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authorjustheuristic <justheuristic@gmail.com>2022-09-18 01:24:27 +0300
committerjustheuristic <justheuristic@gmail.com>2022-09-18 01:24:27 +0300
commit28a9313ddcf09c40d6cea75b3fd932ef09b4c715 (patch)
treeb268f18585e4ca43cb39496fd4f7d345c49ad64f /tests
parent95dafc6475bc36490e213269d1028adfd4f75363 (diff)
cast before allclose
Diffstat (limited to 'tests')
-rw-r--r--tests/test_modules.py4
1 files changed, 2 insertions, 2 deletions
diff --git a/tests/test_modules.py b/tests/test_modules.py
index dbadea9..bb65edb 100644
--- a/tests/test_modules.py
+++ b/tests/test_modules.py
@@ -541,7 +541,7 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
mlp = MLP8bit(
32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
)
- w1, w2 = mlp.fc1.weight.clone(), mlp.fc2.weight.clone() # note: we grad original weights before quantization,
+ w1, w2 = mlp.fc1.weight.clone().cuda(), mlp.fc2.weight.clone().cuda() # grab weights before quantization,
mlp = mlp.cuda().half() # and this line triggers quantization
for i in range(100):
@@ -567,7 +567,7 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
mlp.zero_grad()
(o1 * grad_proj).sum().backward()
- grad_ref = grad_proj.flatten(2) @ w2.to(grad_proj.device) @ w1.to(grad_proj.device)
+ grad_ref = grad_proj.flatten(2) @ w2.to() @ w1.to(grad_proj.device)
assert torch.allclose(b1.grad, grad_ref)