linear.py

# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Linear API"""
from typing import Any, Callable, Dict, Optional, Tuple, Union

import torch

import transformer_engine_torch as tex

from .base import (
    get_workspace,
    get_ub,
    TransformerEngineBaseModule,
    _2X_ACC_FPROP,
    _2X_ACC_DGRAD,
    _2X_ACC_WGRAD,
)
from ._common import _noop_cat
from ..fp8 import get_fp8_te_dtype, FP8GlobalStateManager
from ..utils import (
    divide,
    cast_if_needed,
    assert_dim_for_fp8_exec,
    clear_tensor_data,
    init_method_constant,
    requires_grad,
)
from ..distributed import (
    set_tensor_model_parallel_attributes,
    get_distributed_world_size,
    allreduce,
    reduce_scatter_along_first_dim,
    gather_along_first_dim,
    _fsdp_scatter_tensors,
    _fsdp_gather_tensors,
)
from ..cpp_extensions import (
    fp8_gemm,
    gemm,
    fp8_cast_transpose_fused,
    cast_to_fp8,
)
from ..constants import GemmParallelModes, dist_group_type
from ..jit import no_torch_dynamo
from ..graph import is_graph_capturing
from ..float8_tensor import Float8Tensor
from ..export import is_in_onnx_export_mode
from ..tensor import QuantizedTensor

__all__ = ["Linear"]


class _Linear(torch.autograd.Function):
    """Linear semi-top level module
    Calls custom cuda extensions.
    """

    @staticmethod
    def forward(
        ctx,
        weight: Union[Float8Tensor, torch.Tensor],
        weight_fp8: Optional[Float8Tensor],
        inp: torch.Tensor,
        bias: torch.Tensor,
        use_bias: bool,
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
        fp8_meta: Dict[str, Any],
        fuse_wgrad_accumulation: bool,
        cpu_offloading: bool,
        tp_group: Union[dist_group_type, None],
        tp_size: int,
        sequence_parallel: bool,
        tensor_parallel: bool,
        activation_dtype: torch.dtype,
        parallel_mode: Union[str, None],
        is_grad_enabled: bool,
        ub_overlap_rs: bool,
        ub_overlap_ag: bool,
        ub_name: str,
        fp8_output: bool,
        fsdp_group: Union[dist_group_type, None],
    ) -> torch.Tensor:
        is_input_fp8 = isinstance(inp, Float8Tensor)

        # Make sure input dimensions are compatible
        in_features = weight.shape[-1]
        assert inp.shape[-1] == in_features, "GEMM not possible"
        inputmat = inp.view(-1, in_features)
        if fp8:
            assert_dim_for_fp8_exec(inputmat)
            assert_dim_for_fp8_exec(weight)

        tp_world_size = get_distributed_world_size(tp_group)
        ub_overlap_rs = False if tp_world_size == 1 else ub_overlap_rs

        # Cast input to expected dtype
        inputmat = cast_if_needed(inputmat, activation_dtype)
        inputmat_t = None
        inputmat_no_fp8 = inputmat
        inputmat_scale_inv = None

        if fp8:
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if isinstance(inputmat, Float8Tensor):
                inputmat_scale_inv = inputmat._scale_inv
            else:
                inputmat_scale_inv = torch.empty([1], dtype=torch.float32, device=inputmat.device)
                if (
                    not fp8_meta["recipe"].override_linear_precision.wgrad
                    and is_grad_enabled
                    and weight.requires_grad
                    and not sequence_parallel
                ):
                    # FP8 input for forward, FP8 input transpose for backward wgrad
                    inputmat, inputmat_t = fp8_cast_transpose_fused(
                        inputmat,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                        scale_inv=inputmat_scale_inv,
                    )
                else:
                    # FP8 input for forward
                    inputmat = cast_to_fp8(
                        inputmat,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                        scale_inv=inputmat_scale_inv,
                    )

            # Hack for ONNX export
            # Note: ONNX models are represented as a graph of tensor
            # operations, so the in-place scale-inv update doesn't fit
            # very well. We work around this by making it look like
            # the scale-inv tensor is initialized with a copy.
            # Note: ONNX export expects FP8 scales can be represented
            # with constant ops. However, copying into a buffer
            # involves an expand op for array broadcasting. We work
            # around this by filling the buffer instead.
            if is_in_onnx_export_mode():
                inputmat_scale_inv.fill_(inputmat_scale_inv.item())

        # Column Parallel Linear
        if parallel_mode == "column" and sequence_parallel:
            inputmat_total, _ = gather_along_first_dim(inputmat, tp_group)
        else:
            inputmat_total = inputmat
        if fp8:
            bias_dtype = torch.bfloat16 if activation_dtype == torch.float32 else activation_dtype
            bias = cast_if_needed(bias, bias_dtype) if use_bias else bias

            # Use FP8 weights
            if weight_fp8 is None:
                weight_fp8 = weight

            assert isinstance(weight_fp8, Float8Tensor)

            if fp8_output:
                proj_out_index, meta_tensor, proj_out_tetype, proj_out_pttype = (
                    tex.FP8FwdTensors.GEMM1_OUTPUT,
                    fp8_meta["scaling_fwd"],
                    fp8_dtype_forward,
                    torch.uint8,
                )
            else:
                proj_out_index, meta_tensor, proj_out_tetype, proj_out_pttype = (
                    None,
                    None,
                    None,
                    activation_dtype,
                )

            if ub_overlap_rs:
                ub_obj_projout = get_ub(ub_name + "_fprop")
                out = ub_obj_projout.get_ubuf_output(1)
                dim_size = list(inputmat_total.size())
                dim_size[0] = dim_size[0] // tp_world_size
                dim_size[1] = weight_fp8.size(0)
                rs_out = torch.empty(dim_size, dtype=activation_dtype, device=inputmat_total.device)
                if ub_obj_projout.is_p2p_overlap():
                    if ub_obj_projout.is_atomic_gemm():
                        ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS_P2P
                    else:
                        ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P
                else:
                    if ub_obj_projout.is_atomic_gemm():
                        ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS
                    else:
                        ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS
                if ub_obj_projout.is_fp8_ubuf():
                    proj_out_index = tex.FP8FwdTensors.GEMM1_OUTPUT
                    meta_tensor = fp8_meta["scaling_fwd"]
                    proj_out_tetype = fp8_dtype_forward
                    proj_out_pttype = torch.uint8
                    ub_obj_projout.set_ubuf_scale_inv(meta_tensor.scale_inv[proj_out_index])
            else:
                dim_size = list(inputmat_total.size())
                dim_size[1] = weight_fp8.size(0)
                out = torch.empty(dim_size, dtype=proj_out_pttype, device=inputmat_total.device)

            _ = fp8_gemm(
                weight_fp8._data,
                weight_fp8._scale_inv,
                0,
                weight_fp8._fp8_dtype,
                (
                    inputmat_total._data
                    if isinstance(inputmat_total, Float8Tensor)
                    else inputmat_total
                ),
                inputmat_scale_inv,
                0,
                fp8_dtype_forward,
                proj_out_pttype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
                use_split_accumulator=_2X_ACC_FPROP,
                out=out,
                ub_algo=ub_algo if ub_overlap_rs else None,
                ub=ub_obj_projout if ub_overlap_rs else None,
                extra_output_tensor=rs_out if ub_overlap_rs else None,
                out_index=proj_out_index,
                fp8_meta_tensor=meta_tensor,
                D_dtype=proj_out_tetype,
            )
            if fp8_output:
                out = Float8Tensor(
                    data=out,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=tex.FP8FwdTensors.GEMM1_OUTPUT,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=activation_dtype,
                )
        else:
            # Cast for native AMP
            weight = cast_if_needed(weight, activation_dtype)
            bias = cast_if_needed(bias, activation_dtype) if use_bias else bias

            if fp8_calibration:
                # amax of input
                amin, amax = inputmat_total.aminmax()
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = torch.max(
                    -amin, amax
                ).float()
                # amax of weight
                amin, amax = weight.aminmax()
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = torch.max(
                    -amin, amax
                ).float()

            if ub_overlap_rs:
                ub_obj_projout = get_ub(ub_name + "_fprop")
                out = ub_obj_projout.get_ubuf_output(1)
                dim_size = list(inputmat_total.size())
                dim_size[0] = dim_size[0] // get_distributed_world_size(tp_group)
                dim_size[1] = weight.size(0)
                rs_out = torch.empty(dim_size, dtype=activation_dtype, device=inputmat_total.device)
                if ub_obj_projout.is_p2p_overlap():
                    ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P
                else:
                    ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS
            else:
                dim_size = list(inputmat_total.size())
                dim_size[1] = weight.size(0)
                out = torch.empty(dim_size, dtype=activation_dtype, device=inputmat_total.device)

            _ = gemm(
                weight,
                inputmat_total,
                activation_dtype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
                out=out,
                ub_algo=ub_algo if ub_overlap_rs else None,
                ub=ub_obj_projout if ub_overlap_rs else None,
                extra_output_tensor=rs_out if ub_overlap_rs else None,
            )

        if is_grad_enabled:
            saved_inputmat = None
            saved_inputmat_t = None
            if weight.requires_grad:
                if fp8 and not fp8_meta["recipe"].override_linear_precision.wgrad:
                    if inputmat_t is None:
                        saved_inputmat = inputmat
                    else:
                        saved_inputmat_t = inputmat_t
                        if cpu_offloading:
                            saved_inputmat_t.activation_offloading = True
                else:
                    saved_inputmat = inputmat_no_fp8

                if cpu_offloading:
                    if fp8 and weight_fp8 is not None:
                        weight_fp8.weight_offloading = True
                    weight.weight_offloading = True

                    if saved_inputmat is not None:
                        saved_inputmat.activation_offloading = True

            # Scatter intermediate/activation tensors saved for the backward pass
            # NOTE: FSDP sharding is not valid for models initialized with primary Fp8 weights
            ctx.fsdp_group = fsdp_group
            ctx.fsdp_shapes = _fsdp_scatter_tensors(
                fsdp_group,
                saved_inputmat,  # None if fp8 == False
                saved_inputmat_t,  # None if fp8 == False AND not is_grad_enabled
                weight_fp8 if fp8 and not isinstance(weight, Float8Tensor) else None,
            )

            ctx.save_for_backward(
                saved_inputmat,
                saved_inputmat_t,
                inputmat_scale_inv,
                weight,
                weight_fp8,
                weight.main_grad if cpu_offloading and fuse_wgrad_accumulation else None,
            )

            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
            ctx.fp8_meta = fp8_meta
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
            ctx.cpu_offloading = cpu_offloading
            ctx.is_first_microbatch = is_first_microbatch
            ctx.use_bias = use_bias
            ctx.sequence_parallel = sequence_parallel
            ctx.tensor_parallel = tensor_parallel
            ctx.inp_shape = inp.shape
            ctx.parallel_mode = parallel_mode
            ctx.tp_group = tp_group
            ctx.ub_overlap_ag = ub_overlap_ag
            ctx.ub_name = ub_name
            ctx.tp_size = tp_size
            ctx.requires_dgrad = inp.requires_grad
            ctx.is_input_fp8 = is_input_fp8
            ctx.reduce_and_update_bwd_fp8_tensors = False
            if ctx.fp8 and requires_grad(inp, weight, bias):
                ctx.reduce_and_update_bwd_fp8_tensors = (
                    ctx.reduce_and_update_bwd_fp8_tensors
                    or FP8GlobalStateManager.is_first_fp8_module()
                )

        # Row Parallel Linear
        if ub_overlap_rs:
            out = rs_out
        elif parallel_mode == "row" and sequence_parallel:
            out, _ = reduce_scatter_along_first_dim(out, tp_group)
        elif parallel_mode == "row" and tensor_parallel:
            out, _ = allreduce(out, tp_group)

        # [*, in_features] -> [*, out_features] except first dimension changes for SP
        return out.view(-1, *inp.shape[1:-1], out.shape[-1])

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
        if isinstance(grad_output, Float8Tensor):
            ctx.fp8_meta["scaling_bwd"].scale_inv[
                tex.FP8BwdTensors.GRAD_OUTPUT1
            ] = grad_output._scale_inv

        with torch.cuda.nvtx.range("_Linear_backward"):
            (
                inputmat,
                inputmat_t,
                inputmat_scale_inv,
                weight,
                weight_fp8,
                main_grad,
            ) = ctx.saved_tensors

            # Gather intermediate/activation tensors if needed
            # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already
            #       shards/unshards the base weights so we don't do it ourselves
            _fsdp_gather_tensors(
                ctx.fsdp_group,
                ctx.fsdp_shapes,
                inputmat,
                inputmat_t,
                weight_fp8 if ctx.fp8 and not isinstance(weight, Float8Tensor) else None,
            )

            if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation:
                weight = torch.nn.Parameter(weight, weight.requires_grad)
                weight.main_grad = main_grad

            tp_world_size = get_distributed_world_size(ctx.tp_group)
            ctx.ub_overlap_ag = False if tp_world_size == 1 else ctx.ub_overlap_ag
            if ctx.ub_overlap_ag:
                dim_size = list(grad_output.size())
                dim_size[0] = dim_size[0] * tp_world_size
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad")
                if ctx.ub_obj_gradout.is_atomic_gemm():
                    ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_AG_P2P
                else:
                    ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P

            (
                grad_output,
                grad_output_c,
                grad_output_t,
                grad_bias,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
                ctx, grad_output, ctx.parallel_mode == "row"
            )

            # Column Parallel Linear
            # Overlap input AG with dgrad
            inputmat_total = None
            inputmat_t_total = None
            handle = None
            if weight.requires_grad and ctx.parallel_mode == "column" and ctx.sequence_parallel:
                inputmat_total, handle = gather_along_first_dim(
                    inputmat, ctx.tp_group, async_op=ctx.requires_dgrad
                )
            else:
                inputmat_total = inputmat
                inputmat_t_total = inputmat_t

            if ctx.is_first_microbatch is not None:
                accumulate_wgrad_into_param_main_grad = (
                    ctx.fuse_wgrad_accumulation and not ctx.is_first_microbatch
                )
            else:
                accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation

            if ctx.fp8:
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)

            if ctx.requires_dgrad:
                if ctx.fp8:
                    if ctx.is_input_fp8:
                        out_index, meta_tensor, output_te_dtype, output_dtype = (
                            tex.FP8BwdTensors.GRAD_INPUT1,
                            ctx.fp8_meta["scaling_bwd"],
                            fp8_dtype_backward,
                            torch.uint8,
                        )
                    else:
                        out_index, meta_tensor, output_te_dtype, output_dtype = (
                            None,
                            None,
                            None,
                            ctx.activation_dtype,
                        )
                    dgrad, _ = fp8_gemm(
                        weight_fp8.transpose_2d(),
                        weight_fp8._scale_inv,
                        0,
                        weight_fp8._fp8_dtype,
                        grad_output_c,
                        ctx.fp8_meta["scaling_bwd"].scale_inv,
                        tex.FP8BwdTensors.GRAD_OUTPUT1,
                        fp8_dtype_backward,
                        output_dtype,
                        get_workspace(),
                        use_split_accumulator=_2X_ACC_DGRAD,
                        ub_algo=ub_algo if ctx.ub_overlap_ag else None,
                        ub=ctx.ub_obj_gradout if ctx.ub_overlap_ag else None,
                        out_index=out_index,
                        fp8_meta_tensor=meta_tensor,
                        D_dtype=output_te_dtype,
                    )
                    if output_dtype == torch.uint8:
                        dgrad = Float8Tensor(
                            data=dgrad,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=tex.FP8BwdTensors.GRAD_INPUT1,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=ctx.activation_dtype,
                        )
                else:
                    dgrad, _, _ = gemm(
                        weight,
                        grad_output,
                        ctx.activation_dtype,
                        get_workspace(),
                        layout="NN",
                        grad=True,
                        ub_algo=(
                            tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P
                            if ctx.ub_overlap_ag
                            else None
                        ),
                        ub=ctx.ub_obj_gradout if ctx.ub_overlap_ag else None,
                    )

                # Overlap dgrad-RS/AR with wgrad
                if ctx.parallel_mode == "column" and ctx.sequence_parallel:
                    if handle is not None:
                        handle.wait()
                    dgrad, handle = reduce_scatter_along_first_dim(
                        dgrad, ctx.tp_group, async_op=True
                    )
                elif ctx.parallel_mode == "column" and ctx.tensor_parallel:
                    dgrad, handle = allreduce(dgrad, ctx.tp_group, async_op=True)

            if weight.requires_grad:
                if ctx.fp8:
                    # WGRAD
                    if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                        if ctx.ub_overlap_ag:
                            if isinstance(grad_output_c, Float8Tensor):
                                grad_output_t = grad_output_c.transpose_2d()
                            else:
                                grad_output_t = tex.fp8_transpose(grad_output_c, fp8_dtype_backward)
                        if inputmat_t_total is None:
                            if isinstance(inputmat_total, Float8Tensor):
                                inputmat_t_total = inputmat_total.transpose_2d()
                            else:
                                inputmat_t_total = tex.fp8_transpose(
                                    inputmat_total, fp8_dtype_backward
                                )
                        wgrad, _ = fp8_gemm(
                            (
                                inputmat_t_total._data
                                if isinstance(inputmat_t_total, Float8Tensor)
                                else inputmat_t_total
                            ),
                            inputmat_scale_inv,
                            0,
                            fp8_dtype_forward,
                            grad_output_t,
                            ctx.fp8_meta["scaling_bwd"].scale_inv,
                            tex.FP8BwdTensors.GRAD_OUTPUT1,
                            fp8_dtype_backward,
                            ctx.activation_dtype,
                            get_workspace(),
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                            use_split_accumulator=_2X_ACC_WGRAD,
                        )
                    else:
                        wgrad, _, _ = gemm(
                            inputmat_total,
                            grad_output,
                            ctx.activation_dtype,
                            get_workspace(),
                            layout="NT",
                            grad=True,
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                        )
                else:
                    # WGRAD
                    wgrad, grad_bias, _ = gemm(
                        inputmat_total,
                        grad_output,
                        ctx.activation_dtype,
                        get_workspace(),
                        layout="NT",
                        grad=True,
                        use_bias=ctx.use_bias,
                        accumulate=accumulate_wgrad_into_param_main_grad,
                        out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                    )

                # Deallocate input tensor
                clear_tensor_data(inputmat_total)
                clear_tensor_data(inputmat_t_total)

            # Column Parallel Linear
            if ctx.parallel_mode == "column" and ctx.tensor_parallel and handle is not None:
                handle.wait()

            if not ctx.use_bias:
                grad_bias = None

        if weight.requires_grad:
            # Handle custom DDP from mcore.
            if ctx.fuse_wgrad_accumulation and hasattr(weight, "grad_added_to_main_grad"):
                weight.grad_added_to_main_grad = True
                if getattr(weight, "zero_out_wgrad", False):
                    wgrad = torch.zeros(
                        weight.main_grad.shape,
                        dtype=weight.dtype,
                        device=torch.cuda.current_device(),
                        requires_grad=False,
                    )
                else:
                    wgrad = torch.empty(
                        weight.main_grad.shape,
                        dtype=weight.dtype,
                        device=torch.cuda.current_device(),
                        requires_grad=False,
                    )
            elif ctx.fuse_wgrad_accumulation:
                wgrad = None
        else:
            wgrad = None

        if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing():
            FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False)

        # Scatter fp8 weight buffers
        if ctx.fp8 and not isinstance(weight, Float8Tensor):
            _fsdp_scatter_tensors(ctx.fsdp_group, weight_fp8)

        return (
            wgrad,
            None,  # weight_fp8
            dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None,
            grad_bias,
            None,  # use_bias
            None,  # is_first_microbatch
            None,  # fp8
            None,  # fp8_calibration
            None,  # fp8_meta
            None,  # fuse_wgrad_accumulation
            None,  # cpu_offloading
            None,  # tp_group
            None,  # tp_size
            None,  # sequence_parallel
            None,  # tensor_parallel
            None,  # activation_dtype
            None,  # parallel_mode
            None,  # is_grad_enabled
            None,  # ub_overlap_rs
            None,  # ub_overlap_ag
            None,  # ub_name
            None,  # fp8_output
            None,  # fsdp_group
        )


class Linear(TransformerEngineBaseModule):
    """Applies a linear transformation to the incoming data :math:`y = xA^T + b`

    On NVIDIA GPUs it is a drop-in replacement for `torch.nn.Linear`.

    Parameters
    ----------
    in_features : int
                 size of each input sample.
    out_features : int
                  size of each output sample.
    bias : bool, default = `True`
          if set to `False`, the layer will not learn an additive bias.
    init_method : Callable, default = `None`
                 used for initializing weights in the following way: `init_method(weight)`.
                 When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    get_rng_state_tracker : Callable, default = `None`
                 used to get the random number generator state tracker for initializing weights.
    rng_tracker_name : str, default = `None`
                 the param passed to get_rng_state_tracker to get the specific rng tracker.
    parameters_split : Optional[Union[Tuple[str, ...], Dict[str, int]]], default = None
                      Configuration for splitting the weight and bias tensors along dim 0 into
                      multiple PyTorch parameters. If a list or tuple of strings is provided,
                      they are used to make the names of equally-sized parameters. If a dict
                      (preferably an OrderedDict) is provided, the keys are used as names and
                      values as split sizes along dim 0. The resulting parameters will have
                      names that end in `_weight` or `_bias`, so trailing underscores are
                      stripped from any provided names.
    device : Union[torch.device, str], default = "cuda"
          The device on which the parameters of the model will be allocated. It is the user's
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.

    Parallelism parameters
    ----------------------
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.
    parallel_mode : {None, 'column', 'row'}, default = `None`
                   used to decide whether this Linear layer is Column Parallel Linear or Row
                   Parallel Linear as described `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
                   When set to `None`, no communication is performed.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient. When enabled, it is assumed that the weights
                             have an additional `main_grad` attribute (used instead of the
                             regular `grad`) which is a pre-allocated buffer of the correct
                             size to accumulate gradients in.
    return_bias : bool, default = `False`
                 when set to `True`, this module will not apply the additive bias itself, but
                 instead return the bias value during the forward pass together with the
                 output of the linear transformation :math:`y = xA^T`. This is useful when
                 the bias addition can be fused to subsequent operations.
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.

    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        sequence_parallel: bool = False,
        fuse_wgrad_accumulation: bool = False,
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
        get_rng_state_tracker: Optional[Callable] = None,
        rng_tracker_name: Optional[str] = None,
        init_method: Optional[Callable] = None,
        bias: bool = True,
        return_bias: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        parallel_mode: Optional[str] = None,
        parameters_split: Optional[Union[Tuple[str, ...], Dict[str, int]]] = None,
        device: Union[torch.device, str] = "cuda",
        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
        ub_name: Optional[str] = None,
    ) -> None:
        super().__init__()

        params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
        self.in_features = in_features
        self.out_features = out_features
        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
        self.use_bias = bias
        self.return_bias = return_bias
        self.apply_bias = bias and not return_bias
        self.ub_overlap_rs = ub_overlap_rs
        self.ub_overlap_ag = ub_overlap_ag
        if ub_overlap_rs or ub_overlap_ag:
            assert ub_name is not None, "Userbuffer name [string] is not set."
        self.ub_name = ub_name
        self.get_rng_state_tracker = get_rng_state_tracker
        self.rng_tracker_name = rng_tracker_name

        if device == "meta":
            assert parameters_split is None, "Cannot split module parameters on 'meta' device."
        if tp_group is None:
            self.tp_size = tp_size
            if tp_size == 1:
                self.set_tensor_parallel_group(tp_group)
        else:
            self.tp_size = get_distributed_world_size(tp_group)
            self.set_tensor_parallel_group(tp_group)
        self.set_nccl_overlap_warning_if_tp()

        self.parallel_mode = parallel_mode
        assert (
            self.parallel_mode in GemmParallelModes
        ), f"parallel_mode {parallel_mode} not supported"

        if self.parallel_mode == "column":
            self.out_features = divide(self.out_features, self.tp_size)
        elif self.parallel_mode == "row":
            self.in_features = divide(self.in_features, self.tp_size)

        self.sequence_parallel = (self.tp_size > 1) and sequence_parallel

        # Initialize params in FP8
        with_fp8_params = FP8GlobalStateManager.with_fp8_parameters()

        # Contiguous buffers for params
        weight_tensor = torch.empty(
            self.out_features,
            self.in_features,
            device=device,
            dtype=params_dtype,
        )
        bias_tensor = None
        if self.use_bias:
            bias_tensor = torch.empty(
                self.out_features,
                device=device,
                dtype=params_dtype,
            )

        # Configure parameter splits
        self.weight_names = []
        self.bias_names = []
        self.parameter_split_sizes = []
        if parameters_split is None:
            # Split into a single parameter by default
            self.weight_names = ["weight"]
            self.bias_names = ["bias"]
            self.parameter_split_sizes = [out_features]
        elif not parameters_split:
            raise ValueError("Cannot split weight buffer into 0 parameters")
        elif isinstance(parameters_split, dict):
            # Split parameters with provided sizes
            for name, split_size in parameters_split.items():
                self.weight_names.append(f"{name.rstrip('_')}_weight")
                self.bias_names.append(f"{name.rstrip('_')}_bias")
                self.parameter_split_sizes.append(split_size)
        elif all(isinstance(name, str) for name in parameters_split):
            # Split parameters evenly
            split_size = out_features // len(parameters_split)
            for name in parameters_split:
                self.weight_names.append(f"{name.rstrip('_')}_weight")
                self.bias_names.append(f"{name.rstrip('_')}_bias")
                self.parameter_split_sizes.append(split_size)
        else:
            raise TypeError("Invalid configuration for parameters split")

        # Make sure parameter splits are valid
        if sum(self.parameter_split_sizes) != out_features:
            raise ValueError(
                f"Trying to split weight buffer ({out_features=}) "
                f"with split sizes {self.parameter_split_sizes}"
            )

        # Adjust parameter splits for tensor-parallel distribution
        if self.parallel_mode == "column":
            for i, size in enumerate(self.parameter_split_sizes):
                if size % self.tp_size != 0:
                    raise RuntimeError(
                        f"Attempting to distribute a parameter with out_features={size} "
                        f"between {self.tp_size} tensor-parallel processes"
                    )
                self.parameter_split_sizes[i] = size // self.tp_size

        # Construct weight parameters
        # Note: Register weights together so that they are adjacent to
        # each other in Linear.parameters(). This makes it more likely
        # that they will stay contiguous if the weights are
        # manipulated externally, e.g. by FSDP.
        offset = 0
        for i, split_size in enumerate(self.parameter_split_sizes):
            split_start = offset
            offset += split_size
            split_end = offset

            # Check if parameters are subviews of buffers
            is_subview = (split_start, split_end) != (0, self.out_features)
            if is_subview and with_fp8_params:
                raise RuntimeError("Splitting Float8Tensor into multiple params is not supported")

            # Construct weight parameter
            self.register_parameter(
                self.weight_names[i],
                torch.nn.Parameter(weight_tensor[split_start:split_end]),
                init_fn=init_method,
                get_rng_state_tracker=get_rng_state_tracker,
                fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT,
            )

        # Construct bias parameters if needed
        if self.use_bias:
            offset = 0
            for i, split_size in enumerate(self.parameter_split_sizes):
                split_start = offset
                offset += split_size
                split_end = offset
                self.register_parameter(
                    self.bias_names[i],
                    torch.nn.Parameter(bias_tensor[split_start:split_end]),
                    init_fn=init_method_constant(0.0),
                )
        else:
            for name in self.bias_names:
                bias = torch.Tensor().to(dtype=params_dtype, device=device)
                setattr(self, name, bias)

        if with_fp8_params:
            self.init_fp8_metadata()

        self.reset_parameters(defer_init=(device == "meta"))

        # For RPL, bias has to be added after TP collectives
        # So it cannot be fused with the GEMM
        if self.parallel_mode == "row" and self.apply_bias:
            self.gemm_bias_unfused_add = True
        else:
            self.gemm_bias_unfused_add = False

    def reset_parameters(self, defer_init=False):
        super().reset_parameters(defer_init=defer_init)

        if not defer_init:
            # Set parallelism attributes for linear weights
            for weight in self.weight_names:
                set_tensor_model_parallel_attributes(
                    tensor=getattr(self, weight),
                    is_parallel=True,
                    dim=1 if self.parallel_mode == "row" else 0,
                    stride=1,
                )

            # Set parallelism attributes for linear biases
            if self.use_bias:
                for bias in self.bias_names:
                    if self.parallel_mode == "row":
                        setattr(getattr(self, bias), "sequence_parallel", self.sequence_parallel)
                    elif self.parallel_mode == "column":
                        set_tensor_model_parallel_attributes(getattr(self, bias), True, 0, 1)

    @no_torch_dynamo()
    def forward(
        self,
        inp: torch.Tensor,
        is_first_microbatch: Optional[bool] = None,
        fp8_output: Optional[bool] = False,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
        """
        Apply the linear transformation to the input.

        Parameters
        ----------
        inp : torch.Tensor
             Input tensor.
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
        """

        skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor()
        if skip_fp8_weight_update is not None:
            is_first_microbatch = False

        with self.prepare_forward(
            inp,
            is_first_microbatch,
            allow_non_contiguous=isinstance(inp, QuantizedTensor),
        ) as inp:

            # Get concatenated weight and bias tensors
            unfused_weights = [getattr(self, name) for name in self.weight_names]
            if any(isinstance(w, QuantizedTensor) for w in unfused_weights):
                if self.fp8:
                    if len(unfused_weights) != 1:
                        raise RuntimeError(
                            "Splitting QuantizedTensor into multiple params is not supported"
                        )
                else:
                    unfused_weights = [w.dequantize() for w in unfused_weights]
            weight_tensor = _noop_cat(unfused_weights)
            if self.use_bias:
                bias_tensor = _noop_cat(
                    [getattr(self, name) for name in self.bias_names],
                )
            else:
                bias_tensor = getattr(self, self.bias_names[0])  # Unused

            # Initialize FP8 weights if needed
            weight_fp8 = None
            if self.fp8:
                if isinstance(weight_tensor, Float8Tensor):
                    # Make sure transpose cache is valid, if present
                    # Note: Transpose cache may have been invalidated
                    # externally, e.g. by optimizer.
                    if weight_tensor._transpose is not None:
                        weight_tensor.transpose_2d(
                            fill_cache=True,
                            noop_flag=skip_fp8_weight_update,
                        )
                else:
                    # FP8 cast to workspace buffer
                    update_workspace = is_first_microbatch is None or is_first_microbatch
                    weight_fp8 = self.get_fp8_workspace(
                        tensor=weight_tensor,
                        fp8_meta_forward=True,
                        fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT,
                        cache_name=(None if is_first_microbatch is None else "weight"),
                        update_workspace=update_workspace,
                        skip_update_flag=skip_fp8_weight_update,
                        fsdp_group=self.fsdp_group,
                    )

            from ..cpu_offload import CPUOffloadEnabled

            if torch.is_grad_enabled():
                linear_fn = _Linear.apply
                args = []
            else:
                linear_fn = _Linear.forward
                args = [None]
            args += (
                weight_tensor,
                weight_fp8,
                inp,
                bias_tensor,
                self.apply_bias and not self.gemm_bias_unfused_add,
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
                self.fp8_meta,
                self.fuse_wgrad_accumulation,
                CPUOffloadEnabled,
                self.tp_group,
                self.tp_size,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.parallel_mode,
                torch.is_grad_enabled(),
                self.ub_overlap_rs,
                self.ub_overlap_ag,
                self.ub_name,
                fp8_output,
                self.fsdp_group,
            )
            out = linear_fn(*args)

        if self.gemm_bias_unfused_add:
            out = out + cast_if_needed(bias_tensor, self.activation_dtype)

        if self.return_bias:
            return out, cast_if_needed(bias_tensor, self.activation_dtype)
        return out