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-rw-r--r--bitsandbytes/autograd/_functions.py2
-rw-r--r--tests/test_autograd.py4
2 files changed, 4 insertions, 2 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index 5a83dfd..36c392b 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -231,7 +231,7 @@ class MatMul8bitLt(torch.autograd.Function):
# Cast A to fp16
if A.dtype != torch.float16:
- warnings.warn(f"MatMul8bitLt: input matrix will be cast from {A.dtype} to float16")
+ warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
# 1. Quantize A
if len(A.shape) == 3:
diff --git a/tests/test_autograd.py b/tests/test_autograd.py
index 5171c4f..4e4282a 100644
--- a/tests/test_autograd.py
+++ b/tests/test_autograd.py
@@ -372,8 +372,10 @@ def test_matmullt(
n = out_bnb.numel()
err = torch.abs(out_bnb - out_torch).mean().item()
# print(f'abs error {err:.4f}')
+ out_error_rate = 0.0175 if dtype == torch.float16 else 0.02
+
idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
- assert (idx == 0).sum().item() <= n * 0.0175
+ assert (idx == 0).sum().item() <= n * out_error_rate
idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
assert (idx == 0).sum().item() <= n * 0.001