Fixed gradient accumulation test.

bitsandbytes/autograd/_functions.py

@@ -456,7 +456,6 @@ class MatMul8bitLt(torch.autograd.Function):
 
         Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
         if req_gradB:
-            #grad_B = torch.matmul(grad_output.t(), A)
             CxAt, SAt = F.transform(CAt, formatB, transpose=True)
             C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
             gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)

tests/test_modules.py

@@ -332,12 +332,13 @@ def test_linear8bitlt_inference(threshold):
 def test_linear8bitlt_accumulated_gradient():
     l1 = torch.nn.Sequential(*[bnb.nn.Linear8bitLt(32, 32).cuda().half() for i in range(2)])
     l2 = torch.nn.Sequential(*[torch.nn.Linear(32, 32).cuda().half() for i in range(2)])
-    l2[0].weight = torch.nn.Parameter(l1[0].weight.clone())
-    l2[0].bias = torch.nn.Parameter(l1[0].bias.clone())
-    l2[1].weight = torch.nn.Parameter(l1[1].weight.clone())
-    l2[1].bias = torch.nn.Parameter(l1[1].bias.clone())
-    opt1 = bnb.optim.Adam8bit(l1.parameters(), lr=0.001)
-    opt2 = bnb.optim.Adam8bit(l2.parameters(), lr=0.001)
+    l1[0].weight.data.copy_(l2[0].weight.data)
+    l1[1].weight.data.copy_(l2[1].weight.data)
+    l1[0].bias.data.copy_(l2[0].bias.data)
+    l1[1].bias.data.copy_(l2[1].bias.data)
+
+    opt1 = bnb.optim.Adam32bit(l1.parameters(), lr=0.001)
+    opt2 = bnb.optim.Adam32bit(l2.parameters(), lr=0.001)
 
     acc_steps = 10
 
@@ -353,7 +354,6 @@ def test_linear8bitlt_accumulated_gradient():
             assert l1[0].state.CxB is not None
             assert l1[1].state.CxB is not None
 
-        print(i)
         if i > 0 and i % acc_steps == 0:
             opt1.step()
             opt1.zero_grad(True)
@@ -368,9 +368,11 @@ def test_linear8bitlt_accumulated_gradient():
             # we do this copy because otherwise we have small divergences over time that add up
             l1[0].weight.data.copy_(l2[0].weight.data)
             l1[1].weight.data.copy_(l2[1].weight.data)
+            l1[0].bias.data.copy_(l2[0].bias.data)
+            l1[1].bias.data.copy_(l2[1].bias.data)
         else:
-            torch.testing.assert_close(l1[0].weight.grad, l2[0].weight.grad)
-            torch.testing.assert_close(l1[1].weight.grad, l2[1].weight.grad)
+            torch.testing.assert_close(l1[0].weight.grad, l2[0].weight.grad, atol=1e-3, rtol=1e-3)
+            torch.testing.assert_close(l1[1].weight.grad, l2[1].weight.grad, atol=1e-3, rtol=1e-3)
 
 
 @pytest.mark.parametrize("threshold", [0.0, 2.0])