Fixed unsafe use of eval. #8

CHANGELOG.md

@@ -41,8 +41,9 @@ Docs:
 ### 0.26.0:
 
 Features:
- - Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer
+ - Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer.
- - Added AdamW (copy of Adam with weight decay init 1e-2)
+ - Added AdamW (copy of Adam with weight decay init 1e-2). #10
 
 Bug fixes:
- - Fixed a bug where weight decay was incorrectly applied to 32-bit Adam
+ - Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
+ - Fixed an unsafe use of eval. #8

bitsandbytes/optim/optimizer.py

@@ -242,8 +242,9 @@ class Optimizer2State(Optimizer8bit):
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if isinstance(betas, str):
-            betas = eval(betas)
+            # format: '(beta1, beta2)'
-            print(betas, 'parsed')
+            betas = betas.replace('(', '').replace(')', '').strip().split(',')
+            betas = [float(b) for b in betas]
         for i in range(len(betas)):
             if not 0.0 <= betas[i] < 1.0:
                 raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")

tests/test_optim.py

@@ -392,3 +392,18 @@ def test_benchmark_blockwise(dim1, dim2, gtype, optim_name):
     #assert s < 3.9
 
 
+
+def test_str_betas():
+    betas = (0.80, 0.95)
+    strbetas = '(0.80, 0.95)'
+
+    layer = torch.nn.Linear(10, 10)
+
+    base = bnb.optim.Adam(layer.parameters(), betas=betas)
+    strbase = bnb.optim.Adam(layer.parameters(), betas=strbetas)
+    assert base.defaults['betas'][0] == 0.8
+    assert base.defaults['betas'][1] == 0.95
+    assert strbase.defaults['betas'][0] == 0.8
+    assert strbase.defaults['betas'][1] == 0.95
+
+