diff options
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 22 |
1 files changed, 12 insertions, 10 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 5503749..e641583 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -191,6 +191,7 @@ 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) + #state.B = (state.CB.float()*(state.SCB.view(-1, 1)/127)).half() #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() @@ -214,7 +215,6 @@ class MatMul8bitLt(torch.autograd.Function): state.CxB, state.SB = F.transform(state.CB, to_order=formatB) subA = None - C32A, SA = F.transform(CA, 'col32') # 2. Quantize B if state.has_fp16_weights: @@ -233,14 +233,15 @@ class MatMul8bitLt(torch.autograd.Function): # 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() + state.idx = outlier_idx + #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, state.idx.int()) + state.subB = (outliers*state.SCB.view(-1, 1)/127.0).t().contiguous().half() CA[:, state.idx.long()] = 0 CAt[:, state.idx.long()] = 0 subA = A[:, state.idx.long()] @@ -253,11 +254,12 @@ class MatMul8bitLt(torch.autograd.Function): output_shape = (input_shape[0], shapeB[0]) # 3. Matmul + C32A, SA = F.transform(CA, 'col32') out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB) output = F.mm_dequant(out32, Sout32, SCA, state.SCB) # 4. Mixed-precision decomposition matmul - if state.threshold > 0.0 and coo_tensorA is not None and subA is not None: + if coo_tensorA is not None and subA is not None: output += torch.matmul(subA, state.subB) # 5. Save state |