def backward()

in bitsandbytes/autograd/_functions.py [0:0]

• 67 lines of code
• 12 McCabe index (conditional complexity)

    def backward(ctx, grad_output):
        A, B = ctx.saved_tensors
        quant_type = ctx.quant_type
        precision = ctx.precision
        grad_A = grad_B = None

        if B.requires_grad:
            if len(A.shape) == 3:
                dims = [0, 1]
                # bsi -> ibs
                permute_dim = [0, 2, 1]
            else:
                dims = [0]
                # bs -> sb
                permute_dim = [1, 0]

            if precision[1] != 8:
                with torch.no_grad():
                    grad_B = torch.matmul(A.permute(permute_dim), grad_output)
            else:
                if len(B.shape) == 2 and len(A.shape) == 3:
                    grad_output = grad_output.contiguous()
                    if not grad_output.is_contiguous():
                        grad_output.contiguous()
                    qgrad_output, S1 = F.vectorwise_quant(
                        grad_output.view(-1, grad_output.shape[2]),
                        dim=0,
                        quant_type=quant_type,
                    )
                    if not A.is_contiguous():
                        A = A.contiguous()
                    qA, S2 = F.vectorwise_quant(A.view(-1, A.shape[2]), dim=0, quant_type=quant_type)
                    igrad_B = F.igemm(qA.t(), qgrad_output)
                    grad_B = F.vectorwise_mm_dequant(igrad_B, S2.t(), S1, grad_output.dtype, quant_type)
                else:
                    qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
                    qA, S2 = F.vectorwise_quant(A, dim=dims, quant_type=quant_type)
                    igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
                    grad_B = F.vectorwise_mm_dequant(
                        igrad_B,
                        S2.permute(permute_dim),
                        S1,
                        grad_output.dtype,
                        quant_type,
                    )

        if A.requires_grad:
            if len(grad_output.shape) == 3:
                dims = [2]
            else:
                dims = [1]

            if len(B.shape) == 3:
                # bio -> boi
                permute_dim = [0, 2, 1]
                dim_B = dims
            else:
                # io -> oi
                permute_dim = [1, 0]
                dim_B = [1]

            if precision[2] != 8:
                with torch.no_grad():
                    grad_A = torch.matmul(grad_output, B.permute(permute_dim))
            else:
                qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
                qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
                igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
                grad_A = F.vectorwise_mm_dequant(
                    igrad_A,
                    S1,
                    S3.permute(permute_dim),
                    grad_output.dtype,
                    quant_type,
                )

        return grad_A, grad_B, None, None, None