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| author | Tim Dettmers <tim.dettmers@gmail.com> | 2022-07-26 19:15:35 -0700 |
|---|---|---|
| committer | Tim Dettmers <tim.dettmers@gmail.com> | 2022-07-26 19:15:35 -0700 |
| commit | 47a73d94c3d3284f6073b0ff189ed5bc9e3a8762 (patch) | |
| tree | ac2d412eb428325a6e1b574a05ff207bc64d1765 /bitsandbytes/autograd | |
| parent | 32fa459ed7c812c79e847145004061f21b7ac0d9 (diff) | |
Matmullt with direct outlier extraction for 8-bit inference.
Diffstat (limited to 'bitsandbytes/autograd')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 52 |
1 files changed, 34 insertions, 18 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 815a4f1..5503749 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/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: |