Added normal with extra value.

bitsandbytes/functional.py

@@ -9,7 +9,7 @@ import random
 import torch
 import itertools
 import math
-import scipy.stats
+from scipy.stats import norm
 import numpy as np
 
 from functools import reduce  # Required in Python 3
@@ -181,7 +181,7 @@ def create_custom_map(seed=0, scale=0.01):
     #v = [1.6072478919002173, 1.1864907014855421, 0.9099343314196248, 0.6898544638558411, 0.4990924080314459, 0.32505049268156666, 0.16039309503073892] # 0.946 24.207
     #v = [1.6118251211466303, 1.188665228776879, 0.9112895004060624, 0.690763326564427, 0.4997008778346997, 0.3254280317127771, 0.16057446047146948] # 0.9465 24.30
     #v = [1.6027040905517569, 1.184321770169049, 0.9085808314549837, 0.6889461706317986, 0.4984841229538408, 0.32467299997597887, 0.1602117348657326] # 0.9455 24.293
-    #v = [1.6072478919002173, 1.1864907014855421, 0.9099343314196248, 0.6898544638558411, 0.4990924080314459, 0.32505049268156666, 0.16039309503073892] # 0.946 24.37 22.88
+    v = [1.6072478919002173, 1.1864907014855421, 0.9099343314196248, 0.6898544638558411, 0.4990924080314459, 0.32505049268156666, 0.16039309503073892] # 0.946 24.37 22.88
 
     # 7B evo start 
     #v = [1.62129629, 1.18870191, 0.90848106, 0.69108646, 0.50515268, 0.34927819905,  0.14122701] # 22.06
@@ -197,9 +197,7 @@ def create_custom_map(seed=0, scale=0.01):
     #v = [1.5993337549066253, 1.1965624035328402, 0.9000864380418481, 0.6925840978034195, 0.5011181210961458, 0.32040328389777434, 0.13570386022711237]
 
     # theoretically optiomal (0.93333)
-    v = [1.501085946044025, 1.1331700302595604, 0.8761428492468408, 0.6670160135425023, 0.48373855304610314, 0.3155014472579608, 0.15580024666388428] # 0.9333333333333333
-
-
+    #v = [1.501085946044025, 1.1331700302595604, 0.8761428492468408, 0.6670160135425023, 0.48373855304610314, 0.3155014472579608, 0.15580024666388428] # 0.9333333333333333
 
     if seed > 0:
         v = np.array(v)
@@ -220,6 +218,26 @@ def create_custom_map(seed=0, scale=0.01):
     assert values.numel() == 256
     return values
 
+def create_normal_map(offset=0.966666, use_extra_value=True):
+
+    if use_extra_value:
+        # one more positive value, this is an asymmetric type
+        v1 = norm.ppf(torch.linspace(offset, 0.5, 9)[:-1]).tolist()
+        v2 = [0]*(256-15) ## we have 15 non-zero values in this data type
+        v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist()
+        v = v1 + v2 + v3
+    else:
+        v1 = norm.ppf(torch.linspace(offset, 0.5, 8)[:-1]).tolist()
+        v2 = [0]*(256-14) ## we have 14 non-zero values in this data type
+        v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist()
+        v = v1 + v2 + v3
+
+    values = torch.Tensor(v)
+    values = values.sort().values
+    values /= values.max()
+    assert values.numel() == 256
+    return values
+
 def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8):
     e = exponent_bits
     p = precision_bits

tests/test_functional.py

@@ -2318,6 +2318,3 @@ def test_bench_fp4_dequant():
     #    torch.matmul(b, a.t())
     #torch.cuda.synchronize()
     #print((time.time()-t0)/iters*1e6)
-
-
-