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2049fc8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | 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}")
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