diff options
Diffstat (limited to 'bitsandbytes/autograd/_functions.py')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 55 |
1 files changed, 20 insertions, 35 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 01e7073..4dbf129 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -201,13 +201,14 @@ class MatmulLtState: class MatMul8bitLt(torch.autograd.Function): @staticmethod - def forward(ctx, A, B, out=None, state=MatmulLtState()): + def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()): # default to pytorch behavior if inputs are empty ctx.is_empty = False if prod(A.shape) == 0: ctx.is_empty = True ctx.A = A ctx.B = B + ctx.bias = bias if A.shape[-1] == B.shape[0]: return torch.empty(A.shape[:-1]+B.shape[1:], dtype=torch.float16, device=A.device) else: @@ -220,6 +221,7 @@ class MatMul8bitLt(torch.autograd.Function): # 5. Save state requires_gradA = A.requires_grad requires_gradB = B.requires_grad + requires_gradBias = bias is not None and bias.requires_grad formatB = state.formatB input_shape = A.shape if state.outlier_pool is None: @@ -247,28 +249,7 @@ class MatMul8bitLt(torch.autograd.Function): if state.CxB is None: # 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() - # 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 + state.CxB, state.SB = F.transform(state.CB, to_order=formatB) else: if not state.has_fp16_weights and state.CxB is None: state.CxB, state.SB = F.transform(state.CB, to_order=formatB) @@ -326,7 +307,8 @@ class MatMul8bitLt(torch.autograd.Function): # 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) + # we apply the fused bias here + output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias) # 4. Mixed-precision decomposition matmul if coo_tensorA is not None and subA is not None: @@ -337,7 +319,7 @@ class MatMul8bitLt(torch.autograd.Function): ctx.formatB = formatB ctx.grad_shape = input_shape - ctx.req_grads = [requires_gradA, requires_gradB] + ctx.req_grads = [requires_gradA, requires_gradB, requires_gradBias] if requires_gradA or requires_gradB: ctx.tensors = (CAt, subA) @@ -347,15 +329,16 @@ class MatMul8bitLt(torch.autograd.Function): ctx.tensor_states = (None, None) ctx.save_for_backward(None, None) - # clone_func = torch.clone if len(output_shape) == 3 else lambda x : x - clone_func = torch.clone + clone_func = torch.clone if len(output_shape) == 3 else lambda x : x + #clone_func = torch.clone return clone_func(output.view(output_shape)) @staticmethod def backward(ctx, grad_output): if ctx.is_empty: - return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None - req_gradA, req_gradB = ctx.req_grads + bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias)) + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None + req_gradA, req_gradB, req_gradBias = ctx.req_grads CAt, subA = ctx.tensors SCAt, idx = ctx.tensor_states formatB = ctx.formatB @@ -369,7 +352,7 @@ class MatMul8bitLt(torch.autograd.Function): -1, grad_output.shape[-1] ).contiguous() - grad_A = grad_B = None + grad_A = grad_B = grad_bias = None Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output) if req_gradB: @@ -387,11 +370,12 @@ class MatMul8bitLt(torch.autograd.Function): state.CBt, to_order=formatB, transpose=True ) gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt) - grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view( - ctx.grad_shape - ) + grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape) + + if req_gradBias: + grad_bias = grad_output.sum(0) - return grad_A, grad_B, None, None + return grad_A, grad_B, None, grad_bias, None matmul = MatMul8bitLt.apply @@ -403,8 +387,9 @@ def matmul( out: tensor = None, state: MatmulLtState = None, threshold=0.0, + bias=None ): state = state or MatmulLtState() if threshold > 0.0: state.threshold = threshold - return MatMul8bitLt.apply(A, B, out, state) + return MatMul8bitLt.apply(A, B, out, bias, state) |