From 9d60b3c5279641ba936facd710c722ebe52fcf40 Mon Sep 17 00:00:00 2001
From: Tim Dettmers <tim.dettmers@gmail.com>
Date: Wed, 17 Aug 2022 03:45:57 -0700
Subject: Fixed bug in Linear8bitLt, when the bias is None.

---
 bitsandbytes/nn/modules.py |  6 +++---
 setup.py                   |  2 +-
 tests/test_modules.py      | 23 +++++++++++++++++++++++
 3 files changed, 27 insertions(+), 4 deletions(-)

diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
index 24ecf39..b222f54 100644
--- a/bitsandbytes/nn/modules.py
+++ b/bitsandbytes/nn/modules.py
@@ -248,10 +248,10 @@ class Linear8bitLt(nn.Linear):
 
         if self.weight.CB is not None:
             self.init_8bit_state()
-        if self.bias.dtype != torch.float16:
+
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != torch.float16:
             self.bias.data = self.bias.data.half()
-        # assert not self.state.has_fp16_weights
-        # if not self.state.has_fp16_weights: assert self.state.CB is not None or self.state.CxB is not None
 
         out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
 
diff --git a/setup.py b/setup.py
index 2b25720..ef33f8a 100644
--- a/setup.py
+++ b/setup.py
@@ -18,7 +18,7 @@ def read(fname):
 
 setup(
     name=f"bitsandbytes",
-    version=f"0.32.0",
+    version=f"0.32.1",
     author="Tim Dettmers",
     author_email="dettmers@cs.washington.edu",
     description="8-bit optimizers and matrix multiplication routines.",
diff --git a/tests/test_modules.py b/tests/test_modules.py
index 7faadb8..c0b3311 100644
--- a/tests/test_modules.py
+++ b/tests/test_modules.py
@@ -549,3 +549,26 @@ def test_linear8bitlt_no_fp16_weights(threshold):
     assert mlp.fc2.weight.dtype == torch.int8
     assert mlp.fc1.weight.device.type == "cuda"
     assert mlp.fc2.weight.device.type == "cuda"
+
+
+def test_linear8bitlt_fp32_bias():
+    # casts model to fp16 -> int8 automatically
+    l1 = bnb.nn.Linear8bitLt(32, 64, has_fp16_weights=False).cuda()
+    assert l1.weight.dtype == torch.int8
+    assert l1.bias.dtype == torch.float32
+
+    for i in range(100):
+        b1 = torch.randn(16, 8, 32, device="cuda").half()
+        # casts bias to fp32
+        o1 = l1(b1)
+        assert l1.bias.dtype == torch.float16
+
+    # casts model to fp16 -> int8 automatically
+    l1 = bnb.nn.Linear8bitLt(32, 64, has_fp16_weights=False, bias=False).cuda()
+    assert l1.weight.dtype == torch.int8
+    assert l1.bias is None
+
+    for i in range(100):
+        b1 = torch.randn(16, 8, 32, device="cuda").half()
+        o1 = l1(b1)
+        assert l1.bias is None
-- 
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