Added pre/post call to all lib calls. Fixes #120

bitsandbytes/functional.py

@@ -770,6 +770,8 @@ def optimizer_update_32bit(
             f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}'
         )
 
+    prev_device = pre_call(g.device)
+    is_on_gpu([g, p, state1, state2, unorm_vec])
     if g.dtype == torch.float32 and state1.dtype == torch.float32:
         str2optimizer32bit[optimizer_name][0](
             get_ptr(g),
@@ -812,6 +814,7 @@ def optimizer_update_32bit(
         raise ValueError(
             f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
         )
+    post_call(prev_device)
 
 
 def optimizer_update_8bit(
@@ -890,6 +893,8 @@ def optimizer_update_8bit(
     if max_unorm > 0.0:
         param_norm = torch.norm(p.data.float())
 
+    prev_device = pre_call(g.device)
+    is_on_gpu([g, p, state1, state2, unorm_vec, qmap1, qmap2, max1, max2, new_max1, new_max2])
     if g.dtype == torch.float32 and state1.dtype == torch.uint8:
         str2optimizer8bit[optimizer_name][0](
             get_ptr(p),
@@ -942,6 +947,7 @@ def optimizer_update_8bit(
         raise ValueError(
             f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
         )
+    post_call(prev_device)
 
 
 def optimizer_update_8bit_blockwise(
@@ -964,6 +970,8 @@ def optimizer_update_8bit_blockwise(
     skip_zeros=False,
 ) -> None:
 
+    prev_device = pre_call(g.device)
+    is_on_gpu([g, p, state1, state2, qmap1, qmap2, absmax1, absmax2])
     if g.dtype == torch.float32 and state1.dtype == torch.uint8:
         str2optimizer8bit_blockwise[optimizer_name][0](
             get_ptr(p),
@@ -1008,6 +1016,7 @@ def optimizer_update_8bit_blockwise(
         raise ValueError(
             f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
         )
+    post_call(prev_device)
 
 
 def percentile_clipping(
@@ -1023,6 +1032,7 @@ def percentile_clipping(
         The current optimiation steps (number of past gradient norms).
 
     """
+    prev_device = pre_call(grad.device)
     is_on_gpu([grad, gnorm_vec])
     if grad.dtype == torch.float32:
         lib.cpercentile_clipping_g32(
@@ -1040,6 +1050,7 @@ def percentile_clipping(
         )
     else:
         raise ValueError(f"Gradient type {grad.dtype} not supported!")
+    post_call(prev_device)
 
     current_gnorm = torch.sqrt(gnorm_vec[step % 100])
     vals, idx = torch.sort(gnorm_vec)
@@ -1796,6 +1807,7 @@ def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None):
             (cooA.rows, B.shape[1]), device=B.device, dtype=cooA.values.dtype
         )
     nnz = cooA.nnz
+    prev_device = pre_call(B.device)
     assert cooA.rowidx.numel() == nnz
     assert cooA.colidx.numel() == nnz
     assert cooA.values.numel() == nnz
@@ -1872,6 +1884,7 @@ def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None):
             ccolsB,
         )
     # else: assertion error
+    post_call(prev_device)
 
     return out