chore: import upstream snapshot with attribution

2026-07-13 12:34:46 +08:00
commit f4e68ed970
84 changed files with 14896 additions and 0 deletions
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""Bit-exact correctness test for INT8×INT8→INT32 TensorOps kernel.
+
+Pure integer matmul must produce EXACT results — no floating-point
+tolerance, no cosine similarity. Every element must match bit-for-bit.
+
+Usage:
+    cd /path/to/cider
+    python tests/test_bitexact.py
+"""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+import mlx.core as mx
+from cider import int8_matmul_int32
+
+# ── Test shapes ─────────────────────────────────────────────────
+SHAPES = [
+    # (M, K, N)
+    (1,    2,    2),       # minimal
+    (2,    4,    2),       # tiny
+    (1,    128,  128),     # single row
+    (1,    4096, 4096),    # decode-size
+    (4,    2048, 2048),    # small batch
+    (16,   4096, 4096),    # medium
+    (32,   4096, 4096),    # tile boundary (small→large)
+    (64,   4096, 4096),    # large tile
+    (128,  4096, 4096),    # full large tile
+    (16,   4096, 8192),    # wide N
+    (16,   8192, 4096),    # wide K
+    (64,   2048, 4096),    # mixed
+    (7,    513,  1025),    # non-aligned (prime-ish)
+    (33,   4097, 4095),    # non-aligned near 4096
+]
+
+
+def test_bitexact():
+    print("=" * 70)
+    print("INT8×INT8→INT32 Bit-Exact Tests")
+    print("=" * 70)
+    passed, failed = 0, 0
+
+    for M, K, N in SHAPES:
+        np.random.seed(42)
+        a_np = np.random.randint(-128, 128, (M, K), dtype=np.int8)
+        b_np = np.random.randint(-128, 128, (N, K), dtype=np.int8)
+
+        # GPU kernel
+        c_gpu = int8_matmul_int32(mx.array(a_np), mx.array(b_np))
+        mx.eval(c_gpu)
+        c_gpu_np = np.array(c_gpu)
+
+        # Numpy reference (int64 to avoid overflow in accumulation)
+        c_ref = (a_np.astype(np.int64) @ b_np.astype(np.int64).T).astype(np.int32)
+
+        # Bit-exact check
+        match = np.array_equal(c_gpu_np, c_ref)
+        if match:
+            passed += 1
+            tag = "PASS"
+        else:
+            failed += 1
+            diff = (c_gpu_np != c_ref)
+            n_diff = diff.sum()
+            # Show first mismatch
+            idx = np.argwhere(diff)[0]
+            tag = f"FAIL ({n_diff}/{M*N} mismatches, first@{tuple(idx)}: gpu={c_gpu_np[tuple(idx)]} ref={c_ref[tuple(idx)]})"
+
+        print(f"  M={M:>4d} K={K:>4d} N={N:>4d}  [{tag}]")
+
+    print(f"\nBit-exact: {passed}/{passed+failed} passed")
+    return failed == 0
+
+
+if __name__ == "__main__":
+    ok = test_bitexact()
+    print("\n" + "=" * 70)
+    if ok:
+        print("ALL BIT-EXACT TESTS PASSED ✓")
+    else:
+        print("SOME TESTS FAILED")
+        sys.exit(1)