forked from kent/consciousness
replace try_lock() with lock_blocking() across UI thread
Add lock_blocking() to TrackedMutex: blocks current thread using block_in_place + futures::executor::block_on, safe for sync contexts. Replace all try_lock() calls with lock_blocking() in slash commands, UI rendering, and status reads. Lock hold times are fast enough that blocking briefly is fine, and this eliminates the spurious 'lock unavailable' paths that were never actually hit. Kept rx_mutex.try_lock() in mod.rs (std::sync::Mutex for stderr rx).
This commit is contained in:
Kent Overstreet
parent
5210f7dd66
commit
4225294d16
28 changed files with 4199 additions and 67 deletions
238
sa-schedule-layer-variation.py
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238
sa-schedule-layer-variation.py
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"""After removing the known gauge freedoms (per-head d_h rotation tying
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W_Q/W_K/W_V/W_O together, per-layer d_ff rotation tying gate/up/down),
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measure per-family Frobenius distance between consecutive layers within a
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middle block. Families with low post-alignment distance are candidates for
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"shared operator" across the block; high distance → carries the schedule.
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Normalize each matrix by its Frobenius norm first (so scale differences
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don't dominate). We want to see direction of drift, not magnitude.
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Gauge freedoms being removed:
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- Per-head d_h rotation R ∈ O(d_h): W_Q^h, W_K^h, W_V^h → R W^h;
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W_O^h → W_O^h R^T. Softmax attention is invariant under this.
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- Per-layer d_ff rotation S ∈ O(d_ff): gate_proj, up_proj → S W;
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down_proj → W S^T. SwiGLU/GLU is NOT fully invariant under d_ff
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rotation (because the elementwise gate*up is coordinate-dependent),
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so this is an approximate alignment — still better than raw.
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Families that have no gauge freedom (layernorm γ, q_norm, k_norm): compare
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directly after scale normalization.
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"""
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import argparse
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import json
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import numpy as np
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import torch
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from transformers import AutoModelForCausalLM
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def procrustes(M):
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"""Orthogonal matrix R maximizing tr(R M). Given SVD M = U Σ V^T, R = U V^T."""
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U, _, Vh = np.linalg.svd(M, full_matrices=False)
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return U @ Vh
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def fro(x):
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return float(np.linalg.norm(x))
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def normalize_fro(x, eps=1e-12):
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n = fro(x)
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return x / max(n, eps)
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@torch.no_grad()
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="Qwen/Qwen3-4B")
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ap.add_argument("--block-start", type=int, default=10)
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ap.add_argument("--block-end", type=int, default=25,
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help="inclusive; this is mid-block of 36-layer model")
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ap.add_argument("--out", default="/tmp/sa-layer-variation.json")
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args = ap.parse_args()
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print(f"Loading {args.model} ...", flush=True)
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model = AutoModelForCausalLM.from_pretrained(
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args.model,
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torch_dtype=torch.float32,
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device_map="cpu",
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trust_remote_code=True,
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attn_implementation="eager",
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)
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cfg = model.config
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num_layers = cfg.num_hidden_layers
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num_heads = cfg.num_attention_heads
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num_kv_heads = getattr(cfg, "num_key_value_heads", num_heads)
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hidden = cfg.hidden_size
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head_dim = getattr(cfg, "head_dim", hidden // num_heads)
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intermediate = cfg.intermediate_size
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print(f" L={num_layers} H={num_heads} kv={num_kv_heads} hd={head_dim} "
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f"hidden={hidden} ff={intermediate}", flush=True)
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# Collect per-layer weight matrices as numpy float32.
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def get_np(name, idx):
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w = getattr(model.model.layers[idx], name, None)
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if w is None:
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return None
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return w
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layers = {}
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for L in range(num_layers):
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layer = model.model.layers[L]
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attn = layer.self_attn
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mlp = layer.mlp
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layers[L] = {
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"q_proj": attn.q_proj.weight.detach().numpy().astype(np.float32), # (nh*hd, hidden)
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"k_proj": attn.k_proj.weight.detach().numpy().astype(np.float32), # (nkv*hd, hidden)
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"v_proj": attn.v_proj.weight.detach().numpy().astype(np.float32),
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"o_proj": attn.o_proj.weight.detach().numpy().astype(np.float32), # (hidden, nh*hd)
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"gate_proj": mlp.gate_proj.weight.detach().numpy().astype(np.float32),
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"up_proj": mlp.up_proj.weight.detach().numpy().astype(np.float32),
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"down_proj": mlp.down_proj.weight.detach().numpy().astype(np.float32),
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"input_ln": layer.input_layernorm.weight.detach().numpy().astype(np.float32),
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"post_attn_ln": layer.post_attention_layernorm.weight.detach().numpy().astype(np.float32),
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}
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# Qwen3 has q_norm / k_norm inside self_attn
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q_norm = getattr(attn, "q_norm", None)
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k_norm = getattr(attn, "k_norm", None)
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if q_norm is not None:
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layers[L]["q_norm"] = q_norm.weight.detach().numpy().astype(np.float32)
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if k_norm is not None:
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layers[L]["k_norm"] = k_norm.weight.detach().numpy().astype(np.float32)
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del model # free memory
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block = list(range(args.block_start, args.block_end + 1))
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pairs = [(block[i], block[i + 1]) for i in range(len(block) - 1)]
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print(f"\nAnalyzing block layers {args.block_start}..{args.block_end} "
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f"({len(pairs)} consecutive pairs)\n")
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# ------------------------------------------------------------------
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# Reshape attention weights per-head for rotation alignment
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# ------------------------------------------------------------------
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def per_head_split(W_qkv, n_heads_for_this):
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# W is (n*hd, hidden). Reshape to (n, hd, hidden).
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return W_qkv.reshape(n_heads_for_this, head_dim, hidden)
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def per_head_split_o(W_o):
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# W is (hidden, n*hd). Reshape to (n, hidden, hd).
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return W_o.reshape(hidden, num_heads, head_dim).transpose(1, 0, 2)
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# Replicate k/v head index to query head index space (GQA)
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def kv_to_q_index(h):
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return (h * num_kv_heads) // num_heads
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family_residuals = {fam: [] for fam in
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["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",
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"input_ln", "post_attn_ln", "q_norm", "k_norm"]}
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for (L1, L2) in pairs:
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A = layers[L1]
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B = layers[L2]
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# Per-head attention alignment:
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Q1 = per_head_split(A["q_proj"], num_heads)
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Q2 = per_head_split(B["q_proj"], num_heads)
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K1 = per_head_split(A["k_proj"], num_kv_heads)
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K2 = per_head_split(B["k_proj"], num_kv_heads)
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V1 = per_head_split(A["v_proj"], num_kv_heads)
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V2 = per_head_split(B["v_proj"], num_kv_heads)
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O1 = per_head_split_o(A["o_proj"]) # (num_heads, hidden, hd)
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O2 = per_head_split_o(B["o_proj"])
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q_res = []
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k_res = []
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v_res = []
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o_res = []
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for h in range(num_heads):
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kv_h = kv_to_q_index(h)
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# Normalize each matrix by its Frobenius norm
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qa = normalize_fro(Q1[h])
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qb = normalize_fro(Q2[h])
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ka = normalize_fro(K1[kv_h])
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kb = normalize_fro(K2[kv_h])
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va = normalize_fro(V1[kv_h])
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vb = normalize_fro(V2[kv_h])
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oa = normalize_fro(O1[h])
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ob = normalize_fro(O2[h])
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# Cross-correlation for Procrustes: find R (hd × hd) maximizing
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# tr(R [Qa Qb^T + Ka Kb^T + Va Vb^T + (Oa^T Ob)])
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# Q, K, V are (hd, hidden); Q2 Q1^T would be (hd, hd); etc.
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M = qa @ qb.T + ka @ kb.T + va @ vb.T + (oa.T @ ob) # all (hd, hd)
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# Wait: for Q we want tr(R qa qb^T). So the matrix in the max-trace
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# Procrustes is qb @ qa.T? Let me be careful.
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# max_R tr(R M) achieved at R = U V^T with SVD M = U Σ V^T.
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# Here we want R such that R qa ≈ qb → minimize ||R qa - qb||²
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# = const - 2 tr(R qa qb^T). So max tr(R qa qb^T) gives the
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# correct R. Redo M as sum of qa qb^T terms.
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M = qa @ qb.T + ka @ kb.T + va @ vb.T
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# For O: want W_O^h R^T ≈ W_O^h_target, i.e. oa R^T ≈ ob
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# → min ||oa R^T - ob||² = const - 2 tr(R oa^T ob); max that.
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# So O contributes oa^T @ ob to the cross-correlation matrix.
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M = M + oa.T @ ob
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R = procrustes(M)
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# Apply R and measure residual (Frobenius distance) per-matrix
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q_res.append(fro(R @ qa - qb))
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k_res.append(fro(R @ ka - kb))
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v_res.append(fro(R @ va - vb))
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o_res.append(fro(oa @ R.T - ob))
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family_residuals["q_proj"].append(float(np.mean(q_res)))
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family_residuals["k_proj"].append(float(np.mean(k_res)))
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family_residuals["v_proj"].append(float(np.mean(v_res)))
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family_residuals["o_proj"].append(float(np.mean(o_res)))
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# MLP d_ff rotation alignment: find S (d_ff × d_ff) orthogonal with
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# S gate_a ≈ gate_b and S up_a ≈ up_b simultaneously; adjust down_proj.
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# Each is (d_ff, hidden).
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ga = normalize_fro(A["gate_proj"])
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gb = normalize_fro(B["gate_proj"])
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ua = normalize_fro(A["up_proj"])
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ub = normalize_fro(B["up_proj"])
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da = normalize_fro(A["down_proj"]) # (hidden, d_ff)
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db = normalize_fro(B["down_proj"])
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# M_ff = ga @ gb^T + ua @ ub^T + da^T @ db (all d_ff × d_ff)
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M_ff = ga @ gb.T + ua @ ub.T + da.T @ db
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S = procrustes(M_ff)
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family_residuals["gate_proj"].append(fro(S @ ga - gb))
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family_residuals["up_proj"].append(fro(S @ ua - ub))
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family_residuals["down_proj"].append(fro(da @ S.T - db))
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# LayerNorm γ vectors — no rotation gauge; just scale-normalize and diff
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for ln_name in ["input_ln", "post_attn_ln", "q_norm", "k_norm"]:
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if ln_name in A and ln_name in B:
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va_ = normalize_fro(A[ln_name])
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vb_ = normalize_fro(B[ln_name])
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family_residuals[ln_name].append(fro(va_ - vb_))
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# Report
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print("=== Per-family Frobenius residual between consecutive layers, "
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f"block L={args.block_start}..{args.block_end}, after alignment + scale-norm ===\n")
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print(f" (Residual = Frobenius distance between L and L+1 after rotation alignment;")
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print(f" lower = more shared across block; higher = carries layer-to-layer drift)\n")
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print(f" {'family':>14} {'mean':>8} {'min':>8} {'max':>8} {'std':>8} n")
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# Report families sorted by mean variation
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items = [(fam, np.array(v)) for fam, v in family_residuals.items() if len(v) > 0]
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items.sort(key=lambda kv: float(kv[1].mean()))
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for fam, v in items:
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print(f" {fam:>14} {v.mean():>8.4f} {v.min():>8.4f} {v.max():>8.4f} {v.std():>8.4f} {len(v)}")
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print(f"\n Families ranked least-to-most variation:")
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for i, (fam, v) in enumerate(items):
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print(f" {i+1}. {fam} (mean residual {v.mean():.4f})")
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# Save
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with open(args.out, "w") as f:
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json.dump({
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"model": args.model,
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"block_start": args.block_start,
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"block_end": args.block_end,
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"family_residuals": {k: list(v) for k, v in family_residuals.items()},
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}, f, indent=2)
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print(f"\nSaved: {args.out}")
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if __name__ == "__main__":
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main()
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