diff --git a/bitsandbytes/__init__.py b/bitsandbytes/__init__.py index 21cfbb0..ddd9bf0 100644 --- a/bitsandbytes/__init__.py +++ b/bitsandbytes/__init__.py @@ -10,7 +10,8 @@ from .autograd._functions import ( matmul, matmul_cublas, mm_cublas, - matmul_fp8 + matmul_fp8, + matmul_mixed ) from .cextension import COMPILED_WITH_CUDA from .nn import modules diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index aa50b21..c68b18b 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -461,6 +461,190 @@ class MatMulFP8(torch.autograd.Function): return grad_A, grad_B, None, None, None +class MatMul8bitMixed(torch.autograd.Function): + @staticmethod + 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=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device) + + # 1. Quantize A + # 2. Quantize B + # 3. Matmul + # 4. Mixed-precision decomposition matmul + # 5. Save state + formatB = state.formatB + input_shape = A.shape + if state.outlier_pool is None: + state.outlier_pool = GlobalOutlierPooler.get_instance() + + # Cast A to fp16 + if A.dtype != torch.float16: + warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization") + + # 1. Quantize A + if len(A.shape) == 3: + A = A.view(-1, A.shape[-1]).contiguous() + CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant( + A.to(torch.float16), threshold=state.threshold + ) + + if state.threshold > 0.0 and coo_tensorA is not None: + if state.has_fp16_weights: + idx = torch.unique(coo_tensorA.colidx).long() + CA[:, idx] = 0 + CAt[:, idx] = 0 + subA = A[:, idx] + state.subB = B[:, idx].t().contiguous() + state.idx = idx + else: + 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) + else: + if not state.has_fp16_weights and state.CxB is None: + state.CxB, state.SB = F.transform(state.CB, to_order=formatB) + subA = None + + # 2. Quantize B + if state.has_fp16_weights: + has_grad = True if (getattr(B, "grad", None) is not None) else False + is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1) + if is_transposed: + B = B.contiguous() + + if (state.is_training and not has_grad) or state.CxB is None: + state.reset_grads() + ( + CB, + state.CBt, + state.SCB, + state.SCBt, + coo_tensorB, + ) = F.double_quant(B.to(torch.float16)) + state.CxB, state.SB = F.transform(CB, to_order=formatB) + 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.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() + .to(A.dtype) + ) + CA[:, state.idx.long()] = 0 + CAt[:, state.idx.long()] = 0 + subA = A[:, state.idx.long()] + + shapeB = state.SB[0] + + if len(input_shape) == 3: + output_shape = (input_shape[0], input_shape[1], shapeB[0]) + else: + 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) + # we apply the fused bias here + + if bias is None or bias.dtype == torch.float16: + output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias) + output = output.to(A.dtype) + else: # apply bias separately + output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None) + output = output.to(A.dtype).add_(bias) + + # 4. Mixed-precision decomposition matmul + if coo_tensorA is not None and subA is not None: + output += torch.matmul(subA, state.subB) + + # 5. Save state + ctx.state = state + + ctx.formatB = formatB + ctx.grad_shape = input_shape + ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype + + if any(ctx.needs_input_grad[:2]): + ctx.tensors = (CAt, subA, A) + ctx.tensor_states = (SCAt, state.idx) + else: + ctx.tensors = [None, None, None] + ctx.tensor_states = (None, None) + ctx.save_for_backward(None, None) + + + clone_func = torch.clone if len(output_shape) == 3 else lambda x : x + return clone_func(output.view(output_shape)) + + @staticmethod + def backward(ctx, grad_output): + if ctx.is_empty: + 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.needs_input_grad + CAt, subA, A = ctx.tensors + SCAt, idx = ctx.tensor_states + formatB = ctx.formatB + state = ctx.state + grad_A = grad_B = grad_bias = None + + if req_gradBias: + # compute grad_bias first before changing grad_output dtype + grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) + + # Cast grad_output to fp16 + if len(grad_output.shape) == 3: + grad_output = grad_output.reshape( + -1, grad_output.shape[-1] + ).contiguous() + + Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16)) + + if req_gradB: + grad_B = torch.matmul(grad_output.t(), A) + + if req_gradA: + if state.CBt is not None: + C32grad, Sgrad = F.transform(Cgrad, "col32") + if state.CxBt is None: + state.CxBt, state.SBt = F.transform( + 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).to(ctx.dtype_A) + + elif state.CB is not None: + CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1. / 127.0)) + grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A) + else: + raise Exception('State must contain either CBt or CB matrix for backward') + + return grad_A, grad_B, None, grad_bias, None + + def matmul( A: tensor, B: tensor, @@ -479,7 +663,7 @@ def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tens return MatMulFP8.apply(A, B, out, fw_code, bw_code) -def matmul( +def matmul_mixed( A: tensor, B: tensor, out: tensor = None, @@ -490,4 +674,4 @@ def matmul( state = state or MatmulLtState() if threshold > 0.0: state.threshold = threshold - return MatMul8bitLt.apply(A, B, out, bias, state) + return MatMul8bitMixed.apply(A, B, out, bias, state) diff --git a/tests/test_autograd.py b/tests/test_autograd.py index 4d3e67a..d05b4a6 100644 --- a/tests/test_autograd.py +++ b/tests/test_autograd.py @@ -239,7 +239,7 @@ dim4 = torch.randint(32, 96, size=(n,)).tolist() dim2.append(0) decomp = [0.0, 6.0] -funcs = [(torch.matmul, bnb.matmul)] +funcs = [(torch.matmul, bnb.matmul_mixed)] str_funcs = ["matmul"] req_grad = [(False, False), (True, False), (True, True), (False, True)] req_grad = list(product([True, False], repeat=3))