From 1ace114d0c39f28c22222833f9fa6ccfca22c24c Mon Sep 17 00:00:00 2001
From: Vladimir Protsenko <protsenkovi@gmail.com>
Date: Sat, 14 Feb 2026 14:48:59 +0000
Subject: [PATCH] optic mul error test

---
 src/basic_optic_mm_test.py | 93 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)
 create mode 100644 src/basic_optic_mm_test.py

diff --git a/src/basic_optic_mm_test.py b/src/basic_optic_mm_test.py
new file mode 100644
index 0000000..bc0a883
--- /dev/null
+++ b/src/basic_optic_mm_test.py
@@ -0,0 +1,93 @@
+import torch
+import torch.nn as nn
+import optical_matrix_multiplication as omm
+import matplotlib.pyplot as plt
+device = 'cpu' 
+
+h_dim = 64
+pixel_size = 3.6e-6
+batch_size = 100
+test_lengths = [59, 64, 128, 256, 512]
+
+for max_seq_len in test_lengths:
+    if max_seq_len < 512:
+        sim_scores = omm.OpticalMul(
+                omm.Config(right_matrix_count_columns = max_seq_len,
+                            right_matrix_count_rows = h_dim,
+                            right_matrix_width = pixel_size * max_seq_len,
+                            right_matrix_height = pixel_size * h_dim,
+                            min_height_gap = pixel_size,
+                            right_matrix_split_x = 2,
+                            right_matrix_split_y = 2,
+                            left_matrix_split_x = 2,
+                            left_matrix_split_y = 2,
+                            result_matrix_split = 2,
+                            distance = 0.01,
+                            lens_size = 8192)
+            )
+        sim_output = omm.OpticalMul(
+            omm.Config(right_matrix_count_columns = h_dim,
+                        right_matrix_count_rows = max_seq_len,
+                        right_matrix_width = pixel_size * h_dim,
+                        right_matrix_height = pixel_size * max_seq_len,
+                        min_height_gap = pixel_size,
+                        right_matrix_split_x = 2,
+                        right_matrix_split_y = 2,
+                        left_matrix_split_x = 2,
+                        left_matrix_split_y = 2,
+                        result_matrix_split = 2,
+                        distance = 0.01,
+                        lens_size = 8192)
+        )
+    else:
+        sim_scores = omm.OpticalMul(
+            omm.Config(right_matrix_count_columns = max_seq_len,
+                        right_matrix_count_rows = h_dim,
+                        right_matrix_width = pixel_size * max_seq_len,
+                        right_matrix_height = pixel_size * h_dim,
+                        min_height_gap = pixel_size,
+                        right_matrix_split_x = 2,
+                        right_matrix_split_y = 2,
+                        left_matrix_split_x = 2,
+                        left_matrix_split_y = 2,
+                        result_matrix_split = 2,
+                        distance = 0.15,
+                        lens_size = 8192 * 2)
+        )
+        sim_output = omm.OpticalMul(
+            omm.Config(right_matrix_count_columns = h_dim,
+                        right_matrix_count_rows = max_seq_len,
+                        right_matrix_width = pixel_size * h_dim,
+                        right_matrix_height = pixel_size * max_seq_len,
+                        min_height_gap = pixel_size,
+                        right_matrix_split_x = 2,
+                        right_matrix_split_y = 2,
+                        left_matrix_split_x = 2,
+                        left_matrix_split_y = 2,
+                        result_matrix_split = 2,
+                        distance = 0.15,
+                        lens_size = 8192 * 2)
+        )
+
+    def cko(x,y):
+        x = x**2
+        y = y**2
+        return (((x / x.mean() - y / y.mean())**2).mean())**0.5 * 100
+
+    sim_scores = sim_scores.to(device=device)
+    sim_output = sim_output.to(device=device)
+
+    q = torch.rand((batch_size, 1, max_seq_len, h_dim)).to(device=device)
+    k = torch.rand((batch_size, 1, max_seq_len, h_dim)).to(device=device).transpose(-2, -1)
+    true_scores = q @ k
+    opt_scores = sim_scores(q, k)
+    CKO_scores = cko(true_scores, opt_scores).detach().cpu().numpy()
+
+    scores = torch.rand((batch_size, 1, max_seq_len, max_seq_len)).to(device=device)
+    v = torch.rand((batch_size, 1, max_seq_len, h_dim)).to(device=device)
+    true_o = scores @ v
+    opt_o = sim_output(scores, v)
+    CKO_o = cko(true_o, opt_o).detach().cpu().numpy()
+
+    print(f"CKO sim_scores[{h_dim},{max_seq_len}] [{q.shape[-2]}, {q.shape[-1]}]x[{k.shape[-2]}, {k.shape[-1]}]: {CKO_scores}")
+    print(f"CKO sim_output[{max_seq_len},{h_dim}] [{true_scores.shape[-2]}, {true_scores.shape[-1]}]x[{v.shape[-2]}, {v.shape[-1]}]: {CKO_o}")
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