base.py

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

"""Base modules and utilities for TransformerEngine PyTorch API"""
import io
import math
import os
import pickle
import warnings
from enum import Enum
from abc import ABC, abstractmethod
from typing import Any, Dict, Generator, List, Optional, Set, Tuple, Union
from contextlib import contextmanager
import logging
from types import MethodType

import torch
import torch.nn.functional as F
from torch.distributed.tensor import DTensor

import transformer_engine_torch as tex
from transformer_engine.common.recipe import Recipe

from ._common import _ParameterInitMeta, noop_cat
from ..quantization import (
    MXFP8BlockScalingRecipeState,
    DelayedScalingRecipeState,
    Float8CurrentScalingRecipeState,
    Float8BlockScalingRecipeState,
    NVFP4BlockScalingRecipeState,
    FP8GlobalStateManager,
    RecipeState,
)
from ..distributed import (
    gather_along_first_dim,
    is_fp8_activation_recompute_enabled,
    in_fp8_activation_recompute_phase,
    _fsdp_gather_tensors,
)
from ..constants import dist_group_type
from ..quantized_tensor import QuantizedTensor, QuantizedTensorStorage, Quantizer
from ..tensor.float8_tensor import Float8Quantizer, Float8CurrentScalingQuantizer
from ..tensor.mxfp8_tensor import MXFP8Quantizer
from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
from ..tensor.storage.float8_tensor_storage import Float8TensorStorage
from ..tensor.storage.mxfp8_tensor_storage import MXFP8TensorStorage
from ..utils import is_non_tn_fp8_gemm_supported, torch_get_autocast_gpu_dtype
from ..tensor.storage.float8_blockwise_tensor_storage import Float8BlockwiseQTensorStorage
from ...common.recipe import DelayedScaling, Recipe
from ...debug.pytorch.debug_state import TEDebugState
from ...debug.pytorch.debug_quantization import DebugQuantizer, DebugQuantizedTensor
from ...debug.pytorch.utils import next_iter_when_debug_should_be_run, any_feature_enabled

__all__ = ["initialize_ub", "destroy_ub", "UserBufferQuantizationMode"]

_2X_ACC_FPROP = False
_2X_ACC_DGRAD = True
_2X_ACC_WGRAD = True
_multi_stream_cublas_workspace = []
_dummy_wgrads = {}
_cublas_workspace = None
_ub_communicators = None
_NUM_MAX_UB_STREAMS = 3
_MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY = None, None
layers_atomic_ring_exchange = []


class UserBufferQuantizationMode(Enum):
    """
    UserBufferQuantizationMode is an enum that represents the quantization mode of the UserBuffer.
    """

    NONE = "none"
    FP8 = "fp8"


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:
        # 32 MiB for NVFP4 GEMM, plus additional 1024 B for alignment and misc scales
        return 32 * 1024 * 1024 + 1024
    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.uint8, device="cuda"
        )
    return _cublas_workspace


def get_multi_stream_cublas_workspace() -> List[torch.Tensor]:
    """Returns workspace for multi-stream cublas."""
    global _multi_stream_cublas_workspace
    if not _multi_stream_cublas_workspace:
        for _ in range(tex.get_num_cublas_streams()):
            _multi_stream_cublas_workspace.append(
                torch.empty(get_cublas_workspace_size_bytes(), dtype=torch.uint8, device="cuda")
            )
    return _multi_stream_cublas_workspace


def get_dummy_wgrad(shape: list, dtype: torch.dtype, zero=False) -> torch.Tensor:
    """Returns a dummy tensor of given shape."""
    assert len(shape) == 2
    global _dummy_wgrads
    if (shape[0], shape[1], dtype) not in _dummy_wgrads:
        _dummy_wgrads[(shape[0], shape[1], dtype)] = torch.empty(
            shape,
            dtype=dtype,
            device="cuda",
            requires_grad=False,
        )
    if zero:
        _dummy_wgrads[(shape[0], shape[1], dtype)].fill_(0)
    return _dummy_wgrads[(shape[0], shape[1], dtype)].detach()


def initialize_ub(
    shape: list,
    tp_size: int,
    use_fp8: bool = False,
    quantization_modes: List[UserBufferQuantizationMode] = None,
    dtype: torch.dtype = torch.bfloat16,
    ub_cfgs: Optional[Union[dict, List[dict]]] = None,
    bootstrap_backend: Union[str, torch.distributed.Backend] = None,
) -> None:
    r"""
    Initialize the Userbuffers communicator for overlapping tensor-parallel communications with
    GEMM compute in te.Linear, te.LayerNormLinear and te.LayerNormMLP modules.

    Parameters
    ----------
    shape : list
            shape of the communication buffer, typically set to be the same as the global shape of
            the input tensor to a te.TransformerLayer forward pass, with the sequence and batch
            dimensions collapsed together -- i.e.: `(sequence_length * batch_size, hidden_size)`
    tp_size : int
              number of GPUs in the tensor-parallel process group
    use_fp8 : bool = False
              allocate the communication buffer for FP8 GEMM inputs/outputs.
              DEPRECATED: Please use `quantization_modes` instead.
    quantization_modes : List[UserBufferQuantizationMode] = None
              if a list of UserBufferQuantizationMode is provided, a UB communicator is created for each quantization setting in the list.
              falls back to the legacy `use_fp8` parameter if `None` is provided.
    dtype : torch.dtype = torch.bfloat16
            non-FP8 data type of the communication buffer when `use_fp8 = False`
    ub_cfgs: dict = None
             Configuration dictionary with the structure
             ```
             {
                <gemm_name> : {
                    "method": <"ring_exchange" or "pipeline">,
                    "is_reduce_scatter": bool,
                    "num_sm": int,
                    "cga_size": int,
                    "set_sm_margin": bool,
                    "num_splits": int,
                    "aggregate": bool,
                    "atomic_gemm": bool,
                    "use_ce": bool,
                    "fp8_buf": bool,
                }
             }
             ```
             for `te.TransformerLayer` GEMM layers in `["qkv_fprop", "qkv_dgrad", "qkv_wgrad",
             "proj_fprop", "proj_dgrad", "proj_wgrad", "fc1_fprop", "fc1_dgrad", "fc2_dgrad",
             "fc2_fprop", "fc2_wgrad"]`.
             a list may be provided to specify different overlap configurations for different the quantization settings in `quantization_modes`
    bootstrap_backend : str = None
                        `torch.distributed` communication backend for the all-gather, broadcast and
                        barrier collectives during Userbuffers initialization. Not all backends are
                        valid for every cluster configuration and distributed launch method even if
                        they are available in PyTorch. When left unset, the initialization prefers
                        to use the MPI backend, falling back first on Gloo and then NCCL if MPI is
                        not available. Setting `NVTE_UB_WITH_MPI=1` when building TE overrides this
                        option and always initializes Userbuffers with direct MPI calls in C++,
                        which also requires `MPI_HOME=/path/to/mpi/root` to be set at compile time.
    """
    if not tex.device_supports_multicast():
        assert bool(int(os.getenv("UB_SKIPMC", "0"))), (
            "CUDA device, driver and/or toolkit version does not support comm+GEMM overlap with "
            + "CUDA Multicast. Launch app with UB_SKIPMC=1 to try CUDA IPC instead."
        )

    if not quantization_modes:
        warnings.warn(
            "Initializing Userbuffers with use_fp8 is deprecated. Please use quantization_modes"
            " instead.",
            DeprecationWarning,
        )
        quantization_modes = [
            UserBufferQuantizationMode.FP8 if use_fp8 else UserBufferQuantizationMode.NONE
        ]
    else:
        assert isinstance(quantization_modes, list), "quantization_modes must be a list"
        assert all(
            isinstance(mode, UserBufferQuantizationMode) for mode in quantization_modes
        ), "quantization_modes must be a list of UserBufferQuantizationMode"

    if isinstance(ub_cfgs, dict) or ub_cfgs is None:
        ub_cfgs = [ub_cfgs] * len(quantization_modes)
    else:
        assert len(ub_cfgs) == len(
            quantization_modes
        ), "Number of ub_cfgs settings must match number of quantization configurations"

    global _ub_communicators
    assert _ub_communicators is None, "UB communicators are already initialized."
    _ub_communicators = {}

    if tex.ubuf_built_with_mpi():
        # We're bootstrapping with direct calls to MPI in Userbuffers code so we need to force
        # an MPI_Init() here by creating a new MPI process group...
        assert torch.distributed.is_mpi_available()
        _ = torch.distributed.new_group(backend="mpi")
        helper = tex.CommOverlapHelper()
    else:
        # Bootstrapping with torch.distributed API, so check backend and construct
        # intra/inter-node process groups...
        assert (
            torch.distributed.is_initialized()
        ), "torch.distributed must be initialized before Userbuffers"
        if bootstrap_backend is None:
            bootstrap_backend = "nccl"
            if torch.distributed.is_mpi_available():
                bootstrap_backend = "mpi"
            elif torch.distributed.is_gloo_available():
                bootstrap_backend = "gloo"
        else:
            assert bootstrap_backend in [
                "gloo",
                "mpi",
                "nccl",
            ], "Invalid torch.distributed backend for bootstrapping Userbuffers!"
            assert torch.distributed.is_backend_available(bootstrap_backend), (
                f"PyTorch must be compiled with '{bootstrap_backend}' support in order to "
                f"bootstrap Userbuffers with '{bootstrap_backend}' collectives."
            )

        world_group = torch.distributed.new_group(backend=bootstrap_backend)
        world_rank = torch.distributed.get_rank(world_group)
        world_size = torch.distributed.get_world_size(world_group)

        num_domains = world_size // tp_size
        mydomain_idx = world_rank // tp_size
        if num_domains > 1:
            ranks_per_domain_list = [
                [i * tp_size + t for t in range(tp_size)] for i in range(num_domains)
            ]
            tp_domain_group, _ = torch.distributed.new_subgroups_by_enumeration(
                ranks_per_domain_list, backend=bootstrap_backend
            )
            local_rank = torch.distributed.get_rank(tp_domain_group)
            tp_domain_ranks = torch.distributed.get_process_group_ranks(tp_domain_group)

            helper = tex.CommOverlapHelper(world_group, tp_domain_group)
        else:
            # TP model on single NVLink domain, no replication, no data-parallelism
            mydomain_idx = 0
            local_rank = world_rank
            tp_domain_ranks = list(range(world_size))

            helper = tex.CommOverlapHelper(world_group)

        if world_rank == 0:
            print(f"!!! [UB] Number of TP domains: {num_domains}\n", end="", flush=True)
        if local_rank == 0:
            print(
                f"!!! [UB] Global ranks on TP domain {mydomain_idx}: {tp_domain_ranks}\n",
                end="",
                flush=True,
            )

    # Allocate cuBLAS workspace with expanded size for chunking in overlapping GEMM calls
    global _cublas_workspace
    if _cublas_workspace is None:
        _cublas_workspace = get_workspace().repeat(_NUM_MAX_UB_STREAMS)
    elif _cublas_workspace.numel() != get_cublas_workspace_size_bytes() * _NUM_MAX_UB_STREAMS:
        # This ensures we don't do `.repeat()` on an already expanded workspace
        _cublas_workspace = torch.empty(
            get_cublas_workspace_size_bytes(), dtype=torch.uint8, device="cuda"
        ).repeat(_NUM_MAX_UB_STREAMS)

    # Default buffer precision: AllGather buffers use fp8 when using fp8 recipe
    layers_all_gather_overlap = [
        "qkv_fprop",
        "qkv_dgrad",
        "proj_dgrad",
        "proj_wgrad",
        "fc1_fprop",
        "fc1_dgrad",
        "fc2_dgrad",
        "fc2_wgrad",
    ]
    layers_reduce_scatter_overlap = ["proj_fprop", "fc2_fprop", "qkv_wgrad", "fc1_wgrad"]
    dgrad_reduce_scatter_overlap = ["qkv_dgrad", "fc1_dgrad"]
    # Default overlap methods for layers
    methods = {
        "ring_exchange": [
            "qkv_fprop",
            "fc1_fprop",
            "proj_dgrad",
            "fc2_dgrad",
        ],
        "pipeline": ["proj_fprop", "fc2_fprop"],
        "bulk": ["qkv_dgrad", "qkv_wgrad", "fc1_dgrad", "fc1_wgrad"],
        "external": ["proj_wgrad", "fc2_wgrad"],
    }

    # AG-RS overlap pairs of layers forming a tensor-parallel block
    ag_rs_pairs = {"qkv_fprop": "proj_fprop", "fc1_fprop": "fc2_fprop"}
    rs_ag_pairs = {v: k for k, v in ag_rs_pairs.items()}
    external_gemm_to_overlap = {"proj_wgrad": "proj_dgrad", "fc2_wgrad": "fc2_dgrad"}
    global layers_atomic_ring_exchange
    layers_atomic_ring_exchange = []

    def get_method(name):
        for method, names in methods.items():
            if name in names:
                return method
        raise KeyError(f"Given layer name {name} does not exist.")

    def get_default_config(name):
        global _MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY
        method = get_method(name)
        is_reduce_scatter = name in layers_reduce_scatter_overlap
        if _MIN_STREAM_PRIORITY is None or _MAX_STREAM_PRIORITY is None:
            _MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY = tex.get_stream_priority_range()
        default_cfg = {
            "method": method,
            "is_reduce_scatter": is_reduce_scatter,
            "num_sm": 1 if method == "ring_exchange" else 16,
            "cga_size": 1 if method == "ring_exchange" else 2,
            "set_sm_margin": not method == "ring_exchange",
            "num_splits": tp_size if method == "ring_exchange" else 4,
            "aggregate": False,
            "atomic_gemm": False,
            "use_ce": True,
            "fp8_buf": name in layers_all_gather_overlap,
            "comm_priority": _MAX_STREAM_PRIORITY,
            "gemm_priority": _MIN_STREAM_PRIORITY,
            "pipeline_rs_overlap_first_gemm": False,
        }
        return default_cfg

    def add_ub(
        name: str,
        quantization_mode: UserBufferQuantizationMode,
        method: str,
        is_reduce_scatter: bool,
        num_sm: int = 16,
        cga_size: int = 2,
        set_sm_margin: bool = False,
        num_splits: int = 0,
        aggregate: bool = False,
        atomic_gemm: bool = False,
        use_ce: bool = True,
        fp8_buf: bool = False,
        comm_priority: int = 0,
        gemm_priority: int = 0,
        pipeline_rs_overlap_first_gemm: bool = False,
    ) -> None:
        if atomic_gemm:
            warnings.warn(
                "Atomic GEMM uses a beta API from cublas and is not tested for all use cases."
            )
            assert (
                quantization_mode == UserBufferQuantizationMode.FP8
            ), "Atomic GEMM overlap supported only for FP8 GEMM."
            if method in ("bulk", "external"):
                warnings.warn(
                    f"At {name}, atoimic GEMM not is supported for a bulk overlap."
                    "Defaulting to `atomic_gemm=False`."
                )
                atomic_gemm = 0
        if not is_reduce_scatter and method == "pipeline":
            raise ValueError(
                f"At {name}, `pipeline` overlap method is not supported for AllGather."
            )
        # Check if both AG and RS overlaps use `atomic GEMM`` + `p2p ring-exchange`.
        # Using atomic GEMM + p2p ring-exchange in only one of the pair breaks functionality.
        global layers_atomic_ring_exchange
        if atomic_gemm and method == "ring_exchange" and name in ag_rs_pairs:
            layers_atomic_ring_exchange += [name, ag_rs_pairs[name]]
        if name in rs_ag_pairs:
            assert_message = (
                f"At {name}, atomic AG-GEMM overlap with `ring_exchange` shuffles GEMM chunk "
                "outputs, and  RS-GEMM overlap un-suffle them. When one of the GEMM-AG and "
                "GEMM-RS overlaps forming a TP block (e.g., qkv_fprop and proj_fprop) uses "
                "`atomic gemm` and `ring_exhcnage`, its pair must use the same overlap config "
                "for functionality."
            )
            if name in layers_atomic_ring_exchange:
                assert atomic_gemm and method == "ring_exchange", assert_message
            else:
                if atomic_gemm and method == "ring_exchange":
                    assert rs_ag_pairs[name] in layers_atomic_ring_exchange, assert_message

        if name in external_gemm_to_overlap:
            assert method == "external", (
                f"At {name}, `external` overlap method is specified, but the selected method is"
                f" {method}"
            )
            assert external_gemm_to_overlap[name] in methods["ring_exchange"], (
                f"At {name}, `external` overlap method is specified, but the external gemm"
                f" {external_gemm_to_overlap[name]} is not using `ring_exchange` overlap method"
            )

        buffer_dtype = (
            torch.uint8
            if (quantization_mode == UserBufferQuantizationMode.FP8 and fp8_buf)
            else dtype
        )
        if method == "ring_exchange":
            ub_obj = tex.CommOverlapP2P(
                shape,  # Communication buffer shape
                buffer_dtype,  # Communication buffer data type
                helper,  # Helper for torch.distributed callbacks during bootstrapping
                tp_size,  # Tensor-parallel group size (may be different than local_size)
                tex.CommOverlapType.RS if is_reduce_scatter else tex.CommOverlapType.AG,
                num_max_streams=_NUM_MAX_UB_STREAMS,
                comm_cga_size=cga_size,
                num_comm_sm=num_sm,
                set_sm_margin=set_sm_margin,
                atomic_gemm=atomic_gemm,
                use_ce=use_ce,
                aggregate=aggregate,
                gemm_priority=gemm_priority,
                comm_priority=comm_priority,
            )
        else:
            ub_obj = tex.CommOverlap(
                shape,  # Communication buffer shape
                buffer_dtype,  # Communication buffer data type
                helper,  # Helper for torch.distributed callbacks during bootstrapping
                tp_size,  # Tensor-parallel group size (may be different than local_size)
                num_splits=num_splits,
                num_max_streams=_NUM_MAX_UB_STREAMS,
                comm_cga_size=cga_size,
                num_comm_sm=num_sm,
                set_sm_margin=set_sm_margin,
                atomic_gemm=atomic_gemm,
                gemm_priority=gemm_priority,
                comm_priority=comm_priority,
                rs_overlap_first_gemm=pipeline_rs_overlap_first_gemm,
            )
        _ub_communicators[(name, quantization_mode)] = ub_obj

    for quantization_mode, user_ub_cfg in zip(quantization_modes, ub_cfgs):
        if user_ub_cfg is not None:
            for name in dgrad_reduce_scatter_overlap:
                if (
                    name in user_ub_cfg
                    and "method" in user_ub_cfg[name]
                    and user_ub_cfg[name]["method"] != "bulk"
                ):
                    wgrad_name = name.replace("dgrad", "wgrad")
                    assert wgrad_name not in user_ub_cfg
                    layers_reduce_scatter_overlap.remove(wgrad_name)
                    layers_all_gather_overlap.remove(name)
                    layers_reduce_scatter_overlap.append(name)
                    methods["bulk"].remove(name)
                    new_method = user_ub_cfg[name]["method"]
                    methods[new_method].append(name)

        for name in (
            methods["ring_exchange"] + methods["pipeline"] + methods["bulk"] + methods["external"]
        ):
            ub_cfg = get_default_config(name)
            if user_ub_cfg is not None and name in user_ub_cfg:
                fp8_buf = (name in layers_all_gather_overlap) or (
                    user_ub_cfg[name].get("fp8_buf", False) and name in methods["pipeline"]
                )
                ub_cfg.update(user_ub_cfg[name])
                ub_cfg["fp8_buf"] = fp8_buf
            add_ub(name, quantization_mode, **ub_cfg)


def get_ub(name: str, use_fp8: bool):
    """Get userbuffer communicator corresponding to give key."""
    # For now use `use_fp8` boolean input as it matches the current design in the modules
    # So favour simplicity until the correct design becomes clear.
    # This is mainly an internal API so we don't need to worry about future changes
    key = (name, UserBufferQuantizationMode.FP8 if use_fp8 else UserBufferQuantizationMode.NONE)
    assert _ub_communicators is not None, "UB manager is not initialized."
    assert key in _ub_communicators, f"UB for {name} with use_fp8={use_fp8} is not registered."
    return _ub_communicators[key]


def destroy_ub():
    """Destroy all allocated userbuffer communicators."""
    global _ub_communicators
    _ub_communicators = None
    global layers_atomic_ring_exchange
    layers_atomic_ring_exchange = []


def fill_userbuffers_buffer_for_all_gather(
    comm,
    local_tensor: torch.Tensor,
    quantizer: Optional[Quantizer],
    process_group,
) -> tuple[torch.Tensor | QuantizedTensorStorage, torch.Tensor | QuantizedTensorStorage]:
    """Fill local shard of Userbuffers buffer with data for all-gather

    Returns the full tensor and the local shard, both using the
    Userbuffers buffer as their underlying data. These tensors should
    be used carefully (e.g. only immediately before and after a
    Userbuffers operation) since the underlying data may be
    overwritten by other Userbuffers operations.

    May perform blocking communication if needed for the gathered
    tensor's metadata, e.g. scaling factors.

    """

    # Tensor dimensions
    local_shape = local_tensor.size()
    if not local_shape:
        raise ValueError(f"Invalid local tensor (shape={tuple(local_shape)})")
    process_group_size = torch.distributed.get_world_size(process_group)
    global_shape = list(local_shape)
    global_shape[0] *= process_group_size

    # Unquantized data
    if quantizer is None:
        if isinstance(local_tensor, QuantizedTensorStorage):
            local_tensor = local_tensor.dequantize()
        if comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather unquantized tensor, "
                "but Userbuffers is initialized with FP8 buffers"
            )
        comm.copy_into_buffer(local_tensor, local_chunk=True)
        global_tensor = comm.get_buffer(shape=global_shape)
        return global_tensor, local_tensor

    # FP8 data
    if isinstance(quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)):
        if not isinstance(local_tensor, Float8TensorStorage):
            if isinstance(local_tensor, QuantizedTensorStorage):
                local_tensor.dequantize()
            quantizer.set_usage(rowwise=True, columnwise=False)
            local_tensor = quantizer(local_tensor)
        if not comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather FP8 tensor, "
                "but Userbuffers is not initialized with FP8 buffers"
            )
        comm.copy_into_buffer(local_tensor._data, local_chunk=True)
        global_tensor_data = comm.get_buffer(shape=global_shape)
        global_tensor = Float8TensorStorage(
            data=global_tensor_data,
            fp8_scale_inv=local_tensor._scale_inv,
            fp8_dtype=local_tensor._fp8_dtype,
            quantizer=quantizer,
        )
        return global_tensor, local_tensor

    # MXFP8 data
    if isinstance(quantizer, MXFP8Quantizer):

        # Cast to MXFP8 if needed
        if not isinstance(local_tensor, MXFP8TensorStorage):
            if isinstance(local_tensor, QuantizedTensorStorage):
                local_tensor.dequantize()
            local_tensor = quantizer(local_tensor)
        if not comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather MXFP8 tensor, "
                "but Userbuffers is not initialized with FP8 buffers"
            )

        # Check which MXFP8 buffer to communicate
        if quantizer.rowwise_usage == quantizer.columnwise_usage:
            raise ValueError(
                "Userbuffers can only communicate one MXFP8 buffer at a time, "
                f"but quantizer has rowwise_usage={quantizer.rowwise_usage}, "
                f"columnwise_usage={quantizer.columnwise_usage}"
            )
        with_rowwise_data = quantizer.rowwise_usage

        # Copy MXFP8 data to local chunk of Userbuffers buffer
        local_data = (
            local_tensor._rowwise_data if with_rowwise_data else local_tensor._columnwise_data
        )
        comm.copy_into_buffer(local_data, local_chunk=True)

        # Gather scaling-inverses
        if math.prod(local_shape[:-1]) % 128 != 0:
            raise ValueError(
                "Userbuffers requires MXFP8 tensor dims that are divisible by 128, "
                f"but got MXFP8 tensor with shape={tuple(local_shape)}"
            )
        local_scale_inv = (
            local_tensor._rowwise_scale_inv
            if with_rowwise_data
            else local_tensor._columnwise_scale_inv
        )
        local_scale_inv_size = list(local_scale_inv.size())
        global_scale_inv = torch.empty(
            [process_group_size * local_scale_inv_size[0]] + local_scale_inv_size[1:],
            dtype=local_scale_inv.dtype,
            device=local_scale_inv.device,
        )
        torch.distributed.all_gather_into_tensor(
            global_scale_inv,
            local_scale_inv,
            group=process_group,
        )

        # Construct MXFP8 tensor with Userbuffers buffer
        rowwise_data, rowwise_scale_inv = None, None
        columnwise_data, columnwise_scale_inv = None, None
        global_data = comm.get_buffer(shape=global_shape)
        if with_rowwise_data:
            rowwise_data, rowwise_scale_inv = global_data, global_scale_inv
        else:
            columnwise_data, columnwise_scale_inv = global_data, global_scale_inv
        global_tensor = MXFP8TensorStorage(
            rowwise_data=rowwise_data,
            rowwise_scale_inv=rowwise_scale_inv,
            columnwise_data=columnwise_data,
            columnwise_scale_inv=columnwise_scale_inv,
            fp8_dtype=local_tensor._fp8_dtype,
            quantizer=quantizer,
        )
        return global_tensor, local_tensor

    # Unsupported data format
    raise ValueError(f"Unsupported quantizer for Userbuffers ({quantizer})")


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

    def __init__(self) -> None:
        super().__init__()
        assert torch.cuda.is_available(), "TransformerEngine needs CUDA."
        self.name = None
        self.next_iter_when_debug_should_be_run = 0
        self.fp8_initialized = False
        self.fp8 = False
        self.fp8_calibration = False
        self.fp8_meta = {}
        self.fp8_meta["fp8_checkpoint"] = False
        self.fp8_meta["fp8_group"] = None
        self.fp8_meta_tensors_initialized = False
        self.quantizers = {"scaling_fwd": {}, "scaling_bwd": {}}
        self.tp_group = None
        self.tp_size = 1
        self.sequence_parallel = False
        self.param_init_meta = {}
        self.primary_weights_in_fp8 = FP8GlobalStateManager.with_fp8_parameters()
        self.preserve_high_precision_init_val = FP8GlobalStateManager.with_high_precision_init_val()
        self.fsdp_wrapped = False
        self.fsdp_group = None
        self._fp8_workspaces: Dict[str, QuantizedTensor] = {}
        self.activation_dtype: Optional[torch.dtype] = None
        self.wgrad_accumulation_and_reduce_hooks = []
        self.wgrad_store = None

        if not TEDebugState.debug_enabled:
            TEDebugState.initialize()

    # Names of attributes that can be set quickly (see __setattr__
    # method)
    _fast_setattr_names: Set[str] = {
        "activation_dtype",
        "fp8",
        "fp8_initialized",
        "fp8_calibration",
        "fp8_parameters",
    }

    def __setattr__(self, name: str, value: Any) -> None:
        if name in TransformerEngineBaseModule._fast_setattr_names:
            # torch.nn.Module has a custom __setattr__ that handles
            # modules, parameters, and buffers. This is unnecessary
            # overhead when setting plain attrs.
            self.__dict__[name] = value
        else:
            # Default case
            super().__setattr__(name, value)

    def adjust_amax_history_length(self, length: int, fwd: Optional[bool] = None) -> None:
        """
        Delayed scaling only.

        Increase or decrease size of amax history based on given `length`.

        .. warning::
            This changes the underlying amax memory location.
        """
        if fwd is None:
            fp8_meta_tensor_keys = ("scaling_fwd", "scaling_bwd")
        else:
            fp8_meta_tensor_keys = ("scaling_fwd" if fwd else "scaling_bwd",)

        for meta_key in fp8_meta_tensor_keys:
            if meta_key not in self.fp8_meta:
                # Handles non-parameter FP8 modules, e.g. DPA.
                continue
            curr_len = self.fp8_meta[meta_key].amax_history.shape[0]
            if length == curr_len:
                continue
            if length < curr_len:
                self.fp8_meta[meta_key].amax_history = (
                    self.fp8_meta[meta_key].amax_history[:length].clone()
                )
            elif length > curr_len:
                extra_rows = length - curr_len
                self.fp8_meta[meta_key].amax_history = F.pad(
                    self.fp8_meta[meta_key].amax_history, pad=(0, 0, 0, extra_rows)
                )

            # Update quantizers with new amax pointers.
            self.quantizers[meta_key] = self.fp8_meta[meta_key].make_quantizers()
            # Make sure weight tensors has correct quantizers
            self._update_weight_quantizers()

            # Update the global buffers with new amax and history pointers.
            if FP8GlobalStateManager.get_buffer_info() in self.fp8_meta:
                fwd_pos, fwd_key, bwd_pos, bwd_key = self.fp8_meta[
                    FP8GlobalStateManager.get_buffer_info()
                ]
                for pos, buffer_key in zip((fwd_pos, bwd_pos), (fwd_key, bwd_key)):
                    if buffer_key in FP8GlobalStateManager.global_amax_buffer:
                        assert (
                            buffer_key in FP8GlobalStateManager.global_amax_history_buffer
                        ), "TE internal error during amax history change."
                        FP8GlobalStateManager.global_amax_buffer[buffer_key][pos] = self.fp8_meta[
                            meta_key
                        ].amax_history[0]
                        FP8GlobalStateManager.global_amax_history_buffer[buffer_key][pos] = (
                            self.fp8_meta[meta_key].amax_history
                        )

    def set_meta_tensor(self, fwd: bool, recipe: Recipe) -> None:
        """Init scales and amaxes for fwd | bwd."""
        fp8_meta_tensor_key = "scaling_fwd" if fwd else "scaling_bwd"

        # Return early if recipe state matches recipe
        if self.fp8_meta_tensors_initialized:
            recipe_state = self.fp8_meta[fp8_meta_tensor_key]
            if recipe.delayed() and isinstance(recipe_state, DelayedScalingRecipeState):
                self.adjust_amax_history_length(recipe.amax_history_len, fwd=fwd)
                return
            if recipe.mxfp8() and isinstance(recipe_state, MXFP8BlockScalingRecipeState):
                return
            if recipe.float8_current_scaling() and isinstance(
                recipe_state, Float8CurrentScalingRecipeState
            ):
                return
            if recipe.float8_block_scaling() and isinstance(
                recipe_state, Float8BlockScalingRecipeState
            ):
                return
            if recipe.nvfp4() and isinstance(recipe_state, NVFP4BlockScalingRecipeState):
                return

        # 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

        # Initialize recipe state and quantizers
        recipe_state = RecipeState.create(
            recipe,
            mode=("forward" if fwd else "backward"),
            num_quantizers=num_fp8_tensors,
        )

        self.fp8_meta[fp8_meta_tensor_key] = recipe_state
        self.quantizers[fp8_meta_tensor_key] = recipe_state.make_quantizers()

    def _update_weight_quantizers(self) -> None:
        """Update the quantizers for the weight tensors."""
        weight_tensors = self._get_weight_tensors()
        weight_quantizers = self._get_weight_quantizers()
        assert len(weight_tensors) == len(weight_quantizers), (
            f"Number of weight tensors ({len(weight_tensors)}) and quantizers "
            f"({len(weight_quantizers)}) must match"
        )
        for weight, quantizer in zip(weight_tensors, weight_quantizers):
            if quantizer is not None and isinstance(weight, QuantizedTensorStorage):
                weight.update_quantizer(quantizer)

    def _get_weight_tensors(self) -> List[Union[torch.Tensor, QuantizedTensorStorage]]:
        """Get the weight tensors of the module."""
        raise NotImplementedError(
            f"{self.__class__.__name__} class does not implement _get_weight_tensors function"
        )

    def _get_weight_quantizers(self) -> List[Quantizer]:
        """Get the weight quantizers of the module."""
        raise NotImplementedError(
            f"{self.__class__.__name__} class does not implement _get_weight_quantizers function"
        )

    def init_fp8_meta_tensors(self, recipe: Recipe) -> None:
        """Init scales and amaxes."""
        self.set_meta_tensor(True, recipe)
        self.set_meta_tensor(False, recipe)

        self.fp8_meta_tensors_initialized = True

    def get_fp8_meta_tensors(self) -> None:
        """Get scales and amaxes."""
        fwd_key, bwd_key = "scaling_fwd", "scaling_bwd"
        if fwd_key not in self.fp8_meta or bwd_key not in self.fp8_meta:
            return None

        fp8_meta_tensors = {fwd_key: [], bwd_key: []}
        with torch.no_grad():
            for key in (fwd_key, bwd_key):
                fp8_meta_tensors[key].append(self.fp8_meta[key].scale.clone())
                fp8_meta_tensors[key].append(self.fp8_meta[key].amax_history.clone())
        return fp8_meta_tensors

    def reset_fp8_meta_tensors(self, fp8_meta_tensors=None) -> None:
        """Reset scales and amaxes."""

        def reset(key):
            if key in self.fp8_meta:
                if fp8_meta_tensors is None:
                    self.fp8_meta[key].scale.copy_(torch.ones_like(self.fp8_meta[key].scale))
                    self.fp8_meta[key].amax_history.copy_(
                        torch.zeros_like(self.fp8_meta[key].amax_history)
                    )
                else:
                    assert key in fp8_meta_tensors, "Cannot reset fp8 tensors."
                    self.fp8_meta[key].scale.copy_(fp8_meta_tensors[key][0])
                    self.fp8_meta[key].amax_history.copy_(fp8_meta_tensors[key][1])

        with torch.no_grad():
            reset("scaling_fwd")
            reset("scaling_bwd")

    def get_extra_state(self) -> torch.Tensor:
        """Save before checkpointing."""

        # This implementation is working around a few issues:
        #
        # (1) PyTorch's "extra state" infrastructure might be able to
        #     support any picklable type, but they make no guarantees.
        #     We have experienced problems (e.g. in ONNX export) with
        #     non-tensor extra state.
        # (2) PyTorch's checkpointing infrastructure does not remap
        #     devices for "extra state" like it does for "state dict".
        #     Thus, we want to avoid putting extra state on the GPU
        #     since it may be loaded on the wrong device.
        # (3) The extra state consists of many small tensors. If we
        #     want to copy them all to CPU, then we need to avoid the
        #     overhead of many GPU-CPU memory transfers.
        #
        # See: https://github.com/NVIDIA/TransformerEngine/pull/351
        # See: https://github.com/NVIDIA/TransformerEngine/pull/363

        def to_cpu(src: torch.Tensor) -> torch.Tensor:
            """Helper function to make CPU copy of tensor

            Memory transfer is asynchronous w.r.t. host, so GPU should
            be synchronized before using result.

            """
            dst = torch.empty_like(src, device="cpu")
            dst.copy_(src, non_blocking=True)
            return dst

        # Store FP8 state if needed
        state = None
        fp8_checkpoint = self.fp8_meta["fp8_checkpoint"] or self.fp8 or self.fp8_calibration
        if not fp8_checkpoint:
            return torch.empty(0, dtype=torch.uint8)

        # Copy tensors to CPU and store
        state = {}
        state["recipe"] = self.fp8_meta["recipe"]
        if state["recipe"].delayed():
            state["scale_fwd"] = to_cpu(self.fp8_meta["scaling_fwd"].scale)
            state["amax_history_fwd"] = to_cpu(self.fp8_meta["scaling_fwd"].amax_history)
            state["scale_bwd"] = to_cpu(self.fp8_meta["scaling_bwd"].scale)
            state["amax_history_bwd"] = to_cpu(self.fp8_meta["scaling_bwd"].amax_history)

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

        # Serialize state into byte tensor
        torch.cuda.synchronize()
        state_serialized = bytearray(pickle.dumps(state))
        state_serialized = torch.frombuffer(state_serialized, dtype=torch.uint8)
        return state_serialized

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

        # Maintain backwards compatibility with older checkpoints.
        if state is None:
            return

        # Load state
        if isinstance(state, torch.Tensor):
            # No FP8 is indicated by an empty tensor we don't need to unpickle.
            if state.numel() == 0:
                return
            # Default format: byte tensor with pickled data
            state = pickle.loads(state.detach().cpu().numpy().tobytes())
        elif isinstance(state, io.BytesIO):
            # Deprecated format with io.BytesIO
            state.seek(0)
            state = torch.load(state, map_location="cuda")
        else:
            raise RuntimeError("Unsupported checkpoint format.")

        if state is None:
            return

        # TE 1.x checkpoint compatibility: add DelayedScaling recipe if missing
        if "recipe" not in state:
            # TE 1.x only supported delayed scaling, which was the default recipe
            state["recipe"] = DelayedScaling()
            # TE 1.x also saved scale_inv, which is not needed with Recipe object
            state.pop("scale_inv_fwd", None)
            state.pop("scale_inv_bwd", None)

        # Load extra items
        self.fp8_meta.update(state["extra_fp8_variables"])
        self.fp8_meta["recipe"] = state["recipe"]
        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["recipe"])

        def copy_tensor(src: torch.Tensor, dst: torch.Tensor) -> None:
            """Helper function to copy tensor from CPU

            Memory transfer is asynchronous w.r.t. host, so GPU should
            be synchronized before using result.

            """
            dst.copy_(src, non_blocking=True)

        # Load tensors
        if self.fp8_meta["recipe"].delayed():
            copy_tensor(state["scale_fwd"], self.fp8_meta["scaling_fwd"].scale)
            copy_tensor(state["amax_history_fwd"], self.fp8_meta["scaling_fwd"].amax_history)
            copy_tensor(state["scale_bwd"], self.fp8_meta["scaling_bwd"].scale)
            copy_tensor(state["amax_history_bwd"], self.fp8_meta["scaling_bwd"].amax_history)
        torch.cuda.synchronize()

    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
        if self.activation_dtype == inp.dtype:
            return

        dtype = inp.dtype
        if not self.allow_different_data_and_param_types:
            for name, param in self.named_parameters():
                if param is not None:
                    assert dtype == param.dtype, (
                        "Data types for parameters must match when outside of autocasted region. "
                        f" Found input dtype: {dtype} and {name!r} dtype: {param.dtype}"
                    )
        self.activation_dtype = dtype

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
        self.tp_group = tp_group
        self.tp_group_initialized = True

    def _get_fp8_params(self) -> Union[List[torch.Tensor], None]:
        """returns the FP8 weights."""
        fp8_params = []
        for param in self.parameters(recurse=False):
            if isinstance(param, QuantizedTensor) and param.requires_grad:
                fp8_params.append(param)
        if len(fp8_params) == 0:
            return None
        return fp8_params

    # This routine is shared across FP8 and FP8_calibration paths so should not actually
    # assume FP8 execution.
    def init_fp8_metadata(self, num_gemms: int = 1) -> None:
        """Initialize fp8 related metadata and tensors during fprop."""
        _original_recipe = self.fp8_meta.get("recipe", None)

        self.fp8_parameters = FP8GlobalStateManager.with_fp8_parameters()
        self.fp8 = FP8GlobalStateManager.is_fp8_enabled()
        self.fp8_calibration = FP8GlobalStateManager.is_fp8_calibration()
        fp8_enabled = self.fp8 or self.fp8_calibration
        self.fp8_meta["fp8_checkpoint"] = self.fp8 or self.fp8_calibration

        if self.fp8_parameters or fp8_enabled:
            if (
                self.fp8_initialized
                and FP8GlobalStateManager.get_fp8_recipe() == self.fp8_meta["recipe"]
            ):
                # FP8 init has already been run and recipe is the same, don't do anything.
                return
            self.fp8_meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()
        else:
            # If fp8 isn't enabled, turn off and return.
            self.fp8_initialized = False
            return

        if self.fp8_parameters and not self.fp8_initialized:
            self.fp8_meta["num_gemms"] = num_gemms
            self.init_fp8_meta_tensors(self.fp8_meta["recipe"])

        if fp8_enabled:
            # Set FP8 and other FP8 metadata
            self.fp8_meta["num_gemms"] = num_gemms
            self.fp8_meta["fp8_group"] = FP8GlobalStateManager.get_fp8_group()

            # Set FP8_MAX per tensor according to recipe
            if hasattr(self.fp8_meta["recipe"], "fp8_format"):
                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_meta["recipe"])
            self.fp8_initialized = True

            self.fp8_meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()

        _current_recipe = self.fp8_meta["recipe"]
        if _original_recipe is not None and not (
            issubclass(_current_recipe.__class__, _original_recipe.__class__)
            or issubclass(_original_recipe.__class__, _current_recipe.__class__)
        ):
            warnings.warn(
                f"Recipe type changed from {_original_recipe.__class__.__name__} "
                f"to {_current_recipe.__class__.__name__}. "
                "This may affect model behavior."
            )
            # Clear cached workspaces as they were created with the old recipe/quantizer type
            self._fp8_workspaces.clear()

    @contextmanager
    def prepare_forward(
        self,
        inp: torch.Tensor,
        num_gemms: int = 1,
        allow_non_contiguous: bool = False,
        allow_different_data_and_param_types: bool = False,
    ) -> Generator[torch.Tensor, None, 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.
        """
        self.allow_different_data_and_param_types = allow_different_data_and_param_types
        self.forwarded_at_least_once = True
        # Activation recomputation is used and this is the second forward phase.
        if self.fp8 and in_fp8_activation_recompute_phase():
            FP8GlobalStateManager.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.init_fp8_metadata(num_gemms=num_gemms)
            self._check_weight_tensor_recipe_correspondence()

            if self.fp8 and self.sequence_parallel and self.fp8_meta["recipe"].delayed():
                assert self.fp8_meta["recipe"].reduce_amax, (
                    "Amax reduction across tensor parallel group is "
                    "necessary when using sequence parallelism with FP8."
                )

            if self.fp8 and not FP8GlobalStateManager.fp8_graph_capturing():
                FP8GlobalStateManager.add_fp8_tensors_to_global_buffer(self.fp8_meta)

            # Activation recomputation is used and this is the first forward phase.
            if self.fp8 and self.training and is_fp8_activation_recompute_enabled():
                FP8GlobalStateManager.copy_forward_fp8_meta_tensors_for_recompute(self.fp8_meta)

        with torch.cuda.nvtx.range(self.__class__.__name__ + " forward"):
            if not allow_non_contiguous and not inp.is_contiguous():
                inp = inp.contiguous()
            yield inp

        if self.fp8 and in_fp8_activation_recompute_phase():
            FP8GlobalStateManager.restore_fp8_meta_tensors(self.fp8_meta)

    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,
        quantizer: Optional[Quantizer],
    ) -> 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`.
            R2: bias gradient on R1.

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

        # Non-FP8 case: bgrad is fused with wgrad for this case.
        if not ctx.fp8 and not ctx.debug:
            if gather_grad_output:
                if not ctx.ub_overlap_ag:  # Perform NCCL all-gather
                    grad_output, _ = gather_along_first_dim(grad_output, ctx.tp_group)
                else:  # Initialize Userbuffers all-gather
                    grad_output, _ = fill_userbuffers_buffer_for_all_gather(
                        ctx.ub_obj_gradout,
                        grad_output,
                        None,
                        ctx.tp_group,
                    )
            return grad_output, None

        # FP8 with all-gather: unfused bgrad, fused cast + transpose
        # Also supports debug quantization, which is handled inside gather_along_first_dim.
        if gather_grad_output:
            grad_bias = None
            if ctx.use_bias:
                grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
            if ctx.ub_overlap_ag:
                # Quantize the gradient if needed
                if not isinstance(
                    grad_output,
                    (
                        QuantizedTensor,
                        Float8TensorStorage,
                        MXFP8TensorStorage,
                        Float8BlockwiseQTensorStorage,
                    ),
                ):
                    grad_output = quantizer(grad_output)

                # Copy into communication buffer, and replace original gradient with it
                grad_output, _ = fill_userbuffers_buffer_for_all_gather(
                    ctx.ub_obj_gradout,
                    grad_output,
                    quantizer,
                    ctx.tp_group,
                )
            else:
                grad_output, _ = gather_along_first_dim(
                    grad_output,
                    ctx.tp_group,
                    quantizer=quantizer,
                )
            return grad_output, grad_bias

        # Debug without all-gather: unfused cast and bgrad
        # bgrad only if wgrad is in FP8, otherwise it is fused with wgrad and we return None
        if ctx.debug:
            grad_output_ = quantizer(grad_output)
            if (
                isinstance(
                    grad_output_.get_tensor(True),
                    (
                        QuantizedTensor,
                        Float8TensorStorage,
                        MXFP8TensorStorage,
                        Float8BlockwiseQTensorStorage,
                    ),
                )
                and ctx.use_bias
            ):
                grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
            else:
                grad_bias = None
            grad_output = grad_output_
            return grad_output, grad_bias

        # FP8 without all-gather: fused bgrad + cast + transpose
        grad_bias = None
        if ctx.use_bias:
            if isinstance(
                grad_output,
                (
                    QuantizedTensor,
                    Float8TensorStorage,
                    MXFP8TensorStorage,
                    Float8BlockwiseQTensorStorage,
                ),
            ):
                grad_bias = grad_output.dequantize().view(-1, grad_output.shape[-1]).sum(dim=0)
            else:
                if isinstance(quantizer, Float8BlockQuantizer):
                    # unfuse bgrad for now until cast_transpose + dgrad calculation is ready for Float8BlockQuantizer.
                    grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
                else:
                    grad_bias, grad_output = tex.bgrad_quantize(grad_output, quantizer)
        if not isinstance(grad_output, QuantizedTensorStorage):
            grad_output = quantizer(grad_output)
        return grad_output, grad_bias

    def register_parameter(self, name, param, **kwargs):
        """
        Thin wrapper around PyTorch parameter registration to stash additional parameter
        metedata used in deferred initialization.
        """
        super().register_parameter(name, param)
        # Initialize param_init_meta exactly once during the init. FSDP2 can call
        # register parameter again to change parameters to DTensors. And it calls
        # it without custom fp8 specific kwargs that we need. And so we dont want
        # to reset/loose our fp8 init attributes.
        if hasattr(self, "param_init_meta") and name not in self.param_init_meta:
            self.param_init_meta[name] = _ParameterInitMeta(**kwargs)

    def reset_parameters(self, defer_init: Optional[bool] = False) -> None:
        """
        Reset all module parameters to initial values. Unless deferred initialization
        is specified, all parameters on a 'meta' device are also materialized on a real cuda
        device before the values are reset to initial.
        """
        if defer_init:
            return

        for name, param in self.named_parameters(recurse=False):
            # Check if parameter is a DTensor (FSDP2) or regular tensor
            is_dtensor = isinstance(param, DTensor)
            dtensor_param = param if is_dtensor else None
            # Need to update/quantize local tensor in case of DTensor
            param = param._local_tensor if is_dtensor else param
            # Ensure parameter is on a real device
            if param.device == torch.device("meta"):
                param = torch.empty_like(param, device="cuda")
            # Initialize the parameter values on device
            init_fn = self.param_init_meta[name].init_fn
            get_rng_state_tracker = self.param_init_meta[name].get_rng_state_tracker
            if get_rng_state_tracker is None:
                init_fn(param)
            else:
                if hasattr(self, "rng_tracker_name") and self.rng_tracker_name:
                    with get_rng_state_tracker().fork(self.rng_tracker_name):
                        init_fn(param)
                else:
                    with get_rng_state_tracker().fork():
                        init_fn(param)

            # Wrap parameters in QuantizedTensor if needed
            fp8_meta_index = self.param_init_meta[name].fp8_meta_index
            high_precision_init_val = None
            if self.primary_weights_in_fp8 and fp8_meta_index is not None:

                # Keep high-precision values on CPU if needed
                if self.preserve_high_precision_init_val:
                    high_precision_init_val = param.detach().cpu()

                # Configure quantizer
                quantizer = self.quantizers["scaling_fwd"][fp8_meta_index]
                if quantizer is None:
                    raise RuntimeError("Weight quantizer has not been initialized")
                quantizer.set_usage(rowwise=True, columnwise=torch.is_grad_enabled())
                quantizer.internal = False
                if is_dtensor and isinstance(quantizer, Float8CurrentScalingQuantizer):
                    device_mesh = dtensor_param.device_mesh
                    amax_reduction_group = (
                        device_mesh.get_group(mesh_dim="shard")
                        if device_mesh.ndim > 1
                        else device_mesh.get_group()
                    )
                    quantizer.amax_reduction_group = amax_reduction_group
                    quantizer.with_amax_reduction = True
                # Quantize parameter
                param = quantizer(param)

            # Redo parameter wrap in case we broke it above
            # NOTE: Currently this can only be broken when primary weights are in Fp8 but
            #       re-applying the nn.Parameter() wrap is a no-op when the input is already
            #       a parameter so we always re-apply it just for extra safety.
            if is_dtensor:
                # recreate the DTensor from the parameter.
                dtensor_param = DTensor.from_local(
                    param,
                    device_mesh=dtensor_param.device_mesh,
                    placements=dtensor_param.placements,
                    shape=dtensor_param.size(),
                    stride=dtensor_param.stride(),
                )
                dtensor_param = torch.nn.Parameter(dtensor_param)
            else:
                param = torch.nn.Parameter(param)

            # Keep high-precision values on CPU if needed
            if high_precision_init_val is not None:

                # - Master weights are initialized from model weights, if we use fp8 primary
                #   weights to initialize master weights, the numerical values of master weights
                #   are not consistent with the numerical values when we initialize them from
                #   bf16/fp16 weights.
                # - So we add a `_high_precision_init_val` attribute to each model weight to store
                #   the original bf16/fp16 weight on cpu before casting it to fp8. And users can
                #   use `get_high_precision_init_val` to get this cpu tensor.
                # - This cpu tensor is not needed once the master weight is initialized, so users
                #   should call `clear_high_precision_init_val` to remove it after master weight
                #   is initialized.

                def get(self):
                    if hasattr(self, "_high_precision_init_val"):
                        return self._high_precision_init_val
                    return None

                def clear(self):
                    if hasattr(self, "_high_precision_init_val"):
                        del self._high_precision_init_val

                param._high_precision_init_val = high_precision_init_val
                param.get_high_precision_init_val = MethodType(get, param)
                param.clear_high_precision_init_val = MethodType(clear, param)
                # Update the parameter based on its type

            if not is_dtensor:
                setattr(self, name, param)
            else:
                setattr(self, name, dtensor_param)

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

    def get_weight_workspace(
        self,
        *,
        tensor: Optional[torch.Tensor] = None,
        quantizer: Optional[Quantizer] = None,
        cache_name: Optional[str] = None,
        update_workspace: bool = True,
        skip_update_flag: Optional[torch.Tensor] = None,
        fsdp_group: Optional[dist_group_type] = None,
        workspace_dtype: Optional[torch.dtype] = None,
    ) -> QuantizedTensor:
        """Get workspace buffer for weights and maybe update its values

        The workspace buffer may be cached for future function calls.

        Parameters
        ----------
        tensor : torch.Tensor, optional
            Values to copy into workspace. Required if the workspace
            is being constructed or updated.
        quantizer: Quantizer, optional
            Quantizer used to cast the weights. Required if the
            workspace is being constructed or updated.
        cache_name: str, optional
            Key for caching.
        update_workspace: bool, default = `True`
            Update workspace with values from `tensor`.
        skip_update_flag: torch.Tensor, optional
            GPU flag to skip updating the workspace. Take precedence
            over `update_workspace` if provided.
        fsdp_group: bool, default = None
            FSDP process group that the weights are distributed over.
        workspace_dtype: torch.dtype, default = None
            If weight workspace contains high-precision tensor - for example
            for debug quantization, this is dtype of the tensor.
        """

        # Handle case where weights are already quantized
        # Note: Make sure weights have required usages, but do not
        # destroy unnecessary usages since they may be used later.
        if isinstance(tensor, QuantizedTensor):
            update_rowwise_usage = True if quantizer.rowwise_usage else None
            update_columnwise_usage = True if quantizer.columnwise_usage else None
            tensor.update_usage(
                rowwise_usage=update_rowwise_usage,
                columnwise_usage=update_columnwise_usage,
            )
            return tensor

        # Try getting workspace from cache
        out = None
        if cache_name is not None:
            out = self._fp8_workspaces.get(cache_name, None)

        # Reset cache if workspace is invalid
        if out is not None and quantizer is not None:
            reset_cache = False
            if isinstance(out, Float8TensorStorage):
                if (
                    not is_non_tn_fp8_gemm_supported()
                    and quantizer.columnwise_usage
                    and out._transpose is None
                ):
                    reset_cache = True
            elif isinstance(out, MXFP8TensorStorage):
                if quantizer.rowwise_usage and out._rowwise_data is None:
                    reset_cache = True
                elif quantizer.columnwise_usage and out._columnwise_data is None:
                    reset_cache = True
            if isinstance(out, DebugQuantizedTensor) != isinstance(quantizer, DebugQuantizer):
                reset_cache = True
            if reset_cache:
                out = None
                del self._fp8_workspaces[cache_name]

        # Gather cached Fp8 workspace if it's distributed
        # NOTE: FSDP sharding is supported only for Fp8 buffers and will not work
        #       for models initialized with Fp8 primary weights.
        if (
            out is not None
            and tensor is not None
            and fsdp_group is not None
            and out.data.shape != tensor.data.shape
        ):
            _fsdp_gather_tensors(fsdp_group, [tensor.data.shape], out)

        # Construct workspace if needed
        if out is None:
            if tensor is None or quantizer is None:
                raise ValueError(
                    "tensor and quantizer kwargs must be provided to construct FP8 workspace"
                )

            if cache_name is not None:
                # Ensure the tensor in the cache is an instance of torch.Tensor,
                # as it persists beyond a single forward pass.
                # Setting internal=True would cause the data to be removed in prepare_for_saving(...).
                quantizer_internal = quantizer.internal
                quantizer.internal = False
            out = quantizer.quantize(tensor, dtype=workspace_dtype)
            if cache_name is not None:
                quantizer.internal = quantizer_internal

            # Update cache
            if cache_name is not None:
                self._fp8_workspaces[cache_name] = out
            return out

        # Update workspace if needed
        if skip_update_flag is not None:
            update_workspace = True
        if update_workspace:
            if tensor is None:
                raise ValueError("tensor kwarg must be provided to update FP8 workspace")
            if hasattr(out, "quantize_"):
                out.quantize_(tensor, noop_flag=skip_update_flag)
            else:
                tex.quantize(tensor, quantizer, out, skip_update_flag)
        return out

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """
        This function loads tensors and extra state including fp8 metadata.
        This metadata is essential for copying fp8 tensors, as the copy_ function
        uses the scale_inv parameter from fp8_meta to set the correct scaling factor
        for the new tensor.
        Hence, this extra state must be loaded before the tensor copying process,
        not after, as is typically done in _load_from_state_dict.
        Tensors are copied into fp8 tensors only when self.primary_weights_in_fp8=True,
        otherwise, this behavior is not required.
        """
        if self.primary_weights_in_fp8:
            extra_state_key = prefix + torch.nn.modules.module._EXTRA_STATE_KEY_SUFFIX
            if extra_state_key in state_dict:
                self.set_extra_state(state_dict[extra_state_key])
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def register_wgrad_accumulation_and_reduce_hooks(self, wgrad_accumulation_and_reduce_hook):
        """
        This method is used to manually control the weight gradient accumulation and reduce.
        This method should be called before the backward() method.
        Set the skip_wgrad_accumulation_and_reduce to True to skip the weight gradient accumulation
        and reduce in backward();
        And register the wgrad_accumulation_and_reduce_func to be called in backward_dw() method.
        """
        self.wgrad_accumulation_and_reduce_hooks.append(wgrad_accumulation_and_reduce_hook)

    def need_backward_dw(self):
        """
        Check if this module needs to execute the delayed weight gradient computation.
        This method should be used at the beginning of self.backward_dw() to determine if it
        should actually be executed or just return without doing anything.
        User can also manually call this method to check that before calling into backward_dw().
        """
        return self.wgrad_store is not None and self.wgrad_store.delay_wgrad_compute()

    def backward_dw(self):
        """
        Execute the delayed weight gradient computation.
        This method is called after the main backward pass to compute weight gradients.
        """
        if not self.need_backward_dw():
            return
        with torch.cuda.nvtx.range(f"_{self.__class__.__name__}_wgrad"):
            (wgrad, bgrad), _ = self.wgrad_store.pop()
            if not self.fuse_wgrad_accumulation:
                weight_tensor = noop_cat(self._get_weight_tensors())
                weight_tensor.grad = wgrad.to(weight_tensor.dtype)
            if self.use_bias:
                bias_tensor = noop_cat([getattr(self, name) for name in self.bias_names])
                if bias_tensor.grad is None:
                    bias_tensor.grad = bgrad.to(bias_tensor.dtype)
            del wgrad
            del bgrad
            for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
                wgrad_accumulation_and_reduce_hook()

    def is_debug_iter(self) -> bool:
        """
        This function checks if the debug should be enabled for this layer.
        """
        debug = TEDebugState.debug_enabled
        if not debug:
            return False
        self._validate_name()

        # If layer is run first time in new iteration,
        # we need to check if the debug should be enabled for this layer -
        # maybe in previous iterations debug features returned information
        # that no feature will be active for this layer for multiple next iterations.
        started_new_iteration = TEDebugState.get_iteration() != getattr(
            self, "debug_last_iteration", None
        )
        if started_new_iteration:
            if self.next_iter_when_debug_should_be_run is None:
                debug = False
            else:
                debug = TEDebugState.get_iteration() >= self.next_iter_when_debug_should_be_run
            self.debug_last_iteration = TEDebugState.get_iteration()
            self.debug_enabled_in_this_iteration = debug
        else:
            # If this is the same iteration as previous invocation of the module,
            # we use the debug value from the first invocation in the iteration.
            debug = self.debug_enabled_in_this_iteration

        return debug

    def no_debug_features_active(self, quantizers):
        """
        Checks if any debug feature is active for this layer.
        """
        run_current = any_feature_enabled(quantizers)

        # Sometimes features inform that they will not be enabled for particular layer
        # for multiple next iterations.
        self.next_iter_when_debug_should_be_run = next_iter_when_debug_should_be_run(quantizers)

        if not run_current:
            return True

        if self.primary_weights_in_fp8:
            raise RuntimeError("FP8 weights are not supported in debug mode.")
        return False

    def _validate_name(self):
        """
        Validate name passed to the module.
        This is invoked in the forward() method as module names are assigned after Model is initialized in Megatron-LM.
        If no name is assigned, it creates a default name with layer count as the variable.
        """
        if self.name is not None:
            return
        assert TEDebugState.debug_enabled
        import nvdlfw_inspect.api as debug_api

        if self.name is None:
            debug_api.log_message(
                "Names are not provided to debug modules. ",
                "Creating and using generic names. Pass names to debug modules for better"
                " insight. ",
                level=logging.WARNING,
            )
            self.name = f"Layer_{TEDebugState.get_layer_count()}"

    def _check_weight_tensor_recipe_correspondence(self) -> None:
        """
        Verify that the weight tensor types match their corresponding recipe type.
        This is invoked in the forward().

        This establishes a 1:1 correspondence between recipe types and tensor types:
        - DelayedScaling → Float8Tensor
        - Float8CurrentScaling → Float8Tensor
        - MXFP8BlockScaling → MXFP8Tensor
        - Float8BlockScaling → Float8BlockTensor

        Example case to check: recipe is DelayedScaling (DelayedScaling is set in autocast()),
        but the weight tensor is MXFP8Tensor (MXFP8BlockScaling is set in quantized_model_init()).
        """
        if not self.fp8 and not self.fp8_calibration:
            return
        if not hasattr(self, "weight_names") or not self.weight_names:
            return

        recipe = self.fp8_meta["recipe"]
        weight_tensors = [getattr(self, name) for name in self.weight_names]
        for i, tensor in enumerate(weight_tensors):
            if isinstance(tensor, QuantizedTensorStorage):
                quantizer = tensor._get_quantizer()
                if quantizer is None:
                    continue
                compatible_recipe_class = quantizer._get_compatible_recipe()
                if compatible_recipe_class is None:
                    continue
                if not isinstance(recipe, compatible_recipe_class):
                    raise RuntimeError(
                        f"Recipe mismatch for '{self.weight_names[i]}': tensor supports recipe"
                        f" {compatible_recipe_class.__name__}, but got {recipe.__class__.__name__}."
                        " Please check the recipes assigned during quantized_model_init() and"
                        " autocast() calls."
                    )