Added k<256 quantile estimate.

1 files changed, 37 insertions, 6 deletions

diff --git a/tests/test_functional.py b/tests/test_functional.py
index bd4dafe..99885da 100644
--- a/tests/test_functional.py
+++ b/tests/test_functional.py
@@ -6,9 +6,11 @@ from itertools import product
 import einops
 import pytest
 import torch
+import numpy as np
 
 import bitsandbytes as bnb
 from bitsandbytes import functional as F
+from scipy.stats import norm
 
 torch.set_printoptions(
     precision=5, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000
@@ -2094,8 +2096,12 @@ def test_fp8_quant():
 
 def test_few_bit_quant():
 
+    print('')
     for bits in range(2, 9):
-        for method in ['linear', 'fp8']:
+        print('='*30, bits, '='*30)
+        for method in ['linear', 'fp8', 'dynamic', 'quantile']:
+            abserrs = []
+            relerrs = []
             code = None
             if method == 'linear':
                 code = F.create_linear_map(True, bits=bits).cuda()
@@ -2103,10 +2109,21 @@ def test_few_bit_quant():
                 ebits = math.ceil(bits/2)
                 pbits = bits-ebits-1
                 code = F.create_fp8_map(True, ebits, pbits, bits).cuda()
-                print(ebits, pbits, bits)
-                print(code)
+            elif method == 'dynamic':
+                code = F.create_dynamic_map(True, bits-0, bits).cuda()
+            elif method == 'quantile':
+                values = torch.randn(2048, 2048, device='cuda')
+                q = F.estimate_quantiles(values, offset= 1/(2*(2**bits)), num_quantiles=2**bits)
+                gap = 256-q.numel()
+                q = q.tolist()
+                for i in range(gap):
+                    q.append(0)
+                q = torch.Tensor(q).cuda()
+
+                q /= q.abs().max()
+                code, idx = torch.sort(q)
+            print(method, (code==0).sum())
             assert code.numel() == 256
-            print(bits)
             for i in range(10):
 
                 values = torch.randn(1, 32, device='cuda')
@@ -2127,11 +2144,25 @@ def test_few_bit_quant():
                 v2 = F.dequantize(q2, S2)
 
                 idx = torch.isclose(q1.int(), q2.int())
+                err2 = torch.abs(v2-values)
+                abserrs.append(err2.mean().item())
+                relerrs.append((err2/(1e-10+values).abs()).mean().item())
                 if idx.sum():
                     # some weird cases
                     err1 = torch.abs(v1-values).mean()
-                    err2 = torch.abs(v2-values).mean()
-                    assert err2 <= err1
+                    assert err2.mean() <= err1
 
                 else:
                     torch.testing.assert_allclose(q1, q2)
+            print(method, 'abserr:', sum(abserrs)/len(abserrs), 'relerr:', sum(relerrs)/len(relerrs))
+
+
+def test_kbit_quantile_estimation():
+    for i in range(100):
+        data = torch.randn(1024, 1024, device='cuda')
+        for bits in range(2, 9):
+            p = np.linspace(1.3e-4, 1-1.3e-4, 2**bits)
+            val1 = torch.Tensor(norm.ppf(p)).cuda()
+            val2 = F.estimate_quantiles(data, offset=0, num_quantiles=2**bits)
+            err = torch.abs(val1-val2).mean()
+            assert err < 0.035