module.py

# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Top level Transformer Engine PyTorch modules"""
import os
import pickle
import warnings
from abc import ABC, abstractmethod
from typing import Union, Optional, Callable, Tuple, Dict, Any, Mapping
from functools import partial
from contextlib import contextmanager

import numpy as np
import torch
from torch.nn.parameter import Parameter
from torch.nn import init

import transformer_engine_extensions as tex
from .fp8 import (
    is_fp8_enabled,
    is_fp8_calibration,
    get_fp8_recipe,
    get_fp8_group,
    get_default_fp8_recipe,
    get_fp8_te_dtype,
    is_first_fp8_module,
    new_fp8_context_id,
    get_fp8_context_id,
    set_fp8_context_id,
    add_amax_to_global_buffer,
    copy_amax_from_global_buffer,
    global_amax_reduction,
    setup_amax_forward_global_reduce_func,
    amax_and_scale_update,
    get_global_fp8_buffer,
    set_global_fp8_buffer,
    set_amax_buffer_key_deletion,
    delete_key_from_amax_buffer,
    copy_forward_fp8_meta_tensors_for_recompute,
    get_old_fp8_meta_tensors_for_recompute,
    restore_fp8_meta_tensors,
)
from .jit import (
    bias_gelu_fused,
    bgrad_dgelu_fused,
    set_jit_fusion_options,
    warmup_jit_bias_gelu_all_dtypes,
)
from .utils import (
    divide,
    get_default_init_method,
    cast_if_needed,
)
from .distributed import (
    set_tensor_model_parallel_attributes,
    get_distributed_world_size,
    allreduce,
    initialize_affine_weight_gpu,
    reduce_scatter_along_first_dim,
    gather_along_first_dim,
    gather_along_last_dim,
    is_fp8_activation_recompute_enabled,
    in_fp8_activation_recompute_phase,
)
from .cpp_extensions import (
    fp8_gemm,
    gemm,
    fp8_cast_transpose_fused,
    fp8_cast_transpose_bgrad_fused,
    fp8_gelu,
    fp8_cast_transpose_bgrad_dgelu_fused,
    layernorm_fwd_fp8,
    layernorm_fwd_fp8_inf,
    layernorm_fwd_inf,
    cast_to_fp8,
    cast_from_fp8,
)
from .constants import GemmParallelModes, dist_group_type, TE_DType

_2X_ACC_FPROP = False
_2X_ACC_DGRAD = True
_2X_ACC_WGRAD = True
_cublas_workspace = None


def get_cublas_workspace_size_bytes() -> None:
    """Return 32 MiB if using hopper, 4 MiB for all other architectures."""
    if torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 9:
        return 33_554_432
    return 4_194_304


def get_workspace() -> torch.Tensor:
    """Returns workspace for cublas."""
    global _cublas_workspace
    if _cublas_workspace is None:
        _cublas_workspace = torch.empty(
            get_cublas_workspace_size_bytes(), dtype=torch.int8, device="cuda"
        )
    return _cublas_workspace

@contextmanager
def _prepare_backward(fp8: bool,
                      fp8_meta: Dict[str, Any],
                      reduce_amax_across_tp_group: bool,
                      tp_group: Union[dist_group_type, None],
                      name: str = ""):
    """Checks and prep for BWD."""
    if fp8:
        # Update amax and scale; Skip all setup for global amax reduction
        if not fp8_meta["recipe"].reduce_amax:
            amax_and_scale_update(fp8_meta, False)
        else:
            # From previous iteration
            copy_amax_from_global_buffer(fp8_meta, forward=False)
            amax_and_scale_update(fp8_meta, False)
            set_amax_buffer_key_deletion(fp8_meta, forward=False)

            # Get new backward key.
            fp8_meta["autocast_id_bwd"] = fp8_meta["autocast_id_fwd_stack"].pop(0)

            add_amax_to_global_buffer(fp8_meta, forward=False)

    with torch.cuda.nvtx.range(name + " backward"):
        yield

    if not fp8 or not fp8_meta["recipe"].reduce_amax:
        return

    if fp8_meta["first_module"]:
        global_amax_reduction(
            fp8_meta, reduce_amax_across_tp_group, tp_group, forward=False
        )
        delete_key_from_amax_buffer(forward=False)


class TransformerEngineBaseModule(torch.nn.Module, ABC):
    """Base TE module."""

    def __init__(self) -> None:
        super().__init__()
        assert torch.cuda.is_available(), "TransformerEngine needs CUDA."
        self.fp8_initialized = False
        self.fp8 = False
        self.fp8_calibration = False
        self.fp8_meta = {}
        self.fp8_meta["fp8_group"] = None
        self.fp8_meta["recipe"] = get_default_fp8_recipe()
        self.fp8_meta_tensors_initialized = False
        self.tp_group = None
        self.tp_group_initialized = False
        self.tp_size = 1
        self.sequence_parallel = False
        self.fp8_weight_shapes = []
        self.fp8_meta["autocast_id_fwd_stack"] = []

    def set_meta_tensor(self, fwd: bool) -> None:
        """Init scales and amaxes for fwd | bwd."""
        fp8_meta_tensor_key = "scaling_fwd" if fwd else "scaling_bwd"
        # Max. number of fp8 tensors per GEMM = 3 (input, weight, output) for fwd and
        # 2 (grad_output and grad_input) for bwd
        num_fp8_tensors = (
            self.fp8_meta["num_gemms"] * 3 if fwd else self.fp8_meta["num_gemms"] * 2
        )

        self.fp8_meta[fp8_meta_tensor_key] = tex.FP8TensorMeta()
        self.fp8_meta[fp8_meta_tensor_key].scale = torch.ones(
            num_fp8_tensors, dtype=torch.float32, device="cuda"
        )
        self.fp8_meta[fp8_meta_tensor_key].scale_inv = torch.ones(
            num_fp8_tensors, dtype=torch.float32, device="cuda"
        )
        self.fp8_meta[fp8_meta_tensor_key].amax_history = torch.zeros(
            self.fp8_meta["recipe"].amax_history_len,
            num_fp8_tensors,
            dtype=torch.float32,
            device="cuda",
        )

        # Needed for calculation of scale inverses to
        # preserve scale_inv when caching FP8 weights
        if fwd:
            # [True, False, True]: -> [input, weight, output]
            self.fp8_meta[fp8_meta_tensor_key + "_non_weight_mask"] = torch.BoolTensor(
                [True, False, True] * self.fp8_meta["num_gemms"]
            ).cuda()
        else:
            # [True, True]: -> [grad_output, grad_input]
            self.fp8_meta[fp8_meta_tensor_key + "_non_weight_mask"] = torch.BoolTensor(
                [True, True] * self.fp8_meta["num_gemms"]
            ).cuda()

    def init_fp8_meta_tensors(self) -> None:
        """Init scales and amaxes."""
        # Checkpoint loaded
        if self.fp8_meta_tensors_initialized:
            return

        self.set_meta_tensor(True)
        self.set_meta_tensor(False)

    def get_extra_state(self) -> torch.Tensor:
        """Save before checkpointing."""
        state = None
        if self.fp8 or self.fp8_calibration:
            state = {}
            state["scale_fwd"] = self.fp8_meta["scaling_fwd"].scale
            state["amax_history_fwd"] = self.fp8_meta["scaling_fwd"].amax_history
            state["scale_bwd"] = self.fp8_meta["scaling_bwd"].scale
            state["amax_history_bwd"] = self.fp8_meta["scaling_bwd"].amax_history
            state["global_fp8_buffer"] = get_global_fp8_buffer()

            # Store other pickelable values.
            extra = {}
            for k, v in self.fp8_meta.items():
                if isinstance(v, (bool, int, float, str)):
                    extra[k] = v
            state["extra_fp8_variables"] = extra

        state_serialized = pickle.dumps(state)
        state_tensor = torch.tensor(np.frombuffer(state_serialized, dtype=np.uint8))

        return state_tensor

    def set_extra_state(self, state: torch.Tensor) -> None:
        """Load previous state."""
        if state is None:
            return

        # Maintain backward compatibility with v0.2.0 and older.
        if isinstance(state, list):
            warnings.warn(
                "This checkpoint format is deprecated and will be"
                "removed in a future release of Transformer Engine"
            )

            # Retrieve checkpointed items.
            scale_fwd = state[0]
            amax_history_fwd = state[1]
            scale_bwd = state[2]
            amax_history_bwd = state[3]
            self.fp8_meta["recipe"].amax_history_len = amax_history_fwd.shape[0]
            self.fp8_meta["num_gemms"] = (
                amax_history_fwd.shape[1] // 2
            )  # Two FWD tensors per GEMM

            # Initialize before loading
            self.init_fp8_meta_tensors()
            self.fp8_meta["scaling_fwd"].scale.copy_(scale_fwd)
            self.fp8_meta["scaling_fwd"].amax_history.copy_(amax_history_fwd)
            self.fp8_meta["scaling_bwd"].scale.copy_(scale_bwd)
            self.fp8_meta["scaling_bwd"].amax_history.copy_(amax_history_bwd)
            self.fp8_meta_tensors_initialized = True

            # Restore global FP8 buffer state.
            set_global_fp8_buffer(state[4])
            self.fp8_meta["update_amax_and_scale_fwd"] = state[5]
            self.fp8_meta["global_fp8_buffer_pos_fwd"] = state[6]
            self.fp8_meta["global_fp8_buffer_pos_bwd"] = state[7]
            self.fp8_meta["autocast_id_fwd"] = state[8]
            self.fp8_meta["autocast_id_bwd"] = state[9]
            return

        if isinstance(state, torch.Tensor):
            state = pickle.loads(state.detach().numpy().tobytes())
            if state is None:
                return

        # Restore global FP8 buffer states.
        set_global_fp8_buffer(state["global_fp8_buffer"])
        # Load extra items.
        self.fp8_meta.update(state["extra_fp8_variables"])
        self.fp8_meta["recipe"].amax_history_len = state["amax_history_fwd"].shape[0]
        if "global_fp8_buffer_pos_fwd_recompute" in self.fp8_meta:
            del self.fp8_meta["global_fp8_buffer_pos_fwd_recompute"]

        # Initialize before loading.
        self.init_fp8_meta_tensors()
        self.fp8_meta["scaling_fwd"].scale.copy_(state["scale_fwd"])
        self.fp8_meta["scaling_fwd"].amax_history.copy_(state["amax_history_fwd"])
        self.fp8_meta["scaling_bwd"].scale.copy_(state["scale_bwd"])
        self.fp8_meta["scaling_bwd"].amax_history.copy_(state["amax_history_bwd"])
        self.fp8_meta_tensors_initialized = True

    def set_activation_dtype(self, inp: torch.Tensor) -> None:
        """Get activation data type for AMP."""
        # Native AMP (`torch.autocast`) gets highest priority
        if torch.is_autocast_enabled():
            self.activation_dtype = torch.get_autocast_gpu_dtype()
            return

        # All checks after this have already been performed once, thus skip
        # We assume that user doesn't change input types across iterations
        if hasattr(self, "activation_dtype"):
            return

        assert all(
            (
                (inp.dtype == param.dtype) if param is not None else True
                for param in self.parameters()
            )
        ), (
            "Data type for activations and weights must "
            "match when outside of autocasted region"
        )
        assert all(
            (
                (inp.dtype == buf.dtype) if buf is not None else True
                for buf in self.buffers()
            )
        ), (
            "Data type for activations and buffers must "
            "match when outside of autocasted region"
        )
        self.activation_dtype = inp.dtype

    def set_fp8_weights(self) -> None:
        """Initializes FP8 weights for the module as class attributes. These
        are not parameters or buffers since we do not want functions such as
        `.to(dtype)` or `.to(device)` to effect them. These also do not need
        to be checkpointed. During `init` phase of the module, the attribute
        `fp8_weight_shapes` must be populated with the tensor shapes for FP8
        weights. This function will iterate over those shapes and initialize
        respective attributed named `weight1_fp8`, `weight2_fp8`, ...
        """
        if not self.fp8:
            return

        for i, shape in enumerate(self.fp8_weight_shapes, start=1):
            weight_cast_attr = f"weight{i}_fp8"
            weight_transpose_attr = f"weight{i}_t_fp8"

            if (
                hasattr(self, weight_cast_attr)
                and getattr(self, weight_cast_attr).shape == shape
            ):
                return

            setattr(
                self,
                weight_cast_attr,
                torch.empty(
                    shape,
                    device=torch.cuda.current_device(),
                    dtype=torch.int8,
                ),
            )
            setattr(
                self,
                weight_transpose_attr,
                torch.empty(
                    shape[1],
                    shape[0],
                    device=torch.cuda.current_device(),
                    dtype=torch.int8,
                ),
            )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
        """Set TP group."""
        self.tp_group = tp_group
        self.tp_group_initialized = True

    # This routine is shared across FP8 and FP8_calibration paths so should not actually
    # assume FP8 execution.
    def fp8_init(self, num_gemms: int = 1) -> None:
        """Initialize fp8 related metadata and tensors during fprop."""
        if is_fp8_enabled() or is_fp8_calibration():
            # FP8 init has already been run and recipe is the same, don't do anything.
            if self.fp8_initialized and get_fp8_recipe() == self.fp8_meta["recipe"]:
                return

            # Set FP8, recipe, and other FP8 metadata
            self.fp8 = is_fp8_enabled()
            self.fp8_calibration = is_fp8_calibration()
            self.fp8_meta["recipe"] = get_fp8_recipe()
            self.fp8_meta["num_gemms"] = num_gemms
            self.fp8_meta["fp8_group"] = get_fp8_group()

            # Set FP8_MAX per tensor according to recipe
            self.fp8_meta["fp8_max_fwd"] = self.fp8_meta["recipe"].fp8_format.value.max_fwd
            self.fp8_meta["fp8_max_bwd"] = self.fp8_meta["recipe"].fp8_format.value.max_bwd

            # Allocate scales and amaxes
            self.init_fp8_meta_tensors()
            self.fp8_initialized = True
        else:
            # If fp8 isn't enabled, turn off and return.
            self.fp8_initialized = False
            return

    @contextmanager
    def prepare_forward(
        self,
        inp: torch.Tensor,
        is_first_microbatch: Union[bool, None],
        num_gemms: int = 1,
    ) -> None:
        """Checks and prep for FWD.
        The context manager is needed because there isn't a way for a module to know
        if it's the last FP8 module in the forward autocast. It is useful
        to setup the forward aggregated amax reduction for every module
        just in case. The autocast exit will pick up the most recent one.
        """

        # Activation recomputation is used and this is the second forward phase.
        if self.fp8 and in_fp8_activation_recompute_phase():
            get_old_fp8_meta_tensors_for_recompute(self.fp8_meta)
        else:
            assert inp.is_cuda, "TransformerEngine needs CUDA."

            if self.tp_size > 1:
                assert self.tp_group_initialized, "TP group not initialized."

            self.set_activation_dtype(inp)
            self.fp8_init(num_gemms=num_gemms)
            self.set_fp8_weights()

            update_weight_scale_inv = is_first_microbatch is None or is_first_microbatch

            # Previous iteration was grad_enabled
            if self.fp8_meta.get("update_amax_and_scale_fwd", False):
                if self.fp8_meta["recipe"].reduce_amax:
                    copy_amax_from_global_buffer(self.fp8_meta, forward=True)
                    amax_and_scale_update(
                        self.fp8_meta, True, update_weight_scale_inv=update_weight_scale_inv
                    )
                    set_amax_buffer_key_deletion(self.fp8_meta, forward=True)
                else:
                    amax_and_scale_update(
                        self.fp8_meta, True, update_weight_scale_inv=update_weight_scale_inv
                    )

            if self.fp8 and self.training:
                # Setup for amax reduction
                if self.fp8_meta["recipe"].reduce_amax:
                    self.fp8_meta["first_module"] = is_first_fp8_module()
                    if self.fp8_meta["first_module"]:
                        self.fp8_meta["autocast_id_fwd"] = new_fp8_context_id()
                        set_fp8_context_id(self.fp8_meta["autocast_id_fwd"])
                    else:
                        self.fp8_meta["autocast_id_fwd"] = get_fp8_context_id()
                    self.fp8_meta["autocast_id_fwd_stack"].append(
                        self.fp8_meta["autocast_id_fwd"]
                    )
                    add_amax_to_global_buffer(self.fp8_meta, forward=True)
                self.fp8_meta["update_amax_and_scale_fwd"] = True
            else:
                self.fp8_meta["update_amax_and_scale_fwd"] = False

            # Activation recomputation is used and this is the first forward phase.
            if (
                self.fp8
                and self.training
                and is_fp8_activation_recompute_enabled()
                and not in_fp8_activation_recompute_phase()
            ):
                copy_forward_fp8_meta_tensors_for_recompute(self.fp8_meta)

        with torch.cuda.nvtx.range(self.__class__.__name__ + " forward"):
            yield inp.contiguous()

        if self.fp8 and in_fp8_activation_recompute_phase():
            restore_fp8_meta_tensors(self.fp8_meta)
            return

        if self.fp8 and self.training and self.fp8_meta["recipe"].reduce_amax:
            set_fp8_context_id(self.fp8_meta["autocast_id_fwd"])
            reduce_func = partial(
                global_amax_reduction,
                self.fp8_meta,
                self.sequence_parallel,
                self.tp_group,
                forward=True,
            )
            setup_amax_forward_global_reduce_func(reduce_func)

    def set_nccl_overlap_warning_if_tp(self) -> None:
        """When using TP, the NCCL communication needs to be scheduled
        before the GEMM for there to be a guaranteed overlap. From the
        host side in TE, the comm calls are always launched first, but
        to ensure that the GEMM isn't scheduled first, the environment
        variable `CUDA_DEVICE_MAX_CONNECTIONS` needs to be set to 1 to
        force a single channel.
        """
        if self.tp_size == 1:
            return
        num_cuda_work_queues = int(os.getenv("CUDA_DEVICE_MAX_CONNECTIONS", "0"))
        if num_cuda_work_queues != 1:
            warnings.warn(
                "To guarantee overlapping TP and SP collectives with the backward"
                "GEMMs, set environment variable CUDA_DEVICE_MAX_CONNECTIONS = 1"
            )

    @staticmethod
    def grad_output_preprocess(
        ctx, grad_output: torch.Tensor, row_parallel_mode: bool
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        """Utility function for backward.
        Returns tuple in order (all optional/None based on training precion/recipe):
            R1: gathered `grad_output` in higher precision.
            R2: gathered `grad_output` in FP8.
            R3: R2 transposed.
            R4: bias gradient on R1.

        """
        grad_output = grad_output.contiguous()
        grad_output_mat = grad_output.view((-1, grad_output.shape[-1]))
        gather_grad_output = row_parallel_mode and ctx.sequence_parallel

        # No-FP8 case: bgrad is fused with wgrad for this case.
        if not ctx.fp8:
            if gather_grad_output:
                grad_output_mat, _ = gather_along_first_dim(
                    grad_output_mat, ctx.tp_group
                )
            return grad_output_mat, None, None, None

        fp8_dtype_backward = get_fp8_te_dtype(
            ctx.fp8_meta["recipe"], fprop_tensor=False
        )

        # FP8 case with non-FP8 wgrad
        if (
            gather_grad_output
            and ctx.fp8_meta["recipe"].override_linear_precision.wgrad
        ):
            grad_output_mat, _ = gather_along_first_dim(grad_output_mat, ctx.tp_group)
        # FP8 case with gather: unfused bgrad, cast, transpose for efficient gather
        elif gather_grad_output:
            if ctx.use_bias:
                grad_bias = grad_output_mat.sum(dim=0)
            else:
                grad_bias = None
            grad_output_c = cast_to_fp8(
                grad_output_mat,
                ctx.fp8_meta["scaling_bwd"],
                tex.FP8BwdTensors.GRAD_OUTPUT1,
                fp8_dtype_backward,
            )
            grad_output_c, _ = gather_along_first_dim(grad_output_c, ctx.tp_group)
            grad_output_t = tex.fp8_transpose(grad_output_c, fp8_dtype_backward)

            return grad_output_mat, grad_output_c, grad_output_t, grad_bias

        # FP8 case without gather: cast, transpose, bgrad fused
        if ctx.use_bias:
            grad_bias, grad_output_c, grad_output_t = fp8_cast_transpose_bgrad_fused(
                grad_output_mat,
                ctx.fp8_meta["scaling_bwd"],
                tex.FP8BwdTensors.GRAD_OUTPUT1,
                fp8_dtype_backward,
            )
        else:
            if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                grad_output_c, grad_output_t = fp8_cast_transpose_fused(
                    grad_output_mat,
                    ctx.fp8_meta["scaling_bwd"],
                    tex.FP8BwdTensors.GRAD_OUTPUT1,
                    fp8_dtype_backward,
                )
            else:
                grad_output_t = None
                grad_output_c = cast_to_fp8(
                    grad_output_mat,
                    ctx.fp8_meta["scaling_bwd"],
                    tex.FP8BwdTensors.GRAD_OUTPUT1,
                    fp8_dtype_backward,
                )
            grad_bias = None

        return grad_output_mat, grad_output_c, grad_output_t, grad_bias

    @abstractmethod
    def forward(self):
        """Needs override."""



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

    @staticmethod
    def forward(
        ctx,
        inp: torch.Tensor,
        ln_weight: torch.Tensor,
        ln_bias: torch.Tensor,
        weight: torch.Tensor,
        weight_fp8: Union[torch.Tensor, None],
        weight_t_fp8: Union[torch.Tensor, None],
        bias: torch.Tensor,
        use_bias: bool,
        eps: float,
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
        fp8_meta: Dict[str, Any],
        fuse_wgrad_accumulation: bool,
        tp_group: Union[dist_group_type, None],
        sequence_parallel: bool,
        tensor_parallel: bool,
        activation_dtype: torch.dtype,
        parallel_mode: Union[str, None],
        return_layernorm_output: bool,
        is_training: bool,
        fwd_ln_sm_margin: int,
        bwd_ln_sm_margin: int,
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        # Make sure input dimensions are compatible
        in_features = ln_weight.numel()
        assert inp.shape[-1] == in_features, "GEMM not possible"
        inputmat = inp.view((-1, in_features))

        update_fp8_weights = is_first_microbatch is None or is_first_microbatch

        # Cast for native AMP
        inputmat = cast_if_needed(inputmat, activation_dtype)
        ln_weight = cast_if_needed(ln_weight, activation_dtype)
        ln_bias = cast_if_needed(ln_bias, activation_dtype)

        # If residual connection is after LN, we need `ln_out`
        # tensor in higher precision, this comes at the cost
        # of an extra fp8 cast.
        if fp8:
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)

            if not return_layernorm_output:
                if is_training:
                    ln_out, mu, rsigma = layernorm_fwd_fp8(
                        inputmat,
                        ln_weight,
                        ln_bias,
                        eps,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                        fwd_ln_sm_margin,
                    )
                else:
                    mu = rsigma = None
                    ln_out = layernorm_fwd_fp8_inf(
                        inputmat,
                        ln_weight,
                        ln_bias,
                        eps,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                    )
            else:
                if is_training:
                    ln_out_return, mu, rsigma = tex.layernorm_fwd(
                        inputmat, ln_weight, ln_bias, eps, fwd_ln_sm_margin
                    )
                else:
                    ln_out_return, mu, rsigma = layernorm_fwd_inf(
                        inputmat, ln_weight, ln_bias, eps
                    ), None, None

                ln_out = cast_to_fp8(
                    ln_out_return,
                    fp8_meta["scaling_fwd"],
                    tex.FP8FwdTensors.GEMM1_INPUT,
                    fp8_dtype_forward,
                )
        else:
            if is_training:
                ln_out, mu, rsigma = tex.layernorm_fwd(
                    inputmat, ln_weight, ln_bias, eps, fwd_ln_sm_margin
                )
            else:
                ln_out, mu, rsigma = layernorm_fwd_inf(
                        inputmat, ln_weight, ln_bias, eps
                ), None, None
            ln_out_return = ln_out

        # Column Parallel Linear
        if parallel_mode == "column" and sequence_parallel:
            ln_out_total, _ = gather_along_first_dim(ln_out, tp_group)
        else:
            ln_out_total = ln_out

        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

            if update_fp8_weights:
                if is_training:
                    fp8_cast_transpose_fused(
                        weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward,
                        cast_out=weight_fp8,
                        transpose_out=weight_t_fp8,
                    )
                else:
                    weight_t_fp8 = None
                    weight_fp8 = cast_to_fp8(
                        weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward)

            out = fp8_gemm(
                weight_fp8,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_WEIGHT,
                fp8_dtype_forward,
                ln_out_total,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_INPUT,
                fp8_dtype_forward,
                activation_dtype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
                use_split_accumulator=_2X_ACC_FPROP,
            )
        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
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = \
                    torch.amax(ln_out_total).float()
                # amax of weight
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = \
                    torch.amax(weight).float()

            out, _, _ = gemm(
                weight,
                ln_out_total,
                activation_dtype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
            )

        if is_training:
            ctx.save_for_backward(
                inputmat,
                ln_weight,
                mu,
                rsigma,
                weight,
                weight_t_fp8,
                ln_out,
                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
            )

            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
            ctx.fp8_meta = fp8_meta
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
            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.return_layernorm_output = return_layernorm_output
            ctx.bwd_ln_sm_margin = bwd_ln_sm_margin

        # Row Parallel Linear
        if 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
        out = out.view(-1, *inp.shape[1:-1], out.shape[-1])

        if return_layernorm_output:
            return out, ln_out_return.view_as(inp)
        return out


    @staticmethod
    def backward(
        ctx, *grad_outputs: Tuple[torch.Tensor, ...]
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        with _prepare_backward(ctx.fp8, ctx.fp8_meta, ctx.sequence_parallel, ctx.tp_group,
                               name="_LayerNormLinear"):
            (
                inputmat,
                ln_weight,
                mu,
                rsigma,
                weight,
                weight_t_fp8,
                ln_out,
                fwd_scale_inverses,
            ) = ctx.saved_tensors

            (
                grad_output,
                grad_output_c,
                grad_output_t,
                grad_bias,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
                ctx, grad_outputs[0], ctx.parallel_mode == "row"
            )

            # Column Parallel Linear
            # Overlap input AG with dgrad
            if ctx.parallel_mode == "column" and ctx.sequence_parallel:
                ln_out_total, handle = gather_along_first_dim(
                    ln_out, ctx.tp_group, async_op=True
                )
            else:
                ln_out_total = ln_out

            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
                )

                # DGRAD: Evaluated unconditionally to feed into Linear backward
                dgrad = fp8_gemm(
                    weight_t_fp8,
                    fwd_scale_inverses,
                    tex.FP8FwdTensors.GEMM1_WEIGHT,
                    fp8_dtype_forward,
                    grad_output_c,
                    ctx.fp8_meta["scaling_bwd"].scale_inv,
                    tex.FP8BwdTensors.GRAD_OUTPUT1,
                    fp8_dtype_backward,
                    ctx.activation_dtype,
                    get_workspace(),
                    use_split_accumulator=_2X_ACC_DGRAD,
                )
            else:
                # DGRAD: Evaluated unconditionally to feed into Linear backward
                dgrad, _, _ = gemm(
                    weight,
                    grad_output,
                    ctx.activation_dtype,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                )

            # Overlap dgrad-RS/AR with wgrad
            if ctx.parallel_mode == "column" and ctx.sequence_parallel:
                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:
                        ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward)
                        wgrad = fp8_gemm(
                            ln_out_total_t,
                            fwd_scale_inverses,
                            tex.FP8FwdTensors.GEMM1_INPUT,
                            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,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                            use_split_accumulator=_2X_ACC_WGRAD,
                        )
                    else:
                        ln_out_total_c = cast_from_fp8(
                            ln_out_total,
                            ctx.fp8_meta["scaling_fwd"],
                            tex.FP8FwdTensors.GEMM1_INPUT,
                            fp8_dtype_forward,
                            TE_DType[ctx.activation_dtype],
                        )
                        wgrad, _, _ = gemm(
                            ln_out_total_c,
                            grad_output,
                            ctx.activation_dtype,
                            get_workspace(),
                            layout="NT",
                            grad=True,
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                        )
                else:
                    # WGRAD
                    wgrad, grad_bias, _ = gemm(
                        ln_out_total,
                        grad_output,
                        ctx.activation_dtype,
                        get_workspace(),
                        layout="NT",
                        grad=True,
                        use_bias=ctx.use_bias,
                        accumulate=accumulate_wgrad_into_param_main_grad,
                        fp32_output=ctx.fuse_wgrad_accumulation,
                        out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                    )

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

            # LayerNorm gradient
            d_ln_out = dgrad.view(inputmat.shape)

            # Residual gradient
            if ctx.return_layernorm_output:
                d_ln_out = d_ln_out + grad_outputs[1].view_as(d_ln_out)

            dxmat, dgamma, dbeta = tex.layernorm_bwd(
                d_ln_out, inputmat, mu, rsigma, ln_weight, ctx.bwd_ln_sm_margin
            )

            if not ctx.use_bias:
                grad_bias = None

        return (
            dxmat.view(ctx.inp_shape),
            dgamma,
            dbeta,
            wgrad if weight.requires_grad else None,
            None,
            None,
            grad_bias,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


class LayerNormLinear(TransformerEngineBaseModule):
    """
    Applies layer normalization followed by linear transformation to the incoming data.

    Parameters
    ----------
    in_features : int
                 size of each input sample.
    out_features : int
                  size of each output sample.
    eps : float, default = 1e-5
         a value added to the denominator of layer normalization for numerical stability.
    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)`.
    return_layernorm_output : bool, default = `False`
                             if set to `True`, output of layernorm is returned from the forward
                             together with the output of the linear transformation.
                             Example use case: residual connection for transformer module is
                             taken post layernorm.

    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.
    skip_weight_param_allocation: bool, default = `False`
                                 if set to `True`, weight parameter is not allocated and must be
                                 passed as a keyword argument `weight` during the forward pass.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient.
    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.float32`
                  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,
        eps: float = 1e-5,
        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,
        init_method: Optional[Callable] = None,
        bias: bool = True,
        return_bias: bool = False,
        params_dtype: torch.dtype = torch.float32,
        parallel_mode: Optional[str] = None,
        return_layernorm_output: bool = False,
        skip_weight_param_allocation: bool = False,
    ) -> None:
        super().__init__()
        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.return_layernorm_output = return_layernorm_output
        self.skip_weight_param_allocation = skip_weight_param_allocation

        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)

        if init_method is None:
            init_method = get_default_init_method()

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

        self.eps = eps
        self.layer_norm_weight = Parameter(
            torch.empty(
                in_features,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        self.layer_norm_bias = Parameter(
            torch.empty(
                in_features,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        setattr(self.layer_norm_weight, "sequence_parallel", self.sequence_parallel)
        setattr(self.layer_norm_bias, "sequence_parallel", self.sequence_parallel)
        self.reset_layer_norm_parameters()

        if not skip_weight_param_allocation:
            self.weight = Parameter(
                torch.empty(
                    self.out_features,
                    self.in_features,
                    device=torch.cuda.current_device(),
                    dtype=params_dtype,
                )
            )

            initialize_affine_weight_gpu(
                self.weight,
                init_method,
                get_rng_state_tracker,
                partition_dim=1 if self.parallel_mode == "row" else 0,
                stride=1,
            )

            if self.use_bias or self.return_bias:
                self.bias = Parameter(
                    torch.empty(
                        self.out_features,
                        device=torch.cuda.current_device(),
                        dtype=params_dtype,
                    )
                )
                if self.parallel_mode == "column":
                    set_tensor_model_parallel_attributes(self.bias, True, 0, 1)
            else:
                self.register_buffer("bias", torch.Tensor().type(params_dtype), persistent=False)

            with torch.no_grad():
                self.bias.zero_()

        self.fp8_weight_shapes.append(torch.Size((self.out_features, self.in_features)))

        # 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.use_bias:
            self.gemm_bias_unfused_add = True
            self.use_bias = False
        else:
            self.gemm_bias_unfused_add = False

        # These many SMs are subtracted from the total SM count when calling forward
        # and backward LayerNorm C APIs. These envvars can be used to prevent the LN
        # kernels from using all SMs in the device. This is useful for cases such as
        # communication overlap with LN.
        self.fwd_ln_sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))
        self.bwd_ln_sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0"))

    def reset_layer_norm_parameters(self) -> None:
        """Init LN params"""
        init.ones_(self.layer_norm_weight)
        init.zeros_(self.layer_norm_bias)

    def forward(
        self,
        inp: torch.Tensor,
        weight: Optional[torch.Tensor] = None,
        bias: Optional[torch.Tensor] = None,
        is_first_microbatch: Optional[bool] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
        """
        Apply layer normalization to the input followed by a linear transformation.

        Parameters
        ----------
        inp : torch.Tensor
             Input tensor.
        weight : torch.Tensor, default = None
                An optional weight tensor for the module. This argument is compulsory if module
                is initialized with `skip_weight_param_allocation=True`
        bias : torch.Tensor, default = None
              An optional bias tensor for the module. This argument is compulsory if module
              is initialized with `skip_weight_param_allocation=True` and one of `use_bias`
              or `return_bias`
        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)
        """

        with self.prepare_forward(inp, is_first_microbatch) as inp:
            bias_tensor = bias if bias is not None else self.bias

            if self.training:
                fwd_fn = _LayerNormLinear.apply
                args = []
            else:
                fwd_fn = _LayerNormLinear.forward
                args = [None]
            args += (
                inp,
                self.layer_norm_weight,
                self.layer_norm_bias,
                weight if weight is not None else self.weight,
                self.weight1_fp8 if self.fp8 else None,
                self.weight1_t_fp8 if self.fp8 else None,
                bias_tensor,
                self.use_bias,
                self.eps,
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
                self.fp8_meta,
                self.fuse_wgrad_accumulation,
                self.tp_group,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.parallel_mode,
                self.return_layernorm_output,
                self.training,
                self.fwd_ln_sm_margin,
                self.bwd_ln_sm_margin,
            )
            out = fwd_fn(*args)

        if self.return_layernorm_output:
            out, ln_out = out

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

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

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

    @staticmethod
    def forward(
        ctx,
        weight: torch.Tensor,
        weight_fp8: Union[torch.Tensor, None],
        weight_t_fp8: Union[torch.Tensor, None],
        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,
        tp_group: Union[dist_group_type, None],
        sequence_parallel: bool,
        tensor_parallel: bool,
        activation_dtype: torch.dtype,
        parallel_mode: Union[str, None],
        is_training: bool,
    ) -> torch.Tensor:
        # 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))

        update_fp8_weights = is_first_microbatch is None or is_first_microbatch

        # Cast for native AMP
        inputmat = cast_if_needed(inputmat, activation_dtype)
        inputmat_no_fp8 = inputmat

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

            if not fp8_meta["recipe"].override_linear_precision.wgrad:
                if is_training:
                    inputmat, inputmat_t = fp8_cast_transpose_fused(
                        inputmat,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                    )
                else:
                    inputmat = cast_to_fp8(
                        inputmat,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                    )
            else:
                inputmat, inputmat_t = cast_to_fp8(
                    inputmat,
                    fp8_meta["scaling_fwd"],
                    tex.FP8FwdTensors.GEMM1_INPUT,
                    fp8_dtype_forward,
                ), None

        # 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

            if update_fp8_weights:
                if is_training:
                    fp8_cast_transpose_fused(
                        weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward,
                        cast_out=weight_fp8,
                        transpose_out=weight_t_fp8,
                    )
                else:
                    weight_t_fp8 = None
                    weight_fp8 = cast_to_fp8(
                        weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward,
                    )

            out = fp8_gemm(
                weight_fp8,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_WEIGHT,
                fp8_dtype_forward,
                inputmat,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_INPUT,
                fp8_dtype_forward,
                activation_dtype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
                use_split_accumulator=_2X_ACC_FPROP,
            )
        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
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = \
                    torch.amax(inputmat_total).float()
                # amax of weight
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = \
                    torch.amax(weight).float()

            out, _, _ = gemm(
                weight,
                inputmat_total,
                activation_dtype,
                get_workspace(),
                bias=bias,
                use_bias=use_bias,
            )

        if is_training:
            fp8_wgrad = fp8 and not fp8_meta["recipe"].override_linear_precision.wgrad
            ctx.save_for_backward(
                inputmat_no_fp8 if weight.requires_grad and not fp8_wgrad else None,
                inputmat_t if weight.requires_grad and fp8_wgrad else None,
                weight,
                weight_t_fp8 if fp8 else None,
                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
            )
            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
            ctx.fp8_meta = fp8_meta
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
            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.requires_wgrad = weight.requires_grad

        # Row Parallel Linear
        if 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], ...]:
        with _prepare_backward(ctx.fp8, ctx.fp8_meta, ctx.sequence_parallel, ctx.tp_group,
                               name="_Linear"):
            (
                inputmat,
                inputmat_t,
                weight,
                weight_t_fp8,
                fwd_scale_inverses,
            ) = ctx.saved_tensors

            (
                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
            if ctx.parallel_mode == "column" and ctx.sequence_parallel:
                if ctx.fp8 and not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                    inputmat_t_total, handle = gather_along_last_dim(
                        inputmat_t, ctx.tp_group, async_op=True
                    )
                else:
                    inputmat_total, handle = gather_along_first_dim(
                        inputmat, ctx.tp_group, async_op=True
                    )
            else:
                inputmat_t_total = inputmat_t
                inputmat_total = inputmat

            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
                )

                # DGRAD
                dgrad = fp8_gemm(
                    weight_t_fp8,
                    fwd_scale_inverses,
                    tex.FP8FwdTensors.GEMM1_WEIGHT,
                    fp8_dtype_forward,
                    grad_output_c,
                    ctx.fp8_meta["scaling_bwd"].scale_inv,
                    tex.FP8BwdTensors.GRAD_OUTPUT1,
                    fp8_dtype_backward,
                    ctx.activation_dtype,
                    get_workspace(),
                    use_split_accumulator=_2X_ACC_DGRAD,
                )
            else:
                # DGRAD
                dgrad, _, _ = gemm(
                    weight,
                    grad_output,
                    ctx.activation_dtype,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                )

            # Overlap dgrad-RS/AR with wgrad
            if ctx.parallel_mode == "column" and ctx.sequence_parallel:
                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 ctx.requires_wgrad:
                if ctx.fp8:
                    # WGRAD
                    if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                        wgrad = fp8_gemm(
                            inputmat_t_total,
                            fwd_scale_inverses,
                            tex.FP8FwdTensors.GEMM1_INPUT,
                            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,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            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,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            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,
                        fp32_output=ctx.fuse_wgrad_accumulation,
                        out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                    )

            # 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

        return (
            wgrad if ctx.requires_wgrad else None,
            None,
            None,
            dgrad.view(ctx.inp_shape),
            grad_bias,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


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)`.

    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.
    skip_weight_param_allocation: bool, default = `False`
                                 if set to `True`, weight parameter is not allocated and must be
                                 passed as a keyword argument `weight` during the forward pass.

    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.float32`
                  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,
        init_method: Optional[Callable] = None,
        bias: bool = True,
        return_bias: bool = False,
        params_dtype: torch.dtype = torch.float32,
        parallel_mode: Optional[str] = None,
        skip_weight_param_allocation: bool = False,
    ) -> None:
        super().__init__()
        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.skip_weight_param_allocation = skip_weight_param_allocation

        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)

        if init_method is None:
            init_method = get_default_init_method()

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

        if not skip_weight_param_allocation:
            self.weight = Parameter(
                torch.empty(
                    self.out_features,
                    self.in_features,
                    device=torch.cuda.current_device(),
                    dtype=params_dtype,
                )
            )

            initialize_affine_weight_gpu(
                self.weight,
                init_method,
                get_rng_state_tracker,
                partition_dim=1 if self.parallel_mode == "row" else 0,
                stride=1,
            )

            if self.use_bias or self.return_bias:
                self.bias = Parameter(
                    torch.empty(
                        self.out_features,
                        device=torch.cuda.current_device(),
                        dtype=params_dtype,
                    )
                )
                if self.parallel_mode == "column":
                    set_tensor_model_parallel_attributes(self.bias, True, 0, 1)
            else:
                self.register_buffer("bias", torch.Tensor().type(params_dtype), persistent=False)

            with torch.no_grad():
                self.bias.zero_()

        self.fp8_weight_shapes.append(torch.Size((self.out_features, self.in_features)))

        # 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.use_bias:
            self.gemm_bias_unfused_add = True
            self.use_bias = False
        else:
            self.gemm_bias_unfused_add = False

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

        Parameters
        ----------
        inp : torch.Tensor
             Input tensor.
        weight : torch.Tensor, default = None
                An optional weight tensor for the module. This argument is compulsory if module
                is initialized with `skip_weight_param_allocation=True`
        bias : torch.Tensor, default = None
              An optional bias tensor for the module. This argument is compulsory if module
              is initialized with `skip_weight_param_allocation=True` and one of `use_bias`
              or `return_bias`
        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)
        """

        with self.prepare_forward(inp, is_first_microbatch) as inp:
            bias_tensor = bias if bias is not None else self.bias

            if self.training:
                linear_fn = _Linear.apply
                args = []
            else:
                linear_fn = _Linear.forward
                args = [None]
            args += (
                weight if weight is not None else self.weight,
                self.weight1_fp8 if self.fp8 else None,
                self.weight1_t_fp8 if self.fp8 else None,
                inp,
                bias_tensor,
                self.use_bias,
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
                self.fp8_meta,
                self.fuse_wgrad_accumulation,
                self.tp_group,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.parallel_mode,
                self.training,
            )
            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


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

    @staticmethod
    def forward(
        ctx,
        inp: torch.Tensor,
        ln_weight: torch.Tensor,
        ln_bias: torch.Tensor,
        fc1_weight: torch.Tensor,
        fc1_weight_fp8: Union[torch.Tensor, None],
        fc1_weight_t_fp8: Union[torch.Tensor, None],
        fc1_bias: torch.Tensor,
        fc2_weight: torch.Tensor,
        fc2_weight_fp8: Union[torch.Tensor, None],
        fc2_weight_t_fp8: Union[torch.Tensor, None],
        fc2_bias: torch.Tensor,
        use_bias: bool,
        eps: float,
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
        fp8_meta: Dict[str, Any],
        fuse_wgrad_accumulation: bool,
        tp_group: Union[dist_group_type, None],
        sequence_parallel: bool,
        tensor_parallel: bool,
        activation_dtype: torch.dtype,
        return_layernorm_output: bool,
        bias_gelu_nvfusion: bool,
        set_parallel_mode: bool,
        is_training: bool,
        fwd_ln_sm_margin: int,
        bwd_ln_sm_margin: int,
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        # Make sure input dimensions are compatible
        in_features = ln_weight.numel()
        assert inp.shape[-1] == in_features, "GEMM not possible"
        inputmat = inp.view((-1, in_features))

        update_fp8_weights = is_first_microbatch is None or is_first_microbatch

        # Cast for native AMP
        inputmat = cast_if_needed(inputmat, activation_dtype)
        ln_weight = cast_if_needed(ln_weight, activation_dtype)
        ln_bias = cast_if_needed(ln_bias, activation_dtype)

        # If residual connection is after LN, we need `ln_out`
        # tensor in higher precision, this comes at the cost
        # of an extra fp8 cast.
        if fp8:
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if not return_layernorm_output:
                if is_training:
                    ln_out, mu, rsigma = layernorm_fwd_fp8(
                        inputmat,
                        ln_weight,
                        ln_bias,
                        eps,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                        fwd_ln_sm_margin,
                    )
                else:
                    ln_out = layernorm_fwd_fp8_inf(
                        inputmat,
                        ln_weight,
                        ln_bias,
                        eps,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_INPUT,
                        fp8_dtype_forward,
                    )
            else:
                ln_out_return, mu, rsigma = tex.layernorm_fwd(
                    inputmat, ln_weight, ln_bias, eps, fwd_ln_sm_margin
                )
                ln_out = cast_to_fp8(
                    ln_out_return,
                    fp8_meta["scaling_fwd"],
                    tex.FP8FwdTensors.GEMM1_INPUT,
                    fp8_dtype_forward,
                )
        else:
            if is_training:
                ln_out, mu, rsigma = tex.layernorm_fwd(
                    inputmat, ln_weight, ln_bias, eps, fwd_ln_sm_margin
                )
            else:
                ln_out, mu, rsigma = layernorm_fwd_inf(
                        inputmat, ln_weight, ln_bias, eps
                        ), None, None

            ln_out_return = ln_out
        # Column Parallel Linear
        if set_parallel_mode and sequence_parallel:
            ln_out_total, _ = gather_along_first_dim(ln_out, tp_group)
        else:
            ln_out_total = ln_out

        if fp8:
            bias_dtype = (
                torch.bfloat16
                if activation_dtype == torch.float32
                else activation_dtype
            )
            fc1_bias = cast_if_needed(fc1_bias, bias_dtype)
            fc2_bias = cast_if_needed(fc2_bias, bias_dtype) if use_bias else fc2_bias

            if update_fp8_weights:
                if is_training:
                    fp8_cast_transpose_fused(
                        fc1_weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward,
                        cast_out=fc1_weight_fp8,
                        transpose_out=fc1_weight_t_fp8,
                    )

                    fp8_cast_transpose_fused(
                        fc2_weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM2_WEIGHT,
                        fp8_dtype_forward,
                        cast_out=fc2_weight_fp8,
                        transpose_out=fc2_weight_t_fp8,
                    )
                else:
                    fc1_weight_t_fp8 = None
                    fc1_weight_fp8 = cast_to_fp8(
                        fc1_weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM1_WEIGHT,
                        fp8_dtype_forward,
                    )
                    fc2_weight_t_fp8 = None
                    fc2_weight_fp8 = cast_to_fp8(
                        fc2_weight,
                        fp8_meta["scaling_fwd"],
                        tex.FP8FwdTensors.GEMM2_WEIGHT,
                        fp8_dtype_forward,
                    )

            fc1_out = fp8_gemm(
                fc1_weight_fp8,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_WEIGHT,
                fp8_dtype_forward,
                ln_out_total,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM1_INPUT,
                fp8_dtype_forward,
                activation_dtype,
                get_workspace(),
                bias=fc1_bias,
                use_bias=True,
                use_split_accumulator=_2X_ACC_FPROP,
            )

            gelu_out = fp8_gelu(
                fc1_out,
                fp8_meta["scaling_fwd"],
                tex.FP8FwdTensors.GEMM2_INPUT,
                fp8_dtype_forward,
            )

            fc2_out = fp8_gemm(
                fc2_weight_fp8,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM2_WEIGHT,
                fp8_dtype_forward,
                gelu_out,
                fp8_meta["scaling_fwd"].scale_inv,
                tex.FP8FwdTensors.GEMM2_INPUT,
                fp8_dtype_forward,
                activation_dtype,
                get_workspace(),
                bias=fc2_bias,
                use_bias=use_bias,
                use_split_accumulator=_2X_ACC_FPROP,
            )
        else:
            # Cast for native AMP
            fc1_weight = cast_if_needed(fc1_weight, activation_dtype)
            fc2_weight = cast_if_needed(fc2_weight, activation_dtype)
            fc1_bias = cast_if_needed(fc1_bias, activation_dtype)
            fc2_bias = (
                cast_if_needed(fc2_bias, activation_dtype) if use_bias else fc2_bias
            )

            if fp8_calibration:
                # amax of fc1 input
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = \
                    torch.amax(ln_out_total).float()
                # amax of fc1 weight
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = \
                    torch.amax(fc1_weight).float()

            fc1_outputs = gemm(
                fc1_weight,
                ln_out_total,
                activation_dtype,
                get_workspace(),
                bias=fc1_bias,
                use_bias=not bias_gelu_nvfusion,
                gelu=not bias_gelu_nvfusion,
            )

            if bias_gelu_nvfusion and is_training:
                fc1_out, _, _ = fc1_outputs
                gelu_out = bias_gelu_fused(fc1_out, fc1_bias)
            else:
                gelu_out, _, fc1_out = fc1_outputs

            if fp8_calibration:
                # amax of fc2 input
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM2_INPUT] = \
                    torch.amax(gelu_out).float()
                # amax of fc2 weight
                fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM2_WEIGHT] = \
                    torch.amax(fc2_weight).float()

            fc2_out, _, _ = gemm(
                fc2_weight,
                gelu_out,
                activation_dtype,
                get_workspace(),
                bias=fc2_bias,
                use_bias=use_bias,
            )
        if is_training:
            ctx.save_for_backward(
                inputmat,
                ln_weight,
                mu,
                rsigma,
                ln_out,
                fc1_out,
                gelu_out,
                fc1_weight,
                fc1_weight_t_fp8,
                fc2_weight,
                fc2_weight_t_fp8,
                fc1_bias,
                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
            )
            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
            ctx.fp8_meta = fp8_meta
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
            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.tp_group = tp_group
            ctx.bias_gelu_nvfusion = bias_gelu_nvfusion
            ctx.return_layernorm_output = return_layernorm_output
            ctx.set_parallel_mode = set_parallel_mode
            ctx.bwd_ln_sm_margin = bwd_ln_sm_margin

        # Row Parallel Linear
        if set_parallel_mode and sequence_parallel:
            fc2_out, _ = reduce_scatter_along_first_dim(fc2_out, tp_group)
        elif set_parallel_mode and tensor_parallel:
            fc2_out, _ = allreduce(fc2_out, tp_group)

        # [*, in_features] -> [*, out_features] except first dimension changes for SP
        fc2_out = fc2_out.view(-1, *inp.shape[1:-1], fc2_out.shape[-1])

        if return_layernorm_output:
            return fc2_out, ln_out_return.view_as(inp)
        return fc2_out


    @staticmethod
    def backward(
        ctx, *grad_outputs: Tuple[torch.Tensor, ...]
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        with _prepare_backward(ctx.fp8, ctx.fp8_meta, ctx.sequence_parallel, ctx.tp_group,
                               name="_LayerNormMLP"):
            (
                inputmat,
                ln_weight,
                mu,
                rsigma,
                ln_out,
                fc1_out,
                gelu_out,
                fc1_weight,
                fc1_weight_t_fp8,
                fc2_weight,
                fc2_weight_t_fp8,
                fc1_bias,
                fwd_scale_inverses,
            ) = ctx.saved_tensors

            (
                grad_output,
                grad_output_c,
                grad_output_t,
                fc2_bias_grad,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
                ctx, grad_outputs[0], True
            )

            # Column Parallel Linear
            # Overlap input AG with dgrad
            if ctx.set_parallel_mode and ctx.sequence_parallel:
                ln_out_total, handle = gather_along_first_dim(
                    ln_out, ctx.tp_group, async_op=True
                )
            else:
                ln_out_total = ln_out

            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
                )

                # FC2 DGRAD; Unconditional
                fc2_dgrad = fp8_gemm(
                    fc2_weight_t_fp8,
                    fwd_scale_inverses,
                    tex.FP8FwdTensors.GEMM2_WEIGHT,
                    fp8_dtype_forward,
                    grad_output_c,
                    ctx.fp8_meta["scaling_bwd"].scale_inv,
                    tex.FP8BwdTensors.GRAD_OUTPUT1,
                    fp8_dtype_backward,
                    ctx.activation_dtype,
                    get_workspace(),
                    use_split_accumulator=_2X_ACC_DGRAD,
                )

                # FC2 WGRAD
                if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                    if fc2_weight.requires_grad:
                        gelu_out_t = tex.fp8_transpose(gelu_out, fp8_dtype_forward)
                        fc2_wgrad = fp8_gemm(
                            gelu_out_t,
                            fwd_scale_inverses,
                            tex.FP8FwdTensors.GEMM2_INPUT,
                            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,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=fc2_weight.main_grad
                            if ctx.fuse_wgrad_accumulation
                            else None,
                            use_split_accumulator=_2X_ACC_WGRAD,
                        )

                    fc1_bias_grad, dgelu, dgelu_t = fp8_cast_transpose_bgrad_dgelu_fused(
                        fc2_dgrad,
                        fc1_out,
                        ctx.fp8_meta["scaling_bwd"],
                        tex.FP8BwdTensors.GRAD_OUTPUT2,
                        fp8_dtype_backward,
                    )
                else:
                    if fc2_weight.requires_grad:
                        gelu_out_c = cast_from_fp8(
                            gelu_out,
                            ctx.fp8_meta["scaling_fwd"],
                            tex.FP8FwdTensors.GEMM2_INPUT,
                            fp8_dtype_forward,
                            TE_DType[ctx.activation_dtype],
                        )
                        fc2_wgrad, _, _ = gemm(
                            gelu_out_c,
                            grad_output,
                            ctx.activation_dtype,
                            get_workspace(),
                            layout="NT",
                            grad=True,
                            use_bias=ctx.use_bias,
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=fc2_weight.main_grad
                            if ctx.fuse_wgrad_accumulation
                            else None,
                        )

                    fc1_bias_grad, dgelu_no_fp8 = bgrad_dgelu_fused(
                        fc2_dgrad, fc1_out, fc1_bias
                    )

                    dgelu = cast_to_fp8(
                        dgelu_no_fp8,
                        ctx.fp8_meta["scaling_bwd"],
                        tex.FP8BwdTensors.GRAD_OUTPUT2,
                        fp8_dtype_backward,
                    )
                    dgelu_t = None

                # FC1 DGRAD: Unconditional
                fc1_dgrad = fp8_gemm(
                    fc1_weight_t_fp8,
                    fwd_scale_inverses,
                    tex.FP8FwdTensors.GEMM1_WEIGHT,
                    fp8_dtype_forward,
                    dgelu,
                    ctx.fp8_meta["scaling_bwd"].scale_inv,
                    tex.FP8BwdTensors.GRAD_OUTPUT2,
                    fp8_dtype_backward,
                    ctx.activation_dtype,
                    get_workspace(),
                    use_split_accumulator=_2X_ACC_DGRAD,
                )
            else:
                # FC2 DGRAD; Unconditional
                fc2_dgrad, _, _ = gemm(
                    fc2_weight,
                    grad_output,
                    ctx.activation_dtype,
                    get_workspace(),
                    layout="NN",
                    gelu=not ctx.bias_gelu_nvfusion,
                    grad=True,
                    gelu_input=fc1_out,
                )

                # FC2 WGRAD
                if fc2_weight.requires_grad:
                    fc2_wgrad, fc2_bias_grad, _ = gemm(
                        gelu_out,
                        grad_output,
                        ctx.activation_dtype,
                        get_workspace(),
                        layout="NT",
                        grad=True,
                        use_bias=ctx.use_bias,
                        accumulate=accumulate_wgrad_into_param_main_grad,
                        fp32_output=ctx.fuse_wgrad_accumulation,
                        out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                    )

                if ctx.bias_gelu_nvfusion:
                    fc1_bias_grad, dgelu = bgrad_dgelu_fused(fc2_dgrad, fc1_out, fc1_bias)
                else:
                    dgelu = fc2_dgrad

                # FC1 DGRAD: Unconditional
                fc1_dgrad, _, _ = gemm(
                    fc1_weight,
                    dgelu,
                    ctx.activation_dtype,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                )

            # Overlap dgrad-RS/AR with wgrad
            if ctx.set_parallel_mode and ctx.sequence_parallel:
                handle.wait()
                fc1_dgrad, handle = reduce_scatter_along_first_dim(
                    fc1_dgrad, ctx.tp_group, async_op=True
                )
            elif ctx.set_parallel_mode and ctx.tensor_parallel:
                fc1_dgrad, handle = allreduce(fc1_dgrad, ctx.tp_group, async_op=True)

            if fc1_weight.requires_grad:
                if ctx.fp8:
                    # FC1 WGRAD
                    if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                        ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward)
                        fc1_wgrad = fp8_gemm(
                            ln_out_total_t,
                            fwd_scale_inverses,
                            tex.FP8FwdTensors.GEMM1_INPUT,
                            fp8_dtype_forward,
                            dgelu_t,
                            ctx.fp8_meta["scaling_bwd"].scale_inv,
                            tex.FP8BwdTensors.GRAD_OUTPUT2,
                            fp8_dtype_backward,
                            ctx.activation_dtype,
                            get_workspace(),
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=fc1_weight.main_grad
                            if ctx.fuse_wgrad_accumulation
                            else None,
                            use_split_accumulator=_2X_ACC_WGRAD,
                        )
                    else:
                        ln_out_total_c = cast_from_fp8(
                            ln_out_total,
                            ctx.fp8_meta["scaling_fwd"],
                            tex.FP8FwdTensors.GEMM1_INPUT,
                            fp8_dtype_forward,
                            TE_DType[ctx.activation_dtype],
                        )
                        fc1_wgrad, _, _ = gemm(
                            ln_out_total_c,
                            dgelu_no_fp8,
                            ctx.activation_dtype,
                            get_workspace(),
                            layout="NT",
                            grad=True,
                            accumulate=accumulate_wgrad_into_param_main_grad,
                            fp32_output=ctx.fuse_wgrad_accumulation,
                            out=fc1_weight.main_grad
                            if ctx.fuse_wgrad_accumulation
                            else None,
                        )
                else:
                    # FC1 WGRAD
                    fc1_wgrad_outputs = gemm(
                        ln_out_total,
                        dgelu,
                        ctx.activation_dtype,
                        get_workspace(),
                        layout="NT",
                        grad=True,
                        use_bias=not ctx.bias_gelu_nvfusion,
                        accumulate=accumulate_wgrad_into_param_main_grad,
                        fp32_output=ctx.fuse_wgrad_accumulation,
                        out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
                    )

                    if ctx.bias_gelu_nvfusion:
                        fc1_wgrad, _, _ = fc1_wgrad_outputs
                    else:
                        fc1_wgrad, fc1_bias_grad, _ = fc1_wgrad_outputs

            # Column Parallel Linear
            if ctx.set_parallel_mode and ctx.tensor_parallel and handle is not None:
                handle.wait()

            # LayerNorm gradient
            d_ln_out = fc1_dgrad.view(inputmat.shape)

            # Residual gradient
            if ctx.return_layernorm_output:
                d_ln_out = d_ln_out + grad_outputs[1].view_as(d_ln_out)

            dxmat, dgamma, dbeta = tex.layernorm_bwd(
                d_ln_out, inputmat, mu, rsigma, ln_weight, ctx.bwd_ln_sm_margin
            )

            if not ctx.use_bias:
                fc2_bias_grad = None

        return (
            dxmat.view(ctx.inp_shape),
            dgamma,
            dbeta,
            fc1_wgrad if fc1_weight.requires_grad else None,
            None,
            None,
            fc1_bias_grad,
            fc2_wgrad if fc2_weight.requires_grad else None,
            None,
            None,
            fc2_bias_grad,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


class LayerNormMLP(TransformerEngineBaseModule):
    """
    Applies layer normalization on the input followed by the MLP module, consisting of
    2 successive linear transformations, separated by the GeLU activation.

    Parameters
    ----------
    hidden_size : int
                 size of each input sample.
    ffn_hidden_size : int
                     intermediate size to which input samples are projected.
    eps : float, default = 1e-5
         a value added to the denominator of layer normalization for numerical stability.
    bias : bool, default = `True`
          if set to `False`, the FC2 layer will not learn an additive bias.
    init_method : Callable, default = `None`
                 used for initializing FC1 weights in the following way: `init_method(weight)`.
                 When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    output_layer_init_method : Callable, default = `None`
                              used for initializing FC2 weights in the following way:
                              `output_layer_init_method(weight)`. When set to `None`, defaults to
                              `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    return_layernorm_output : bool, default = `False`
                             if set to `True`, output of layernorm is returned from the forward
                             together with the output of the linear transformation.
                             Example use case: residual connection for transformer module
                             is taken post layernorm.

    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, FC1 is used as Column Parallel and FC2 is used as Row
                      Parallel as described `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
    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.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient.
    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.float32`
                  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.
    seq_length: int
               sequence length of input samples. Needed for JIT Warmup, a technique where jit fused
               functions are warmed up before training to ensure same kernels are used for forward
               propogation and activation recompute phase.
    micro_batch_size: int
                     batch size per training step. Needed for JIT Warmup, a technique where jit
                     fused functions are warmed up before training to ensure same kernels are
                     used for forward propogation and activation recompute phase.
    """

    def __init__(
        self,
        hidden_size: int,
        ffn_hidden_size: int,
        eps: float = 1e-5,
        sequence_parallel: bool = False,
        return_bias: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
        init_method: Optional[Callable] = None,
        bias: bool = True,
        output_layer_init_method: Optional[Callable] = None,
        fuse_wgrad_accumulation: bool = False,
        params_dtype: torch.dtype = torch.float32,
        return_layernorm_output: bool = False,
        seq_length: Optional[int] = None,
        micro_batch_size: Optional[int] = None,
        set_parallel_mode: bool = False,
    ) -> None:
        super().__init__()

        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
        self.use_bias = bias
        self.return_bias = return_bias
        self.return_layernorm_output = return_layernorm_output
        self.bias_gelu_nvfusion = bool(int(os.getenv("NVTE_BIAS_GELU_NVFUSION", "1")))
        self.set_parallel_mode = set_parallel_mode

        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()

        if init_method is None:
            init_method = get_default_init_method()
        if output_layer_init_method is None:
            output_layer_init_method = get_default_init_method()

        self.sequence_parallel = (self.tp_size > 1) and sequence_parallel
        self.size_per_partition = divide(ffn_hidden_size, self.tp_size)

        # LN init
        self.eps = eps
        self.layer_norm_weight = Parameter(
            torch.empty(
                hidden_size,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        self.layer_norm_bias = Parameter(
            torch.empty(
                hidden_size,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        setattr(self.layer_norm_weight, "sequence_parallel", self.sequence_parallel)
        setattr(self.layer_norm_bias, "sequence_parallel", self.sequence_parallel)
        self.reset_layer_norm_parameters()

        # FC1 init
        self.fc1_weight = Parameter(
            torch.empty(
                self.size_per_partition,
                hidden_size,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        self.fp8_weight_shapes.append(self.fc1_weight.shape)

        initialize_affine_weight_gpu(
            self.fc1_weight,
            init_method,
            get_rng_state_tracker,
            partition_dim=0,
            stride=1,
        )

        self.fc1_bias = Parameter(
            torch.empty(
                self.size_per_partition,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        set_tensor_model_parallel_attributes(self.fc1_bias, True, 0, 1)

        with torch.no_grad():
            self.fc1_bias.zero_()

        # FC2 init
        self.fc2_weight = Parameter(
            torch.empty(
                hidden_size,
                self.size_per_partition,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        self.fp8_weight_shapes.append(self.fc2_weight.shape)

        initialize_affine_weight_gpu(
            self.fc2_weight,
            output_layer_init_method,
            get_rng_state_tracker,
            partition_dim=1,
            stride=1,
        )

        if self.use_bias or self.return_bias:
            self.fc2_bias = Parameter(
                torch.empty(
                    hidden_size, device=torch.cuda.current_device(), dtype=params_dtype
                )
            )
        else:
            self.register_buffer("fc2_bias", torch.Tensor().type(params_dtype), persistent=False)

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

        with torch.no_grad():
            self.fc2_bias.zero_()

        if self.bias_gelu_nvfusion:
            set_jit_fusion_options()
            if seq_length and micro_batch_size:
                warmup_jit_bias_gelu_all_dtypes(
                    self.size_per_partition, seq_length, micro_batch_size
                )

        # These many SMs are subtracted from the total SM count when calling forward
        # and backward LayerNorm C APIs. These envvars can be used to prevent the LN
        # kernels from using all SMs in the device. This is useful for cases such as
        # communication overlap with LN.
        self.fwd_ln_sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))
        self.bwd_ln_sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0"))

    def reset_layer_norm_parameters(self) -> None:
        """Init LN params"""
        init.ones_(self.layer_norm_weight)
        init.zeros_(self.layer_norm_bias)

    def forward(
        self, inp: torch.Tensor, is_first_microbatch: Optional[bool] = None
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
        """
        Apply layer normalization to the input followed by a feedforward network (MLP Block).

        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)
        """

        with self.prepare_forward(inp, is_first_microbatch, num_gemms=2) as inp:
            if self.training:
                fwd_fn = _LayerNormMLP.apply
                args = []
            else:
                fwd_fn = _LayerNormMLP.forward
                args = [None]
            args += (
                inp,
                self.layer_norm_weight,
                self.layer_norm_bias,
                self.fc1_weight,
                self.weight1_fp8 if self.fp8 else None,
                self.weight1_t_fp8 if self.fp8 else None,
                self.fc1_bias,
                self.fc2_weight,
                self.weight2_fp8 if self.fp8 else None,
                self.weight2_t_fp8 if self.fp8 else None,
                self.fc2_bias,
                self.use_bias,
                self.eps,
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
                self.fp8_meta,
                self.fuse_wgrad_accumulation,
                self.tp_group,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.return_layernorm_output,
                self.bias_gelu_nvfusion,
                self.set_parallel_mode,
                self.training,
                self.fwd_ln_sm_margin,
                self.bwd_ln_sm_margin,
            )
            out = fwd_fn(*args)

        if self.return_layernorm_output:
            out, ln_out = out

        if self.gemm_bias_unfused_add:
            out = out + cast_if_needed(self.fc2_bias, self.activation_dtype)

        if self.return_bias:
            if self.return_layernorm_output:
                return out, cast_if_needed(self.fc2_bias, self.activation_dtype), ln_out
            return out, cast_if_needed(self.fc2_bias, self.activation_dtype)
        if self.return_layernorm_output:
            return out, ln_out
        return out


class _LayerNorm(torch.autograd.Function):
    """functional LayerNorm"""

    @staticmethod
    def forward(
        ctx,
        inp: torch.Tensor,
        ln_weight: torch.Tensor,
        ln_bias: torch.Tensor,
        eps: float,
        fwd_ln_sm_margin: int,
        bwd_ln_sm_margin: int,
    ) -> torch.Tensor:
        # Make sure input dimensions are compatible
        in_features = ln_weight.numel()
        assert inp.is_cuda, "TransformerEngine needs CUDA."
        assert inp.shape[-1] == in_features, "LayerNorm not possible"
        inputmat = inp.view((-1, in_features))

        ln_out, mu, rsigma = tex.layernorm_fwd(inputmat, ln_weight, ln_bias, eps, fwd_ln_sm_margin)
        ctx.save_for_backward(inputmat, ln_weight, mu, rsigma)
        ctx.inp_shape = inp.shape
        ctx.bwd_ln_sm_margin = bwd_ln_sm_margin
        return ln_out.view_as(inp)

    @staticmethod
    def backward(
        ctx, grad_output: torch.Tensor
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        inputmat, ln_weight, mu, rsigma = ctx.saved_tensors
        grad_output = grad_output.contiguous()
        d_ln_out = grad_output.view(inputmat.shape)
        dxmat, dgamma, dbeta = tex.layernorm_bwd(
            d_ln_out, inputmat, mu, rsigma, ln_weight, ctx.bwd_ln_sm_margin
        )
        return dxmat.view(ctx.inp_shape), dgamma, dbeta, None, None, None


class LayerNorm(torch.nn.Module):
    r"""
    Applies Layer Normalization over a mini-batch of inputs as described in
    the paper `Layer Normalization <https://arxiv.org/abs/1607.06450>`__

    .. math::
        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

    :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of
    size :attr:`hidden_size`

    Parameters
    ----------
    hidden_size : int
                size of each input sample.
    eps : float, default = 1e-5
        a value added to the denominator of layer normalization for numerical stability.
    sequence_parallel : bool, default = `False`
                        if set to `True`, uses sequence parallelism.
    params_dtype : torch.dtype, default = `torch.float32`
                    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,
        hidden_size: int,
        eps: float = 1e-5,
        sequence_parallel: bool = False,
        params_dtype: torch.dtype = torch.float32,
    ) -> None:
        super().__init__()
        self.eps = eps
        self.weight = Parameter(
            torch.empty(
                hidden_size,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        self.bias = Parameter(
            torch.empty(
                hidden_size,
                device=torch.cuda.current_device(),
                dtype=params_dtype,
            )
        )
        setattr(self.weight, "sequence_parallel", sequence_parallel)
        setattr(self.bias, "sequence_parallel", sequence_parallel)
        self.reset_layer_norm_parameters()

        # These many SMs are subtracted from the total SM count when calling forward
        # and backward LayerNorm C APIs. These envvars can be used to prevent the LN
        # kernels from using all SMs in the device. This is useful for cases such as
        # communication overlap with LN.
        self.fwd_ln_sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))
        self.bwd_ln_sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0"))

    def load_state_dict(
        self,
        state_dict: Mapping[str, Any],
        strict: bool = True,
    ) -> None:
        """Override PyTorch loader to maintain backward compatibility
        with previous version of LayerNorm parameter names.
        """
        if "layer_norm_weight" in state_dict:
            state_dict["weight"] = state_dict["layer_norm_weight"]
            del state_dict["layer_norm_weight"]
        if "layer_norm_bias" in state_dict:
            state_dict["bias"] = state_dict["layer_norm_bias"]
            del state_dict["layer_norm_bias"]

        super().load_state_dict(state_dict, strict)

    def reset_layer_norm_parameters(self) -> None:
        """Init LN params"""
        init.ones_(self.weight)
        init.zeros_(self.bias)


    def forward(self, inp: torch.Tensor) -> torch.Tensor:
        """LayerNorm FWD"""
        # Maintain backward compatibility.
        if hasattr(self, "layer_norm_weight"):
            setattr(self, "weight", self.layer_norm_weight)
        if hasattr(self, "layer_norm_bias"):
            setattr(self, "bias", self.layer_norm_bias)

        return _LayerNorm.apply(
            inp,
            self.weight,
            self.bias,
            self.eps,
            self.fwd_ln_sm_margin,
            self.bwd_ln_sm_margin,
        )