bitsandbytes-rocm/bitsandbytes/research/autograd/_functions.py

import operator
import warnings
from dataclasses import dataclass
from functools import reduce  # Required in Python 3

import torch

import bitsandbytes.functional as F

from bitsandbytes.autograd._functions import MatmulLtState, GlobalOutlierPooler


# math.prod not compatible with python < 3.8
def prod(iterable):
    return reduce(operator.mul, iterable, 1)

tensor = torch.Tensor

class MatMulFP8Mixed(torch.autograd.Function):
    # forward is the same, but we added the fallback for pre-turing GPUs
    # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")

    @staticmethod
    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
        # default of 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

            B_shape = B.shape
            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. Dequantize
        # 2. MatmulnN
        cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz)
        fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype)

        cB, state = F.quantize(B.float(), code=fw_code)
        fp8B = F.dequantize(cB, state).to(B.dtype)

        output = torch.matmul(fp8A, fp8B)

        # output is half

        # 3. Save state
        ctx.fw_code = fw_code
        ctx.bw_code = bw_code
        ctx.bsz = bsz
        ctx.bsz2 = bsz2
        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype

        if any(ctx.needs_input_grad[:2]):
            # NOTE: we send back A, and re-quant.
            ctx.tensors = (A, fp8B)
        else:
            ctx.tensors = (None, None)

        return output

    @staticmethod
    def backward(ctx, grad_output):
        if ctx.is_empty:
            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None

        req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
        A, B = ctx.tensors

        grad_A, grad_B = None, None

        # TODO: Fix blocksize to be output_dim
        cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2)
        fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype)

        # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
        # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)

        # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
        # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
        # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
        # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])

        # not supported by PyTorch. TODO: create work-around
        if req_gradA: 
            grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)

        if req_gradB:
            if len(A.shape) == 3:
                At = A.transpose(2, 1).contiguous()
            else:
                At = A.transpose(1, 0).contiguous()
            # cA, state = F.quantize(At.float(), code=ctx.fw_code)
            # fp8At = F.dequantize(cA, state).to(A.dtype)
            grad_B = torch.matmul(At.to(grad_output.dtype), grad_output).to(B.dtype)

        return grad_A, grad_B, None, None, None, None, None


class MatMulFP8Global(torch.autograd.Function):
    # forward is the same, but we added the fallback for pre-turing GPUs
    # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")

    @staticmethod
    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
        # default of 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

            B_shape = B.shape
            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. Dequantize
        # 2. MatmulnN
        cA, state = F.quantize(A.float(), code=fw_code)
        fp8A = F.dequantize(cA, state).to(A.dtype)

        cB, state = F.quantize(B.float(), code=fw_code)
        fp8B = F.dequantize(cB, state).to(B.dtype)

        output = torch.matmul(fp8A, fp8B)

        # output is half

        # 3. Save state
        ctx.fw_code = fw_code
        ctx.bw_code = bw_code
        ctx.bsz = bsz
        ctx.bsz2 = bsz2
        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype

        if any(ctx.needs_input_grad[:2]):
            # NOTE: we send back A, and re-quant.
            ctx.tensors = (A, fp8B)
        else:
            ctx.tensors = (None, None)

        return output

    @staticmethod
    def backward(ctx, grad_output):
        if ctx.is_empty:
            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None

        req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
        A, B = ctx.tensors

        grad_A, grad_B = None, None

        # TODO: Fix blocksize to be output_dim
        cgrad_out, state = F.quantize(grad_output.float(), code=ctx.bw_code)
        fp8out = F.dequantize(cgrad_out, state).to(grad_output.dtype)

        # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
        # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)

        # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
        # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
        # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
        # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])

        # not supported by PyTorch. TODO: create work-around
        if req_gradA: 
            grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)

        if req_gradB:
            if len(A.shape) == 3:
                At = A.transpose(2, 1).contiguous()
            else:
                At = A.transpose(1, 0).contiguous()
            cA, state = F.quantize(At.float(), code=ctx.fw_code)
            fp8At = F.dequantize(cA, state).to(A.dtype)
            grad_B = torch.matmul(fp8At.to(fp8out.dtype), fp8out).to(B.dtype)

        return grad_A, grad_B, None, None, None, None, None


class SwitchBackBnb(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:
            #print('A shape', A.shape)
            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:
            #print('B shape', B.shape)
            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:
            # print('back A shape', A.shape)
            # print('grad output t shape', grad_output.t().shape)
            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
                    )
                # print('back B shape', state.CxBt.shape)
                # print('back grad shape', C32grad.shape)
                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 get_block_sizes(input_matrix, weight_matrix):
    input_features = input_matrix.shape[-1]
    output_features = (weight_matrix.shape[0] if weight_matrix.shape[1] == input_features else weight_matrix.shape[1])
    array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
    bsz, bsz2 = 1024, 1024
    for i, k in enumerate(array):
        if input_features > array[i + 1]:
            bsz = k
            break
    for i, k in enumerate(array):
        if output_features > array[i + 1]:
            bsz2 = k
            break

    return bsz, bsz2

def matmul_fp8_global(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
    if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
    return MatMulFP8Global.apply(A, B, out, fw_code, bw_code, bsz, bsz2)

def matmul_fp8_mixed(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
    if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
    return MatMulFP8Mixed.apply(A, B, out, fw_code, bw_code, bsz, bsz2)

def switchback_bnb(
    A: tensor,
    B: tensor,
    out: tensor = None,
    state: MatmulLtState = None,
    threshold=0.0,
    bias=None
):
    state = state or MatmulLtState()
    if threshold > 0.0:
        state.threshold = threshold
    return SwitchBackBnb.apply(A, B, out, bias, state)