pytorch_tanh":
+        alpha = np.sqrt(2.0 / np.pi)
+        return lambda z: 0.5 * (1.0 + np.tanh(alpha * (z + 0.044715 * z**3)))
+    elif name == "gelu":
+        from scipy.special import erf
+        return lambda z: 0.5 * (1.0 + erf(z / np.sqrt(2.0)))
+    else:
+        raise ValueError(f"Unsupported activation: {name}")
+
+
+def get_activation(name):
+    g_fn = get_gating_function(name)
+    return lambda z: z * g_fn(z)
+
+
+# ---------------------------------------------------------------------------
+# 1. Load and extract functional parameters
+# ---------------------------------------------------------------------------
+
+def load_neurons(source, single_file, multi_dir):
+    """Load source neurons."""
+    neurons = []
+    if source == "single":
+        w = load_file(single_file)
+        neurons.append(
+            {
+                k: v.float().numpy()
+                for k, v in {
+                    "W1": w["layer1.weight"],
+                    "b1": w["layer1.bias"],
+                    "W2": w["layer2.weight"],
+                    "b2": w["layer2.bias"],
+                }.items()
+            }
+        )
+    elif source == "multi":
+        for f in sorted(Path(multi_dir).glob("neuron_*.safetensors")):
+            w = load_file(str(f))
+            neurons.append(
+                {
+                    k: v.float().numpy()
+                    for k, v in {
+                        "W1": w["layer1.weight"],
+                        "b1": w["layer1.bias"],
+                        "W2": w["layer2.weight"],
+                        "b2": w["layer2.bias"],
+                    }.items()
+                }
+            )
+    return neurons
+
+
+def extract_functional_params(W1, b1, W2, b2, n_samples=10000):
+    """Extract piecewise linear parameters from reference neurons."""
+    xs = np.linspace(-4, 4, n_samples)
+    ys = []
+
+    for x in xs:
+        h = np.maximum(0, W1 @ np.array([[x]]) + b1.reshape(-1, 1))
+        y = (W2 @ h + b2.reshape(-1, 1)).item()
+        ys.append(y)
+
+    ys = np.array(ys)
+    slopes = np.gradient(ys, xs)
+    slope_changes = np.abs(np.gradient(slopes, xs))
+
+    from scipy.signal import find_peaks
+    peaks, _ = find_peaks(slope_changes, height=np.max(slope_changes) * 0.1, distance=100)
+
+    if len(peaks) >= 2:
+        idx1, idx2 = sorted(peaks[:2])
+    elif len(peaks) == 1:
+        idx1, idx2 = 0, peaks[0]
+    else:
+        idx1, idx2 = n_samples // 3, 2 * n_samples // 3
+
+    boundary_x1 = float(xs[idx1])
+    boundary_x2 = float(xs[idx2])
+
+    left_slope = float(np.mean(slopes[:idx1])) if idx1 > 0 else float(slopes[0])
+    mid_slope = float(np.mean(slopes[idx1:idx2]))
+    right_slope = float(np.mean(slopes[idx2:]))
+    y_boundary2 = float(ys[idx2])
+
+    return {
+        "boundary_x1": boundary_x1,
+        "boundary_x2": boundary_x2,
+        "left_slope": left_slope,
+        "mid_slope": mid_slope,
+        "right_slope": right_slope,
+        "y_boundary2": y_boundary2,
+    }
+
+
+# ---------------------------------------------------------------------------
+# 2. Construct functional dense layer (Sign-Symmetric Aligned Pairs)
+# ---------------------------------------------------------------------------
+
+def construct_functional_layer(
+    functional_params,
+    hidden_size,
+    intermediate_size,
+    gating_fn,
+    activation_fn,
+    has_bias=False,
+    source_weights=None,
+    rng_seed: int = 0,
+):
+    p = functional_params
+    boundary = p["boundary_x1"]
+    left_slope = p["left_slope"]
+    right_slope = p["right_slope"]
+
+    W_gate = np.zeros((intermediate_size, hidden_size), dtype=np.float32)
+    W_up = np.zeros((intermediate_size, hidden_size), dtype=np.float32)
+    W_down = np.zeros((hidden_size, intermediate_size), dtype=np.float32)
+
+    if SINGLE_BOUNDARY_MODE:
+        n_carrier = intermediate_size // 2
+        n_transition = intermediate_size - n_carrier
+        slope_diff = left_slope - right_slope
+    else:
+        n_carrier = intermediate_size // 3
+        n_transition = intermediate_size - n_carrier
+        slope_diff = p["left_slope"] - p["mid_slope"]
+
+    _fill_swiglu(
+        W_gate, W_up, W_down,
+        n_carrier, n_transition,
+        hidden_size, intermediate_size,
+        boundary, right_slope, slope_diff,
+        gating_fn, activation_fn,
+        rng_seed=rng_seed,
+    )
+
+    return W_gate, W_up, W_down
+
+
+def _fill_swiglu(
+    W_gate, W_up, W_down,
+    n_carrier, n_transition,
+    hidden_size, intermediate_size,
+    boundary, right_slope, slope_diff,
+    gating_fn, activation_fn,
+    rng_seed: int = 0,
+):
+    """
+    Fills SwiGLU projections using clean sign-symmetric paired frames.
+    """
+    H = hidden_size
+    rng = np.random.default_rng(rng_seed)
+
+    # Re-apportion matrices to ensure strict pairs
+    n_pairs_carrier = n_carrier // 2
+    if n_pairs_carrier == 0: n_pairs_carrier = 1
+    n_neurons_carrier = 2 * n_pairs_carrier
+    
+    n_neurons_transition = intermediate_size - n_neurons_carrier
+    n_pairs_transition = n_neurons_transition // 2
+
+    # 1. Generate unified random projection frame vectors
+    n_total_pairs = n_pairs_carrier + n_pairs_transition
+    dirs = rng.standard_normal((n_total_pairs, H)).astype(np.float32)
+    dirs /= np.linalg.norm(dirs, axis=1, keepdims=True)
+
+    # 2. Monte-Carlo gain calibration for the odd function: z^3 * (2*gate_fn(g*z) - 1)
+    cal_rng = np.random.default_rng(0xCA1_5EED)
+    z_samples = cal_rng.standard_normal(100_000)
+
+    # Carrier calibration
+    g_c = 1.0
+    gain_c = float(np.mean((z_samples**3) * (2 * gating_fn(g_c * z_samples) - 1)))
+    u_c = 1.0
+    v_c = (right_slope * 4.0 * H) / (n_neurons_carrier * u_c * g_c * gain_c) if abs(gain_c) > 1e-6 else 0.0
+
+    # Transition calibration
+    g_t = 2.0  
+    gain_t = float(np.mean((z_samples**3) * (2 * gating_fn(g_t * z_samples) - 1)))
+    u_t = 1.0
+    v_t = (slope_diff * 4.0 * H) / (n_neurons_transition * u_t * g_t * gain_t) if abs(gain_t) > 1e-6 else 0.0
+
+    # 3. Populate carrier pairs
+    for i in range(n_pairs_carrier):
+        d = dirs[i]
+        idx1 = 2 * i
+        idx2 = 2 * i + 1
+
+        W_gate[idx1, :] = g_c * d
+        W_up[idx1, :]   = u_c * d
+        W_down[:, idx1] = (v_c / 2.0) * d
+
+        W_gate[idx2, :] = -g_c * d
+        W_up[idx2, :]   = u_c * d
+        W_down[:, idx2] = (v_c / 2.0) * d
+
+    # 4. Populate transition pairs
+    for i in range(n_pairs_transition):
+        d = dirs[n_pairs_carrier + i]
+        idx1 = n_neurons_carrier + 2 * i
+        idx2 = n_neurons_carrier + 2 * i + 1
+
+        W_gate[idx1, :] = g_t * d
+        W_up[idx1, :]   = u_t * d
+        W_down[:, idx1] = (v_t / 2.0) * d
+
+        W_gate[idx2, :] = -g_t * d
+        W_up[idx2, :]   = u_t * d
+        W_down[:, idx2] = (v_t / 2.0) * d
+
+    # 5. Global Zero-Mean Normalization
+    W_down -= W_down.mean(axis=0, keepdims=True)
+
+    # 6. Empirical Validation Test Pass
+    test_rng = np.random.default_rng(rng_seed + 99)
+    x_test = test_rng.standard_normal((1024, H)).astype(np.float32)
+    gate_act = activation_fn(x_test @ W_gate.T)
+    up_act = x_test @ W_up.T
+    out_test = (gate_act * up_act) @ W_down.T
+
+    target_scale = max(abs(right_slope), 0.05)
+    empirical_scale = np.sqrt(np.var(out_test) / np.var(x_test))
+    if empirical_scale > 1e-7:
+        correction_factor = target_scale / empirical_scale
+        W_down *= correction_factor
+        print(f"    [Stability Check] Layer {rng_seed - 42:02d} W_down scaling verification factor: {correction_factor:.4f}")
+
+    print(f"    Carrier Pairs:    {n_pairs_carrier} (g_c={g_c}, u_c={u_c}, v_c={v_c:.4f})")
+    print(f"    Transition Pairs: {n_pairs_transition} (g_t={g_t}, u_t={u_t}, v_t={v_t:.4f})")
+
+
+# ---------------------------------------------------------------------------
+# 3. Execution Pipeline
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate functional SwiGLU weights for Gemma")
+    parser.add_argument("--source", default=NEURON_SOURCE, choices=["single", "multi"])
+    parser.add_argument("--single-file", default=SINGLE_FILE)
+    parser.add_argument("--multi-dir", default=MULTI_DIR)
+    parser.add_argument("--n-layers", type=int, default=N_LAYERS)
+    parser.add_argument("--hidden-size", type=int, default=HIDDEN_SIZE)
+    parser.add_argument("--intermediate-size", type=int, default=INTERMEDIATE_SIZE)
+    parser.add_argument("--output-dir", default=OUTPUT_DIR)
+    parser.add_argument("--seed", type=int, default=RANDOM_SEED)
+    parser.add_argument("--target-layers", type=int, nargs="+", default=None)
+    parser.add_argument("--has-bias", action="store_true", default=False)
+    parser.add_argument("--base-model", default=None, help="Base model directory path to inspect config for layers and dimensions")
+    parser.add_argument("--activation", default=None, choices=["silu", "gelu_pytorch_tanh", "gelu"], help="Override activation function (otherwise inferred from base-model or default silu)")
+    args = parser.parse_args()
+
+    # Detect model parameters if base-model is provided
+    inferred_n_layers = args.n_layers
+    inferred_hidden_size = args.hidden_size
+    inferred_intermediate_size = args.intermediate_size
+    inferred_activation = args.activation if args.activation else DEFAULT_ACTIVATION
+    use_double_wide_mlp = False
+    interleave_period = INTERLEAVE_PERIOD
+    global_layer_offset = GLOBAL_LAYER_OFFSET
+
+    if args.base_model:
+        print(f"[config] Reading config from base model: {args.base_model}")
+        base_path = Path(args.base_model)
+        config_file = base_path / "config.json"
+        if not config_file.exists():
+            raise FileNotFoundError(f"Config not found at {config_file}")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        text_config = config.get("text_config", {})
+        
+        def get_val(key, default_None):
+            return text_config.get(key, config.get(key, default_None))
+
+        inferred_n_layers = get_val("num_hidden_layers", inferred_n_layers)
+        inferred_hidden_size = get_val("hidden_size", inferred_hidden_size)
+        inferred_intermediate_size = get_val("intermediate_size", inferred_intermediate_size)
+        use_double_wide_mlp = get_val("use_double_wide_mlp", False)
+        # Gemma 4 31B stores interleave info as attention_pattern or sliding_window counts;
+        # fall back to module-level constants if not present in config.
+        interleave_period = get_val("attention_pattern_period", interleave_period)
+        global_layer_offset = get_val("global_layer_offset", global_layer_offset)
+
+        if not args.activation:
+            inferred_activation = get_val("hidden_activation", inferred_activation)
+
+        print(f"  Detected configuration:")
+        print(f"    Layers: {inferred_n_layers}")
+        print(f"    Hidden Size: {inferred_hidden_size}")
+        print(f"    Base Intermediate Size: {inferred_intermediate_size}")
+        print(f"    Activation: {inferred_activation}")
+        print(f"    Double Wide MLP: {use_double_wide_mlp}")
+        print(f"    Interleave Period: {interleave_period}  (global offset: {global_layer_offset})")
+
+    out = Path(args.output_dir)
+    out.mkdir(exist_ok=True)
+
+    print("=" * 60)
+    print("FUNCTIONAL PAIR-ALIGNED SwiGLU Generation (Gemma 4 31B)")
+    print("=" * 60)
+
+    print("[1] Loading source neurons...")
+    neurons = load_neurons(args.source, args.single_file, args.multi_dir)
+    
+    print("[2] Extracting functional behavior...")
+    functional_params = []
+    for i, n in enumerate(neurons):
+        p = extract_functional_params(n["W1"], n["b1"], n["W2"], n["b2"])
+        functional_params.append(p)
+
+    source_weights = neurons[0]
+    layer_indices = args.target_layers if args.target_layers is not None else range(inferred_n_layers)
+    gating_fn = get_gating_function(inferred_activation)
+    activation_fn = get_activation(inferred_activation)
+
+    print(f"\n[3] Encoding {len(layer_indices)} layers using activation: {inferred_activation}...")
+    for layer_idx in layer_indices:
+        neuron_idx = (layer_idx * len(neurons)) // inferred_n_layers
+        base_params = functional_params[neuron_idx]
+
+        # Determine intermediate size: global attention layers use double-wide MLP.
+        # Gemma 4 31B interleaves 5 SWA + 1 global every INTERLEAVE_PERIOD layers.
+        if use_double_wide_mlp and is_global_attention_layer(layer_idx, interleave_period, global_layer_offset):
+            layer_intermediate_size = inferred_intermediate_size * 2
+            layer_type = "global"
+        else:
+            layer_intermediate_size = inferred_intermediate_size
+            layer_type = "swa" if use_double_wide_mlp else "std"
+
+        print(f"  Layer {layer_idx:02d} [{layer_type}]: intermediate_size = {layer_intermediate_size}")
+
+        W_gate, W_up, W_down = construct_functional_layer(
+            base_params, inferred_hidden_size, layer_intermediate_size,
+            gating_fn, activation_fn,
+            has_bias=False, source_weights=source_weights, rng_seed=args.seed + layer_idx,
+        )
+
+        out_path = out / f"layer_{layer_idx:02d}.safetensors"
+        tensors = {
+            "gate_proj.weight": torch.tensor(W_gate),
+            "up_proj.weight": torch.tensor(W_up),
+            "down_proj.weight": torch.tensor(W_down),
+        }
+        save_file(tensors, str(out_path))
+
+    with open(out / "meta.json", "w") as f:
+        json.dump({
+            "config": {
+                "hidden_size": inferred_hidden_size,
+                "intermediate_size": inferred_intermediate_size,
+                "n_layers": inferred_n_layers,
+                "activation": inferred_activation,
+                "use_double_wide_mlp": use_double_wide_mlp,
+                "interleave_period": interleave_period,
+                "global_layer_offset": global_layer_offset,
+                "encoding": "sign_symmetric_pairs"
+            }
+        }, f, indent=2)
+
+    print(f"\nComplete! Balanced weights saved safely to: {out}/")
+
+
+if __name__ == "__main__":
+    main()