diff options
Diffstat (limited to 'bitsandbytes/functional.py')
| -rw-r--r-- | bitsandbytes/functional.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py index 48ab40c..9fe1345 100644 --- a/bitsandbytes/functional.py +++ b/bitsandbytes/functional.py @@ -486,13 +486,13 @@ def optimizer_update_8bit_blockwise(optimizer_name: str, g: Tensor, p: Tensor, s str2optimizer8bit_blockwise[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2), - get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), + get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_bool(skip_zeros), ct.c_int32(g.numel())) elif g.dtype == torch.float16 and state1.dtype == torch.uint8: str2optimizer8bit_blockwise[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2), - get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), + get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_bool(skip_zeros), ct.c_int32(g.numel())) else: raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}') |