linear.py

# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Linear API"""
from typing import Callable, Dict, Optional, Tuple, Union
from functools import reduce
from operator import mul as multiply_op

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, _fix_gathered_fp8_transpose
from ..fp8 import FP8GlobalStateManager
from ..utils import (
    cast_if_needed,
    clear_tensor_data,
    divide,
    init_method_constant,
    non_tn_fp8_gemm_supported,
    assert_dim_for_fp8_exec,
    nvtx_range_pop,
    nvtx_range_push,
    requires_grad,
)
from ..distributed import (
    set_tensor_model_parallel_attributes,
    get_distributed_world_size,
    allreduce,
    reduce_scatter_along_first_dim,
    gather_along_first_dim,
    is_fp8_activation_recompute_enabled,
    in_fp8_activation_recompute_phase,
    _fsdp_scatter_tensors,
    _fsdp_gather_tensors,
)
from ..cpp_extensions import (
    general_gemm,
)
from ..constants import GemmParallelModes, dist_group_type
from ..jit import no_torch_dynamo
from ..graph import is_graph_capturing
from ..tensor.quantized_tensor import (
    QuantizedTensor,
    Quantizer,
    prepare_for_saving,
    restore_from_saved,
)

from ..cpu_offload import is_cpu_offload_enabled, set_offloading_param

__all__ = ["Linear"]


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

    @staticmethod
    def forward(
        ctx,
        weight: torch.Tensor,
        inp: torch.Tensor,
        bias: Optional[torch.Tensor],
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
        input_quantizer: Optional[Quantizer],
        weight_quantizer: Optional[Quantizer],
        output_quantizer: Optional[Quantizer],
        grad_output_quantizer: Optional[Quantizer],
        grad_input_quantizer: Optional[Quantizer],
        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_fprop: bool,
        ub_overlap_ag_dgrad: bool,
        ub_overlap_ag_fprop: bool,
        ub_overlap_rs_dgrad: bool,
        ub_bulk_dgrad: bool,
        ub_bulk_wgrad: bool,
        ub_name: str,
        fp8_output: bool,  # pylint: disable=unused-argument
        fsdp_group: Union[dist_group_type, None],
        module: torch.nn.Module,
        skip_fp8_weight_update: bool,
    ) -> torch.Tensor:
        # pylint: disable=missing-function-docstring

        # NVTX label for profiling
        nvtx_label = "transformer_engine._Linear.forward"
        if ub_name is not None:
            nvtx_label = f"{nvtx_label}.{ub_name}"

        # Make sure input dimensions are compatible
        out_features, in_features = weight.shape
        inp_shape = inp.shape
        assert inp_shape[-1] == in_features, "GEMM not possible"

        tp_world_size = get_distributed_world_size(tp_group)
        backward_needs_input = is_grad_enabled and weight.requires_grad

        # Prepare input tensor
        # Note: Cast to expected dtype and perform tensor-parallel communication
        nvtx_range_push(f"{nvtx_label}.input_cast_comm")
        inputmat = inp.view(-1, in_features)
        inputmat_total = None
        with_input_all_gather_nccl = (
            parallel_mode == "column" and sequence_parallel and not ub_overlap_ag_fprop
        )
        own_quantized_input = False
        if fp8:
            assert_dim_for_fp8_exec(inputmat, weight)
            if (
                any([ub_overlap_ag_fprop, ub_overlap_rs_fprop])
                and not FP8GlobalStateManager.get_fp8_recipe().delayed()
            ):
                raise NotImplementedError(
                    "Comm+GEMM overlap is only supported with FP8 delayed scaling"
                )

            if input_quantizer is None:
                raise ValueError("Missing quantizer for input tensor")
            if with_input_all_gather_nccl:
                assert not isinstance(
                    inputmat, QuantizedTensor
                ), "All gather of fp8 input is not supported"
                input_quantizer.set_usage(rowwise=True, columnwise=False)
                inputmat_total, _ = gather_along_first_dim(
                    inputmat,
                    tp_group,
                    quantizer=input_quantizer,
                )
            else:
                input_quantizer.set_usage(
                    rowwise=True,
                    columnwise=backward_needs_input,
                )
                if not isinstance(inputmat, QuantizedTensor):
                    inputmat = input_quantizer(inputmat)
                elif backward_needs_input:
                    inputmat.update_usage(rowwise_usage=True, columnwise_usage=True)
                inputmat_total = inputmat
        else:
            inputmat = cast_if_needed(inp, activation_dtype)
            if with_input_all_gather_nccl:
                inputmat_total, _ = gather_along_first_dim(inputmat, tp_group)
            else:
                inputmat_total = inputmat
        nvtx_range_pop(f"{nvtx_label}.input_cast_comm")

        # Cast weight to expected dtype
        weightmat = weight
        if not fp8:
            weightmat = cast_if_needed(weightmat, activation_dtype)
        else:
            if not isinstance(weight, QuantizedTensor):
                # Configure quantizer
                if weight_quantizer is not None:
                    columnwise_usage = is_grad_enabled and inp.requires_grad
                    if not columnwise_usage:
                        columnwise_usage = (
                            is_fp8_activation_recompute_enabled()
                            and not in_fp8_activation_recompute_phase()
                        )
                    weight_quantizer.set_usage(rowwise=True, columnwise=columnwise_usage)

                # FP8 cast to workspace buffer
                update_workspace = is_first_microbatch is None or is_first_microbatch
                weightmat = module.get_weight_workspace(
                    tensor=weight,
                    quantizer=weight_quantizer,
                    cache_name=(None if is_first_microbatch is None else "weight"),
                    update_workspace=update_workspace,
                    skip_update_flag=skip_fp8_weight_update,
                    fsdp_group=fsdp_group,
                )

        # Cast bias to expected dtype
        bias_dtype = activation_dtype
        if fp8 and activation_dtype == torch.float32:
            bias_dtype = torch.bfloat16
        bias = cast_if_needed(bias, bias_dtype) if bias is not None else bias

        # Configure output quantizer
        if output_quantizer is not None:
            output_quantizer.set_usage(rowwise=True, columnwise=False)

        # Calibrate quantizers if needed
        if not fp8 and fp8_calibration:
            if input_quantizer is not None:
                input_quantizer.calibrate(inputmat_total)
            if weight_quantizer is not None:
                weight_quantizer.calibrate(weight)

        ub_obj = None
        ub_type = None
        rs_out = None
        out_dtype = activation_dtype
        if ub_overlap_rs_fprop:
            ub_obj = get_ub(ub_name + "_fprop")
            ub_type = tex.CommOverlapType.RS
            out_shape = [reduce(multiply_op, inp_shape[:-1]) // tp_world_size, out_features]
            rs_out = torch.empty(out_shape, dtype=activation_dtype, device=inputmat_total.device)

        elif ub_overlap_ag_fprop:
            ub_obj = get_ub(ub_name + "_fprop")
            ub_type = tex.CommOverlapType.AG
            if fp8:
                assert ub_obj.is_fp8_ubuf(), "AG overlap with FP8 GEMM inputs requires FP8 buffer."
            ub_obj.copy_into_buffer(inputmat_total, input_quantizer, local_chunk=True)
            inputmat_total = ub_obj.get_buffer(input_quantizer)

        nvtx_range_push(f"{nvtx_label}.gemm")
        out, *_, rs_out = general_gemm(
            weightmat,
            inputmat_total,
            get_workspace(),
            quantization_params=output_quantizer,
            out_dtype=out_dtype,
            bias=bias,
            use_split_accumulator=_2X_ACC_FPROP,
            ub=ub_obj,
            ub_type=ub_type,
            extra_output=rs_out,
        )
        nvtx_range_pop(f"{nvtx_label}.gemm")

        if is_grad_enabled:
            saved_inputmat = None
            if backward_needs_input:
                if own_quantized_input and isinstance(inputmat, QuantizedTensor):
                    inputmat.update_usage(rowwise_usage=False)
                saved_inputmat = inputmat

            if cpu_offloading:
                set_offloading_param(weight, "weight_offloading", True)
                set_offloading_param(weightmat, "weight_offloading", True)
                if saved_inputmat is not None:
                    set_offloading_param(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
            nvtx_range_push(f"{nvtx_label}.fsdp_scatter")
            ctx.fsdp_group = fsdp_group
            ctx.fsdp_shapes = _fsdp_scatter_tensors(
                fsdp_group,
                saved_inputmat,
                weightmat if fp8 and not isinstance(weight, QuantizedTensor) else None,
            )
            nvtx_range_pop(f"{nvtx_label}.fsdp_scatter")

            # TODO(ksivamani): Check memory usage
            tensors_to_save, tensor_objects = prepare_for_saving(
                saved_inputmat,
                weightmat,
                weight,
                bias,
            )
            ctx.save_for_backward(*tensors_to_save)
            ctx.tensor_objects = tensor_objects

            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
            ctx.input_quantizer = input_quantizer
            ctx.grad_output_quantizer = grad_output_quantizer
            ctx.grad_input_quantizer = grad_input_quantizer
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
            if fuse_wgrad_accumulation and weight.requires_grad:
                ctx.main_grad = weight.main_grad

            ctx.cpu_offloading = cpu_offloading
            ctx.is_first_microbatch = is_first_microbatch
            ctx.use_bias = bias is not None
            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_dgrad
            ctx.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad
            ctx.ub_bulk_dgrad = ub_bulk_dgrad
            ctx.ub_bulk_wgrad = ub_bulk_wgrad
            ctx.ub_name = ub_name
            ctx.tp_size = tp_size
            ctx.requires_dgrad = inp.requires_grad
            ctx.requires_wgrad = weight.requires_grad
            ctx.reduce_and_update_bwd_fp8_tensors = False
            ctx.owns_input = saved_inputmat is not inp
            ctx.is_input_fp8 = not own_quantized_input
            if ctx.fp8 and requires_grad(inp, weight, bias):
                _first_fp8_module = FP8GlobalStateManager.IS_FIRST_FP8_MODULE
                ctx.reduce_and_update_bwd_fp8_tensors = FP8GlobalStateManager.is_first_fp8_module()
                if in_fp8_activation_recompute_phase():
                    FP8GlobalStateManager.IS_FIRST_FP8_MODULE = _first_fp8_module

        # Row Parallel Linear
        if ub_overlap_rs_fprop:
            out = rs_out
        elif parallel_mode == "row":
            nvtx_range_push(f"{nvtx_label}.row_parallel_comm")
            if sequence_parallel:
                out, _ = reduce_scatter_along_first_dim(out, tp_group)
            elif tensor_parallel:
                out, _ = allreduce(out, tp_group)
            nvtx_range_pop(f"{nvtx_label}.row_parallel_comm")

        out = out.view(-1, *inp_shape[1:-1], out_features)
        return out

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
        # pylint: disable=missing-function-docstring

        # NVTX label for profiling
        nvtx_label = "transformer_engine._Linear.backward"
        if ctx.ub_name is not None:
            nvtx_label = f"{nvtx_label}.{ctx.ub_name}"

        with torch.cuda.nvtx.range("_Linear_backward"):
            if (
                ctx.fp8
                and any(
                    [
                        ctx.ub_overlap_ag,
                        ctx.ub_overlap_rs_dgrad,
                        ctx.ub_bulk_dgrad,
                        ctx.ub_bulk_wgrad,
                    ]
                )
                and not FP8GlobalStateManager.get_fp8_recipe().delayed()
            ):
                raise NotImplementedError(
                    "Comm+GEMM overlap is only supported with FP8 delayed scaling"
                )

            saved_tensors = ctx.saved_tensors
            inputmat, weight_fp8, weight, bias = (  # pylint: disable=unbalanced-tuple-unpacking
                restore_from_saved(ctx.tensor_objects, saved_tensors)
            )
            # Delete the references to tensor objects once they've been consumed
            # by the `restore_from_saved` method to construct back the actual tensors.
            ctx.tensor_objects = None

            # Since main_grad can be modified inplace, it should not be a part of saved_tensors
            main_grad = (
                ctx.main_grad
                if weight is not None and ctx.fuse_wgrad_accumulation and ctx.requires_wgrad
                else None
            )

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

            # 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
            nvtx_range_push(f"{nvtx_label}.fsdp_gather")
            _fsdp_gather_tensors(
                ctx.fsdp_group,
                ctx.fsdp_shapes,
                inputmat,
                weight_fp8,
            )
            nvtx_range_pop(f"{nvtx_label}.fsdp_gather")

            ctx.ub_obj_gradout = None
            ub_obj_dgrad = None
            ub_obj_wgrad = None
            ub_type_dgrad = None
            ub_type_wgrad = None
            dgrad_shape = [reduce(multiply_op, ctx.inp_shape[:-1]), ctx.inp_shape[-1]]
            rs_out = None
            dgrad_bulk = None
            if ctx.ub_overlap_ag:
                # Overlap grad_output all-gather with dgrad compute
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad")
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.AG

            elif ctx.ub_overlap_rs_dgrad:
                # Overlap dgrad reduce-scatter with dgrad compute
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad")
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.RS
                rs_out = torch.empty(
                    dgrad_shape, dtype=ctx.activation_dtype, device=grad_output.device
                )

            else:
                if ctx.ub_bulk_dgrad:
                    # Overlap inputmat all-gather with dgrad compute
                    # NOTE: Copying into communication buffer will always prefer rowwise data,
                    #       and will copy columnwise data if rowwise does not exist. In that case,
                    #       the all-gather will apply to the leading dimension of the transpose,
                    #       which then needs to be interleaved correctly before WGRAD.
                    ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad")
                    ub_obj_dgrad = ctx.ub_obj_gradout
                    ub_type_dgrad = tex.CommOverlapType.AG
                    ub_obj_dgrad.copy_into_buffer(inputmat, ctx.input_quantizer, local_chunk=True)

                if ctx.ub_bulk_wgrad:
                    # Overlap dgrad reduce-scatter with wgrad compute
                    ub_obj_wgrad = get_ub(ctx.ub_name + "_wgrad")
                    ub_type_wgrad = tex.CommOverlapType.RS
                    ub_obj_wgrad.set_buffer_params(ctx.grad_input_quantizer)
                    dgrad_bulk = ub_obj_wgrad.get_buffer(ctx.grad_input_quantizer)

            # Prepare grad output tensor
            # Note: Cast to expected dtype and perform tensor-parallel communication
            if ctx.grad_output_quantizer is not None:
                ctx.grad_output_quantizer.set_usage(rowwise=True, columnwise=True)
            (
                grad_output,
                grad_bias,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
                ctx,
                grad_output,
                ctx.parallel_mode == "row",
                ctx.grad_output_quantizer,
            )

            # Prepare input tensor
            # Note: Perform tensor-parallel communication if needed
            inputmat_total = None
            inputmat_total_work = None
            if (
                ctx.requires_wgrad
                and ctx.parallel_mode == "column"
                and ctx.sequence_parallel
                and not ctx.ub_bulk_dgrad
            ):
                quantizer = None
                if ctx.fp8:
                    quantizer = ctx.input_quantizer
                    quantizer.set_usage(rowwise=True, columnwise=True)
                nvtx_range_push(f"{nvtx_label}.column_parallel_comm_input")
                inputmat_total, inputmat_total_work = gather_along_first_dim(
                    inputmat,
                    ctx.tp_group,
                    async_op=True,
                    quantizer=quantizer,
                )
                nvtx_range_pop(f"{nvtx_label}.column_parallel_comm_input")
            else:
                inputmat_total = inputmat

            # Check whether to output wgrad GEMM directly into main grad
            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

            # Compute grad input tensor
            dgrad = None
            dgrad_work = None
            if ctx.requires_dgrad:

                # Update quantizer
                if ctx.grad_input_quantizer is not None:
                    ctx.grad_input_quantizer.set_usage(rowwise=True, columnwise=False)

                # dgrad GEMM
                nvtx_range_push(f"{nvtx_label}.dgrad_gemm")
                dgrad, *_, rs_out = general_gemm(
                    weight_fp8,
                    grad_output,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                    quantization_params=ctx.grad_input_quantizer,
                    out=dgrad_bulk,
                    out_dtype=ctx.activation_dtype,
                    use_split_accumulator=_2X_ACC_DGRAD,
                    ub=ub_obj_dgrad,
                    ub_type=ub_type_dgrad,
                    extra_output=rs_out,
                    bulk_overlap=ctx.ub_bulk_dgrad,
                )
                nvtx_range_pop(f"{nvtx_label}.dgrad_gemm")

                # Launch tensor-parallel communication
                if ctx.ub_overlap_rs_dgrad:
                    dgrad = rs_out
                elif ctx.parallel_mode == "column" and not ctx.ub_bulk_wgrad:
                    nvtx_range_push(f"{nvtx_label}.column_parallel_comm_dgrad")
                    if ctx.sequence_parallel:
                        dgrad, dgrad_work = reduce_scatter_along_first_dim(
                            dgrad,
                            ctx.tp_group,
                            async_op=True,
                        )
                    else:
                        dgrad, dgrad_work = allreduce(dgrad, ctx.tp_group, async_op=True)
                    nvtx_range_pop(f"{nvtx_label}.column_parallel_comm_dgrad")

            # Compute grad weight tensor
            wgrad = None
            if ctx.requires_wgrad:
                if ctx.ub_bulk_dgrad:
                    inputmat_total = ub_obj_dgrad.get_buffer(ctx.input_quantizer)
                    if ctx.fp8:
                        if inputmat._data is None:
                            # All-gather executed on columnwise data and result is in rowwise data,
                            # so we need to fix the interleaving before WGRAD.
                            inputmat_total = _fix_gathered_fp8_transpose(
                                inputmat_total, ctx.tp_size
                            )
                        elif not non_tn_fp8_gemm_supported():
                            # FP8 GEMM on Hopper only supports TN layout so the gathered input must
                            # have a valid transpose.
                            inputmat_total._create_transpose()

                else:
                    if inputmat_total_work is not None:
                        # Synchronize tensor-parallel communication
                        inputmat_total_work.wait()
                        inputmat_total_work = None

                if isinstance(grad_output, QuantizedTensor):
                    # This is a no-op if platform supports non-TN FP8 GEMM or the transpose
                    # already exists.
                    grad_output.update_usage(rowwise_usage=True, columnwise_usage=True)

                if ctx.ub_bulk_wgrad and ub_obj_wgrad.is_fp8_ubuf():
                    rs_out = torch.empty(
                        dgrad_shape, dtype=ctx.activation_dtype, device=grad_output.device
                    )

                # wgrad GEMM
                # Note: Fuse with bgrad computation if needed
                nvtx_range_push(f"{nvtx_label}.wgrad_gemm")
                wgrad, grad_bias_, _, rs_out = general_gemm(
                    inputmat_total,
                    grad_output,
                    get_workspace(),
                    layout="NT",
                    grad=True,
                    out_dtype=(
                        main_grad.dtype if ctx.fuse_wgrad_accumulation else ctx.activation_dtype
                    ),
                    bias=(bias if (grad_bias is None and not ctx.fp8) else None),
                    out=main_grad if ctx.fuse_wgrad_accumulation else None,
                    use_split_accumulator=_2X_ACC_WGRAD,
                    accumulate=accumulate_wgrad_into_param_main_grad,
                    ub=ub_obj_wgrad,
                    ub_type=ub_type_wgrad,
                    extra_output=rs_out,
                    bulk_overlap=ctx.ub_bulk_wgrad,
                )
                nvtx_range_pop(f"{nvtx_label}.wgrad_gemm")

                if ctx.ub_bulk_wgrad:
                    if ub_obj_wgrad.is_fp8_ubuf():
                        dgrad = rs_out
                    else:
                        dgrad = ub_obj_wgrad.get_buffer(ctx.grad_input_quantizer, local_chunk=True)

                if grad_bias is None:
                    grad_bias = grad_bias_
                del grad_bias_

                # Deallocate input tensor
                if ctx.owns_input:
                    clear_tensor_data(inputmat_total)

            # Don't return grad bias if not needed
            if not ctx.use_bias:
                grad_bias = None

            # Synchronize tensor parallel communication
            if inputmat_total_work is not None:
                inputmat_total_work.wait()
                inputmat_total_work = None
            if dgrad_work is not None:
                dgrad_work.wait()
                dgrad_work = None

        if ctx.requires_wgrad:
            # Handle custom DDP from mcore.
            if (
                ctx.fuse_wgrad_accumulation
                and weight is not None
                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, QuantizedTensor):
            _fsdp_scatter_tensors(ctx.fsdp_group, weight_fp8)
        return (
            wgrad,
            dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None,
            grad_bias,
            None,  # is_first_microbatch
            None,  # fp8
            None,  # fp8_calibration
            None,  # input_quantizer
            None,  # weight_quantizer
            None,  # output_quantizer
            None,  # grad_output_quantizer
            None,  # grad_input_quantizer
            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_fprop
            None,  # ub_overlap_ag_dgrad
            None,  # ub_overlap_ag_fprop
            None,  # ub_overlap_rs_dgrad
            None,  # ub_bulk_dgrad
            None,  # ub_bulk_wgrad
            None,  # ub_name
            None,  # fp8_output
            None,  # fsdp_group
            None,  # module
            None,  # skip_fp8_weight_update
        )


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_ag: bool = False,
        ub_overlap_rs: bool = False,
        ub_overlap_rs_dgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: 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.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

        # Column parallel TP overlap options
        self.ub_overlap_ag_fprop = (
            self.parallel_mode == "column" and self.sequence_parallel and ub_overlap_ag
        )
        self.ub_overlap_rs_dgrad = (
            self.parallel_mode == "column" and self.sequence_parallel and ub_overlap_rs_dgrad
        )
        self.ub_bulk_dgrad = (
            self.parallel_mode == "column"
            and self.sequence_parallel
            and ub_bulk_dgrad
            and not self.ub_overlap_rs_dgrad
        )
        self.ub_bulk_wgrad = (
            self.parallel_mode == "column"
            and self.sequence_parallel
            and ub_bulk_wgrad
            and not self.ub_overlap_rs_dgrad
        )

        # Row parallel TP overlap options
        self.ub_overlap_rs_fprop = (
            self.parallel_mode == "row" and self.sequence_parallel and ub_overlap_rs
        )
        self.ub_overlap_ag_dgrad = (
            self.parallel_mode == "row" and self.sequence_parallel and ub_overlap_ag
        )

        if any(
            [
                self.ub_overlap_rs_fprop,
                self.ub_overlap_ag_dgrad,
                self.ub_overlap_ag_fprop,
                self.ub_overlap_rs_dgrad,
                self.ub_bulk_dgrad,
                self.ub_bulk_wgrad,
            ]
        ):
            assert ub_name is not None, f"Comm+GEMM overlap layer '{ub_name}' is not initialized."
        self.ub_name = ub_name

        # 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 QuantizedTensor 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,
        fp8_grad: 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)
        """
        if FP8GlobalStateManager.fp8_graph_capturing():
            skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor()
        else:
            skip_fp8_weight_update = None
        if skip_fp8_weight_update is not None:
            is_first_microbatch = False

        with self.prepare_forward(
            inp,
            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 = None

            (
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_output_quantizer,
                grad_input_quantizer,
            ) = self._get_quantizers(fp8_output, fp8_grad)

            # Make sure weight tensor has correct quantizer
            # Note: Quantizer might have changed if quantization
            # recipe changed
            if weight_quantizer is not None and isinstance(weight_tensor, QuantizedTensor):
                weight_tensor._quantizer = weight_quantizer

            if torch.is_grad_enabled():
                linear_fn = _Linear.apply
                args = []
            else:
                linear_fn = _Linear.forward
                args = [None]
            args += (
                weight_tensor,
                inp,
                bias_tensor if (self.apply_bias and not self.gemm_bias_unfused_add) else None,
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_output_quantizer,
                grad_input_quantizer,
                self.fuse_wgrad_accumulation,
                is_cpu_offload_enabled(),
                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_fprop,
                self.ub_overlap_ag_dgrad,
                self.ub_overlap_ag_fprop,
                self.ub_overlap_rs_dgrad,
                self.ub_bulk_dgrad,
                self.ub_bulk_wgrad,
                self.ub_name,
                fp8_output,
                self.fsdp_group,
                self,
                skip_fp8_weight_update,
            )
            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

    def _get_quantizers(self, fp8_output, fp8_grad):
        if not self.fp8:
            return [None] * 5
        grad_input_quantizer = None
        grad_output_quantizer = None
        output_quantizer = None
        input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
        input_quantizer.internal = False
        weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
        weight_quantizer.internal = True
        if fp8_output:
            output_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_OUTPUT]
        if torch.is_grad_enabled():
            grad_output_quantizer = self.quantizers["scaling_bwd"][tex.FP8BwdTensors.GRAD_OUTPUT1]
            grad_output_quantizer.internal = True
            if fp8_grad:
                grad_input_quantizer = self.quantizers["scaling_bwd"][tex.FP8BwdTensors.GRAD_INPUT1]
        return (
            input_quantizer,
            weight_quantizer,
            output_quantizer,
            grad_output_quantizer,
            grad_input_quantizer,
        )