| import sys |
|
|
| sys.path.insert(0, "/tmp/opencode/tl") |
| import numpy as np |
| import torch |
|
|
| import int4hf as H |
| import mirror3 as M |
| from export_full import load as load_weights |
|
|
| M.ATT_C, M.PKSH, M.SILU_SH, M.SILU_RND = 66572, 0, 11, False |
| M.build_tables() |
|
|
| float_m = H.AutoModelForCausalLM.from_pretrained("/tmp/opencode/tl", dtype=torch.float32).eval() |
| int4_m = H.int4_model() |
|
|
| N = 40 |
|
|
|
|
| def greedy(model, n): |
| ids = torch.tensor([[1]]) |
| out = [] |
| with torch.no_grad(): |
| for _ in range(n): |
| lg = model(ids).logits[0, -1] |
| t = int(lg.argmax()) |
| out.append(t) |
| ids = torch.cat([ids, torch.tensor([[t]])], 1) |
| return out |
|
|
|
|
| def mirror_greedy(n): |
| st = M.State() |
| tok = 1 |
| out = [] |
| for pos in range(n): |
| lg = M.step(tok, pos, st) |
| tok = int(lg.argmax()) |
| out.append(tok) |
| return out |
|
|
|
|
| f = greedy(float_m, N) |
| i4 = greedy(int4_m, N) |
| mi = mirror_greedy(N) |
| print("float puro :", f) |
| print("int4 pesos float :", i4) |
| print("pipeline entero :", mi) |
| agree_i4 = next((k for k in range(N) if f[k] != i4[k]), N) |
| agree_mi = next((k for k in range(N) if f[k] != mi[k]), N) |
| same = sum(1 for k in range(N) if i4[k] == mi[k]) |
| print(f"float vs int4-float: coinciden {agree_i4}/{N} | float vs pipeline: {agree_mi}/{N}") |
| print(f"pipeline vs int4-float (fidelidad del kernel): {same}/{N}") |
|
|
