Matmullt with direct outlier extraction for 8-bit inference.

bitsandbytes/autograd/_functions.py

@@ -191,24 +191,24 @@ class MatMul8bitLt(torch.autograd.Function):
                     # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
                     # we also need to convert it to the turing/ampere format
                     state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
-                if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
-                    # generate outlier index and subB
-                    outlier_idx = torch.unique(coo_tensorA.colidx).long()
-                    state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
-                    if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
-                        # do not use pool for 2nd FFN layer
-                        state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
-                    else:
-                        state.idx = outlier_idx
-                    state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half()
-
-                if state.idx is not None:
-                    # extract outliers
-                    CA[:, state.idx] = 0
-                    CAt[:, state.idx] = 0
-                    subA = A[:, state.idx]
-                else:
-                    subA = None
+                #if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
+                #    # generate outlier index and subB
+                #    outlier_idx = torch.unique(coo_tensorA.colidx).long()
+                #    state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+                #    if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+                #        # do not use pool for 2nd FFN layer
+                #        state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+                #    else:
+                #        state.idx = outlier_idx
+                #    state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half()
+
+                #if state.idx is not None:
+                #    # extract outliers
+                #    CA[:, state.idx] = 0
+                #    CAt[:, state.idx] = 0
+                #    subA = A[:, state.idx]
+                #else:
+                #    subA = None
         else:
             if not state.has_fp16_weights and state.CxB is None:
                 state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
@@ -229,6 +229,22 @@ class MatMul8bitLt(torch.autograd.Function):
         else:
             has_grad = False
 
+        if coo_tensorA is not None and not state.has_fp16_weights:
+            # extract outliers
+
+            outlier_idx = torch.unique(coo_tensorA.colidx)
+            state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+            if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+                # do not use pool for 2nd FFN layer
+                state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+            else:
+                state.idx = outlier_idx
+            outliers = F.extract_outliers(state.CxB, state.SB, outlier_idx).half()
+            state.subB = (outliers*state.SCB.view(-1, 1).half()/127.0).t().contiguous()
+            CA[:, state.idx.long()] = 0
+            CAt[:, state.idx.long()] = 0
+            subA = A[:, state.idx.long()]
+
         shapeB = state.SB[0]
 
         if len(input_shape) == 3: