add memory efficient backward

bitsandbytes/autograd/_functions.py

@@ -381,7 +381,6 @@ class MatMul8bitLt(torch.autograd.Function):
                 grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
 
             elif state.CB is not None:
-                raise NotImplementedError("WIP")
                 CB = state.CB.to(ctx.dtype_B)
                 CB.mul_(state.SCB.unsqueeze(1).div_(127.0).to(CB.dtype))
                 grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)

tests/test_modules.py

@@ -14,13 +14,15 @@ class MockArgs(object):
 
 
 class MLP8bit(torch.nn.Module):
-    def __init__(self, dim1, dim2, has_fp16_weights=True, threshold=0.0):
+    def __init__(self, dim1, dim2, has_fp16_weights=True, memory_efficient_backward=False, threshold=0.0):
         super(MLP8bit, self).__init__()
         self.fc1 = bnb.nn.Linear8bitLt(
-            dim1, dim2, has_fp16_weights=has_fp16_weights, threshold=threshold
+            dim1, dim2, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
+            threshold=threshold
         )
         self.fc2 = bnb.nn.Linear8bitLt(
-            dim2, dim1, has_fp16_weights=has_fp16_weights, threshold=threshold
+            dim2, dim1, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
+            threshold=threshold
         )
 
     def forward(self, x):
@@ -451,9 +453,12 @@ names = ["threshold_{0}".format(vals) for vals in values]
 
 
 @pytest.mark.parametrize("threshold", values, ids=names)
-def test_linear8bitlt_no_fp16_weights(threshold):
+@pytest.mark.parametrize("memory_efficient_backward", [True, False])
+def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
     l1 = (
-        bnb.nn.Linear8bitLt(32, 64, threshold=threshold, has_fp16_weights=False)
+        bnb.nn.Linear8bitLt(
+            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
+        )
         .cuda()
         .half()
     )
@@ -513,7 +518,9 @@ def test_linear8bitlt_no_fp16_weights(threshold):
     assert mlp.fc2.weight.dtype == torch.int8
 
     mlp = (
-        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
+        MLP8bit(
+            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
+        )
         .half()
         .to("cuda")
     )
@@ -532,7 +539,9 @@ def test_linear8bitlt_no_fp16_weights(threshold):
     assert mlp.fc2.weight.device.type == "cuda"
 
     mlp = (
-        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
+        MLP8bit(
+            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
+        )
         .to(torch.float16)
         .to("cuda")
     )
@@ -551,6 +560,7 @@ def test_linear8bitlt_no_fp16_weights(threshold):
     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()