attention.py 402 KB
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# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.

"""Attention."""
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import collections
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from contextlib import nullcontext
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from importlib.metadata import version as get_pkg_version
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from importlib.metadata import PackageNotFoundError
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import math
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import os
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import warnings
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import logging
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import functools
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from dataclasses import dataclass, fields
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import numpy as np
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from packaging.version import Version as PkgVersion
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import torch
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import torch.nn.functional as F
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import transformer_engine_torch as tex
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import transformer_engine as te
from transformer_engine.pytorch.utils import get_cudnn_version
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from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
    fused_attn_fwd_qkvpacked,
    fused_attn_bwd_qkvpacked,
    fused_attn_fwd_kvpacked,
    fused_attn_bwd_kvpacked,
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    fused_attn_fwd,
    fused_attn_bwd,
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    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
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    META_QKV,
    META_DQKV,
    META_O,
    META_DO,
    META_S,
    META_DP,
    META_O_CP,
    META_DQKV_CP,
)
from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    get_fp8_te_dtype,
    get_fp8_torch_dtype,
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)
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from transformer_engine.pytorch.float8_tensor import Float8Tensor
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
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from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
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from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
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    get_default_init_method,
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)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
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    AttnBiasTypes,
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    QKVLayouts,
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    dist_group_type,
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    TE_DType,
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)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
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    get_distributed_rank,
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    checkpoint,
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    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
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    gather_along_first_dim,
    reduce_scatter_along_first_dim,
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)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
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from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
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from transformer_engine.pytorch.graph import is_graph_capturing

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# NVTE_DEBUG = 0/1 # disables/enables debug mode, default = 0
_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
# NVTE_DEBUG_LEVEL = 0/1/2 # enables more and more verbose debug mode, default = 0
_NVTE_DEBUG_LEVEL = int(os.getenv("NVTE_DEBUG_LEVEL", "0"))
_log_level = _NVTE_DEBUG * _NVTE_DEBUG_LEVEL
_log_levels = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG}
_log_level = _log_levels[_log_level if _log_level in [0, 1, 2] else 2]
_formatter = logging.Formatter("[%(levelname)-8s | %(name)-19s]: %(message)s")
_stream_handler = logging.StreamHandler()
_stream_handler.setFormatter(_formatter)
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fa_logger = logging.getLogger()
fa_logger.setLevel(_log_level)
if not fa_logger.hasHandlers():
    fa_logger.addHandler(_stream_handler)


@functools.lru_cache(maxsize=None)
def _get_supported_versions(version_min, version_max):
    return ">= " + str(version_min) + ", " + "<= " + str(version_max)

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_NVTE_FLASH_ATTN = int(os.getenv("NVTE_FLASH_ATTN", "1"))
_NVTE_FUSED_ATTN = int(os.getenv("NVTE_FUSED_ATTN", "1"))
_NVTE_UNFUSED_ATTN = int(os.getenv("NVTE_UNFUSED_ATTN", "1"))
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# Detect flash-attn v2 in the environment
_flash_attn_is_installed = False
_flash_attn_version = PkgVersion("0")
_flash_attn_version_required = PkgVersion("2.1.1")
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_flash_attn_max_version = PkgVersion("2.6.3")
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_flash_attn_2_plus = False
_flash_attn_2_1_plus = False
_flash_attn_2_3_plus = False
_flash_attn_2_4_plus = False
_flash_attn_2_4_1_plus = False
_flash_attn_2_5_7_plus = False
_flash_attn_2_6_0_plus = False
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flash_attn_func = None
flash_attn_varlen_func = None
flash_attn_varlen_fwd = None
flash_attn_varlen_bwd = None
flash_attn_cuda_bwd = None

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try:
    _flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
except PackageNotFoundError:
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    if torch.cuda.is_available() and get_device_compute_capability() >= (8, 0) and _NVTE_FLASH_ATTN:
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        fa_logger.debug(
            "flash-attn v2 is not installed. To use, please install it by"
            """ "pip install flash-attn".""",
        )
else:
    if _flash_attn_version_required <= _flash_attn_version <= _flash_attn_max_version:
        from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
        from flash_attn.flash_attn_interface import (
            _flash_attn_varlen_forward as flash_attn_varlen_fwd,
        )
        from flash_attn.flash_attn_interface import (
            _flash_attn_varlen_backward as flash_attn_varlen_bwd,
        )
        from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd

        _flash_attn_is_installed = True
        _flash_attn_2_plus = _flash_attn_version >= PkgVersion("2")
        _flash_attn_2_1_plus = _flash_attn_version >= PkgVersion("2.1")
        _flash_attn_2_3_plus = _flash_attn_version >= PkgVersion("2.3")
        _flash_attn_2_4_plus = _flash_attn_version >= PkgVersion("2.4")
        _flash_attn_2_4_1_plus = _flash_attn_version >= PkgVersion("2.4.1")
        _flash_attn_2_5_7_plus = _flash_attn_version >= PkgVersion("2.5.7")
        _flash_attn_2_6_0_plus = _flash_attn_version >= PkgVersion("2.6.0")
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    elif (
        torch.cuda.is_available() and get_device_compute_capability() >= (8, 0) and _NVTE_FLASH_ATTN
    ):
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        fa_logger.warning(
            "Supported flash-attn versions are %s. Found flash-attn %s.",
            _get_supported_versions(
                _flash_attn_version_required,
                _flash_attn_max_version,
            ),
            _flash_attn_version,
        )

# Detect flash-attn v3 in the environment
# This section will be removed when FA3 is released as a regular FA package,
# i.e. flashattn-hopper 3.0.0 as flash-attn 3.0.0
_flash_attn_3_is_installed = False
_flash_attn_3_version = PkgVersion("0")
_flash_attn_3_0_0_beta = False
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_use_flash_attn_3 = False
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_flash_attn_3_installation_steps = """\
(1) pip install "git+https://github.com/Dao-AILab/flash-attention.git#egg=flashattn-hopper&subdirectory=hopper"
(2) python_path=`python -c "import site; print(site.getsitepackages()[0])"`
(3) mkdir -p $python_path/flashattn_hopper
(4) wget -P $python_path/flashattn_hopper https://raw.githubusercontent.com/Dao-AILab/flash-attention/main/hopper/flash_attn_interface.py"""
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try:
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    _flash_attn_3_version = PkgVersion(get_pkg_version("flashattn-hopper"))
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except PackageNotFoundError:
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    if torch.cuda.is_available() and get_device_compute_capability() >= (9, 0) and _NVTE_FLASH_ATTN:
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        fa_logger.debug(
            "flash-attn v3 is not installed. To use, please install it by \n%s",
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            _flash_attn_3_installation_steps,
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        )
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else:
    from flashattn_hopper.flash_attn_interface import flash_attn_func as flash_attn_func_v3
    from flashattn_hopper.flash_attn_interface import (
        flash_attn_varlen_func as flash_attn_varlen_func_v3,
    )
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    from flashattn_hopper.flash_attn_interface import (
        _flash_attn_varlen_forward as flash_attn_varlen_fwd_v3,
    )
    from flashattn_hopper.flash_attn_interface import (
        _flash_attn_varlen_backward as flash_attn_varlen_bwd_v3,
    )
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    _flash_attn_3_is_installed = True
    _flash_attn_3_0_0_beta = PkgVersion("3.0.0b") < _flash_attn_3_version < PkgVersion("3.0.0")
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    _use_flash_attn_3 = True
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_attention_backends = {
    "attention_params": None,
    "use_flash_attention": None,
    "use_fused_attention": None,
    "fused_attention_backend": None,
    "use_unfused_attention": None,
    "backend_selection_requires_update": False,
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}
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@dataclass(eq=True)
class AttentionParams:
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    """
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    Attention parameters used to determine which backend to be used.
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    Parameters
    ----------
    qkv_type: Union[torch.Tensor, Float8Tensor], default = `torch.Tensor`
        Type of query/key/value tensors, {`torch.Tensor`, `Float8Tensor`}.
    qkv_dtype: torch.dtype, default = `torch.bfloat16`
        Data type of query/key/value tensors.
    qkv_layout: str, default = "sbh3d"
        Query/key/value tensor memory layout.
    batch_size: int, default = 1
        Batch size.
    num_heads: int, default = 16
        Number of attention heads in the query tensor.
    num_gqa_groups: int, default = 16
        Number of attention heads in key and value tensors.
    max_seqlen_q: int, default = 128
        Maximum sequence length of the query tensor.
    max_seqlen_kv: int, default = 128
        Maximum sequence length of the key and value tensors.
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    head_dim_qk: int, default = 64
        The size of each attention head in query and key tensors.
    head_dim_v: int, default = 64
        The size of each attention head in the value tensor.
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    attn_mask_type: str, default = `no_mask`
        Attention mask type, {`no_mask`, `padding`, `causal`, `padding_causal`,
        `causal_bottom_right`, `padding_causal_bottom_right`, `arbitrary`}
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    window_size: Tuple[int, int], default = None
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        Sliding window attention size.
    alibi_slopes_shape: Optional[Union[torch.Size, List]], default = `None`
        Tensor shape of :attr:`alibi_slopes` in `DotProductAttention`.
    core_attention_bias_type: str, default = `no_bias`
        Attention bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}.
    core_attention_bias_shape: str, default = `1hss`
        Attention bias shape, {`1hss`, `b1ss`, `bhss`}.
    core_attention_bias_requires_grad: bool, default = `True`
        Whether attention bias requires gradient.
    pad_between_seqs: bool, default = `False`
        Whether there is padding between sequences in a batch.
        This only applies to `qkv_format=thd`.
    attention_dropout: float, default = 0.0
        Attention dropout.
    context_parallel: bool, default = `False`
        Whether context parallelism is used or not.
    deterministic: bool, default = `False`
        Whether to run `DotProductAttention` with determinism or not.
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    is_training: bool, default = `True`
        Whether in training mode (`True`) or inference mode (`False`)
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    fp8: bool, default = `False`
        Whether `DotProductAttention` is in an `fp8_autocast` region.
    fp8_meta: Optional[Dict[str Any]], default = `None`
        The FP8 metadata tensor of `DotProductAttention`.
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    """

    qkv_type: Union[torch.Tensor, Float8Tensor] = torch.Tensor
    qkv_dtype: torch.dtype = torch.bfloat16
    qkv_layout: str = "sbh3d"
    batch_size: int = 1
    num_heads: int = 16
    num_gqa_groups: int = 16
    max_seqlen_q: int = 128
    max_seqlen_kv: int = 128
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    head_dim_qk: int = 64
    head_dim_v: int = 64
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    attn_mask_type: str = "no_mask"
    window_size: Union[Tuple[int, int], None] = None
    alibi_slopes_shape: Union[torch.Size, List, None] = None
    core_attention_bias_type: str = "no_bias"
    core_attention_bias_shape: str = "1hss"
    core_attention_bias_requires_grad: bool = True
    pad_between_seqs: bool = False
    attention_dropout: float = 0.0
    context_parallel: bool = False
    deterministic: bool = False
    is_training: bool = True
    fp8: bool = False
    fp8_meta: Union[Dict[str, Any], None] = None


_alibi_cache = {
    "_num_heads": None,
    "_alibi_slopes": None,
    "_max_seqlen_q": None,
    "_max_seqlen_kv": None,
    "_bottom_right_alignment": True,
    "_alibi_bias": None,
    "_alibi_slopes_require_update": False,
    "_alibi_bias_require_update": False,
}


__all__ = ["DotProductAttention", "InferenceParams", "MultiheadAttention"]


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def maybe_contiguous(tensor: torch.Tensor) -> torch.Tensor:
    """Make tensor contiguous if final stride is not 1."""
    return tensor.contiguous() if tensor.stride(-1) != 1 else tensor


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def get_attention_backend(
    attention_params: AttentionParams = None,
):
    """
    Select the appropriate attention backend/sub-backend based on user input and runtime environment.

    Parameters
    ----------
    See `AttentionParams`.
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    Returns
    ----------
    use_flash_attention: bool
        Whether the `FlashAttention` backend has been selected.
    use_fused_attention: bool
        Whether the `FusedAttention` backend has been selected.
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    fused_attention_backend: tex.NVTE_Fused_Attn_Backend
        If `use_fused_attention = True`, one of `FusedAttention` three sub-backends, else `None`.
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    use_unfused_attention: bool
        Whether the `UnfusedDotProductAttention` backend has been selected.
    available_backends: List[bool]
        All available backends that could support the provided input. A list of Booleans
        in the form of [use_flash_attention, use_fused_attention, use_unfused_attention].
    """
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    qkv_type = attention_params.qkv_type
    qkv_dtype = attention_params.qkv_dtype
    qkv_layout = attention_params.qkv_layout
    batch_size = attention_params.batch_size
    num_heads = attention_params.num_heads
    num_gqa_groups = attention_params.num_gqa_groups
    max_seqlen_q = attention_params.max_seqlen_q
    max_seqlen_kv = attention_params.max_seqlen_kv
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    head_dim_qk = attention_params.head_dim_qk
    head_dim_v = attention_params.head_dim_v
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    attn_mask_type = attention_params.attn_mask_type
    window_size = attention_params.window_size
    alibi_slopes_shape = attention_params.alibi_slopes_shape
    core_attention_bias_type = attention_params.core_attention_bias_type
    core_attention_bias_shape = attention_params.core_attention_bias_shape
    core_attention_bias_requires_grad = attention_params.core_attention_bias_requires_grad
    pad_between_seqs = attention_params.pad_between_seqs
    attention_dropout = attention_params.attention_dropout
    context_parallel = attention_params.context_parallel
    deterministic = attention_params.deterministic
    is_training = attention_params.is_training
    fp8 = attention_params.fp8
    fp8_meta = attention_params.fp8_meta

    # Run config
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    logger = logging.getLogger("DotProductAttention")
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    logger.setLevel(_log_level)
    if not logger.hasHandlers():
        logger.addHandler(_stream_handler)
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    device_compute_capability = get_device_compute_capability()
    cudnn_version = get_cudnn_version()
    run_config = {
        "transformer_engine_version": te.__version__,
        "compute_capability": "sm"
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        + str(10 * device_compute_capability[0] + device_compute_capability[1]),
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        "flash_attn_version": (
            str(_flash_attn_version) if _flash_attn_is_installed else "not installed"
        ),
        "flash_attn_3_version": (
            str(_flash_attn_3_version) if _flash_attn_3_is_installed else "not installed"
        ),
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        "cudnn_version": ".".join([str(i) for i in cudnn_version]),
    }
    attention_params_dict = {
        field.name: getattr(attention_params, field.name) for field in fields(attention_params)
    }
    run_config.update(attention_params_dict)
    if fp8:
        run_config["NVTE_FP8_DPA_BWD"] = int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
    logger.debug("Running with config=%s", run_config)
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    # The following sections check if `FlashAttention` supports the provided attention params,
    # regardless of whether FA2 or FA3 is installed. If FA2 or FA3 is not installed but is
    # necessary for performance/functionality, a warning will be issued to prompt users to
    # install an appropriate FA version.
    global _flash_attn_version_required, _flash_attn_max_version, _use_flash_attn_3

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    # Filter: Environment variables
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    use_flash_attention = int(os.getenv("NVTE_FLASH_ATTN", "1"))
    use_fused_attention = int(os.getenv("NVTE_FUSED_ATTN", "1"))
    use_unfused_attention = int(os.getenv("NVTE_UNFUSED_ATTN", "1"))
    if not use_flash_attention and _flash_attn_is_installed:
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        logger.debug("Disabling FlashAttention due to NVTE_FLASH_ATTN=0")
    if not use_fused_attention:
        logger.debug("Disabling FusedAttention due to NVTE_FUSED_ATTN=0")
    if not use_unfused_attention:
        logger.debug("Disabling UnfusedDotProductAttention due to NVTE_UNFUSED_ATTN=0")

    # Filter: ONNX mode
    if is_in_onnx_export_mode():
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        if use_flash_attention and _flash_attn_is_installed:
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            logger.debug("Disabling FlashAttention due to ONNX mode")
        use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention due to ONNX mode")
        use_fused_attention = False

    # Filter: Compute capability
    if device_compute_capability < (8, 0):
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        if use_flash_attention and _flash_attn_is_installed:
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            logger.debug("Disabling FlashAttention as it requires compute capability sm80+")
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        use_flash_attention = False
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        if use_fused_attention:
            logger.debug("Disabling FusedAttention as it requires compute capability sm80+")
            use_fused_attention = False
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    if device_compute_capability < (9, 0):
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        if use_flash_attention and _flash_attn_3_is_installed:
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            logger.debug("Disabling FlashAttention 3 as it requires compute capability sm90+")
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        _use_flash_attn_3 = False
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    # Filter: Data type
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    if qkv_dtype not in [torch.bfloat16, torch.float16] or qkv_type not in [
        torch.Tensor,
        Float8Tensor,
    ]:
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        if use_flash_attention and _flash_attn_is_installed:
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            logger.debug(
                "Disabling FlashAttention due to unsupported QKV data type. "
                "Supported: qkv_dtype = {torch.bfloat16, torch.float16}. "
                "Found: qkv_dtype = %s.",
                qkv_dtype,
            )
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        use_flash_attention = False
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        if use_fused_attention:
            logger.debug(
                "Disabling FusedAttention due to unsupported QKV data type. "
                "Supported: qkv_dtype = {torch.bfloat16, torch.float16}. "
                "Found: qkv_dtype = %s.",
                qkv_dtype,
            )
            use_fused_attention = False
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    # Filter: Execution type
    if fp8 and fp8_meta["recipe"].fp8_dpa:
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        if use_flash_attention and not _use_flash_attn_3:
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            if _flash_attn_is_installed:
                logger.debug("Disabling FlashAttention as FlashAttention 2 does not support FP8")
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            use_flash_attention = False
        if use_flash_attention and _use_flash_attn_3 and is_training:
            logger.debug(
                "Disabling FlashAttention as FlashAttention 3 does not support FP8 training"
            )
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            use_flash_attention = False
        if use_unfused_attention:
            logger.debug("Disabling UnfusedDotProductAttention as it does not support FP8")
            use_unfused_attention = False

    # Filter: Head dimension
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    if use_flash_attention and head_dim_qk != head_dim_v:
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        if _flash_attn_is_installed:
            logger.debug("Disabling FlashAttention as it does not support MLA.")
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        use_flash_attention = False
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    if use_flash_attention and (
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        head_dim_qk > 256
        or head_dim_qk % 8 != 0
        or (head_dim_qk > 192 and device_compute_capability not in ((8, 0), (9, 0)))
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    ):
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        if _flash_attn_is_installed:
            logger.debug(
                "Disabling FlashAttention due to unsupported head_dim_qk and head_dim_v. "
                "Supported: head_dim_qk = head_dim_v, head_dim_qk %%8 = 0, "
                "head_dim_qk <= 256 (>192 requires sm80/90). "
                "Found: head_dim_qk = %s, head_dim_v = %s, on sm%s.",
                head_dim_qk,
                head_dim_v,
                ".".join([str(i) for i in device_compute_capability]),
            )
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        use_flash_attention = False
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    qkv_layout_group = qkv_layout.replace("b", "").replace("s", "").replace("t", "")
    if use_fused_attention and head_dim_qk != head_dim_v and qkv_layout_group != "hd_hd_hd":
        logger.debug(
            "Disabling FusedAttention as MLA is not supported with qkv_layout = %s",
            qkv_layout,
        )
        use_fused_attention = False
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    # Filter: QKV layout
    qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
    if qkv_format == "thd":
        if use_unfused_attention:
            logger.debug("Disabling UnfusedDotProductAttention for qkv_format = thd")
            use_unfused_attention = False
        if use_flash_attention and pad_between_seqs:
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention for qkv_format = thd when there is "
                    "padding between sequences, i.e. [a, a, PAD, b, b, b, PAD, c, PAD]"
                )
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            use_flash_attention = False

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    # Filter: Dropout
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    if attention_dropout != 0.0 and use_flash_attention and _use_flash_attn_3:
        logger.debug("Disabling FlashAttention 3 for dropout")
        _use_flash_attn_3 = False
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    # Filter: Context parallelism
    # qkv_format | attn_mask_type              | attn_bias_type           | supported backends
    # ----------------------------------------------------------------------------------------------------
    # bshd, sbhd | self-attention:             | no_bias, post_scale_bias | FlashAttention, FusedAttention
    #            |     no_mask, causal         |                          |
    #            | cross-attention:            |                          |
    #            |     no_mask                 |                          |
    # thd        | self-attention:             | no_bias                  | FlashAttention, FusedAttention
    #            |     padding, padding_causal |                          | if no padding between sequences,
    #            | cross-attention:            |                          | FusedAttention
    #            |     padding                 |                          | if there is padding between sequences
    # Note: context parallelism requires seq_len % (cp_size * 2) == 0 for each sequence in q, k, v.
    if context_parallel and use_unfused_attention:
        logger.debug(
            "Disabling UnfusedDotProductAttention as it does not support context parallelism"
        )
        use_unfused_attention = False
    if context_parallel and use_flash_attention:
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        if fp8 and fp8_meta["recipe"].fp8_dpa:
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with FP8"
                )
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            use_flash_attention = False
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        if "bottom_right" in attn_mask_type:
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " causal_bottom_right masking"
                )
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            use_flash_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " causal masking for cross-attention"
                )
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            use_flash_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with bias"
                    " type of %s",
                    core_attention_bias_type,
                )
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            use_flash_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
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            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " attention bias for THD format"
                )
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            use_flash_attention = False
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    if context_parallel and use_fused_attention:
        if "bottom_right" in attn_mask_type:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with"
                " causal_bottom_right masking"
            )
            use_fused_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with causal"
                " masking for cross-attention"
            )
            use_fused_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with bias type"
                " of %s",
                core_attention_bias_type,
            )
            use_fused_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with attention"
                " bias for THD format"
            )
            use_fused_attention = False
        elif head_dim_qk != head_dim_v:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with MLA"
            )
            use_fused_attention = False

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    # Filter: Attention mask
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    # attn_mask_type              | attention_mask                       | supported backends
    # ----------------------------------------------------------------------------------------
    # no_mask                     | None                                 | All
    # padding                     |                                      | All
    #     self-attention          | One tensor in shape [b, 1, 1, sq]    |
    #     cross-attention         | Tuple of two tensors in shapes       |
    #                             | [b, 1, 1, sq] and [b, 1, 1, skv]     |
    # causal                      | None                                 |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FusedAttention, UnfusedDotProductAttention
    # padding_causal              | Same as "padding"                    |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FusedAttention, UnfusedDotProductAttention
    # causal_bottom_right         | None                                 | All
    # padding_causal_bottom_right | Same as "padding"                    |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FlashAttention, UnfusedDotProductAttention
    # arbitrary                   | One tensor in shape broadcastable to | UnfusedDotProductAttention
    #                             | [b, h, sq, skv]                      |
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    if attn_mask_type == "arbitrary":
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        if use_flash_attention and _flash_attn_is_installed:
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            logger.debug("Disabling FlashAttention for arbitrary mask")
        use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention for arbitrary mask")
        use_fused_attention = False
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    if (
        use_flash_attention
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        and _use_flash_attn_3
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        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
        logger.warning(
            "Disabling FlashAttention 3 as it only supports bottom-right-diagonal "
            "causal mask since flash-attn 2.1. See "
            "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
        )
        _use_flash_attn_3 = False
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    if (
        use_flash_attention
        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
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        if _flash_attn_2_1_plus:
            logger.warning(
                "Disabling FlashAttention as it only supports bottom-right-diagonal "
                "causal mask since flash-attn 2.1. See "
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False
        if not _flash_attn_is_installed:
            _flash_attn_max_version = PkgVersion("2.1")
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    if (
        use_flash_attention
        and attn_mask_type in ["causal_bottom_right", "padding_causal_bottom_right"]
        and max_seqlen_q != max_seqlen_kv
    ):
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        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.1")
        elif not _flash_attn_2_1_plus and not _use_flash_attn_3:
            logger.warning(
                "Disabling FlashAttention as it only supports top-left-diagonal "
                "causal mask before flash-attn 2.1. See "
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False
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    if (
        use_flash_attention
        and _use_flash_attn_3
        and fp8
        and fp8_meta["recipe"].fp8_dpa
        and "padding" in attn_mask_type
    ):
        logger.debug("Disabling FlashAttention 3 for FP8 and padding masks")
        _use_flash_attn_3 = False
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    # Filter: Sliding window attention
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    #    backend                 |      window_size       | diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | (-1, -1) or (>=0, >=0) | bottom right
    # FusedAttention             | (-1,  0) or (>=0, 0)   | top left
    # UnfusedDotProductAttention | (-1, -1) or (>=0, >=0) | both;
    #                            |                        | converts window_size to an 'arbitrary' mask
    if window_size is None:
        window_size = check_set_window_size(attn_mask_type, window_size)
    else:
        if use_fused_attention and (window_size[0] != -1 or window_size[1] not in [-1, 0]):
            if fp8 and (fp8_meta["recipe"].fp8_dpa or fp8_meta["recipe"].fp8_mha):
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention"
                    " for FP8"
                )
                use_fused_attention = False
            elif window_size[1] != 0 or attention_dropout != 0.0 or qkv_format == "thd":
                logger.debug(
                    "Disabling FusedAttention as it only supports sliding window attention "
                    "with causal mask, no dropout, and qkv_format = bshd/sbhd"
                )
                use_fused_attention = False
            elif max_seqlen_q != max_seqlen_kv and attn_mask_type in [
                "no_mask",
                "padding",
                "causal_bottom_right",
                "padding_causal_bottom_right",
            ]:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s for cross-attention",
                    attn_mask_type,
                )
                use_fused_attention = False
            elif "padding" in attn_mask_type:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s",
                    attn_mask_type,
                )
                use_fused_attention = False
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        if use_flash_attention and (window_size[0] != -1 or window_size[1] not in [-1, 0]):
            if _use_flash_attn_3:
                logger.debug(
                    "Disabling FlashAttention 3 as it does not support sliding window attention"
                )
                _use_flash_attn_3 = False
            if not _flash_attn_is_installed:
                _flash_attn_version_required = PkgVersion("2.3")
            elif not _flash_attn_2_3_plus:
                logger.debug(
                    "Disabling FlashAttention as sliding window attention requires flash-attn 2.3+"
                )
                use_flash_attention = False
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    # Filter: Attention bias
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    #    backend                 |      bias types              | ALiBi diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | no_bias, alibi/alibi_slopes  | bottom right
    # FusedAttention             | no_bias, post_scale_bias     |
    #                            | alibi/alibi_slopes           | top left,
    #                            |                              | bottom_right (converts to a 'post_scale_bias' bias)
    # UnfusedDotProductAttention | no_bias, pre/post_scale_bias |
    #                            | alibi/alibi_slopes           | both; converts to a 'post_scale_bias' bias
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    if use_flash_attention and core_attention_bias_type == "alibi":
749
        if _use_flash_attn_3:
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            logger.debug("Disabling FlashAttention 3 for ALiBi")
            _use_flash_attn_3 = False
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        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.4")
        elif not _flash_attn_2_4_plus:
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            logger.debug("Disabling FlashAttention as ALiBi requires flash-attn 2.4+")
            use_flash_attention = False
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    if use_flash_attention and (
        core_attention_bias_type not in ["no_bias", "alibi"]
        or core_attention_bias_shape is not None
    ):
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        if _flash_attn_is_installed:
            logger.debug("Disabling FlashAttention for pre/post_scale_bias")
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        use_flash_attention = False

    fu_core_attention_bias_type = core_attention_bias_type
    fu_core_attention_bias_shape = core_attention_bias_shape
    fu_core_attention_bias_requires_grad = core_attention_bias_requires_grad
    if (
        use_fused_attention
        and core_attention_bias_type == "alibi"
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        and (alibi_slopes_shape is not None or max_seqlen_q != max_seqlen_kv)
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    ):
        fu_core_attention_bias_type = "post_scale_bias"
        fu_core_attention_bias_requires_grad = False
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        if alibi_slopes_shape is None:
            fu_core_attention_bias_shape = "1hss"
        elif len(alibi_slopes_shape) == 1 and alibi_slopes_shape[0] == num_heads:
            fu_core_attention_bias_shape = "1hss"
        elif (
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            len(alibi_slopes_shape) == 2
            and alibi_slopes_shape[0] == batch_size
            and alibi_slopes_shape[1] == num_heads
        ):
            fu_core_attention_bias_shape = "bhss"

    if (
        use_fused_attention
        and fu_core_attention_bias_type == "post_scale_bias"
        and fu_core_attention_bias_shape != "1hss"
    ):
        if fu_core_attention_bias_requires_grad:
            # remove this line when cuDNN adds bwd support for
            # [1, 1, s, s], [b, 1, s, s] and [b, h, s, s]
            logger.debug("Disabling FusedAttention for dBias in [1, H, S, S] shape")
            use_fused_attention = False
        else:
            # max512 backend will only support [1, h, s, s]
            os.environ["NVTE_FUSED_ATTN_BACKEND"] = "1"

    # Filter: cuDNN support
    fused_attention_backend = None
    if use_fused_attention:
        q_type = TE_DType[qkv_dtype]
        kv_type = q_type
        if fp8 and fp8_meta["recipe"].fp8_dpa:
            q_type = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            kv_type = q_type
        fused_attention_backend = tex.get_fused_attn_backend(
            q_type,
            kv_type,
            QKVLayout[qkv_layout],
            AttnBiasType[fu_core_attention_bias_type],
            AttnMaskType[attn_mask_type],
            attention_dropout,
            num_heads,
            num_gqa_groups,
            max_seqlen_q,
            max_seqlen_kv,
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            head_dim_qk,
            head_dim_v,
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            window_size[0],
            window_size[1],
824
        )
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        if fused_attention_backend == FusedAttnBackend["No_Backend"]:
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            logger.debug("Disabling FusedAttention as no backend supports the provided input")
            use_fused_attention = False
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            fused_attention_backend = None
        if (
            use_fused_attention
            and window_size is not None
            and window_size[0] != -1
            and fused_attention_backend != FusedAttnBackend["F16_arbitrary_seqlen"]
        ):
            logger.debug(
                "Disabling FusedAttention as only sub-backend %s does not support "
                "slidng window attention",
                int(fused_attention_backend),
            )
            use_fused_attention = False
            fused_attention_backend = None
        if (
            use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_max512_seqlen"]
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            and fu_core_attention_bias_type == "post_scale_bias"
            and fu_core_attention_bias_shape != "1hss"
        ):
            logger.debug(
                "Disabling FusedAttention as cuDNN sub-backend 0 only supports post_scale_bias in"
                " [1, H, S, S] shape"
            )
            use_fused_attention = False
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            fused_attention_backend = None
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    # Filter: Determinism
    # backend                      | deterministic
    # ---------------------------------------------
    # FlashAttention               |
    #     flash-attn >=2.0, <2.4.1 | no
    #     flash-attn >=2.4.1       | yes
    # FusedAttention               |
    #     sub-backend 0            | yes
    #     sub-backend 1            | workspace optimization path and sm90+: yes;
    #                              | otherwise: no
    #     sub-backend 2            | no
    # UnfusedDotProductAttention   | yes
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    if use_flash_attention and deterministic:
        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.4.1")
        elif not _flash_attn_2_4_1_plus and not _use_flash_attn_3:
            logger.warning(
                "Disabling FlashAttention as version <2.4.1 does not support deterministic "
                "execution. To use FlashAttention with deterministic behavior, "
                "please install flash-attn >= 2.4.1."
            )
            use_flash_attention = False
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    if use_fused_attention and deterministic:
        if fused_attention_backend == FusedAttnBackend["FP8"] and is_training:
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
        if (
            fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
            and is_training
            and (
                device_compute_capability < (9, 0)
                or core_attention_bias_requires_grad
                or cudnn_version < (8, 9, 5)
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            )
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        ):
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
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    # All available backends
    available_backends = [use_flash_attention, use_fused_attention, use_unfused_attention]
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    # `FusedAttention` and `FlashAttention` are faster backends than `UnfusedDotProductAttention`.
    # When `FusedAttention` does not support the provided attention params, and `FlashAttention`
    # does, we recommend users to install flash-attn if not installed already.
    if not use_fused_attention and use_flash_attention and not _flash_attn_is_installed:
        logger.warning(
            "flash-attn may provide important feature support or performance improvement."
            " Please install flash-attn %s.",
            _get_supported_versions(
                _flash_attn_version_required,
                _flash_attn_max_version,
            ),
        )
    if use_flash_attention and not _flash_attn_is_installed:
        use_flash_attention = False
        available_backends[0] = False

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    logger.debug(
        "Available backends = {FlashAttention=%s, FusedAttention=%s%s,"
        " UnfusedDotProductAttention=%s}",
        bool(available_backends[0]),
        bool(available_backends[1]),
        (
            f" (sub-backend {int(fused_attention_backend)})"
            if fused_attention_backend is not None
            else ""
        ),
        bool(available_backends[2]),
    )
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    # Select FusedAttention for performance
    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
    ):
        if device_compute_capability == (9, 0):
            logger.debug(
                "Disabling FlashAttention to give FusedAttention preference on Hopper+ "
                "for performance reasons"
            )
            use_flash_attention = False
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    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["FP8"]
        and _use_flash_attn_3
    ):
        logger.debug(
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            "Disabling FlashAttention 3 to give FusedAttention preference for performance reasons "
            "in FP8 execution"
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        )
        use_flash_attention = False

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    # Selected backend
    if use_flash_attention:
        use_fused_attention = False
        use_unfused_attention = False
    elif use_fused_attention:
        use_unfused_attention = False
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    selected_backend = "NoBackend"
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    if use_flash_attention:
        selected_backend = "FlashAttention"
    elif use_fused_attention:
        selected_backend = f"FusedAttention (sub-backend {int(fused_attention_backend)})"
    elif use_unfused_attention:
        selected_backend = "UnfusedDotProductAttention"
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    logger.debug("Selected backend = %s", selected_backend)
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    global _attention_backends
    _attention_backends["use_flash_attention"] = use_flash_attention
    _attention_backends["use_fused_attention"] = use_fused_attention
    _attention_backends["fused_attention_backend"] = fused_attention_backend
    _attention_backends["use_unfused_attention"] = use_unfused_attention
    _attention_backends["backend_selection_requires_update"] = False
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    return (
        use_flash_attention,
        use_fused_attention,
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        fused_attention_backend,
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        use_unfused_attention,
        available_backends,
    )


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class InferenceParams:  # pylint: disable=too-few-public-methods
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    """
    Inference parameters that are passed to the main model in order
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    to efficiently calculate and store the context during inference.
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    Parameters
    ----------
    max_batch_size : int
                    maximum batch size during inference.
    max_sequence_length : int
                         maximum sequence length during inference.
    """

    def __init__(self, max_batch_size, max_sequence_length):
        self.max_sequence_length = max_sequence_length
        self.max_batch_size = max_batch_size
        self.sequence_len_offset = 0
        self.batch_size_offset = 0
        self.key_value_memory_dict = {}

    def swap_key_value_dict(self, batch_indices):
        """
        Reorders the KV cache using the specified batch indices.

        Parameters
        ----------
        batch_indices : List[int]
                       Sequence of indices to reorder along the batch dimensions of
                       the KV cache. Must have a length equal to the batch size.
        """
        if len(self.key_value_memory_dict) == 0:
            raise ValueError("should not swap when dict in empty")

        for layer_number, inference_memory in self.key_value_memory_dict.items():
            inference_key_memory, inference_value_memory = inference_memory
            assert (
                len(batch_indices) == inference_key_memory.shape[1]
            )  # make sure batch size is the same
            new_inference_key_memory = inference_key_memory[:, batch_indices]
            new_inference_value_memory = inference_value_memory[:, batch_indices]
            self.key_value_memory_dict[layer_number] = (
                new_inference_key_memory,
                new_inference_value_memory,
            )
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@torch.no_grad()
def get_swa_mask(
    window_size: Tuple[int, int],
    max_seqlen_q: int,
    max_seqlen_kv: int,
    attn_mask_type: str = "no_mask",
    attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
) -> torch.Tensor:
    """
    Convert sliding window `window_size` to an equivalent "`arbitrary`" mask.
    For "`causal`" mask type, the sliding window diagonal is aligned to the top left corner,
    and for other mask types, the bottom right corner.

    Parameters
    ----------
    window_size: Tuple[int, int]
        Sliding window size for local attention, where query at position i attends to keys
        in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
        + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
        window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
        map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
        `attn_mask_type`.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
    attn_mask_type: str, default = `no_mask`
        Attention mask type, {"`no_mask`", "`padding`", "`causal`", "`padding_causal`",
        "`causal_bottom_right`", "`padding_causal_bottom_right`", "`arbitrary`"}
    attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
        default = `None`
        Boolean tensor(s) used to mask out attention softmax input.

    Returns
    ----------
    attention_mask: torch.Tensor
        Combined `attention_mask` (input) and sliding window attention mask.
        The shape is [max_seqlen_q, max_seqlen_kv] when input `attention_mask` is None;
        else, the same shape as input `attention_mask`.
    """
    mask = torch.ones(max_seqlen_q, max_seqlen_kv, dtype=torch.bool, device="cuda")
    if attn_mask_type in ["causal"]:
        left = window_size[0] if window_size[0] != -1 else max_seqlen_q
        right = window_size[1] if window_size[1] != -1 else max_seqlen_q
        mask_upper = torch.triu(mask, diagonal=-left)
        mask_lower = torch.tril(mask_upper, diagonal=right)
    else:
        left = window_size[0] if window_size[0] != -1 else max_seqlen_kv
        right = window_size[1] if window_size[1] != -1 else max_seqlen_kv
        mask_upper = torch.triu(mask, diagonal=max_seqlen_kv - max_seqlen_q - left)
        mask_lower = torch.tril(mask_upper, diagonal=max_seqlen_kv - max_seqlen_q + right)
    attn_mask_type = "arbitrary"
    mask = mask_lower.logical_not()
    if attention_mask is not None:
        mask = torch.logical_and(attention_mask, mask)
    return attn_mask_type, mask


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@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
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    actual_seqlens_q: Optional[torch.Tensor] = None,
    actual_seqlens_kv: Optional[torch.Tensor] = None,
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    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
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    bottom_right_alignment: bool = True,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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    """
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    Parameters
    ----------
    num_heads: int
        Number of heads.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
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    actual_seqlens_q: Optional[torch.Tensor], default = `None`
        Actual sequence lengths for queries, in shape [batch_size].
    actual_seqlens_kv: Optional[torch.Tensor], default = `None`
        Actual sequence lengths for keys and values, in shape [batch_size].
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    alibi_slopes: Optional[torch.Tensor], default = `None`
        Custom ALiBi slopes, FP32, CUDA tensor, in shape [num_heads] or [batch_size, num_heads].
    bias_dtype: Optional[torch.dtype], default = `None`
        Dtype of the generated ALiBi bias. If None, use torch.float32.
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    bottom_right_alignment: bool, default = `True`
        Whether to align the diagonal of the ALiBi bias to the bottom right corner of
        the matrix (`True`) or top left (`False`).
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    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
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        ALiBi bias in FP32 or `bias_dtype`. Its shape is
        (1) [1, num_heads, max_seqlen_q, max_seqlen_kv] if `alibi_slopes` is in [num_heads] shape,
        and `actual_seqlens_q` and `actual_seqlens_kv` are `None`; or
        (2) [batch_size, num_heads, max_seqlen_q, max_seqlen_kv] if `alibi_slopes` is in
        [batch_size, num_heads] shape, or, if `alibi_slopes` is in [num_heads] shape and
        `actual_seqlens_q` and `actual_seqlens_kv` are not `None`.
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    """
    global _alibi_cache
    if _alibi_cache["_alibi_slopes_require_update"]:
        if alibi_slopes is not None:
            _alibi_cache["_alibi_slopes"] = alibi_slopes
        else:
            n = 2 ** math.floor(math.log2(num_heads))
            m_0 = 2.0 ** (-8.0 / n)
            m = torch.pow(m_0, torch.arange(1, 1 + n))

            if n < num_heads:
                m_hat_0 = 2.0 ** (-4.0 / n)
                m_hat = torch.pow(m_hat_0, torch.arange(1, 1 + 2 * (num_heads - n), 2))
                m = torch.cat([m, m_hat])

            _alibi_cache["_alibi_slopes"] = m.to(dtype=torch.float32, device="cuda")
        _alibi_cache["_num_heads"] = num_heads
        _alibi_cache["_alibi_slopes_require_update"] = False

    if _alibi_cache["_alibi_bias_require_update"]:
        assert _alibi_cache["_alibi_slopes"] is not None, "ALiBi slopes can not be None!"
        if _alibi_cache["_alibi_slopes"].dim() == 1:
            slopes_shape = torch.Size([1, _alibi_cache["_alibi_slopes"].shape[0], 1, 1])
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        elif _alibi_cache["_alibi_slopes"].dim() == 2:
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            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
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        else:
            raise ValueError("ALiBi slopes cannot exceed 2 dimensions.")

1155
        bias = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
1156
            1, 1, max_seqlen_q, 1
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        ) - torch.arange(max_seqlen_kv, dtype=torch.int32, device="cuda").view(
            1, 1, 1, max_seqlen_kv
1159
        )
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        if actual_seqlens_q is None and actual_seqlens_kv is None:
            if bottom_right_alignment:
                bias = bias + max_seqlen_kv - max_seqlen_q
        elif actual_seqlens_q is not None and actual_seqlens_kv is not None:
            batch_size = actual_seqlens_q.shape[0]
            bias = bias.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv)
            if bottom_right_alignment:
                bias = bias + (actual_seqlens_kv - actual_seqlens_q).view(batch_size, 1, 1, 1)
        else:
            assert (
                False
            ), "actual_seqlens_q and actual_seqlens_kv need to be both None or torch.Tensors!"
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        bias = bias.abs().mul(-1)
        bias = bias * _alibi_cache["_alibi_slopes"].view(slopes_shape)
        _alibi_cache["_max_seqlen_q"], _alibi_cache["_max_seqlen_kv"] = max_seqlen_q, max_seqlen_kv
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        _alibi_cache["_bottom_right_alignment"] = bottom_right_alignment
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        bias_dtype = torch.float32 if bias_dtype is None else bias_dtype
        _alibi_cache["_alibi_bias"] = bias.contiguous().to(dtype=bias_dtype, device="cuda")
        _alibi_cache["_alibi_bias_require_update"] = False

    return _alibi_cache["_alibi_slopes"], _alibi_cache["_alibi_bias"]
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def get_cu_seqlens(mask: torch.Tensor) -> torch.Tensor:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch.
    """
    mask = mask.squeeze(1).squeeze(1)
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    reduced_mask = mask.logical_not().sum(dim=1)
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    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    return cu_seqlens

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def get_cu_seqlens_and_indices(mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
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    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch, and another int32 tensor of shape [batch_size * max_seqlen, 1, 1]
    containing the indices for the valid tokens.
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    """
    mask = mask.squeeze(1).squeeze(1)
    bs, seqlen = mask.shape

1208
    reduced_mask = mask.logical_not().sum(dim=1)
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    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    mask = mask.reshape(-1)
1214
    indices = mask.logical_not().nonzero()
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    indices = indices.unsqueeze(-1)

    num_nonzeros = indices.shape[0]
    pad_amount = bs * seqlen - num_nonzeros
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    indices = F.pad(
        input=indices, pad=(0, 0, 0, 0, 0, pad_amount), mode="constant", value=float(bs * seqlen)
    )
1222
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    return cu_seqlens, indices


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def get_indices(max_seqlen: int, cu_seqlens: torch.Tensor) -> torch.Tensor:
    """
    Given max_seqlen and cu_seqlens of shape [batch_size + 1], returns an int32
    tensor of shape [batch_size * max_seqlen, 1, 1] containing the indices for
    the valid tokens in a batch.
    """
    bs = len(cu_seqlens) - 1
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
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    indices = [i * max_seqlen + ii for i, j in enumerate(seqlens) for ii in range(j)]
    indices = torch.Tensor(indices).unsqueeze(1).unsqueeze(1).to(dtype=torch.int64, device="cuda")
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    num_nonzeros = indices.shape[0]
    pad_amount = bs * max_seqlen - num_nonzeros
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    indices = F.pad(
        input=indices,
        pad=(0, 0, 0, 0, 0, pad_amount),
        mode="constant",
        value=float(bs * max_seqlen),
    )
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    return indices

1248

1249
_cu_seqlens_cache = {}
1250
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def _get_full_cu_seqlens(
    batch_size: int,
    max_seqlen: int,
    device: torch.device,
) -> torch.Tensor:
    """Cumulative sequence lengths in full data batch

    All sequences in batch have the maximum sequence length.

    """
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    global _cu_seqlens_cache
    if (batch_size, max_seqlen) not in _cu_seqlens_cache:
        _cu_seqlens_cache[(batch_size, max_seqlen)] = torch.arange(
            0,
            (batch_size + 1) * max_seqlen,
            step=max_seqlen,
            dtype=torch.int32,
            device=device,
        )
    return _cu_seqlens_cache[(batch_size, max_seqlen)]
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1274
@torch.compile
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def pack_tensor(
    indices: torch.Tensor,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Packs the given tensor using the `indices`.
    """
    padding_indice = torch.zeros(
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        1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
1285
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
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    if isinstance(tensor, Float8Tensor):
        tensor_data = torch.cat((tensor._data, padding_indice), dim=0)

        packed = Float8Tensor.make_like(tensor, data=torch.gather(tensor_data, 0, indices))
    else:
        tensor = torch.cat((tensor, padding_indice), dim=0)

        packed = torch.gather(tensor, 0, indices)
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    return packed


1297
@torch.compile
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def pack_2_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Packs the given 2 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    return t1_packed, t2_packed


1311
@torch.compile
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def pack_3_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Packs the given 3 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    t3_packed = pack_tensor(indices, t3)
    return t1_packed, t2_packed, t3_packed


1327
@torch.compile
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def unpack_tensor(
    indices: torch.Tensor,
    dim0: int,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Inverse of `pack_tensor`.
    """
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    unpacked = torch.zeros(
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        dim0 + 1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
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    if isinstance(tensor, Float8Tensor):
        unpacked.scatter_(0, indices, tensor._data)
        unpacked = Float8Tensor.make_like(tensor, data=unpacked[0:-1, :, :])
    else:
        unpacked.scatter_(0, indices, tensor)
        unpacked = unpacked[0:-1, :, :]
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    return unpacked


1349
@torch.compile
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def unpack_2_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_2_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    return t1_unpacked, t2_unpacked


1364
@torch.compile
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def unpack_3_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_3_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    t3_unpacked = unpack_tensor(indices, dim0, t3)
    return t1_unpacked, t2_unpacked, t3_unpacked


class PackTensors(torch.autograd.Function):
    """
    Autograd function to pack tensors.
    """
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    @staticmethod
    def forward(
1388
        ctx, indices: torch.Tensor, *tensors: Tuple[torch.Tensor, ...]
1389
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
1390
        # pylint: disable=missing-function-docstring
1391
        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
1392
        ctx.save_for_backward(indices)
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        ctx.dim0 = tensors[0].shape[0]
        if len(tensors) == 1:
            return pack_tensor(indices, *tensors)
        if len(tensors) == 2:
            return pack_2_tensors(indices, *tensors)
        return pack_3_tensors(indices, *tensors)

    @staticmethod
    def backward(ctx, *grad_outputs: Tuple[torch.Tensor, ...]):
1402
        # pylint: disable=missing-function-docstring
1403
        (indices,) = ctx.saved_tensors
1404
        if len(grad_outputs) == 1:
1405
            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
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        if len(grad_outputs) == 2:
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            return None, *unpack_2_tensors(indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(indices, ctx.dim0, *grad_outputs)
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class UnpackTensor(torch.autograd.Function):
    """
    Autograd function to unpack a tensor.
    """
1415

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    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
1423
        # pylint: disable=missing-function-docstring
1424
        ctx.save_for_backward(indices)
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        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
1429
        # pylint: disable=missing-function-docstring
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        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(indices, grad_output)
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def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
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    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
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    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
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            send_op = torch.distributed.P2POp(
                torch.distributed.isend, send_tensor, send_dst, cp_group
            )
            recv_op = torch.distributed.P2POp(
                torch.distributed.irecv, recv_tensor, recv_src, cp_group
            )
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            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
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            recv_op = torch.distributed.P2POp(
                torch.distributed.irecv, recv_tensor, recv_src, cp_group
            )
            send_op = torch.distributed.P2POp(
                torch.distributed.isend, send_tensor, send_dst, cp_group
            )
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            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = torch.distributed.batch_isend_irecv(send_recv_ops)
    else:
        if rank % 2 == 0:
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = send_recv_ops

    return send_recv_reqs


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@jit_fuser
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def flash_attn_fwd_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
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    movedim_src: int,
    movedim_dst: int,
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):
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    """Merge partial outputs of each step in Attention with context parallelism"""
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    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(
        movedim_src, movedim_dst
    )
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    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
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    out_corrected = out_per_step * softmax_lse_corrected_exp
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    out.add_(out_corrected)


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@jit_fuser
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def flash_attn_fwd_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
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    """Merge softmax stats of each step in Attention with context parallelism"""
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    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
    new_scale = max_scale + torch.log(1 + torch.exp(min_scale - max_scale))
    softmax_lse.copy_(new_scale)
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@jit_fuser
def get_cu_seqlens_on_cp_rank(
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    cu_seqlens: torch.Tensor,
    cu_seqlens_padded_on_cp_rank: torch.Tensor,
    cp_size: int,
    cp_rank: int,
    first_half: bool,
    second_half: bool,
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):
    """Compute cu_seqlens of a context parallelism rank"""
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
    seqlens_padded = (cu_seqlens_padded_on_cp_rank[1:] - cu_seqlens_padded_on_cp_rank[:-1]) // 2
    zeros = torch.zeros_like(seqlens)
    cu_seqlens_on_cp_rank = torch.zeros_like(cu_seqlens)
    if first_half:
        seqlens_1 = seqlens - cp_rank * seqlens_padded
        seqlens_1 = seqlens_1.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_1)
    if second_half:
        seqlens_2 = seqlens - (2 * cp_size - cp_rank - 1) * seqlens_padded
        seqlens_2 = seqlens_2.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_2)
    cu_seqlens_on_cp_rank.cumsum_(dim=0)
    return cu_seqlens_on_cp_rank


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@torch.compile
def get_seq_chunk_ids_for_reordering(cp_size, device, to_contiguous):
    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
    To make sure tokens are ordered correctly for compute, we need to reorder sequence chunks
    before or after CP communications (e.g., all-gather, all-to-all). This function is to compute
    sequence chunk ids for reordering.
    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
    if to_contiguous:
        for rank in range(cp_size):
            chunk_ids[rank] = 2 * rank
            chunk_ids[rank + cp_size] = 2 * cp_size - 2 * rank - 1
    else:
        for rank in range(cp_size):
            chunk_ids[2 * rank] = rank
            chunk_ids[2 * rank + 1] = 2 * cp_size - rank - 1
    return chunk_ids


@torch.compile
def reorder_seq_chunks_for_a2a(x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn):
    """Reorder sequence chunk for A2A communication."""
    if before_attn:
        # [cp, b, s, np//cp, hn] -> [b, cp, s, np//cp, hn]
        # or [cp, s, b, np//cp, hn] -> [cp, s, b, np//cp, hn]
        x = x.movedim(0, seq_dim).contiguous()
        # [b, cp, s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
        # or [cp, s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
        x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 2) :])
        # reorder the sequence chunks
        x = torch.index_select(x, dim=seq_dim, index=chunk_ids_for_a2a)
    else:
        # [b, cp*2, s//2, np//cp, hn] -> [cp*2, b, s//2, np//cp, hn]
        # or [cp*2, s//2, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
        x = x.movedim(seq_dim, 0).contiguous()
        # reorder the sequence chunks
        x = torch.index_select(x, dim=0, index=chunk_ids_for_a2a)
        # [cp*2, b, s//2, np//cp, hn] -> [cp, 2, b, s//2, np//cp, hn]
        # or [cp*2, s//2, b, np//cp, hn] -> [cp, 2, s//2, b, np//cp, hn]
        x = x.view(cp_size, 2, *x.shape[1:])
    return x


def flash_attn_a2a_communicate(
    a2a_inputs: Union[torch.Tensor, List[torch.Tensor]],
    chunk_ids_for_a2a: torch.Tensor,
    seq_dim: int,
    cp_size: int,
    cp_group: dist_group_type,
    cp_stream: torch.cuda.Stream,
    before_attn: bool,
) -> Union[torch.Tensor, List[torch.Tensor]]:
    """A2A communication for context parallelism."""
    a2a_inputs = [a2a_inputs] if not isinstance(a2a_inputs, list) else a2a_inputs
    a2a_outputs, a2a_reqs = [None] * len(a2a_inputs), [None] * len(a2a_inputs)
    if before_attn:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # reorder the sequence chunks
                    x = reorder_seq_chunks_for_a2a(
                        x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn
                    )
                    # [b, cp*2, s//2, np//cp, hn] -> [b, cp*s, np//cp, hn]
                    # or [cp*2, s//2, b, np//cp, hn] -> [cp*s, b, np//cp, hn]
                    a2a_outputs[i - 2] = x.view(*x.shape[:seq_dim], -1, *x.shape[(seq_dim + 2) :])
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, s, np, hn] -> [b, s, cp, np//cp, hn]
                # or [s, b, np, hn] -> [s, b, cp, np//cp, hn]
                x = x.view(*x.shape[:-2], cp_size, x.shape[-2] // cp_size, x.shape[-1])
                # [b, s, cp, np//cp, hn] -> [cp, b, s, np//cp, hn]
                # or [s, b, cp, np//cp, hn] -> [cp, s, b, np//cp, hn]
                a2a_inputs[i] = x.movedim(-3, 0).contiguous()
    else:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, cp*s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
                # or [cp*s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
                x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 1) :])
                # reorder the sequence chunks
                a2a_inputs[i] = reorder_seq_chunks_for_a2a(
                    x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # [cp, 2, b, s//2, np//cp, hn] -> [b, 2, s//2, cp, np//cp, hn]
                    # or [cp, 2, s//2, b, np//cp, hn] -> [2, s//2, b, cp, np//cp, hn]
                    x = x.movedim(0, -3).movedim(0, seq_dim).contiguous()
                    # [b, 2, s//2, cp, np//cp, hn] -> [b*s, np, hn]
                    # or [2, s//2, b, cp, np//cp, hn] -> [s*b, np, hn]
                    a2a_outputs[i - 2] = x.view(-1, x.shape[-3] * x.shape[-2], x.shape[-1])
    torch.cuda.current_stream().wait_stream(cp_stream)
    return a2a_outputs[0] if len(a2a_inputs) == 1 else a2a_outputs


1644
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
1645
    """
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    Attention implementation with context parallelism. Exchange KV between CP ranks
    with P2P in ring topology. Split attention compute into multiple steps, and overlap
    current-step compute with next-step communication.
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1653

    This implementation also supports hierarchical CP, which parallelizes attention
    heads in low-level CP groups and parallelizes sequence dimension in high-level CP
    groups. For more details, please refer to `LongVILA <https://arxiv.org/abs/2408.10188>`_
    and `USP <https://arxiv.org/abs/2405.07719>`_.
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    """

    @staticmethod
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    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
1664
        cu_seqlens_kv,
1665
        max_seqlen_q,
1666
        max_seqlen_kv,
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        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
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        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
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        fp8,
        fp8_meta,
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        cp_group,
        cp_global_ranks,
        cp_stream,
1682
    ):
1683
        # pylint: disable=missing-function-docstring
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        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

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        if isinstance(cp_group, list):
            assert (
                qkv_format != "thd"
            ), f"{qkv_format} format is not supported with hierarchical CP implementation yet!"
            assert attn_bias_type == "no_bias", (
                f"{attn_bias_type} bias type is not supported with hierarchical CP implementation"
                " yet!"
            )
            cp_group_a2a = cp_group[0]
            cp_size_a2a = get_distributed_world_size(cp_group_a2a)
            rank_a2a = get_distributed_rank(cp_group_a2a)
            cp_group = cp_group[1]
        else:
            cp_group_a2a = None
            cp_size_a2a = 1
            rank_a2a = 0

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        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
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        send_dst = cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
1708
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        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1710
1711
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
1712

1713
        seq_dim = None
1714
        if qkv_format in ["bshd", "sbhd"]:
1715
            seq_dim = qkv_format.index("s")
1716
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            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        pad_between_seqs_q = not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
        pad_between_seqs_kv = not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
        cu_seqlens_q_padded = cu_seqlens_q_padded // cp_size
        cu_seqlens_kv_padded = cu_seqlens_kv_padded // cp_size
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
1728

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        fused_attn_qkv_dtype = None
        fused_attn_backend = None
        amax_per_step = None
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        qkv_dtype = q.dtype
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
        is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
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        if fp8:
            if use_fused_attention:
                fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_backend = FusedAttnBackend["FP8"]
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                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
                if is_input_fp8:
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                    fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                else:
                    q_f16, k_f16, v_f16 = q, k, v
                    if cp_size_a2a == 1 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                        q = cast_to_fp8(q_f16, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                    if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                        k, v = [
                            cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                            for x in [k_f16, v_f16]
                        ]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_qkv_offset"] = META_QKV
                fp8_meta_kwargs["d_scale_s"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_s"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_o"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_o_offset"] = META_O_CP
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            q_f16 = q
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, q.device, True)
            q, k, v = flash_attn_a2a_communicate(
                [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, True
            )
            if not fp8:
                q_f16 = q
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            elif not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
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                q_f16 = q
                q = cast_to_fp8(q_f16, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)

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        assert qkv_format == "thd" or (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"
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        if causal:
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            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
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                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
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            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
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                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
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        total_tokens_kv = None if qkv_format != "thd" else k.shape[0]
        # remove padded tokens at the end
        k, v = [x if qkv_format != "thd" else x[: cu_seqlens_kv_padded[-1]] for x in [k, v]]
1801
        if attn_bias is not None:
1802
            assert len(attn_bias.shape) == 4, (
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                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
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            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
1809
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
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            attn_bias_ = attn_bias.view(
                *attn_bias.shape[:-2],
                2,
                attn_bias.shape[-2] // 2,
                2 * cp_size,
                attn_bias.shape[-1] // (2 * cp_size),
1816
1817
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
1818
1819
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
1820
            )
1821
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
1822
1823
1824
1825

        softmax_lse_in_packed_format = not use_fused_attention and (
            _flash_attn_2_6_0_plus or _use_flash_attn_3
        )
1826
        flash_attn_fwd = None
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
                fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_3_plus:
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
1842

1843
1844
1845
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
1846
        attn_bias_inputs = [None, None]
1847
1848
1849
1850
        # Flash Attn outputs
        out_per_step = [None for _ in range(cp_size)]
        softmax_lse_per_step = [None for _ in range(cp_size)]
        rng_states = [None for _ in range(cp_size)]
1851
        attn_biases = [None for _ in range(cp_size)]
1852
1853
1854
1855
1856
1857
1858

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
        # synchronize fwd results correction across steps
        fwd_results_correction_done = torch.cuda.Event()

        p2p_comm_buffers = [None for _ in range(cp_size)]
1859
1860
1861
1862
        if use_fused_attention and qkv_format in ["bshd", "sbhd"]:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(-3), v.unsqueeze(-3)), dim=-3)
        else:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(0), v.unsqueeze(0)), dim=0)
1863
1864
        send_recv_reqs = [[], []]

1865
1866
        softmax_lse_ = None
        out = None
1867
        for i in range(cp_size + 1):
1868
            if i < cp_size:
1869
                with torch.cuda.stream(flash_attn_streams[i % 2]):
1870
                    # wait until KV is received
1871
                    for req in send_recv_reqs[(i + 1) % 2]:
1872
1873
                        req.wait()

1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
                    if i < (cp_size - 1):
                        p2p_comm_buffers[i + 1] = torch.empty_like(p2p_comm_buffers[i])
                        send_recv_reqs[i % 2] = flash_attn_p2p_communicate(
                            rank,
                            p2p_comm_buffers[i],
                            send_dst,
                            p2p_comm_buffers[i + 1],
                            recv_src,
                            cp_group,
                            batch_p2p_comm,
                        )

1886
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
                        kv_inputs[i % 2] = cast_to_fp8(
                            p2p_comm_buffers[i],
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                        )
                    if fp8 and use_fused_attention:
1897
1898
1899
1900
                        fp8_meta_kwargs["amax_s"] = amax_per_step
                        fp8_meta_kwargs["amax_s_offset"] = i
                        fp8_meta_kwargs["amax_o"] = amax_per_step
                        fp8_meta_kwargs["amax_o_offset"] = cp_size + i
1901
1902
                    if causal:
                        if i == 0:
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv, cu_seqlens_kv_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1915
                            if use_fused_attention:
1916
1917
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1918
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1919
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1920
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1921
                                        k.shape[0], -1, 2, *k.shape[-2:]
1922
                                    )
1923
1924
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1925
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1926
1927
1928
1929
                                    # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                        -1, k.shape[2], 2, *k.shape[-2:]
                                    )
1930
                                elif qkv_format == "thd":
1931
                                    q_inputs[i % 2] = q
1932
1933
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1934
1935
1936
1937
1938
1939
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1940
                                    ).contiguous()
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type=attn_mask_type,
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
1969
                                )
1970
1971
1972
1973
1974
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
1975
1976
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1977
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1978
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1979
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1980
                                fa_outputs = flash_attn_fwd(
1981
1982
1983
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1984
1985
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1986
                                    max_seqlen_q,
1987
                                    max_seqlen_kv,
1988
                                    causal=True,
1989
                                    **fa_forward_kwargs,
1990
                                )
1991
1992
1993
1994
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
1995
                        elif i <= rank:
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    False,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
2013
                            if use_fused_attention:
2014
2015
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2016
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
2017
2018
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...].contiguous()
2019
2020
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2021
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
2022
2023
                                    # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][0].contiguous()
2024
                                elif qkv_format == "thd":
2025
                                    q_inputs[i % 2] = q
2026
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
2027
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
2028
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
2029
                                    )
2030
2031
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2032
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv // 2,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=(
                                        None
                                        if cu_seqlens_kv_padded is None
                                        else cu_seqlens_kv_padded // 2
                                    ),
                                    **fp8_meta_kwargs,
2065
                                )
2066
2067
2068
2069
2070
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
2071
2072
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
2073
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
2074
2075
                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
2076
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
2077
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
2078
                                    )
2079
2080
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
2081
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, :, 0, ...].contiguous()
2082
                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
2083
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2084
2085
2086
                                if _use_flash_attn_3 or _flash_attn_2_3_plus:
                                    fa_forward_kwargs["window_size"] = (-1, -1)
                                fa_outputs = flash_attn_fwd(
2087
2088
2089
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
2090
2091
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
2092
                                    max_seqlen_q,
2093
                                    max_seqlen_kv // 2,
2094
                                    causal=False,
2095
                                    **fa_forward_kwargs,
2096
                                )
2097
2098
2099
2100
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
2101
                        else:
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, False, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    True,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2119
                            if use_fused_attention:
2120
2121
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
2122
                                    q_inputs[i % 2] = q[:, 1, ...].contiguous()
2123
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
2124
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
2125
                                        k.shape[0], -1, 2, *k.shape[-2:]
2126
                                    )
2127
2128
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2129
                                    q_inputs[i % 2] = q[1].contiguous()
2130
2131
2132
2133
                                    # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                        -1, k.shape[2], 2, *k.shape[-2:]
                                    )
2134
2135
                                elif qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
2136
2137
2138
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
2139
2140
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2141
2142
2143
2144
2145
2146
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
2147
                                    ).contiguous()
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q // 2,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=(
                                        None
                                        if cu_seqlens_q_padded is None
                                        else cu_seqlens_q_padded // 2
                                    ),
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
2180
                                )
2181
2182
2183
2184
2185
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
2186
                            else:
2187
2188
                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
2189
2190
2191
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
2192
2193
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
2194
                                    q_inputs[i % 2] = (
2195
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
2196
                                    )
2197
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
2198
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2199
2200
2201
                                if _use_flash_attn_3 or _flash_attn_2_3_plus:
                                    fa_forward_kwargs["window_size"] = (-1, -1)
                                fa_outputs = flash_attn_fwd(
2202
2203
2204
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
2205
2206
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
2207
                                    max_seqlen_q // 2,
2208
                                    max_seqlen_kv,
2209
                                    causal=False,
2210
                                    **fa_forward_kwargs,
2211
                                )
2212
2213
2214
2215
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
2216
                    else:
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
                        if pad_between_seqs_q:
                            cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                            )
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                        if pad_between_seqs_kv:
                            cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_kv,
                                cu_seqlens_kv_padded,
                                cp_size,
                                (rank - i) % cp_size,
                                True,
                                True,
                            )
                        else:
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2234
                        if use_fused_attention:
2235
2236
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
2237
2238
2239
2240
2241
2242
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
2243
                                ).contiguous()
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
                            out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                is_training,
                                max_seqlen_q,
                                max_seqlen_kv,
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
                                q,
                                (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                ),
                                (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                ),
                                fused_attn_qkv_dtype,
                                fused_attn_backend,
                                attn_scale=softmax_scale,
                                dropout=dropout_p,
                                qkv_layout=qkv_layout,
                                attn_mask_type=attn_mask_type,
                                attn_bias_type=attn_bias_type,
                                attn_bias=attn_bias_inputs[i % 2],
                                cu_seqlens_q_padded=cu_seqlens_q_padded,
                                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                **fp8_meta_kwargs,
2272
                            )
2273
2274
2275
2276
2277
                            if fp8:
                                softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                            else:
                                softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                attn_biases[i] = rest[0] if len(rest) > 0 else None
2278
                        else:
2279
                            # [b, sq, np, hn] -> [b*sq, np, hn]
2280
                            q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
2281
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
2282
                            kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2283
                            fa_outputs = flash_attn_fwd(
2284
2285
2286
                                q_inputs[i % 2],
                                kv_inputs[i % 2][0],
                                kv_inputs[i % 2][1],
2287
2288
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
2289
                                max_seqlen_q,
2290
                                max_seqlen_kv,
2291
                                causal=False,
2292
                                **fa_forward_kwargs,
2293
                            )
2294
2295
2296
2297
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
                            if not _use_flash_attn_3:
                                rng_states[i] = fa_outputs[7]
2298
2299
2300
2301

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
2302
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
2303

2304
2305
                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
2306
                    softmax_lse_per_step[i - 1].squeeze_(-1)
2307
2308
2309
2310
2311
                if qkv_format != "thd" and softmax_lse_in_packed_format:
                    # [np, t] -> [np, b, sq]
                    softmax_lse_per_step[i - 1] = softmax_lse_per_step[i - 1].view(
                        q.shape[-2], q.shape[0], -1
                    )
2312

2313
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
2314
2315
2316
2317
2318
2319
2320
2321
                    if fp8:
                        out_per_step[i - 1] = cast_from_fp8(
                            out_per_step[i - 1],
                            fp8_meta["scaling_fwd"],
                            META_O_CP,
                            fp8_dtype_forward,
                            TE_DType[torch.float32],
                        )
2322
                    if i == 1:
2323
                        out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
2324
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
2325
                        if causal and qkv_format != "thd":
2326
2327
                            # [b, np, sq] -> [b, np, 2, sq//2] lse not in packed format
                            # [np, b, sq] -> [np, b, 2, sq//2] lse in packed format
2328
                            softmax_lse_ = softmax_lse.view(
2329
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
2330
                            )
2331
2332
2333
2334
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
2335
                    else:
2336
                        if qkv_format == "thd":
2337
                            tex.thd_second_half_lse_correction(
2338
2339
2340
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
2341
                                softmax_lse_in_packed_format,
2342
                            )
2343
                        else:
2344
2345
2346
                            flash_attn_fwd_softmax_lse_correction(
                                softmax_lse_[..., 1, :], softmax_lse_per_step[i - 1]
                            )
2347
2348

                if i < cp_size:
2349
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
2350
2351
2352
2353
2354

        torch.cuda.current_stream().wait_stream(flash_attn_streams[1])

        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
2355
            out_ = None
2356
            if qkv_format == "bshd":
2357
2358
2359
                out_per_step[i] = out_per_step[i].view(
                    out.shape[0], -1, *out.shape[-2:]
                )  # pylint: disable=used-before-assignment
2360
2361
2362
2363
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
2364

2365
            if i <= rank or not causal:
2366
                if qkv_format in ["bshd", "sbhd"]:
2367
2368
2369
2370
2371
                    flash_attn_fwd_out_correction(
                        out.view(*out_per_step[i].shape),
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2372
2373
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2374
                    )
2375
                elif qkv_format == "thd":
2376
2377
2378
2379
2380
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2381
                        cu_seqlens_q_padded,
2382
                        False,
2383
                        softmax_lse_in_packed_format,
2384
                    )
2385
            else:
2386
                if qkv_format in ["bshd", "sbhd"]:
2387
2388
2389
2390
2391
                    flash_attn_fwd_out_correction(
                        out_,
                        out_per_step[i],
                        softmax_lse_[..., 1, :],
                        softmax_lse_per_step[i],
2392
2393
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2394
                    )
2395
                elif qkv_format == "thd":
2396
2397
2398
2399
2400
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2401
                        cu_seqlens_q_padded,
2402
                        True,
2403
                        softmax_lse_in_packed_format,
2404
                    )
2405

2406
2407
2408
        if qkv_format != "thd" and softmax_lse_in_packed_format:
            # [np, b, sq] -> [np, t]
            softmax_lse = softmax_lse.view(softmax_lse.shape[0], -1)
2409
        kv = p2p_comm_buffers[-1]
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
        if qkv_format == "bshd":
            out = out.view(out.shape[0], -1, *out.shape[-2:])
            ctx.batch_size = out.shape[0]
        elif qkv_format == "sbhd":
            out = out.view(-1, *out.shape[-3:])
            ctx.batch_size = out.shape[1]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, out.device, False)
            out = flash_attn_a2a_communicate(
                out, chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, False
            )
            if use_fused_attention:
                if qkv_format == "bshd":
                    # [b*s, np, hn] -> [b, s, np, hn]
                    out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                elif qkv_format == "sbhd":
                    # [s*b, np, hn] -> [s, b, np, hn]
                    out = out.view(-1, ctx.batch_size, *out.shape[-2:])
        elif not use_fused_attention:
2430
            out = out.view(-1, *out.shape[-2:])
2431

2432
2433
2434
2435
2436
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
            fp8_meta["scaling_fwd"].amax_history[0][META_S] = amax_cp_fwd[0]
            fp8_meta["scaling_fwd"].amax_history[0][META_O_CP] = amax_cp_fwd[1]

2437
        out_fp8 = None
2438
2439
        out_f16 = out.to(qkv_dtype)
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
2440
2441
            out_fp8 = cast_to_fp8(out_f16, fp8_meta["scaling_fwd"], META_O, fp8_dtype_forward)

2442
        if fp8 and is_output_fp8:
2443
2444
2445
2446
2447
2448
            out_ret = Float8Tensor(
                data=out_fp8,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_O,
                fp8_dtype=fp8_dtype_forward,
2449
                dtype=qkv_dtype,
2450
2451
2452
2453
2454
2455
2456
2457
            )
        else:
            out_ret = out_f16

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
            q_save, kv_save, out_save = q, kv, out_fp8
            fp8_fwd_scales = fp8_meta["scaling_fwd"].scale.clone()
            fp8_fwd_scale_invs = fp8_meta["scaling_fwd"].scale_inv.clone()
2458
        elif fp8 and is_input_fp8:
2459
2460
2461
2462
2463
2464
2465
2466
            q_fp8 = Float8Tensor(
                data=q,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_QKV,
                fp8_dtype=fp8_dtype_forward,
                dtype=q_fp8.dtype,
            )
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
            kv_fp8 = Float8Tensor(
                data=kv,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_QKV,
                fp8_dtype=fp8_dtype_forward,
                dtype=k_fp8.dtype,
            )
            q_save, kv_save, out_save = q_fp8, kv_fp8, out_f16
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None
        else:
2478
            q_f16 = q_f16.view(q.shape)
2479
2480
2481
            q_save, kv_save, out_save = q_f16, kv, out_f16
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None

2482
        ctx.save_for_backward(
2483
2484
2485
            q_save,
            kv_save,
            out_save,
2486
            softmax_lse,
2487
2488
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
2489
2490
            fp8_fwd_scales,
            fp8_fwd_scale_invs,
2491
2492
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
2493
2494
            *rng_states,
            *attn_biases,
2495
        )
2496
2497
2498
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
2499
2500
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
2501
        ctx.cp_stream = cp_stream
2502
        ctx.dropout_p = dropout_p
2503
        ctx.total_tokens_kv = total_tokens_kv
2504
        ctx.max_seqlen_q = max_seqlen_q
2505
        ctx.max_seqlen_kv = max_seqlen_kv
2506
        ctx.softmax_scale = softmax_scale
2507
        ctx.qkv_format = qkv_format
2508
        ctx.attn_mask_type = attn_mask_type
2509
2510
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
2511
        ctx.deterministic = deterministic
2512
        ctx.use_fused_attention = use_fused_attention
2513
2514
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
2515
2516
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
2517
        return out_ret
2518
2519
2520

    @staticmethod
    def backward(ctx, dout):
2521
        # pylint: disable=missing-function-docstring
2522
2523
2524
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

2525
2526
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
2527
2528
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
2529
2530
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

2531
2532
2533
2534
2535
2536
2537
        (*saved_tensors,) = ctx.saved_tensors
        (q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded) = saved_tensors[:6]
        (fp8_fwd_scales, fp8_fwd_scale_invs) = saved_tensors[6:8]
        cu_seqlens_q_per_step = saved_tensors[8 : 8 + cp_size]
        cu_seqlens_kv_per_step = saved_tensors[8 + cp_size : 8 + cp_size * 2]
        rng_states = saved_tensors[8 + cp_size * 2 : 8 + cp_size * 3]
        attn_biases = saved_tensors[8 + cp_size * 3 : 8 + cp_size * 4]
2538

2539
2540
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
2541
2542

        seq_dim = None
2543
        if ctx.qkv_format in ["bshd", "sbhd"]:
2544
            seq_dim = ctx.qkv_format.index("s")
2545
2546
2547
            qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format[:-2] + "2" + ctx.qkv_format[-2:]
        else:
            qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format
2548

2549
        if attn_biases[0] is not None:
2550
2551
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2552
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2553
2554
2555
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2556
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2557
2558
2559
            )
        else:
            attn_dbias = None
2560
            attn_dbias_ = None
2561

2562
2563
2564
2565
        softmax_lse_in_packed_format = not ctx.use_fused_attention and (
            _flash_attn_2_6_0_plus or _use_flash_attn_3
        )

2566
        if causal:
2567
            if ctx.qkv_format == "thd" or softmax_lse_in_packed_format:
2568
                softmax_lse_ = tex.thd_read_second_half_lse(
2569
                    softmax_lse, cu_seqlens_q_padded, softmax_lse_in_packed_format
2570
                )
2571
2572
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2573
2574
2575
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2576
2577
2578
2579
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)
2580
2581
2582
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
2583

2584
        dout_dtype = dout.dtype
2585
2586
2587
2588
2589
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
        fused_attn_dqkv_dtype = None
        amax_per_step = None
        dout_fp8_dtype = None
2590
2591
        if ctx.fp8:
            if ctx.use_fused_attention:
2592
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
2593
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)
2594
                fused_attn_qkv_dtype = fp8_dtype_forward
2595
2596
2597
2598
2599
                fused_attn_dqkv_dtype = fp8_dtype_backward
                fused_attn_backend = FusedAttnBackend["FP8"]
                dq_fp8 = torch.empty((cp_size, *q.shape), dtype=q.dtype, device=q.device)
                dkv_fp8 = torch.empty((cp_size, *kv.shape), dtype=kv.dtype, device=kv.device)
                dkv_fp8_ = torch.empty_like(dkv_fp8)
2600
                if ctx.is_output_fp8:
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = dout._scale_inv
                    dout = dout._data
                else:
                    dout = cast_to_fp8(
                        dout, ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                    )
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_fwd_scale_invs[META_QKV]
                fp8_meta_kwargs["d_scale_s"] = fp8_fwd_scale_invs[META_S]
                fp8_meta_kwargs["d_scale_o"] = fp8_fwd_scale_invs[META_O]
                fp8_meta_kwargs["d_scale_do"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO]
                fp8_meta_kwargs["d_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP]
                fp8_meta_kwargs["q_scale_s"] = fp8_fwd_scales[META_S]
                fp8_meta_kwargs["q_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale[META_DP]
                fp8_meta_kwargs["q_scale_dqkv"] = ctx.fp8_meta["scaling_bwd"].scale[META_DQKV_CP]
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
2622
            if ctx.fp8_meta is not None and ctx.is_input_fp8:
2623
2624
2625
2626
2627
2628
2629
                q, kv = [x.from_float8(x.dtype) for x in [q, kv]]
                if cp_size_a2a == 1:
                    dout = dout.from_float8(dout_dtype)
                else:
                    dout_fp8_dtype = dout._fp8_dtype
                    dout_scale_inv = dout._scale_inv
                    dout = dout._data
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
            dq = torch.empty_like(q)
            if ctx.qkv_format == "thd" and causal:
                dq[cu_seqlens_q_padded[-1] :].fill_(0)
            p2p_comm_buffers = [
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
            ]
            p2p_comm_buffers[0][0].copy_(kv)
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
2641
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
2642
2643
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
        if cp_size_a2a > 1:
            if not ctx.use_fused_attention:
                out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                dout = dout.view(*out.shape)
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, out.device, True)
            out, dout = flash_attn_a2a_communicate(
                [out, dout],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                True,
            )
2658
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
2659
                dout = cast_from_fp8(
2660
2661
2662
2663
2664
2665
                    dout,
                    None,
                    None,
                    dout_fp8_dtype,
                    TE_DType[dout_dtype],
                    scale_inv=dout_scale_inv,  # pylint: disable=used-before-assignment
2666
2667
                )

2668
2669
2670
2671
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

2672
        flash_attn_bwd = None
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2685

2686
2687
2688
2689
2690
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2691
2692
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
            if ctx.fp8:
                if i < cp_size - 1:
                    send_recv_reqs = flash_attn_p2p_communicate(
                        rank,
                        send_tensor[0],
                        send_dst,
                        recv_tensor[0],
                        recv_src,
                        ctx.cp_group,
                        batch_p2p_comm,
                    )
                else:
                    dkv_a2a_req = torch.distributed.all_to_all_single(
                        dkv_fp8,
                        dkv_fp8_,
                        group=ctx.cp_group,
                        async_op=True,
                    )
                    send_recv_reqs = [dkv_a2a_req]
            else:
                if i == 0:
                    send_tensor = send_tensor[0]
                    recv_tensor = recv_tensor[0]
                if i == (cp_size - 1):
                    send_tensor = send_tensor[1]
                    recv_tensor = recv_tensor[1]
                send_recv_reqs = flash_attn_p2p_communicate(
                    rank, send_tensor, send_dst, recv_tensor, recv_src, ctx.cp_group, batch_p2p_comm
                )
2722

2723
            kv = p2p_comm_buffers[i % 2][0]
2724
            dk_, dv_ = None, None
2725
2726
2727
            if ctx.fp8 and ctx.use_fused_attention:
                fp8_meta_kwargs["amax_dp"] = amax_per_step[0][i]
                fp8_meta_kwargs["amax_dqkv"] = amax_per_step[0][i]
2728
            # In reversed order of fwd
2729
            if causal:
2730
                if i == (cp_size - 1):
2731
                    if ctx.use_fused_attention:
2732
2733
2734
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2735
2736
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2737
2738
2739
2740
2741
2742
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2743
2744
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2745
2746
2747
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2748
2749
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
2750
2751
2752
2753
2754
2755
2756
2757
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2758
                        if attn_dbias is not None:
2759
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2760
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2761
                            ctx.max_seqlen_q,
2762
2763
2764
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2765
                            q_,
2766
2767
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2768
2769
                            out_,
                            dout_,
2770
2771
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2772
                            aux_ctx_tensors,
2773
                            fused_attn_backend,
2774
2775
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2776
2777
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2778
                            qkv_layout=qkv_layout,
2779
                            attn_mask_type=ctx.attn_mask_type,
2780
                            attn_bias_type=ctx.attn_bias_type,
2781
2782
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2783
2784
2785
2786
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2787
                        dq_ = torch.zeros_like(q_)
2788
2789
2790
2791
2792
2793
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
2794
2795
2796
2797
2798
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, 0)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2799
2800
2801
2802
2803
2804
2805
2806
2807
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2808
2809
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2810
                            ctx.max_seqlen_q,
2811
                            ctx.max_seqlen_kv,
2812
2813
                            causal=True,
                            **fa_backward_kwargs,
2814
                        )
2815
                elif i >= (cp_size - rank - 1):
2816
                    if ctx.use_fused_attention:
2817
2818
2819
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2820
2821
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
2822
2823
2824
2825
2826
2827
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2828
2829
                            # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                            kv_ = kv[0].contiguous()
2830
2831
2832
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2833
2834
2835
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2836
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2837
2838
2839
2840
2841
2842
2843
2844
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2845
                        if attn_dbias is not None:
2846
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2847
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2848
                            ctx.max_seqlen_q,
2849
2850
2851
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2852
                            q_,
2853
2854
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2855
2856
                            out_,
                            dout_,
2857
2858
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2859
                            aux_ctx_tensors,
2860
                            fused_attn_backend,
2861
2862
2863
2864
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=(
                                None if cu_seqlens_kv_padded is None else cu_seqlens_kv_padded // 2
                            ),
2865
2866
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2867
                            qkv_layout=qkv_layout,
2868
                            attn_mask_type="padding" if padding else "no_mask",
2869
                            attn_bias_type=ctx.attn_bias_type,
2870
2871
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2872
2873
2874
2875
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2876
                        dq_ = torch.zeros_like(q_)
2877
2878
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2879
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2880
2881
2882
                        else:
                            # [2, b, 2, sk//2, np, hn]->[2, b, sk//2, np, hn]->[2, b*sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
2883
2884
2885
2886
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
2887
2888
2889
2890
2891
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, -1)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2892
2893
2894
2895
2896
2897
2898
2899
2900
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2901
2902
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2903
                            ctx.max_seqlen_q,
2904
                            ctx.max_seqlen_kv // 2,
2905
2906
                            causal=False,
                            **fa_backward_kwargs,
2907
2908
2909
                        )
                else:
                    if ctx.use_fused_attention:
2910
2911
2912
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
2913
2914
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2915
2916
2917
2918
2919
2920
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous()
                            dout_ = dout[:, 1, ...].contiguous()
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            q_ = q[1].contiguous()
2921
2922
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2923
2924
2925
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
2926
2927
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2928
2929
2930
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2931
                            kv_ = kv
2932
2933
2934
2935
2936
2937
2938
2939
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse_,
                                softmax_lse_,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse_, rng_states[cp_size - i - 1]]
2940
                        if attn_dbias is not None:
2941
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2942
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2943
                            ctx.max_seqlen_q // 2,
2944
2945
2946
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2947
                            q_,
2948
2949
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2950
2951
                            out_,
                            dout_,
2952
2953
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2954
                            aux_ctx_tensors,
2955
                            fused_attn_backend,
2956
2957
2958
2959
                            cu_seqlens_q_padded=(
                                None if cu_seqlens_q_padded is None else cu_seqlens_q_padded // 2
                            ),
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2960
2961
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2962
                            qkv_layout=qkv_layout,
2963
                            attn_mask_type="padding" if padding else "no_mask",
2964
                            attn_bias_type=ctx.attn_bias_type,
2965
2966
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2967
2968
                        )
                    else:
2969
2970
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2971
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
2972
2973
2974
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
2975
                        dq_ = torch.zeros_like(q_)
2976
2977
2978
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
2979
                        if ctx.qkv_format == "thd":
2980
2981
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2982
2983
2984
2985
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous().view(-1, *out.shape[-2:])
                            dout_ = dout[:, 1, ...].contiguous().view(-1, *dout.shape[-2:])
2986
2987
2988
2989
2990
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, -1)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2991
2992
2993
2994
2995
2996
2997
2998
2999
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse_,
                            dq_,
                            dkv_[0],
                            dkv_[1],
3000
3001
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
3002
                            ctx.max_seqlen_q // 2,
3003
                            ctx.max_seqlen_kv,
3004
3005
                            causal=False,
                            **fa_backward_kwargs,
3006
3007
3008
                        )
            else:
                if ctx.use_fused_attention:
3009
3010
3011
3012
                    if ctx.fp8:
                        aux_ctx_tensors = [softmax_lse, softmax_lse, rng_states[cp_size - i - 1]]
                    else:
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
3013
                    if attn_dbias is not None:
3014
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3015
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3016
                        ctx.max_seqlen_q,
3017
3018
3019
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3020
                        q,
3021
3022
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
3023
3024
                        out,
                        dout,
3025
3026
                        fused_attn_qkv_dtype,
                        fused_attn_dqkv_dtype,
3027
                        aux_ctx_tensors,
3028
                        fused_attn_backend,
3029
3030
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
3031
3032
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
3033
                        qkv_layout=qkv_layout,
3034
                        attn_mask_type=ctx.attn_mask_type,
3035
                        attn_bias_type=ctx.attn_bias_type,
3036
3037
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
3038
3039
3040
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3041
                    q_ = q.view(-1, *q.shape[-2:])
3042
                    dq_ = torch.zeros_like(q_)
3043
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
3044
3045
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
3046
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3047
3048
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
3049
3050
3051
3052
3053
                    if _use_flash_attn_3 or _flash_attn_2_3_plus:
                        fa_backward_kwargs["window_size"] = (-1, -1)
                    if not _use_flash_attn_3:
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
3054
3055
3056
3057
3058
3059
3060
3061
3062
                        dout_,
                        q_,
                        kv_[0],
                        kv_[1],
                        out_,
                        softmax_lse,
                        dq_,
                        dkv_[0],
                        dkv_[1],
3063
3064
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3065
                        ctx.max_seqlen_q,
3066
                        ctx.max_seqlen_kv,
3067
3068
                        causal=False,
                        **fa_backward_kwargs,
3069
3070
                    )

3071
3072
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
3073
            if i >= (cp_size - rank - 1) or not causal:
3074
3075
3076
3077
                # [b*sq, np, hn] -> [b, 2, sq//2, np, hn] if causal
                # [b*sq, np, hn] -> [b, sq, np, hn] if not causal
                dq_ = dq_.view(*dq.shape)
            else:
3078
3079
3080
3081
3082
3083
                if ctx.qkv_format == "bshd":
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])
                elif ctx.qkv_format == "sbhd":
                    # [b*sq//2, np, hn] -> [sq//2, b, np, hn]
                    dq_ = dq_.view(-1, *dq.shape[-3:])
3084

3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
            if ctx.fp8:
                if i >= (cp_size - rank - 1) or not causal:
                    dq.copy_(dq_)
                else:
                    if ctx.qkv_format == "bshd":
                        dq[:, 0, ...].fill_(0)
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[0].fill_(0)
                        dq[1].copy_(dq_)
            elif causal:
3096
                if i > (cp_size - rank - 1):
3097
                    dq.add_(dq_)
3098
3099
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
3100
3101
                        dq.copy_(dq_)
                    else:
3102
3103
3104
3105
3106
3107
                        if ctx.qkv_format == "bshd":
                            dq[:, 0, ...].copy_(dq_[:, 0, ...])
                            dq[:, 1, ...].add_(dq_[:, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dq[0].copy_(dq_[0])
                            dq[1].add_(dq_[1])
3108
                        elif ctx.qkv_format == "thd":
3109
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
3110
                elif i > 0:
3111
3112
3113
3114
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
3115
                    elif ctx.qkv_format == "thd":
3116
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
3117
                else:
3118
3119
3120
3121
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
3122
                    elif ctx.qkv_format == "thd":
3123
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
3124
3125
3126
3127
3128
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
3129

3130
            if attn_dbias is not None:
3131
                idx = (rank + i + 1) % cp_size
3132
                if i == (cp_size - 1) or not causal:
3133
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
3134
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3135
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
3136
3137
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
3138
3139
3140
3141
                    # [b, np, sq, sk//(2*cp)]
                    attn_dbias[..., idx, :].copy_(dbias_)
                else:
                    # [b, np, sq//2, sk//cp] -> [b, np, sq//2, 2, sk//(2*cp)]
3142
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3143
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
3144
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
3145

3146
3147
3148
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
3149

3150
3151
3152
3153
3154
3155
3156
            if ctx.fp8:
                if i < cp_size - 1:
                    dkv = dkv_fp8_[(rank + i + 1) % cp_size]
                else:
                    dkv = dkv_fp8[(rank + i + 1) % cp_size]
            else:
                dkv = p2p_comm_buffers[(i + 1) % 2][1]
3157
            if ctx.use_fused_attention:
3158
3159
3160
                dkv_ = torch.cat(
                    (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                )  # pylint: disable=used-before-assignment
3161
3162
3163
3164
                if ctx.qkv_format in ["bshd", "sbhd"]:
                    # [b, 2, sk//2, 2, np, hn] -> [2, b, 2, sk//2, np, hn] or
                    # [2, sk//2, b, 2, np, hn] -> [2, 2, sk//2, b, np, hn]
                    dkv = dkv.view(2, *dkv.shape[0:-3], *dkv.shape[-2:])
3165
            if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
3166
3167
3168
3169
3170
3171
                if ctx.qkv_format == "bshd":
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                elif ctx.qkv_format == "sbhd":
                    # [2, b*sk//2, np, hn] -> [2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(dkv.shape[0], -1, *dkv.shape[-3:])
3172
3173
3174
3175
            else:
                # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn] if causal
                # [2, b*sk, np, hn] -> [2, b, sk, np, hn] if not causal
                dkv_ = dkv_.view(*dkv.shape)
3176

3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
            if ctx.fp8:
                if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
                    if ctx.qkv_format == "bshd":
                        dkv[:, :, 0, ...].copy_(dkv_)
                        dkv[:, :, 1, ...].fill_(0)
                    elif ctx.qkv_format == "sbhd":
                        dkv[:, 0, ...].copy_(dkv_)
                        dkv[:, 1, ...].fill_(0)
                else:
                    dkv.copy_(dkv_)
            elif causal:
3188
                if i == (cp_size - 1):
3189
                    if rank == 0:
3190
3191
3192
3193
3194
3195
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_[:, :, 0, ...])
                            dkv[:, :, 1, ...].copy_(dkv_[:, :, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_[:, 0, ...])
                            dkv[:, 1, ...].copy_(dkv_[:, 1, ...])
3196
                        elif ctx.qkv_format == "thd":
3197
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
3198
3199
                    else:
                        dkv.add_(dkv_)
3200
3201
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
3202
3203
3204
3205
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
3206
                        elif ctx.qkv_format == "thd":
3207
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
3208
                    else:
3209
3210
3211
3212
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
3213
                        elif ctx.qkv_format == "thd":
3214
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
3215
3216
3217
3218
3219
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
3220
3221
3222
3223
3224
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
        if ctx.fp8 and ctx.use_fused_attention:
            amax_cp_bwd = amax_per_step.amax(dim=1)
            ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP] = amax_cp_bwd[0]
            ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV_CP] = amax_cp_bwd[1]
            if ctx.qkv_format in ["bshd", "sbhd"]:
                # [cp, b, 2, sk//2, 2, np, hn] -> [cp, 2, b, 2, sk//2, np, hn] or
                # [cp, 2, sk//2, b, 2, np, hn] -> [cp, 2, 2, sk//2, b, np, hn]
                dkv_fp8 = dkv_fp8.view(cp_size, 2, *dkv_fp8.shape[1:-3], *dkv_fp8.shape[-2:])
            dq, dkv = [
                cast_from_fp8(
                    x,
                    ctx.fp8_meta["scaling_bwd"],
                    META_DQKV_CP,
                    fp8_dtype_backward,
                    TE_DType[torch.float32],
                )
                for x in [dq_fp8, dkv_fp8]
            ]
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

3245
        if causal:
3246
3247
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
3248
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
3249
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
3250
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
3251
3252
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
3253
                dq = dq.view(-1, *dq.shape[-3:])
3254
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
3255
3256
3257
3258
3259
3260
3261
3262
3263
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

        if ctx.qkv_format == "thd":
            dkv_ = torch.empty(
                2, ctx.total_tokens_kv, *dkv.shape[-2:], dtype=dkv.dtype, device=dkv.device
            )
            dkv_[:, : cu_seqlens_kv_padded[-1]].copy_(dkv)
            dkv_[:, cu_seqlens_kv_padded[-1] :].fill_(0)
            dkv = dkv_
3264

3265
        if ctx.fp8 and ctx.is_input_fp8:
3266
3267
3268
3269
            dq, dkv = [
                cast_to_fp8(x, ctx.fp8_meta["scaling_bwd"], META_DQKV, fp8_dtype_backward)
                for x in [dq, dkv]
            ]
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, q.device, False)
            dq, dk, dv = flash_attn_a2a_communicate(
                [dq, dk, dv],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                False,
            )
            if ctx.qkv_format == "bshd":
                dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
            elif ctx.qkv_format == "sbhd":
                dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

3288
        if ctx.fp8 and ctx.is_input_fp8:
3289
3290
3291
3292
3293
3294
3295
3296
3297
            dq, dk, dv = [
                Float8Tensor(
                    data=x,
                    fp8_meta=ctx.fp8_meta,
                    fp8_meta_forward=False,
                    fp8_meta_index=META_DQKV,
                    fp8_dtype=fp8_dtype_backward,
                    dtype=dout_dtype,
                )
3298
                for x in [dq, dk, dv]
3299
3300
            ]

3301
3302
3303
3304
        if attn_dbias is not None:
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, sq, sk]
            attn_dbias = attn_dbias.view(*attn_dbias.shape[:-2], -1)

3305
3306
3307
        return (
            None,
            dq,
3308
3309
            dk,
            dv,
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3321
            attn_dbias,
3322
3323
3324
3325
3326
            None,
            None,
            None,
            None,
            None,
3327
3328
            None,
            None,
3329
        )
3330
3331


3332
3333
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
3334
):
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
    """Compute KV sequence index range and update window size after all-gather."""
    local_chunk_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    full_seq_end_idx = max_seqlen_kv * cp_size * 2

    if window_size is None:
        window_size = (-1, 0) if causal else (-1, -1)

    if window_size[1] == -1:
        seq_end_idx = full_seq_end_idx
        window_size_right = -1
    else:
        seq_end_idx = min(full_seq_end_idx, local_chunk_end_idx + window_size[1])
        window_size_right = local_chunk_end_idx + window_size[1] - seq_end_idx

    if window_size[0] == -1:
        seq_start_idx = 0
        window_size_left = -1
    else:
        seq_start_idx = max(0, local_chunk_end_idx - max_seqlen_q - window_size[0])
        window_size_left = window_size[0] + seq_end_idx - local_chunk_end_idx

    return (seq_start_idx, seq_end_idx), (window_size_left, window_size_right)
3357
3358
3359
3360


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
3361
3362
    Attention implementation with context parallelism. KV all-gather between CP ranks is exposed.
    Refer section 3.3.2 of `The Llama 3 Herd of Models <https://arxiv.org/abs/2407.21783>`_.
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
3385
3386
        cp_group,
        cp_stream,
3387
    ):
3388
        # pylint: disable=missing-function-docstring
3389
3390
3391
3392
3393
3394
3395
3396
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
3397
        assert not padding, f"{attn_mask_type} mask type is not supported!"
3398
3399
3400
3401
3402
3403
3404
        if use_fused_attention and causal and "bottom_right" not in attn_mask_type:
            attn_mask_type = attn_mask_type + "_bottom_right"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            use_fused_attention or _flash_attn_2_3_plus
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3405

3406
        flash_attn_fwd = None
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432

        assert qkv_format != "thd", f"{qkv_format} format is not supported!"
        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        seq_dim = qkv_format.index("s")
        assert (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"

        max_seqlen_q = max_seqlen_q // (2 * cp_size)
        max_seqlen_kv = max_seqlen_kv // (2 * cp_size)
        cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
        cu_seqlens_q_padded = cu_seqlens_q_padded // (2 * cp_size)

3433
3434
3435
3436
        # [b, s, np, hn] -> [b, 2, s//2, np, hn] or [s, b, np, hn] -> [2, s//2, b, np, hn]
        q = q.view(*q.shape[:seq_dim], 2, q.shape[seq_dim] // 2, *q.shape[(seq_dim + 1) :])
        # [b, s, np, hn] or [s, b, np, hn] -> [s, b, np, hn]
        k, v = [x.movedim(seq_dim, 0).contiguous() for x in [k, v]]
3437

3438
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3439
3440
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
3441
3442

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3443
3444
        k_ag = k_ag.view(2 * cp_size, k.shape[0] // 2, *k.shape[1:])
        v_ag = v_ag.view(2 * cp_size, v.shape[0] // 2, *v.shape[1:])
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, k.device, True)
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        cp_stream.wait_stream(torch.cuda.current_stream())

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
3455
3456

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
3457
3458
3459
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
3460
3461
3462
3463
3464
3465
3466
3467
        out_per_step = [None, None]
        softmax_lse_per_step = [None, None]
        rng_states = [None, None]
        out = torch.empty_like(q)

        for i in range(len(local_seq_chunk_ids) + 1):
            if i < len(local_seq_chunk_ids):
                with torch.cuda.stream(flash_attn_streams[i]):
3468
3469
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3470
3471
3472
3473
3474
3475
3476
3477
3478
                    q_ = q.select(seq_dim, i).contiguous()
                    kv_seq_range_per_step[i], window_size_per_step[i] = (
                        get_kv_seq_info_after_all_gather(
                            local_seq_chunk_ids[i],
                            cp_size,
                            max_seqlen_q,
                            max_seqlen_kv,
                            window_size,
                            causal,
3479
                        )
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
                    )
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv_ = seq_end_idx - seq_start_idx
                    cu_seqlens_kv_per_step[i] = _get_full_cu_seqlens(
                        k.shape[1], max_seqlen_kv_, k.device
                    )
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [s_range, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
3492
3493
3494
3495
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
3496
                            max_seqlen_kv_,
3497
                            cu_seqlens_q,
3498
                            cu_seqlens_kv_per_step[i],
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
                            q_,
                            k_,
                            v_,
                            TE_DType[q.dtype],
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=softmax_scale,
                            dropout=dropout_p,
                            qkv_layout=qkv_layout,
                            attn_mask_type=attn_mask_type,
                            attn_bias_type=attn_bias_type,
                            attn_bias=attn_bias,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
3511
3512
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
3513
3514
3515
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
                            cu_seqlens_q,
                            cu_seqlens_kv_per_step[i],
                            max_seqlen_q,
                            max_seqlen_kv_,
                            causal=causal,
                            window_size=window_size_per_step[i],
                            **fa_forward_kwargs,
3527
                        )
3528
3529
3530
3531
                        out_per_step[i] = fa_outputs[4]
                        softmax_lse_per_step[i] = fa_outputs[5]
                        if not _use_flash_attn_3:
                            rng_states[i] = fa_outputs[7]
3532
3533
3534
3535

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
3536
                        out[:, i - 1].copy_(out_per_step[i - 1].view(out[:, i - 1].shape))
3537
                    elif qkv_format == "sbhd":
3538
                        out[i - 1].copy_(out_per_step[i - 1].view(out[i - 1].shape))
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555

        torch.cuda.current_stream().wait_stream(cp_stream)

        if use_fused_attention:
            if qkv_format == "bshd":
                out = out.view(out.shape[0], -1, *out.shape[-2:])
            elif qkv_format == "sbhd":
                out = out.view(-1, *out.shape[-3:])
        else:
            out = out.view(-1, *out.shape[-2:])

        ctx.save_for_backward(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_q_padded,
3556
            *cu_seqlens_kv_per_step,
3557
3558
3559
3560
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
3561
3562
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
3563
3564
3565
3566
3567
3568
3569
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_bias_type = attn_bias_type
3570
        ctx.attn_mask_type = attn_mask_type
3571
3572
3573
3574
3575
3576
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
        return out

    @staticmethod
    def backward(ctx, dout):
3577
        # pylint: disable=missing-function-docstring
3578
3579
3580
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

3581
3582
3583
3584
3585
3586
        (*saved_tensors,) = ctx.saved_tensors
        (q, k, v, cu_seqlens_q, cu_seqlens_q_padded) = saved_tensors[:5]
        cu_seqlens_kv_per_step = saved_tensors[5:7]
        out_per_step = saved_tensors[7:9]
        softmax_lse_per_step = saved_tensors[9:11]
        rng_states = saved_tensors[11:13]
3587
3588
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
3589

3590
        seq_dim = ctx.qkv_format.index("s")
3591
3592
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

3593
        dout = dout.view(q.shape)
3594
        dq = torch.empty_like(q)
3595
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
        dv = torch.zeros_like(dk)
        dq_per_step = [None, None]
        dk_per_step = [None, None]
        dv_per_step = [None, None]

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), ctx.cp_stream]
        # synchronize dkv update across steps
        dkv_update_done = torch.cuda.Event()

3606
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3607
3608
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
3609
3610

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3611
3612
        k_ag = k_ag.view(2 * cp_size, k.shape[0] // 2, *k.shape[1:])
        v_ag = v_ag.view(2 * cp_size, v.shape[0] // 2, *v.shape[1:])
3613
3614
3615
3616
3617
3618
3619
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, k.device, True)
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
3620
3621
3622

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]

3623
        flash_attn_bwd = None
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3636
3637
3638
3639

        for i in range(len(local_seq_chunk_ids) + 1):
            if i < len(local_seq_chunk_ids):
                with torch.cuda.stream(flash_attn_streams[i]):
3640
3641
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3642
3643
3644
3645
3646
3647
3648
3649
3650
                    q_ = q.select(seq_dim, i).contiguous()
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv = seq_end_idx - seq_start_idx
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [cp*s, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
3651
                    out_ = out_per_step[i]
3652
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
3653
3654
3655
3656
                    if ctx.use_fused_attention:
                        aux_ctx_tensors = [softmax_lse_per_step[i], rng_states[i]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i], _ = fused_attn_bwd(
                            ctx.max_seqlen_q,
3657
                            max_seqlen_kv,
3658
                            cu_seqlens_q,
3659
                            cu_seqlens_kv_per_step[i],
3660
3661
3662
3663
3664
3665
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
                            TE_DType[q.dtype],
3666
                            TE_DType[dout.dtype],
3667
3668
3669
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
3670
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
3671
3672
3673
3674
3675
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
                            qkv_layout=qkv_layout,
                            attn_mask_type=ctx.attn_mask_type,
                            attn_bias_type=ctx.attn_bias_type,
3676
3677
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
3678
3679
                        )
                    else:
3680
                        batch_size = k_.shape[0]
3681
3682
3683
3684
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
3685
3686
3687
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[i]
                        flash_attn_bwd(
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
                            dq_per_step[i],
                            dk_per_step[i],
                            dv_per_step[i],
                            cu_seqlens_q,
3698
                            cu_seqlens_kv_per_step[i],
3699
                            ctx.max_seqlen_q,
3700
                            max_seqlen_kv,
3701
                            causal="causal" in ctx.attn_mask_type,
3702
                            window_size=window_size_per_step[i],
3703
                            **fa_backward_kwargs,
3704
                        )
3705
3706
3707
3708
3709
3710
3711
                        # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                        dq_per_step[i] = dq_per_step[i].view(dq[:, i].shape)
                        # [b*s_range, np, hn] -> [b, s_range, np, hn]
                        dk_per_step[i], dv_per_step[i] = [
                            x.view(batch_size, -1, *x.shape[-2:])
                            for x in [dk_per_step[i], dv_per_step[i]]
                        ]
3712
3713
3714
3715

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
3716
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
3717
                    elif ctx.qkv_format == "sbhd":
3718
3719
3720
3721
3722
3723
                        dq[i - 1].copy_(dq_per_step[i - 1])
                    # [b, s_range, np, hn] or [s_range, b, np, hn] -> [s_range, b, np, hn]
                    dk_per_step[i - 1], dv_per_step[i - 1] = [
                        x.movedim(seq_dim, 0).contiguous()
                        for x in [dk_per_step[i - 1], dv_per_step[i - 1]]
                    ]
3724
3725
3726
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
3727
3728
3729
3730
3731
3732
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i - 1][0],
                        kv_seq_range_per_step[i - 1][1],
                    )
                    dk[seq_start_idx:seq_end_idx].add_(dk_per_step[i - 1])
                    dv[seq_start_idx:seq_end_idx].add_(dv_per_step[i - 1])
3733
3734
3735
3736
3737
                    if i < len(local_seq_chunk_ids):
                        flash_attn_streams[i - 1].record_event(dkv_update_done)

        torch.cuda.current_stream().wait_stream(ctx.cp_stream)

3738
3739
3740
3741
3742
3743
3744
        # [cp*s, b, np, hn] -> [cp*2, s//2, b, np, hn]
        dk = dk.view(2 * cp_size, -1, *dk.shape[-3:])
        dv = dv.view(2 * cp_size, -1, *dv.shape[-3:])
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, dk.device, False)
        dk = torch.index_select(dk, dim=0, index=chunk_ids_for_kv_ag)
        dv = torch.index_select(dv, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
3745
3746
3747
3748
3749
        dk = dk.view(-1, *dk.shape[-3:])
        dv = dv.view(-1, *dv.shape[-3:])
        dk, _ = reduce_scatter_along_first_dim(dk, ctx.cp_group)
        dv, _ = reduce_scatter_along_first_dim(dv, ctx.cp_group)

3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
        dq = dq.view(*dq.shape[:seq_dim], -1, *dq.shape[(seq_dim + 2) :])
        dk = dk.movedim(0, seq_dim).contiguous()
        dv = dv.movedim(0, seq_dim).contiguous()

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


class AttnFuncWithCPAndQKVOA2A(torch.autograd.Function):
    """
    Attention implementation with context parallelism. Like Ulysses, applying A2A to QKVO.
    Refer the paper `DeepSpeed Ulysses <https://arxiv.org/abs/2309.14509>`_.
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
        fp8,
        fp8_meta,
        cp_group,
        cp_stream,
    ):
3810
        # pylint: disable=missing-function-docstring
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
        assert not padding, f"{attn_mask_type} mask type is not supported!"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            window_size == (-1, 0)
            or window_size == (-1, -1)
            or use_fused_attention
            or _flash_attn_2_3_plus
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3827

3828
        flash_attn_fwd = None
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
                fa_forward_kwargs["window_size"] = window_size
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_3_plus:
                    fa_forward_kwargs["window_size"] = window_size
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857

        assert (
            q.shape[-2] % cp_size == 0 and k.shape[-2] % cp_size == 0
        ), "The number of attention heads needs to be divisible by CP size!"

        assert qkv_format != "thd", f"{qkv_format} format is not supported!"
        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        batch_dim = qkv_format.index("b")
        seq_dim = qkv_format.index("s")
        assert (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"

3858
        qkv_dtype = q.dtype
3859
3860
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
3861
3862
3863
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
        is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
3864
3865
3866
3867
3868
        if fp8:
            if use_fused_attention:
                fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_backend = FusedAttnBackend["FP8"]
3869
3870
3871
3872
3873
                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
                if is_input_fp8:
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
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3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
                    fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                elif int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                    q_f16, k_f16, v_f16 = q, k, v
                    q, k, v = [
                        cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                        for x in [q_f16, k_f16, v_f16]
                    ]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_qkv_offset"] = META_QKV
                fp8_meta_kwargs["d_scale_s"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_s"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_o"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_o_offset"] = META_O
                fp8_meta_kwargs["amax_s"] = fp8_meta["scaling_fwd"].amax_history
                fp8_meta_kwargs["amax_s_offset"] = META_S
                fp8_meta_kwargs["amax_o"] = fp8_meta["scaling_fwd"].amax_history
                fp8_meta_kwargs["amax_o_offset"] = META_O
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, q.device, True)
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

3909
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
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3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
            q_f16, k_f16, v_f16 = q, k, v
            q, k, v = [
                cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                for x in [q_f16, k_f16, v_f16]
            ]

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                fused_attn_qkv_dtype,
                fused_attn_backend,
                attn_scale=softmax_scale,
                dropout=dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=attn_mask_type,
                attn_bias_type=attn_bias_type,
                attn_bias=attn_bias,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                window_size=window_size,
                **fp8_meta_kwargs,
            )
        else:
            # [b, cp*s, np//cp, hn] -> [b*cp*s, np//cp, hn]
            q, k, v = [x.view(-1, *x.shape[-2:]) for x in [q, k, v]]
3943
            fa_outputs = flash_attn_fwd(
3944
3945
3946
3947
3948
3949
3950
3951
                q,
                k,
                v,
                cu_seqlens_q,
                cu_seqlens_kv,
                max_seqlen_q,
                max_seqlen_kv,
                causal=causal,
3952
                **fa_forward_kwargs,
3953
            )
3954
3955
            out, softmax_lse = fa_outputs[4], fa_outputs[5]
            rng_state = fa_outputs[7] if not _use_flash_attn_3 else None
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
            aux_ctx_tensors = [softmax_lse, rng_state]
            # [b*cp*s, np//cp, hn] -> [b, cp*s, np//cp, hn]
            out = out.view(batch_size, -1, *out.shape[-2:])

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, out.device, False)
        out = flash_attn_a2a_communicate(
            out, chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, False
        )

        if use_fused_attention:
            if qkv_format == "bshd":
                # [b*s, np, hn] -> [b, s, np, hn]
                out = out.view(batch_size, -1, *out.shape[-2:])
            elif qkv_format == "sbhd":
                # [s*b, np, hn] -> [s, b, np, hn]
                out = out.view(-1, batch_size, *out.shape[-2:])

        if fp8:
3974
            if is_output_fp8:
3975
3976
3977
3978
3979
3980
                out_fp8 = Float8Tensor(
                    data=out,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
3981
                    dtype=qkv_dtype,
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
                )
                out = out_fp8._data
                out_ret = out_fp8
            else:
                out_f16 = cast_from_fp8(
                    out,
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    TE_DType[q_f16.dtype],
                )
                out_ret = out_f16
        else:
            out_ret = out

        if fp8:
            if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                q_save, k_save, v_save, out_save = q, k, v, out
4000
            elif is_input_fp8:
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
                q_fp8, k_fp8, v_fp8 = [
                    Float8Tensor(
                        data=x,
                        fp8_meta=fp8_meta,
                        fp8_meta_forward=True,
                        fp8_meta_index=META_QKV,
                        fp8_dtype=fp8_dtype_forward,
                        dtype=out_fp8.dtype,
                    )
                    for x in [q, k, v]
                ]
                q_save, k_save, v_save, out_save = q_fp8, k_fp8, v_fp8, out_fp8
            else:
                q_save, k_save, v_save, out_save = q_f16, k_f16, v_f16, out_f16
        else:
            q_save, k_save, v_save, out_save = q, k, v, out

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
            fp8_fwd_scales = fp8_meta["scaling_fwd"].scale.clone()
            fp8_fwd_scale_invs = fp8_meta["scaling_fwd"].scale_inv.clone()
        else:
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None

        ctx.save_for_backward(
            q_save,
            k_save,
            v_save,
            out_save,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            fp8_fwd_scales,
            fp8_fwd_scale_invs,
            *aux_ctx_tensors,
        )
        ctx.batch_size = batch_size
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_mask_type = attn_mask_type
        ctx.attn_bias_type = attn_bias_type
        ctx.deterministic = deterministic
        ctx.window_size = window_size
        ctx.use_fused_attention = use_fused_attention
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
4052
4053
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4054
4055
4056
4057
        return out_ret

    @staticmethod
    def backward(ctx, dout):
4058
        # pylint: disable=missing-function-docstring
4059
4060
        cp_size = get_distributed_world_size(ctx.cp_group)

4061
4062
4063
4064
4065
        (*saved_tensors,) = ctx.saved_tensors
        q, k, v, out = saved_tensors[:4]
        cu_seqlens_q, cu_seqlens_kv, cu_seqlens_q_padded, cu_seqlens_kv_padded = saved_tensors[4:8]
        fp8_fwd_scales, fp8_fwd_scale_invs = saved_tensors[8:10]
        aux_ctx_tensors = saved_tensors[10:]
4066
4067
4068
4069
4070

        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format
        causal = "causal" in ctx.attn_mask_type
        seq_dim = ctx.qkv_format.index("s")

4071
4072
4073
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
        fused_attn_qkv_dtype = None
4074
        dout_dtype = dout.dtype
4075
4076
4077
4078
4079
4080
4081
        if ctx.fp8:
            if ctx.use_fused_attention:
                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)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_dqkv_dtype = fp8_dtype_backward
                fused_attn_backend = FusedAttnBackend["FP8"]
4082
                if ctx.is_output_fp8:
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = dout._scale_inv
                    dout_fp8 = dout
                    dout = dout_fp8._data
                else:
                    dout_f16 = dout
                    dout = cast_to_fp8(
                        dout_f16, ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                    )
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_fwd_scale_invs[META_QKV]
                fp8_meta_kwargs["d_scale_s"] = fp8_fwd_scale_invs[META_S]
                fp8_meta_kwargs["d_scale_o"] = fp8_fwd_scale_invs[META_O]
                fp8_meta_kwargs["d_scale_do"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO]
                fp8_meta_kwargs["d_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP]
                fp8_meta_kwargs["q_scale_s"] = fp8_fwd_scales[META_S]
                fp8_meta_kwargs["q_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale[META_DP]
                fp8_meta_kwargs["q_scale_dqkv"] = ctx.fp8_meta["scaling_bwd"].scale[META_DQKV]
                fp8_meta_kwargs["amax_dp"] = ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP]
                fp8_meta_kwargs["amax_dqkv"] = ctx.fp8_meta["scaling_bwd"].amax_history[0][
                    META_DQKV
                ]
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
4108
            if ctx.fp8_meta is not None and ctx.is_output_fp8:
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
                assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                q, k, v, out, dout = [x.from_float8(x.dtype) for x in [q, k, v, out, dout]]
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_dqkv_dtype = TE_DType[dout.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if not ctx.use_fused_attention:
            out = out.view(ctx.batch_size, -1, *out.shape[-2:])
        dout = dout.view(*out.shape)

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, out.device, True)
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )

4126
        flash_attn_bwd = None
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_3_plus:
                    fa_backward_kwargs["window_size"] = ctx.window_size
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172

        if ctx.use_fused_attention:
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                out,
                dout,
                fused_attn_qkv_dtype,
                fused_attn_dqkv_dtype,
                aux_ctx_tensors,
                fused_attn_backend,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                attn_scale=ctx.softmax_scale,
                dropout=ctx.dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=ctx.attn_mask_type,
                attn_bias_type=ctx.attn_bias_type,
                window_size=ctx.window_size,
                deterministic=ctx.deterministic,
                **fp8_meta_kwargs,
            )
        else:
            softmax_lse, rng_state = aux_ctx_tensors
            out, dout = [x.view(-1, *x.shape[-2:]) for x in [out, dout]]
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
4173
4174
4175
            if not _use_flash_attn_3:
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
4189
4190
                causal=causal,
                **fa_backward_kwargs,
4191
4192
4193
4194
4195
4196
4197
4198
            )
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, q.device, False)
        dq, dk, dv = flash_attn_a2a_communicate(
            [dq, dk, dv], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, False
        )

4199
        if ctx.qkv_format == "bshd":
4200
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
4201
        elif ctx.qkv_format == "sbhd":
4202
4203
4204
            dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8:
4205
            if ctx.is_input_fp8:
4206
4207
4208
4209
4210
4211
4212
                dq, dk, dv = [
                    Float8Tensor(
                        data=x,
                        fp8_meta=ctx.fp8_meta,
                        fp8_meta_forward=False,
                        fp8_meta_index=META_DQKV,
                        fp8_dtype=fp8_dtype_backward,
4213
                        dtype=dout_dtype,
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
                    )
                    for x in [dq, dk, dv]
                ]
            else:
                dq, dk, dv = [
                    cast_from_fp8(
                        x,
                        ctx.fp8_meta["scaling_bwd"],
                        META_DQKV,
                        fp8_dtype_backward,
4224
                        TE_DType[dout_dtype],
4225
4226
4227
                    )
                    for x in [dq, dk, dv]
                ]
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4251
4252
4253
            None,
            None,
            None,
4254
4255
4256
        )


4257
def attn_forward_func_with_cp(
4258
4259
4260
4261
4262
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
4263
    cu_seqlens_kv,
4264
    max_seqlen_q,
4265
    max_seqlen_kv,
4266
4267
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
4268
4269
4270
4271
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
4272
    cp_comm_type,
4273
4274
4275
4276
4277
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    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
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    window_size=None,
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    fp8=False,
    fp8_meta=None,
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) -> torch.Tensor:
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    """
    Attention implementation with context parallelism.
    """

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    if cp_comm_type == "a2a+p2p":
        assert isinstance(
            cp_group, list
        ), "Hierarchical CP implementation needs multi-level CP groups!"
        assert len(cp_group) == 2, "Current implementation only supports two-level CP groups!"
        if get_distributed_world_size(cp_group[0]) == 1:
            cp_group = cp_group[1]
            cp_comm_type = "p2p"
        elif get_distributed_world_size(cp_group[1]) == 1:
            cp_group = cp_group[0]
            cp_comm_type = "a2a"
    else:
        assert isinstance(
            cp_group, dist_group_type
        ), f"Unsupported process group for CP communication type {cp_comm_type}!"

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    assert qkv_format in [
        "bshd",
        "sbhd",
        "thd",
    ], f"QKV format of {qkv_format} is not supported with context parallelism!"
    assert (
        qkv_format != "sbhd" or use_fused_attention
    ), "FlashAttention does not support sbhd format!"
    assert (
        qkv_format != "thd"
        or not use_fused_attention
        or attn_mask_type in ["padding", "padding_causal"]
    ), (
        f"Context parallelism is not supported for {attn_mask_type} mask type and "
        f"{qkv_format} format with {'FusedAttention' if use_fused_attention else 'FlashAttention'}!"
    )
    assert attn_bias is None or (use_fused_attention and "padding" not in attn_mask_type), (
        """Attention bias is only supported with FusedAttention and "causal" """
        """or "no_mask" mask types!"""
    )
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    assert (
        cu_seqlens_q_padded is not None and cu_seqlens_kv_padded is not None
    ), "cu_seqlens_q_padded and cu_seqlens_kv_padded cannot be None with context parallelism!"
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    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
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    )
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    assert (
        not sliding_window_attn
        or cp_comm_type == "a2a"
        or (cp_comm_type == "all_gather" and not use_fused_attention)
    ), "The context parallel running configs cannot support sliding window attetnion!"
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    args = [
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
    ]

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    if cp_comm_type in ["p2p", "a2a+p2p"]:
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        args += [fp8, fp8_meta, cp_group, cp_global_ranks, cp_stream]
        out = AttnFuncWithCPAndKVP2P.apply(*args)
    elif cp_comm_type == "all_gather":
        args.pop(5)
        args.pop(8)
        args += [window_size, cp_group, cp_stream]
        out = AttnFuncWithCPAndKVAllGather.apply(*args)
    elif cp_comm_type == "a2a":
        args += [window_size, fp8, fp8_meta, cp_group, cp_stream]
        out = AttnFuncWithCPAndQKVOA2A.apply(*args)
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    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

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    return out


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class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
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    def __init__(
        self,
        dim: int,
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        rotary_percent: float = 1.0,
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        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
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        rotary_base: float = 10000.0,
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    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
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        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
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        seq_len_interpolation_factor: int
            if not None, discrete positions will be interpolated by this factor via the trick in
            https://arxiv.org/abs/2306.15595
        pretrained_max_position_embeddings: int
            pre-trained max_position_embeddings before position interpolation
        """
        super().__init__()
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        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
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        self.seq_len_interpolation_factor = seq_len_interpolation_factor
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        self.rotary_base = rotary_base
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        inv_freq = 1.0 / (
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            self.rotary_base
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            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
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        self.register_buffer("inv_freq", inv_freq)
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        self.pretrained_max_position_embeddings = pretrained_max_position_embeddings

    def forward(self, max_seq_len: int, offset: int = 0):
        """
        Create rotary position embedding frequencies

        Parameters
        ----------
        max_seq_len: int
            sequence length of a sample
        offset: int, default = 0
            fixed offset for freqencies
        """
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        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
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        if (
            self.pretrained_max_position_embeddings is not None
            and self.seq_len_interpolation_factor is not None
        ):
            if (
                max_seq_len
                > self.pretrained_max_position_embeddings * self.seq_len_interpolation_factor
            ):
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                # dynamic linear scaling (length > position we have learned)
                seq *= 1 / (max_seq_len / self.pretrained_max_position_embeddings)
            else:
                # fixed linear scaling
                seq *= 1 / self.seq_len_interpolation_factor

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        freqs = torch.einsum("i , j -> i j", seq, self.inv_freq)
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        # first part even vector components, second part odd vector components,
        #  2 * dim in dimension size
        emb = torch.cat((freqs, freqs), dim=-1)
        # emb [seq_length, .., dim]
        return emb.reshape(emb.size(0), 1, 1, emb.size(1))

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class FusedRoPEFunc(torch.autograd.Function):
    """
    Function for FusedRoPE

    This implementation assumes the input tensor to be in `sbhd`, `bshd` or `thd` format and
    the RoPE tensor to be of shape (s, 1, 1, d). It accepts arbitrary memory layouts to avoid
    the expensive `.contiguous()` calls, thus it may not achieve the best memory access pattern.
    """

    @staticmethod
    def forward(
        ctx,
        t: torch.Tensor,
        freqs: torch.Tensor,
        tensor_format: str = "sbhd",
        cu_seqlens: Union[torch.Tensor, None] = None,
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        cp_size: int = 1,
        cp_rank: int = 0,
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    ) -> torch.Tensor:
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        # pylint: disable=missing-function-docstring
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        if freqs.dtype != torch.float32:
            freqs = freqs.float()
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        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
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            output = tex.fused_rope_forward(t.transpose(0, 1), freqs, True).transpose(0, 1)
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        elif tensor_format == "thd":
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            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs, cp_size, cp_rank)
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        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format
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        ctx.cp_size = cp_size
        ctx.cp_rank = cp_rank
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        return output

    @staticmethod
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    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
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        # pylint: disable=missing-function-docstring
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        freqs, cu_seqlens = ctx.saved_tensors
        if ctx.tensor_format == "sbhd":
            grad_input = tex.fused_rope_backward(grad_output, freqs, False)
        elif ctx.tensor_format == "bshd":
            grad_input = tex.fused_rope_backward(
                grad_output.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif ctx.tensor_format == "thd":
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            grad_input = tex.fused_rope_thd_backward(
                grad_output, cu_seqlens, freqs, ctx.cp_size, ctx.cp_rank
            )
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        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

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        return grad_input, None, None, None, None, None
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def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    """
    change sign so the last dimension becomes [-odd, +even]
    """
    x = x.view(x.shape[:-1] + torch.Size((2, x.shape[-1] // 2)))
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


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def apply_rotary_pos_emb(
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    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
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    cp_size: int = 1,
    cp_rank: int = 0,
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) -> torch.Tensor:
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    """
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    Apply rotary positional embedding tensor to the input tensor.
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    Parameters
    ----------
    t: torch.Tensor
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        Input tensor of shape `[s, b, h, d]`, `[b, s, h, d]` or `[t, h, d]`, on which
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        rotary positional embedding will be applied.
    freqs: torch.Tensor
        Rotary positional embedding tensor of shape `[s2, 1, 1, d2]` and dtype 'float',
        with `s2 >= s` and `d2 <= d`.
    fused: bool, default = False
        Whether to use a fused applying RoPE implementation.
    tensor_format: {'sbhd', 'bshd', 'thd'}, default = 'sbhd'
        is `bshd` if `t` is of shape `[bs, seq, ...]`, or `sbhd` if `t` is
        of shape `[seq, bs, ...]`. 'thd' is only supported when `fused` is True.
    cu_seqlens: torch.Tensor, default = None.
        Cumulative sum of sequence lengths in a batch for `t`, with shape [b + 1] and
        dtype torch.int32. Only valid when `tensor_format` is 'thd'.
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        Should be `cu_seqlens_padded` when cp_size > 1.
    cp_size: int, default = 1.
        Context parallel world size. Only valid when `tensor_format` is 'thd' and `fused` is True.
    cp_rank: int, default = 0.
        Context parallel rank. Only valid when `tensor_format` is 'thd' and `fused` is True.
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    """
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    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
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        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens, cp_size, cp_rank)
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    assert tensor_format in ("sbhd", "bshd"), (
        "Only formats `sbhd` or `bshd` are supported for input tensor `t` "
        f"when fused is False, got {tensor_format}."
    )

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    max_seq_len = freqs.shape[0]
    cur_seq_len = t.shape[1] if tensor_format == "bshd" else t.shape[0]

    # Only apply the rotary embeddings up to the sequence length of the running
    # input.
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    assert (
        cur_seq_len <= max_seq_len
    ), f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
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    freqs = freqs[:cur_seq_len]
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    if tensor_format == "bshd":
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        freqs = freqs.transpose(0, 1)  # [seq, 1, 1, dim] -> [1, seq, 1, dim]
    # cos/sin first then dtype conversion for better precision
    cos_ = torch.cos(freqs).to(t.dtype)
    sin_ = torch.sin(freqs).to(t.dtype)
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    rot_dim = freqs.shape[-1]
    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]

    # first part is cosine component
    # second part is sine component, need to change signs with _rotate_half method
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    t = (t * cos_) + (_rotate_half(t) * sin_)
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    return torch.cat((t, t_pass), dim=-1)


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class _SplitAlongDim(torch.autograd.Function):
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    """"""

    @staticmethod
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    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
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    ) -> Tuple[torch.Tensor, ...]:
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        # pylint: disable=missing-function-docstring
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        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
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        if isinstance(mixed_x_layer, Float8Tensor):
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            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
                    data=x,
                )
                for x in torch.split(
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                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
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                    dim=split_dim,
                )
            )
        return torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
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    @staticmethod
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    def backward(ctx, *grad_outputs):
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        # pylint: disable=missing-function-docstring
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        assert len(grad_outputs) > 0, "No gradients received for backprop!"

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        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
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            assert len(grad_outputs) == len(
                split_sizes
            ), "Unequal number of gradients vs split sections for backprop!"
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        if isinstance(ctx.split_size_or_sections, int):
            split_sizes = [ctx.split_size_or_sections] * len(grad_outputs)
        dims = len(grad_outputs[0].shape)
        split_dim = (ctx.split_dim + dims) % dims

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        if isinstance(grad_outputs[0], Float8Tensor):
            noop_ok = True
            strides = grad_outputs[0].stride()
            data_ptr = grad_outputs[0]._data.untyped_storage().data_ptr()
            shape = list(grad_outputs[0].shape)
            for i, tensor in enumerate(grad_outputs):
                shape_i = shape
                shape_i[split_dim] = split_sizes[i]
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                offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim + 1 :])
                if (
                    tensor.stride() != strides
                    or list(tensor.shape) != shape_i
                    or tensor._data.untyped_storage().data_ptr() != data_ptr
                    or tensor.storage_offset() != offset_size
                ):
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                    noop_ok = False
                    break
            if noop_ok:
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                ret = torch.Tensor().to(
                    device=grad_outputs[0].device, dtype=grad_outputs[0]._data.dtype
                )
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                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
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                ret.set_(
                    grad_outputs[0]._data.untyped_storage(),
                    grad_outputs[0]._data.storage_offset(),
                    new_shape,
                    strides,
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                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
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            return (
                Float8Tensor.make_like(
                    grad_outputs[0], data=torch.cat(grad_outputs_data, dim=split_dim)
                ),
                None,
                None,
            )
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        noop_ok = True
        strides = grad_outputs[0].stride()
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        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
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        shape = list(grad_outputs[0].shape)
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        for i, tensor in enumerate(grad_outputs):
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            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
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            offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim + 1 :])
            if (
                tensor.stride() != strides
                or list(tensor.shape) != shape_i
                or tensor.untyped_storage().data_ptr() != data_ptr
                or tensor.storage_offset() != offset_size
            ):
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                noop_ok = False
                break
        if noop_ok:
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            ret = torch.Tensor().to(device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
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            new_shape = list(shape)
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            new_shape[split_dim] = sum(split_sizes)
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            ret.set_(
                grad_outputs[0].untyped_storage(),
                grad_outputs[0].storage_offset(),
                new_shape,
                strides,
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            )
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            return ret, None, None
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        return torch.cat(grad_outputs, dim=split_dim), None, None
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class UnfusedDotProductAttention(torch.nn.Module):
    """Parallel attention w/o QKV and Proj Gemms
    BMM1 -> softmax + dropout -> BMM2
    """

    def __init__(
        self,
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        softmax_scale: float,
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        attention_type: str = "self",
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        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

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        self.softmax_scale = softmax_scale
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        self.attention_type = attention_type
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        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

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        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
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        # Dropout. Note that for a single iteration, this layer will generate
        # different outputs on different number of parallel partitions but
        # on average it should not be partition dependent.
        self.attention_dropout = torch.nn.Dropout(attention_dropout)

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        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
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            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
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        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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    ) -> torch.Tensor:
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        """Unfused attention fprop"""
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        assert (
            qkv_layout in QKVLayouts
        ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
        if qkv_format == "bshd":
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            # convert to sbhd and use sbhd implementation for now
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            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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4764
4765
4766
4767
4768
4769
4770
4771
4772
4773
4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
4799
4800
4801
4802
4803
4804
4805
4806
4807
4808
4809
        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
        if "padding" in attn_mask_type:
            if self.attention_type == "self":
                assert attention_mask.shape == (
                    batch_size,
                    1,
                    1,
                    max_seqlen_q,
                ), "attention_mask should be a single tensor with [b, 1, 1, sq] shape!"
                attention_mask = torch.logical_or(
                    attention_mask.squeeze(1).unsqueeze(3), attention_mask
                )
            else:
                assert (
                    len(attention_mask) == 2
                    and attention_mask[0].shape == (batch_size, 1, 1, max_seqlen_q)
                    and attention_mask[1].shape == (batch_size, 1, 1, max_seqlen_kv)
                ), (
                    "attention_mask should be a tuple of two tensors with shapes "
                    "[b, 1, 1, sq] and [b, 1, 1, skv]!"
                )
                attention_mask = torch.logical_or(
                    attention_mask[0].squeeze(1).unsqueeze(3), attention_mask[1]
                )
            mask = attention_mask.squeeze(1).logical_not()
            actual_seqlens_q = mask[:, :, 0].sum(dim=1)
            actual_seqlens_kv = mask[:, 0, :].sum(dim=1)
            mask = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
                1, 1, max_seqlen_q, 1
            ) - torch.arange(max_seqlen_kv, dtype=torch.int32, device="cuda").view(
                1, 1, 1, max_seqlen_kv
            )
            if attn_mask_type == "padding_causal":
                attention_mask = torch.logical_or(
                    torch.where(mask.view(1, 1, max_seqlen_q, max_seqlen_kv) < 0, 1, 0),
                    attention_mask,
                )
            if attn_mask_type == "padding_causal_bottom_right":
                attention_mask = torch.logical_or(
                    torch.where(
                        mask.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv)
                        + (actual_seqlens_kv - actual_seqlens_q).view(batch_size, 1, 1, 1)
                        < 0,
                        1,
                        0,
                    ),
                    attention_mask,
                )
4810

4811
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
4812
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
4813
4814
4815
4816
4817
4818
4819
4820
4821

        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

4822
        if key_layer.shape[2] != query_layer.shape[2]:
4823
4824
4825
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
4826
            key_layer = key_layer.repeat_interleave(
4827
4828
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
4829
            value_layer = value_layer.repeat_interleave(
4830
4831
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
4832

4833
        # [sq, b, np, hn] -> [sq, b * np, hn]
4834
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
4835
4836
4837
4838
        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
4839
4840
        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
4841
4842
4843
4844
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
4845
            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
4846
4847
4848
            device=torch.cuda.current_device(),
        )

4849
4850
4851
        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

4852
        scale = self.softmax_scale
4853
        if apply_qk_layer_scaling:
4854
            scale /= self.layer_number
4855
4856

        # Raw attention scores. [b * np, sq, sk]
4857
4858
4859
4860
4861
4862
        if core_attention_bias_type == "no_bias":
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
4863
                alpha=scale,
4864
            ).view(*output_size)
4865
4866
4867
4868
4869
4870
4871

        elif core_attention_bias_type == "pre_scale_bias":
            assert core_attention_bias is not None, "core_attention_bias should not be None!"
            matmul_result = torch.bmm(
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            )
4872
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
4873
            matmul_result *= scale
4874

4875
4876
4877
4878
        elif core_attention_bias_type in ["post_scale_bias", "alibi"]:
            if core_attention_bias_type == "post_scale_bias":
                assert core_attention_bias is not None, "core_attention_bias should not be None!"
            if core_attention_bias_type == "alibi":
4879
                _, core_attention_bias = get_alibi(
4880
4881
4882
                    output_size[1],
                    output_size[2],
                    output_size[3],
4883
4884
                    actual_seqlens_q=actual_seqlens_q if "padding" in attn_mask_type else None,
                    actual_seqlens_kv=actual_seqlens_kv if "padding" in attn_mask_type else None,
4885
4886
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
4887
                )
4888
4889
4890
4891
4892
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
4893
                alpha=scale,
4894
            )
4895
4896
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
4897
            )
4898
4899
4900

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
4901
        attention_probs = self.scale_mask_softmax(
4902
            matmul_result, attention_mask, attn_mask_type, softmax_scale
4903
        )
4904

4905
4906
4907
4908
4909
        # mask out the pad positions in softmax results, mostly for the rows (pad tokens from q)
        # the columns (pad tokens from k) are already zeroed out during softmax
        if "padding" in attn_mask_type:
            attention_probs = attention_probs.masked_fill(attention_mask, 0)

4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
4920
4921
4922
4923
4924
        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        with self.attention_dropout_ctx():
            attention_probs = self.attention_dropout(attention_probs)

        # value_layer -> context layer.
        # [sk, b, np, hn] --> [b, np, sq, hn]
        output_size = (
            value_layer.size(1),
            value_layer.size(2),
            query_layer.size(0),
            value_layer.size(3),
        )

        # change view [sk, b * np, hn]
4925
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
4926
4927

        # change view [b * np, sq, sk]
4928
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
4929
4930
4931
4932
4933
4934
4935

        # matmul: [b * np, sq, hn]
        context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

        # change view [b, np, sq, hn]
        context_layer = context_layer.view(*output_size)

4936
        if qkv_format == "sbhd":
4937
4938
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
4939

4940
4941
4942
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

4943
        if qkv_format == "bshd":
4944
4945
4946
4947
4948
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

            # [b, sq, np, hn] --> [b, sq, hp]
            context_layer = context_layer.view(batch_size, seqlen, -1)
4949
4950
4951
4952
4953
4954

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4955
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4956
4957

    @staticmethod
4958
4959
4960
4961
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4962
        value_layer: torch.Tensor,
4963
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4964
        # pylint: disable=missing-function-docstring
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
4975
        # All inputs received are non-contiguous tensors.
        # The `query_layer` tensor is used to access the
        # full memory region of the QKV tensor.
        qkv = tex.fa_prepare_fwd(query_layer)
        q, k, v = split_tensor_along_dim(qkv, 0, 3)
        query_layer = torch.squeeze(q, 0)
        key_layer = torch.squeeze(k, 0)
        value_layer = torch.squeeze(v, 0)
        return query_layer, key_layer, value_layer

    @staticmethod
4976
4977
4978
4979
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4980
        dv: torch.Tensor,
4981
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4982
        # pylint: disable=missing-function-docstring
4983
4984
4985
4986
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4987

4988
def get_qkv_layout(
4989
4990
4991
4992
4993
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
4994
    """Get qkv layout.
4995

4996
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
    Parameters
    ----------
    q: torch.Tensor
        Query tensor.
    k: torch.Tensor
        Key tensor.
    v: torch.Tensor
        Value tensor.
    qkv_format: str, default = `sbhd`
        Dimension format for `q`, `k` and `v`, {`sbhd`, `bshd`, `thd`}. `s` stands for
        the sequence length dimension, `b` batch size, `h` the number of attention heads,
5007
        `d` head size, and `t` the total number of tokens in a batch, i.e.
5008
5009
5010
5011
5012
5013
5014
5015
5016
5017
5018
5019
5020
5021
5022
        `t = sum(s_i) for i = 0...b-1`.

    Returns
    ----------
    qkv_layout: str
       Memory layout of `q`, `k` and `v`. Each `qkv_format` can be mapped to one of five
       memory layouts. For example, `sb3hd` means `q`, `k`, `v` are created as one chunk
       of memory and that they are interleaved in the `2`nd dimension. `sbhd_sbh2d` means
       `q` and `kv` are created in two chunks and that `q` itself is contiguous and `k`, `v`
       are interleaved with each other in the `3`rd dimension, `k = kv[:,:,:,0,:]` and
       `v = kv[:,:,:,1,:]`.
       Mapping:
       `sbhd`: {`sb3hd`, `sbh3d`, `sbhd_sb2hd`, `sbhd_sbh2d`, `sbhd_sbhd_sbhd`}
       `bshd`: {`bs3hd`, `bsh3d`, `bshd_bs2hd`, `bshd_bsh2d`, `bshd_bshd_bshd`}
       `thd` : {`t3hd`, `th3d`, `thd_t2hd`, `thd_th2d`, `thd_thd_thd`}
5023
5024
5025
5026
5027
5028
5029
5030
5031
    q: torch.Tensor
        Query tensor. It may be different from input `q` as we try to fit tensors to
        a supported layout.
    k: torch.Tensor
        Key tensor. It may be different from input `k` as we try to fit tensors to
        a supported layout.
    v: torch.Tensor
        Value tensor. It may be different from input `v` as we try to fit tensors to
        a supported layout.
5032
    """
5033

5034
5035
    check_last_dim_contiguous = all(x.stride(-1) == 1 for x in [q, k, v])
    assert check_last_dim_contiguous, "q, k and v must have stride 1 in their last dimension!"
5036

5037
    def run_iteratively(q, k, v):
5038
        # check data pointers
5039
5040
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
5041
        check_ptrs_qk = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k])
5042
5043
5044
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

5045
5046
5047
5048
5049
5050
5051
        # check tensor shapes
        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
        check_shapes_kv = shape[:-1] == v.shape[:-1]

        # check tensor strides
5052
5053
        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
5054
5055
        check_strides_kv = tuple(sk / k.shape[-1] for sk in k.stride()[:-1]) == tuple(
            sv / v.shape[-1] for sv in v.stride()[:-1]
5056
        )
5057

5058
5059
5060
5061
5062
5063
        # check tensor offsets for h3d and 3hd layouts
        prod_h_d = q.shape[-1] * q.shape[-2]
        check_3hd_offsets = all(x.storage_offset() == i * prod_h_d for i, x in enumerate([q, k, v]))
        check_h3d_offsets = all(
            x.storage_offset() == i * q.shape[-1] for i, x in enumerate([q, k, v])
        )
5064

5065
5066
5067
5068
5069
5070
        # check tensor offsets for hd_h2d and hd_2hd layouts
        prod_all_dims = [np.prod(x.shape) for x in [q, k]]
        offset = prod_all_dims[0] if check_ptrs_qkv else 0
        prod_h_d = k.shape[-1] * k.shape[-2]
        check_2hd_offsets = all(
            x.storage_offset() == (offset + i * prod_h_d) for i, x in enumerate([k, v])
5071
        )
5072
5073
        check_h2d_offsets = all(
            x.storage_offset() == (offset + i * k.shape[-1]) for i, x in enumerate([k, v])
5074
        )
5075

5076
5077
5078
5079
5080
5081
5082
5083
5084
5085
        # check tensor offsets for hd_hd_hd layouts
        check_hd_offsets_qkv = (
            all(x.storage_offset() == sum(prod_all_dims[:i]) for i, x in enumerate([q, k, v]))
            if check_ptrs_qkv
            else all(x.storage_offset() == 0 for i, x in enumerate([q, k, v]))
        )
        check_hd_offsets_qk = (
            all(x.storage_offset() == sum(prod_all_dims[:i]) for i, x in enumerate([q, k]))
            if not check_ptrs_qkv and check_ptrs_qk
            else all(x.storage_offset() == 0 for i, x in enumerate([q, k]))
5086
        )
5087
5088
5089
5090
        check_hd_offsets_kv = (
            all(x.storage_offset() == sum(prod_all_dims[1 : i + 1]) for i, x in enumerate([k, v]))
            if not check_ptrs_qkv and check_ptrs_kv
            else all(x.storage_offset() == 0 for i, x in enumerate([k, v]))
5091
        )
5092

5093
        if check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_3hd_offsets:
5094
            # sb3hd, bs3hd, t3hd
5095
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-3 in qkv
5096
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
5097
        elif check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_h3d_offsets:
5098
            # sbh3d, bsh3d, th3d
5099
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-2 in qkv
5100
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
5101
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_2hd_offsets:
5102
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
5103
5104
5105
            # two chunks of memory, q and kv, with k, v interleaved at dim=-3 in kv
            # q and kv may be disjoint or consecutive in memory, and when consecutive, they may
            # have the same data pointer, i.e. check_ptrs_qkv=True
5106
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
5107
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_h2d_offsets:
5108
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
5109
5110
5111
            # two chunks of memory, q and kv, with k, v interleaved at dim=-2 in kv
            # q and kv may be disjoint or consecutive in memory, and when consecutive, they may
            # have the same data pointer, i.e. check_ptrs_qkv=True
5112
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
5113
5114
5115
5116
5117
        elif (
            check_strides_kv
            and check_shapes_kv
            and (check_hd_offsets_qkv or check_hd_offsets_kv or check_hd_offsets_qk)
        ):
5118
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
5119
5120
5121
            # three chunks of memory, q, k and v, which may be disjoint or consecutive, and
            # when consecutive, they may have the same data pointer, i.e. check_ptrs_qkv=True or
            # check_ptrs_qk=True or check_ptrs_kv=True
5122
            qkv_layout = "_".join(list([qkv_format]) * 3)
5123
        else:
5124
            qkv_layout = "not_supported"
5125
5126
5127
5128

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
5129
    if qkv_layout == "not_supported":
5130
5131
5132
        # force q,k,v to be contiguous and run get_layout again
        q, k, v = [x.contiguous() for x in [q, k, v]]
        qkv_layout = run_iteratively(q, k, v)
5133
    if qkv_layout == "not_supported":
5134
        raise RuntimeError("The provided qkv memory layout is not supported!")
5135

5136
    return qkv_layout, q, k, v
5137

5138

5139
def check_set_window_size(
5140
5141
5142
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
5143
5144
5145
5146
5147
5148
5149
5150
    """Check if sliding window size is compliant with attention mask type.
    If not, set it to the appropriate size.

         attn_mask_type                              |   window_size
    -------------------------------------------------------------------------
    no_mask, padding, arbitrary                      | (-1, -1) or (>=0, >=0)
    causal, padding_causal                           | (-1,  0) or (>=0, 0)
    causal_bottom_right, padding_causal_bottom_right | (-1,  0) or (>=0, 0)
5151
    """
5152
    orig_window_size = window_size
5153
    if "causal" in attn_mask_type:
5154
        if orig_window_size is None:
5155
            window_size = (-1, 0)
5156
5157
5158
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
5159
5160
5161
5162
            window_size = (orig_window_size[0], 0)
            warnings.warn(
                "window_size should be (-1, 0) or (>=0, 0) for attn_mask_type=" + attn_mask_type
            )
5163
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
5164
5165
5166
5167
            assert False, (
                "window_size should be (-1, 0) or (>=0, 0) for attn_mask_type=" + attn_mask_type
            )
    elif attn_mask_type in ["no_mask", "padding", "arbitrary"]:
5168
5169
5170
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
5171
            window_size = (-1, -1)
5172
5173
5174
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
5175
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
5176
5177
5178
5179
5180
            assert False, (
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
    else:
        assert False, "Invalid attn_mask_type: " + attn_mask_type
5181
    return window_size
5182

5183

5184
class FlashAttention(torch.nn.Module):
5185
    """Dot product attention, using HazyResearch flash-attn package:
5186
    https://github.com/Dao-AILab/flash-attention
5187
5188
5189
5190
    """

    def __init__(
        self,
5191
        softmax_scale: float,
5192
5193
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
5194
5195
        attention_type: str = "self",
        layer_number: Optional[int] = None,
5196
        deterministic: bool = False,
5197
5198
5199
    ) -> None:
        super().__init__()

5200
5201
5202
5203
5204
5205
5206
        if _flash_attn_is_installed:
            assert (
                _flash_attn_version >= _flash_attn_version_required
            ), f"FlashAttention minimum version {_flash_attn_version_required} is required."
            assert (
                _flash_attn_version <= _flash_attn_max_version
            ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
5207

5208
        self.softmax_scale = softmax_scale
5209
5210
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
5211
5212
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
5213
        self.deterministic = deterministic
5214
5215
5216
5217
        self.logger = logging.getLogger("FlashAttention")
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
5218
5219
5220
5221
5222
5223

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5224
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5225
5226
5227
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5228
5229
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5230
        attn_mask_type: str = "causal",
5231
        window_size: Optional[Tuple[int, int]] = None,
5232
        alibi_slopes: Optional[torch.Tensor] = None,
5233
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5234
        cp_global_ranks: List[int] = None,
5235
        cp_stream: torch.cuda.Stream = None,
5236
        cp_comm_type: str = "p2p",
5237
5238
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5239
5240
5241
    ) -> torch.Tensor:
        """flash-attn fprop"""

5242
5243
5244
5245
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FlashAttention only supports FP16 and BF16 data types, or Float8Tensors."
5246
5247
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5248
        ), "FlashAttention currently only supports CUDA tensors."
5249
5250
        assert (
            qkv_layout in QKVLayouts
5251
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
5252

5253
5254
5255
5256
5257
5258
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
5259
        context_parallel = cp_size > 1
5260

5261
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
5262

5263
5264
5265
5266
5267
5268
5269
5270
5271
5272
5273
5274
5275
        if all(not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]):
            if qkv_format == "sbhd":
                # For now just 128, will make it more general in the future
                if (
                    query_layer.shape[-1] == 128
                    and query_layer.shape[0] * query_layer.shape[1] >= 512
                    and qkv_layout == "sbh3d"
                ):
                    query_layer, key_layer, value_layer = _PrepareQKVForFA.apply(
                        query_layer, key_layer, value_layer
                    )
                else:
                    query_layer, key_layer, value_layer = [
5276
                        x.transpose(0, 1) for x in (query_layer, key_layer, value_layer)
5277
                    ]
5278
            if context_parallel:
5279
                query_layer, key_layer, value_layer = [
5280
5281
5282
5283
5284
                    x.contiguous() for x in (query_layer, key_layer, value_layer)
                ]
        else:
            if qkv_format == "sbhd":
                query_layer._data, key_layer._data, value_layer._data = [
5285
                    x.transpose(0, 1)
5286
5287
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
5288
5289
5290
5291
                query_layer, key_layer, value_layer = [
                    Float8Tensor.make_like(x, data=x._data)
                    for x in (query_layer, key_layer, value_layer)
                ]
5292
            if context_parallel:
5293
5294
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
5295
                ]
5296

5297
        batch_size = query_layer.shape[0]
5298

5299
        if qkv_format in ["sbhd", "bshd"]:
5300
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
5301
5302
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
5303
5304
5305

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
5306
5307
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
5308
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
5309
5310
5311
5312
5313
5314
5315
                    for x in [query_layer, key_layer, value_layer]
                ]

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
5316
                    if cu_seqlens_q is None:
5317
5318
5319
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
5320
5321
5322
5323
5324
5325
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask)
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                    cu_seqlens_kv = cu_seqlens_q
                    query_layer, key_layer, value_layer = PackTensors.apply(
                        indices_q, query_layer, key_layer, value_layer
5326
5327
                    )
                else:
5328
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
5329
5330
5331
5332
5333
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask[0])
                        cu_seqlens_kv, indices_kv = get_cu_seqlens_and_indices(attention_mask[1])
5334
5335
5336
5337
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                        indices_kv = get_indices(max_seqlen_kv, cu_seqlens_kv)
                    query_layer = PackTensors.apply(indices_q, query_layer)
5338
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
5339
            else:
5340
5341
5342
5343
5344
5345
5346
5347
5348
5349
5350
5351
5352
                # Cumulative sequence lengths for unpadded data
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
5353
5354
5355
5356
        elif qkv_format == "thd":
            assert (
                cu_seqlens_q is not None and cu_seqlens_kv is not None
            ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
5357
5358
5359
5360
5361
5362
            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                max_seqlen_q = seqlens_q.max().item()
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                max_seqlen_kv = seqlens_kv.max().item()
5363

5364
5365
5366
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
5367
5368
5369
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
5370
            with self.attention_dropout_ctx():
5371
                output = attn_forward_func_with_cp(
5372
5373
5374
5375
5376
5377
5378
5379
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5380
5381
                    cu_seqlens_q,
                    cu_seqlens_kv,
5382
                    self.attention_dropout if self.training else 0.0,
5383
5384
5385
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5386
                    cp_comm_type,
5387
                    softmax_scale=self.softmax_scale,
5388
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
5389
                    attn_mask_type=attn_mask_type,
5390
                    deterministic=self.deterministic,
5391
                    window_size=window_size,
5392
5393
                )
        else:
5394
5395

            from .cpu_offload import CPUOffloadEnabled
5396

5397
5398
5399
5400
5401
5402
            if CPUOffloadEnabled:
                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
                for tensor in tensor_list:
                    if tensor is not None:
                        tensor.activation_offloading = True

5403
            with self.attention_dropout_ctx():
5404
                fa_optional_forward_kwargs = {}
5405
5406
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
5407
5408
5409
5410
                if _flash_attn_2_4_plus:
                    fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                if _flash_attn_2_4_1_plus:
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
5411
5412
5413
5414
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
                    func = flash_attn_func if not _use_flash_attn_3 else flash_attn_func_v3
                else:
5415
5416
                    if _flash_attn_2_5_7_plus:
                        fa_optional_forward_kwargs["block_table"] = None
5417
5418
5419
5420
5421
5422
5423
5424
5425
5426
                    func = (
                        flash_attn_varlen_func
                        if not _use_flash_attn_3
                        else flash_attn_varlen_func_v3
                    )
                    fa_optional_forward_args_thd.append(cu_seqlens_q)
                    fa_optional_forward_args_thd.append(cu_seqlens_kv)
                    fa_optional_forward_args_thd.append(max_seqlen_q)
                    fa_optional_forward_args_thd.append(max_seqlen_kv)
                if _use_flash_attn_3:
5427
5428
5429
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
                    fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
5430
                    activation_dtype = query_layer.dtype
5431
5432
5433
                    if fp8:
                        fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
5434
5435
5436
5437
5438
5439
5440
5441
5442
5443
5444

                        def convert_to_torch_float8(tensor, dtype):
                            out = torch.Tensor().to(device=tensor.device, dtype=dtype)
                            out.set_(
                                tensor._data.untyped_storage(),
                                tensor._data.storage_offset(),
                                tensor._data.shape,
                                tensor._data.stride(),
                            )
                            return out

5445
5446
5447
5448
5449
5450
                        # "fp8_mha" decides outputs in fp8, while inputs are inferred from
                        # the real dtype
                        assert isinstance(key_layer, query_layer.__class__) and isinstance(
                            value_layer, query_layer.__class__
                        ), "q, k, and v must have the same type."
                        if isinstance(query_layer, Float8Tensor):
5451
5452
5453
                            fp8_meta["scaling_fwd"].scale_inv[META_QKV] = query_layer._scale_inv
                        else:
                            query_layer, key_layer, value_layer = (
5454
5455
                                Float8Tensor.to_float8(x, fp8_dtype=fp8_dtype_forward)
                                for x in [query_layer, key_layer, value_layer]
5456
                            )
5457
5458
5459
5460
5461
5462
5463
5464
5465
5466
5467
5468
5469
5470
5471
5472
5473
5474
5475
5476
5477
                        fa_3_optional_forward_kwargs["descale_q"] = query_layer._scale_inv
                        fa_3_optional_forward_kwargs["descale_k"] = key_layer._scale_inv
                        fa_3_optional_forward_kwargs["descale_v"] = value_layer._scale_inv
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
                        )
                    try:
                        output, _ = func(
                            query_layer,
                            key_layer,
                            value_layer,
                            *fa_optional_forward_args_thd,
                            softmax_scale=self.softmax_scale,
                            causal="causal" in attn_mask_type,
                            **fa_3_optional_forward_kwargs,
                        )
                    except TypeError as e:
                        if _flash_attn_3_0_0_beta:
                            e.args = (
                                e.args[0]
5478
                                + ". Please update your flash-attn v3 (beta) installation as it "
5479
5480
5481
5482
5483
                                + "may have added more supported arguments to its API. \n"
                                + _flash_attn_3_installation_steps,
                            ) + e.args[1:]
                        raise

5484
5485
5486
5487
5488
5489
5490
5491
5492
5493
5494
5495
5496
5497
5498
5499
5500
5501
5502
5503
5504
5505
5506
5507
5508
5509
                    if fp8 and fp8_meta["recipe"].fp8_mha:
                        output = cast_to_fp8(
                            output,
                            fp8_meta["scaling_fwd"],
                            META_O,
                            fp8_dtype_forward,
                        )
                        output = Float8Tensor(
                            data=output,
                            fp8_meta=fp8_meta,
                            fp8_meta_forward=True,
                            fp8_meta_index=META_O,
                            fp8_dtype=fp8_dtype_forward,
                            dtype=activation_dtype,
                        )
                else:
                    output = func(
                        query_layer,
                        key_layer,
                        value_layer,
                        *fa_optional_forward_args_thd,
                        self.attention_dropout if self.training else 0.0,
                        softmax_scale=self.softmax_scale,
                        causal="causal" in attn_mask_type,
                        **fa_optional_forward_kwargs,
                    )
5510

5511
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
5512
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
5513

5514
        if qkv_format == "sbhd":
5515
            # (bs)hd -> bs(hd) -> sb(hd)
5516
            if fp8 and fp8_meta["recipe"].fp8_mha:
5517
5518
5519
5520
5521
5522
                output = Float8Tensor.make_like(
                    output,
                    data=output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous(),
                )
5523
            else:
5524
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
5525
        elif qkv_format == "bshd":
5526
            # (bs)hd -> bs(hd)
5527
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
5528
        elif qkv_format == "thd":
5529
            # thd -> t(hd)
5530
            output = output.reshape(output.shape[0], -1)
5531

5532
        return output.contiguous()
5533

5534

5535
def _combine_tensors(
5536
5537
5538
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
5539
5540
5541
5542
5543
5544
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    new_stride = list(tensors[0].stride())
5545
    new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
5546
    if isinstance(tensors[0], Float8Tensor):
5547
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
5548
5549
5550
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
5551
5552
5553
5554
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor)
5555
    else:
5556
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
5557
        combined_tensor.set_(
5558
5559
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
        )
5560
5561

    return combined_tensor
5562

5563

5564
5565
5566
5567
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
5568
5569
5570
5571
5572
    def forward(
        ctx,
        is_training,
        max_seqlen,
        cu_seqlens,
5573
        cu_seqlens_padded,
5574
5575
5576
5577
5578
5579
5580
5581
5582
        qkv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5583
        window_size,
5584
5585
5586
5587
5588
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5589
        deterministic,
5590
    ):
5591
        # pylint: disable=missing-function-docstring
5592
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
5593
5594
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5595
        if fp8:
5596
5597
            is_input_fp8 = isinstance(qkv, Float8Tensor)
            if is_input_fp8:
5598
5599
5600
5601
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = qkv._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            # 1: qkv packed, 2: kv packed, 3: qkv separate
5602
            qkv_group = len(qkv_layout.split("_"))
5603
5604
5605
5606
            assert (
                qkv_group == 1
            ), f"qkv layout should conform to 3hd or h3d, e.g. sb3hd, but found {qkv_layout}."
            if is_input_fp8:
5607
5608
5609
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5610
5611
5612
                qkv_fp8 = cast_to_fp8(
                    qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(qkv.shape)
5613
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5614
5615
5616
5617
5618
5619
5620
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
5621
                cu_seqlens_padded,
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
5634
5635
5636
5637
5638
5639
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5640
                window_size,
5641
5642
                rng_gen,
            )
5643
            if is_output_fp8:
5644
5645
                out_ret = Float8Tensor(
                    data=out_fp8,
5646
5647
5648
5649
5650
5651
5652
5653
5654
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=qkv.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5655
5656
5657
5658
5659
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5660
            out_save = out_ret
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                if is_input_fp8:
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv = cast_from_fp8(
                        qkv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[qkv.dtype],
                    ).view(qkv.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                        fp8_meta["scaling_fwd"],
                        META_O,
                        fp8_dtype_forward,
                        qkv_dtype,
                    ).view(out_fp8.shape)
5679
5680
5681
            fp8_tensors = (
                qkv_fp8,
                out_fp8,
5682
                fp8_meta["scaling_fwd"].scale.clone(),
5683
5684
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5685
5686
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5687
5688
5689
5690
5691
5692
5693
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5694
                cu_seqlens_padded,
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
5705
5706
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
5707
5708
5709
5710
5711
5712
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5713
                window_size,
5714
5715
                rng_gen,
            )
5716
5717
5718
5719
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
5720
5721
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
5722
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
5723
        ctx.save_for_backward(
5724
            *qkvo_tensors, cu_seqlens, cu_seqlens_padded, *fp8_tensors, *aux_ctx_tensors
5725
        )
5726
        ctx.fp8_meta = fp8_meta
5727
5728
5729
5730
5731
5732
5733
5734
        ctx.max_seqlen = max_seqlen
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
5735
        ctx.window_size = window_size
5736
        ctx.fused_attention_backend = (
5737
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5738
        )
5739
        ctx.use_FAv2_bwd = use_FAv2_bwd
5740
        ctx.deterministic = deterministic
5741

5742
        return out_ret
5743
5744
5745

    @staticmethod
    def backward(ctx, d_out):
5746
        # pylint: disable=missing-function-docstring
5747
        if ctx.is_output_fp8:
5748
5749
5750
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5751
5752
5753
            d_out_f8tensor = d_out
            d_out = d_out._data

5754
        d_out = d_out.contiguous()
5755
5756
5757
5758
        (
            qkv,
            out,
            cu_seqlens,
5759
            cu_seqlens_padded,
5760
5761
5762
5763
5764
5765
            qkv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5766
        rest = [None]
5767
5768
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5769
        if ctx.use_FAv2_bwd:
5770
            softmax_lse, rng_state = aux_ctx_tensors
5771
            dqkv = torch.empty_like(qkv)
5772
5773
5774
            d_out, q, k, v, out = [
                maybe_contiguous(x) for x in (d_out, qkv[:, 0], qkv[:, 1], qkv[:, 2], out)
            ]
5775
            flash_attn_cuda_bwd(
5776
5777
5778
5779
5780
5781
5782
5783
5784
5785
5786
5787
5788
5789
5790
5791
5792
5793
5794
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dqkv[:, 0],
                dqkv[:, 1],
                dqkv[:, 2],
                cu_seqlens,
                cu_seqlens,
                ctx.max_seqlen,
                ctx.max_seqlen,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
5795
            )
5796
            dqkv = dqkv[..., : d_out.shape[-1]]
5797
        else:
5798
5799
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
5800
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
5801
                    fp8_dtype_backward = get_fp8_te_dtype(
5802
5803
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5804
                    if ctx.is_output_fp8:
5805
                        d_out_fp8 = d_out
5806
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5807
5808
5809
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5810
5811
5812
5813
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5814
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
5815
5816
5817
5818
5819
5820
5821
5822
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
5823
                        ctx.fused_attention_backend,
5824
                        cu_seqlens_padded,
5825
5826
5827
5828
5829
5830
5831
5832
5833
5834
5835
5836
5837
5838
5839
5840
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5841
5842
                        ctx.window_size,
                        ctx.deterministic,
5843
                    )
5844
                    if ctx.is_input_fp8:
5845
5846
                        dqkv = Float8Tensor(
                            data=dqkv_fp8,
5847
5848
5849
5850
5851
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5852
                        )
5853
                    else:
5854
5855
5856
5857
5858
5859
5860
5861
5862
5863
                        dqkv_c_fp8 = dqkv_fp8.view(
                            -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                        )
                        dqkv = cast_from_fp8(
                            dqkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dqkv_fp8.shape)
5864
5865
5866
5867
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
5868
5869
5870
5871
5872
5873
5874
5875
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
5876
                        ctx.fused_attention_backend,
5877
                        cu_seqlens_padded,
5878
5879
5880
5881
5882
5883
5884
5885
5886
5887
5888
5889
5890
5891
5892
5893
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5894
5895
                        ctx.window_size,
                        ctx.deterministic,
5896
                    )
5897

5898
5899
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
            return (
                None,
                None,
                None,
                None,
                dqkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
5921
5922
                None,
                None,
5923
            )
5924
        # else, return (dqkv, dbias)
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
        return (
            None,
            None,
            None,
            None,
            dqkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5946
5947
            None,
            None,
5948
        )
5949

5950

5951
5952
5953
5954
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
5955
5956
5957
5958
5959
5960
5961
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
5962
5963
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
        q,
        kv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5974
        window_size,
5975
5976
5977
5978
5979
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5980
        deterministic,
5981
    ):
5982
        # pylint: disable=missing-function-docstring
5983
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
5984
5985
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5986
        if fp8:
5987
5988
5989
            assert isinstance(kv, q.__class__), "q and kv must have the same type."
            is_input_fp8 = isinstance(q, Float8Tensor)
            if is_input_fp8:
5990
5991
5992
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
5993
            if is_input_fp8:
5994
5995
5996
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5997
5998
                qkv_group = len(qkv_layout.split("_"))
                assert qkv_group == 2, (
5999
6000
                    "qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd, "
                    f"but found {qkv_layout}."
6001
6002
6003
6004
                )
                q_fp8 = cast_to_fp8(q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward).view(
                    q.shape
                )
6005
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
6006
6007
6008
                kv_fp8 = cast_to_fp8(
                    kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(kv.shape)
6009
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                kv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
6020
6021
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
6034
6035
6036
6037
6038
6039
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6040
                window_size,
6041
6042
                rng_gen,
            )
6043
            if is_output_fp8:
6044
6045
                out_ret = Float8Tensor(
                    data=out_fp8,
6046
6047
6048
6049
6050
6051
6052
6053
6054
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6055
6056
6057
6058
6059
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6060
            out_save = out_ret
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
6072
6073
6074
6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                if is_input_fp8:
                    q = cast_from_fp8(
                        q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[q.dtype],
                    ).view(q.shape)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv = cast_from_fp8(
                        kv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[kv.dtype],
                    ).view(kv.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                        fp8_meta["scaling_fwd"],
                        META_O,
                        fp8_dtype_forward,
                        qkv_dtype,
                    ).view(out_fp8.shape)
6086
6087
6088
6089
            fp8_tensors = (
                q_fp8,
                kv_fp8,
                out_fp8,
6090
                fp8_meta["scaling_fwd"].scale.clone(),
6091
6092
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6093
6094
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                kv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6105
6106
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6107
6108
6109
6110
6111
6112
6113
6114
6115
6116
6117
6118
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
6119
6120
6121
6122
6123
6124
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6125
                window_size,
6126
6127
                rng_gen,
            )
6128
6129
6130
6131
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
6132
6133
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6134
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
6135
6136
6137
6138
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6139
6140
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6141
6142
6143
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6144
        ctx.fp8_meta = fp8_meta
6145
6146
6147
6148
6149
6150
6151
6152
6153
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
6154
        ctx.window_size = window_size
6155
        ctx.fused_attention_backend = (
6156
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6157
        )
6158
        ctx.use_FAv2_bwd = use_FAv2_bwd
6159
        ctx.deterministic = deterministic
6160

6161
        return out_ret
6162
6163
6164

    @staticmethod
    def backward(ctx, d_out):
6165
        # pylint: disable=missing-function-docstring
6166
        if ctx.is_output_fp8:
6167
6168
6169
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6170
6171
6172
            d_out_f8tensor = d_out
            d_out = d_out._data

6173
        d_out = d_out.contiguous()
6174
6175
6176
6177
6178
6179
        (
            q,
            kv,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6180
6181
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6182
6183
6184
6185
6186
6187
6188
            q_fp8,
            kv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6189
        rest = [None]
6190
6191
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6192
        if ctx.use_FAv2_bwd:
6193
            softmax_lse, rng_state = aux_ctx_tensors
6194
6195
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
6196
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, kv[:, 0], kv[:, 1], out)]
6197
            flash_attn_cuda_bwd(
6198
6199
6200
6201
6202
6203
6204
6205
6206
6207
6208
6209
6210
6211
6212
6213
6214
6215
6216
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dkv[:, 0],
                dkv[:, 1],
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
6217
            )
6218
6219
            dq = dq[..., : d_out.shape[-1]]
            dkv = dkv[..., : d_out.shape[-1]]
6220
        else:
6221
6222
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
6223
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
6224
                    fp8_dtype_backward = get_fp8_te_dtype(
6225
6226
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6227
                    if ctx.is_output_fp8:
6228
                        d_out_fp8 = d_out
6229
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6230
6231
6232
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6233
6234
6235
6236
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6237
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        kv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
6249
                        ctx.fused_attention_backend,
6250
6251
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6252
6253
6254
6255
6256
6257
6258
6259
6260
6261
6262
6263
6264
6265
6266
6267
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6268
6269
                        ctx.window_size,
                        ctx.deterministic,
6270
                    )
6271
                    if ctx.is_input_fp8:
6272
6273
                        dq = Float8Tensor(
                            data=dq_fp8,
6274
6275
6276
6277
6278
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6279
6280
6281
                        )
                        dkv = Float8Tensor(
                            data=dkv_fp8,
6282
6283
6284
6285
6286
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6287
                        )
6288
6289
6290
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6291
6292
6293
6294
6295
6296
6297
6298
6299
6300
6301
6302
6303
6304
6305
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dq_fp8.shape)
                        dkv_c_fp8 = dkv_fp8.view(
                            -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                        )
                        dkv = cast_from_fp8(
                            dkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dkv_fp8.shape)
6306
6307
6308
6309
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
6310
6311
6312
6313
6314
6315
6316
6317
6318
6319
6320
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        kv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
6321
                        ctx.fused_attention_backend,
6322
6323
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6324
6325
6326
6327
6328
6329
6330
6331
6332
6333
6334
6335
6336
6337
6338
6339
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6340
6341
                        ctx.window_size,
                        ctx.deterministic,
6342
                    )
6343

6344
6345
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6346
6347
6348
6349
6350
6351
6352
6353
6354
6355
6356
6357
6358
6359
6360
6361
6362
6363
6364
6365
6366
6367
6368
6369
6370
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
6371
6372
                None,
                None,
6373
            )
6374
        # else, return (dqkv, dbias)
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
6386
6387
6388
6389
6390
6391
6392
6393
6394
6395
6396
6397
6398
6399
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
6400
6401
            None,
            None,
6402
6403
        )

6404

6405
6406
6407
6408
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
6409
6410
6411
6412
6413
6414
6415
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
6416
6417
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
6418
6419
6420
6421
6422
6423
6424
6425
6426
6427
6428
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
6429
        window_size,
6430
6431
6432
6433
6434
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
6435
        deterministic,
6436
    ):
6437
        # pylint: disable=missing-function-docstring
6438
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
6439
6440
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
6441
6442
6443
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
6444
6445
6446
6447
6448
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
            is_input_fp8 = isinstance(q, Float8Tensor)
            if is_input_fp8:
6449
6450
6451
6452
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                q_fp8, k_fp8, v_fp8 = q._data, k._data, v._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6453
                qkv_group = len(qkv_layout.split("_"))
6454
                if qkv_group == 1:
6455
6456
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
6457
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
6458
6459
6460
6461
                    qkv_fp8 = cast_to_fp8(
                        qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(qkv.shape)
                    q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1])
6462
6463
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
6464
6465
6466
6467
6468
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    dim = qkv_layout.split("_")[1].find("2")
                    kv = _combine_tensors([k, v], dim)
6469
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
6470
6471
6472
6473
                    kv_fp8 = cast_to_fp8(
                        kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(kv.shape)
                    k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1])
6474
6475
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
6476
6477
6478
6479
6480
6481
6482
6483
6484
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    k_fp8 = cast_to_fp8(
                        k, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(k.shape)
                    v_fp8 = cast_to_fp8(
                        v, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(v.shape)
6485
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
6497
6498
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6499
6500
6501
6502
6503
6504
6505
6506
6507
6508
6509
6510
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
6511
6512
6513
6514
6515
6516
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6517
                window_size,
6518
6519
                rng_gen,
            )
6520
            if is_output_fp8:
6521
6522
                out_ret = Float8Tensor(
                    data=out_fp8,
6523
6524
6525
6526
6527
6528
6529
6530
6531
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6532
6533
6534
6535
6536
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6537
6538
            out_save = out_ret

6539
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
6540
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6541
6542
6543
6544
6545
6546
6547
6548
6549
6550
6551
6552
6553
6554
6555
6556
6557
6558
6559
6560
6561
6562
6563
6564
6565
6566
6567
6568
6569
6570
6571
6572
6573
6574
6575
6576
6577
6578
6579
6580
6581
6582
6583
6584
6585
6586
6587
6588
6589
6590
6591
6592
6593
6594
6595
6596
6597
6598
6599
6600
                if is_input_fp8:
                    qkv_group = len(qkv_layout.split("_"))
                    if qkv_group == 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                        qkv_no_fp8 = cast_from_fp8(
                            qkv_c._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[qkv.dtype],
                        ).view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1])
                        q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                    if qkv_group == 2:
                        q = cast_from_fp8(
                            q._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[q.dtype],
                        ).view(q.shape)
                        dim = qkv_layout.split("_")[1].find("2")
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                        kv_no_fp8 = cast_from_fp8(
                            kv_c._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[kv.dtype],
                        ).view(kv.shape)
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1])
                        k, v = [x.squeeze(dim) for x in [k, v]]
                    if qkv_group == 3:
                        q = cast_from_fp8(
                            q._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[q.dtype],
                        ).view(q.shape)
                        k = cast_from_fp8(
                            k._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[k.dtype],
                        ).view(k.shape)
                        v = cast_from_fp8(
                            v._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[v.dtype],
                        ).view(v.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6601
                        fp8_meta["scaling_fwd"],
6602
                        META_O,
6603
                        fp8_dtype_forward,
6604
6605
                        qkv_dtype,
                    ).view(out_fp8.shape)
6606
6607
6608
6609
6610
6611

            fp8_tensors = (
                q_fp8,
                k_fp8,
                v_fp8,
                out_fp8,
6612
                fp8_meta["scaling_fwd"].scale.clone(),
6613
6614
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6615
6616
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd(
6617
6618
6619
6620
6621
6622
6623
6624
6625
6626
6627
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6628
6629
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6630
6631
6632
6633
6634
6635
6636
6637
6638
6639
6640
6641
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
6642
6643
6644
6645
6646
6647
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6648
                window_size,
6649
6650
                rng_gen,
            )
6651
6652
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
6653

6654
6655
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

6656
        from .cpu_offload import CPUOffloadEnabled
6657

6658
        if CPUOffloadEnabled:
6659
6660
6661
6662
6663
6664
6665
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

6666
            qkv_layout = "sbhd_sbhd_sbhd"
6667
6668
6669
6670
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

6671
6672
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6673
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
6674
6675
6676
6677
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6678
6679
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6680
6681
6682
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6683
        ctx.fp8_meta = fp8_meta
6684
6685
6686
6687
6688
6689
6690
6691
6692
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
6693
        ctx.window_size = window_size
6694
        ctx.fused_attention_backend = (
6695
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6696
        )
6697
        ctx.use_FAv2_bwd = use_FAv2_bwd
6698
        ctx.deterministic = deterministic
6699

6700
        return out_ret
6701
6702
6703

    @staticmethod
    def backward(ctx, d_out):
6704
        # pylint: disable=missing-function-docstring
6705
        if ctx.is_output_fp8:
6706
6707
6708
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6709
6710
6711
            d_out_f8tensor = d_out
            d_out = d_out._data

6712
        d_out = d_out.contiguous()
6713
6714
6715
6716
6717
6718
6719
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6720
6721
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6722
6723
6724
6725
6726
6727
6728
6729
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6730
6731
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6732
        rest = [None]
6733
        if ctx.use_FAv2_bwd:
6734
            softmax_lse, rng_state = aux_ctx_tensors
6735
6736
6737
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
6738
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
6739
            flash_attn_cuda_bwd(
6740
6741
6742
6743
6744
6745
6746
6747
6748
6749
6750
6751
6752
6753
6754
6755
6756
6757
6758
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
6759
            )
6760
6761
6762
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
6763
        else:
6764
6765
6766
6767
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
6768
6769
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6770
                    if ctx.is_output_fp8:
6771
                        d_out_fp8 = d_out
6772
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6773
6774
6775
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6776
6777
6778
6779
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6780
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
6781
6782
6783
6784
6785
6786
6787
6788
6789
6790
6791
6792
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        k_fp8,
                        v_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
6793
                        ctx.fused_attention_backend,
6794
6795
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6796
6797
6798
6799
6800
6801
6802
6803
6804
6805
6806
6807
6808
6809
6810
6811
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6812
6813
                        ctx.window_size,
                        ctx.deterministic,
6814
                    )
6815

6816
                    if ctx.is_input_fp8:
6817
6818
                        dq = Float8Tensor(
                            data=dq_fp8,
6819
6820
6821
6822
6823
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6824
6825
6826
                        )
                        dk = Float8Tensor(
                            data=dk_fp8,
6827
6828
6829
6830
6831
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6832
6833
6834
                        )
                        dv = Float8Tensor(
                            data=dv_fp8,
6835
6836
6837
6838
6839
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6840
                        )
6841
                    else:
6842
                        qkv_group = len(ctx.qkv_layout.split("_"))
6843
                        if qkv_group == 1:
6844
6845
6846
6847
6848
6849
6850
6851
6852
6853
6854
6855
6856
                            dim = ctx.qkv_layout.find("3")
                            dqkv_fp8 = _combine_tensors([dq_fp8, dk_fp8, dv_fp8], dim)
                            dqkv_c_fp8 = dqkv_fp8.view(
                                -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                            )
                            dqkv = cast_from_fp8(
                                dqkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dqkv_fp8.shape)
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1])
6857
6858
6859
6860
                            dq, dk, dv = [x.squeeze(dim) for x in [dq, dk, dv]]
                        if qkv_group == 2:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6861
6862
6863
6864
6865
6866
6867
6868
6869
6870
6871
6872
6873
6874
6875
6876
6877
6878
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
                            dim = ctx.qkv_layout.split("_")[1].find("2")
                            dkv_fp8 = _combine_tensors([dk_fp8, dv_fp8], dim)
                            dkv_c_fp8 = dkv_fp8.view(
                                -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                            )
                            dkv = cast_from_fp8(
                                dkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dkv_fp8.shape)
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1])
6879
6880
6881
6882
                            dk, dv = [x.squeeze(dim) for x in [dk, dv]]
                        if qkv_group == 3:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6883
6884
6885
6886
6887
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
6888
6889
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
6890
6891
6892
6893
6894
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dk_fp8.shape)
6895
6896
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
6897
6898
6899
6900
6901
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dv_fp8.shape)
6902
6903
6904
6905
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
6906
6907
6908
6909
6910
6911
6912
6913
6914
6915
6916
6917
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
6918
                        ctx.fused_attention_backend,
6919
6920
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6921
6922
6923
6924
6925
6926
6927
6928
6929
6930
6931
6932
6933
6934
6935
6936
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6937
6938
                        ctx.window_size,
                        ctx.deterministic,
6939
                    )
6940

6941
6942
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6943
6944
6945
6946
6947
6948
6949
6950
6951
6952
6953
6954
6955
6956
6957
6958
6959
6960
6961
6962
6963
6964
6965
6966
6967
6968
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
6969
6970
                None,
                None,
6971
            )
6972
        # else, return (dqkv, dbias)
6973
6974
6975
6976
6977
6978
6979
6980
6981
6982
6983
6984
6985
6986
6987
6988
6989
6990
6991
6992
6993
6994
6995
6996
6997
6998
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dk,
            dv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
6999
7000
            None,
            None,
7001
        )
7002

7003

7004
class FusedAttention(torch.nn.Module):
7005
7006
7007
7008
7009
7010
7011
7012
7013
    """Dot product attention, with multiple backends:

    1. FusedAttnBackend["F16_max512_seqlen"]
       cuDNN based fused attention for FP16/BF16 and <=512 sequence length.
    2. FusedAttnBackend["F16_arbitrary_seqlen"]
       cuDNN based fused attention for FP16/BF16 and any sequence length.

    Support matrix:

7014
7015
7016
7017
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
7018
    | attn_type     | self/cross              | self/cross                     |
7019
    | qkv_layout    |                         |                                |
7020
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
7021
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
7022
7023
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
7024
7025
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
7026
    | dropout       | yes                     | yes                            |
7027
7028
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
7029
    | output dtype  | fp16/bf16               | fp16/bf16                      |
7030
7031
7032
7033
    """

    def __init__(
        self,
7034
        softmax_scale: float,
7035
7036
7037
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
7038
7039
        layer_number: Optional[int] = None,
        deterministic: bool = False,
7040
7041
7042
    ) -> None:
        super().__init__()

7043
        self.softmax_scale = softmax_scale
7044
7045
7046
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
7047
7048
7049
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
7050
        self.layer_number = 1 if layer_number is None else layer_number
7051
        self.deterministic = deterministic
7052

7053
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
7054
7055
            """
            Temporarily remove fused_attention._extra_state as a missing key
7056
            or an unexpected key when loading Transformer Engine checkpoints.
7057
7058
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
7059
            phased out in Transformer Engine 2.0.
7060
7061
            """
            for key in incompatible_keys.missing_keys:
7062
                if "fused_attention._extra_state" in key:
7063
                    incompatible_keys.missing_keys.remove(key)
7064
7065
7066
7067
7068
7069
7070
            for key in incompatible_keys.unexpected_keys:
                if "fused_attention._extra_state" in key:
                    incompatible_keys.unexpected_keys.remove(key)
                    warnings.warn(
                        "fused_attention._extra_state is not loaded from checkpoint. Please map "
                        "FusedAttention's _extra_state to DotProductAttention's _extra_state."
                    )
7071

7072
7073
        self.register_load_state_dict_post_hook(remove_extra_states_check)

7074
    @no_torch_dynamo()
7075
7076
7077
7078
7079
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7080
7081
7082
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7083
7084
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7085
7086
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7087
        attn_mask_type: str = "causal",
7088
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7089
        window_size: Optional[Tuple[int, int]] = None,
7090
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
7091
7092
7093
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
7094
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7095
7096
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
7097
        cp_comm_type: str = "p2p",
7098
7099
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
7100
7101
    ) -> torch.Tensor:
        """fused attention fprop"""
7102
7103
7104
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
7105
7106
7107
7108
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FusedAttention only supports FP16 and BF16 data types, or Float8Tensors."
7109
7110
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
7111
        ), "FusedAttention only supports CUDA tensors."
7112
7113
        assert (
            qkv_layout in QKVLayouts
7114
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
7115

7116
7117
7118
7119
7120
7121
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
7122
        context_parallel = cp_size > 1
7123

7124
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
7125

7126
7127
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
7128
                batch_size, max_seqlen_q, max_seqlen_kv = (
7129
7130
7131
7132
7133
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
7134
                batch_size, max_seqlen_q, max_seqlen_kv = (
7135
7136
7137
7138
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
7139
7140
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
7141
            if "padding" in attn_mask_type:
7142
7143
                assert not context_parallel, "Padding mask not supported with context parallelism!"

7144
7145
7146
7147
7148
                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if attention_mask is None:
                        raise RuntimeError(
                            "Please provide attention_mask or cu_seqlens for padding!"
                        )
7149
                    if self.attention_type == "self":
7150
7151
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
7152
                    else:
7153
7154
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
7155
            else:
7156
7157
7158
7159
7160
7161
7162
7163
7164
7165
7166
7167
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
7168
7169
7170
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
7171
7172
7173
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
7174
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
7175
7176
7177
7178

        if cu_seqlens_q_padded is None or cu_seqlens_kv_padded is None:
            cu_seqlens_q_padded = cu_seqlens_q
            cu_seqlens_kv_padded = cu_seqlens_kv
7179
7180
7181

        qkv_dtype = TE_DType[query_layer.dtype]

7182
7183
7184
7185
7186
        use_FAv2_bwd = (
            self.use_FAv2_bwd
            and (core_attention_bias_type == "no_bias")
            and (fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen)
        )
7187

7188
7189
7190
7191
7192
7193
7194
7195
7196
7197
7198
        if fp8:
            assert fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_FP8, (
                f"cuDNN attention sub-backend {int(tex.NVTE_Fused_Attn_Backend.NVTE_FP8)}"
                " is required for FP8 attention!"
            )
            assert fp8_meta is not None, "FP8 metadata fp8_meta is required for FP8 attention!"
            assert not context_parallel or fp8_meta["recipe"].reduce_amax, (
                "Amax reduction across TP+CP group is necessary when using context parallelism with"
                " FP8!"
            )

7199
        if context_parallel:
7200
            assert (
7201
7202
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
7203
7204
7205
7206
7207
7208
7209
            ), f"{fused_attention_backend} does not work with context parallelism!"
            assert core_attention_bias_type not in [
                "alibi"
            ], f"{core_attention_bias_type} is not supported with context parallelism!"
            query_layer, key_layer, value_layer = [
                x.contiguous() for x in (query_layer, key_layer, value_layer)
            ]
7210
7211
7212
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
7213
7214
7215
7216
7217
7218
7219
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
7220
7221
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7222
                    self.attention_dropout if self.training else 0.0,
7223
7224
7225
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
7226
                    cp_comm_type,
7227
                    softmax_scale=self.softmax_scale,
7228
                    qkv_format=qkv_format,
7229
                    attn_mask_type=attn_mask_type,
7230
7231
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
7232
                    deterministic=self.deterministic,
7233
                    use_fused_attention=True,
7234
                    window_size=window_size,
7235
7236
                    fp8=fp8,
                    fp8_meta=fp8_meta,
7237
7238
                )
        else:
7239
7240
7241
7242
7243
7244
7245
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
7246
7247
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7248
7249
7250
7251
7252
7253
7254
7255
7256
7257
7258
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_dtype,
                    core_attention_bias,
                    self.softmax_scale,
                    self.attention_dropout if self.training else 0.0,
                    fast_zero_fill,
                    qkv_layout,
                    core_attention_bias_type,
                    attn_mask_type,
7259
                    window_size,
7260
7261
7262
7263
7264
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
7265
                    self.deterministic,
7266
                )
7267

7268
7269
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
7270
7271


7272
class DotProductAttention(TransformerEngineBaseModule):
7273
7274
7275
7276
7277
7278
    """Allows the model to jointly attend to information from different
    representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    .. note::

7279
        Argument :attr:`attention_mask` in the `forward` call is only used when
7280
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
7281
7282
7283

    .. warning::

7284
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
7285
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
7286
7287
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
7288

7289
7290
7291
7292
7293
7294
7295
    .. note::

        Transformer Engine stores the FP8 metadata under a `._extra_state` key when checkpointing.
        As the FP8 attention support expands from one backend to multiple backends, the location
        of that key has also shifted (see `FP8 checkpoint compatibility <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_).


7296
7297
7298
7299
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
7300
7301
7302
    kv_channels : Union[int, Tuple[int, int]]
                the head size in key and value tensors. If the same, :attr:`kv_channels` can be
                an integer; if not, :attr:`kv_channels` should be a tuple of two integers.
7303
7304
7305
7306
7307
7308
7309
7310
    num_gqa_groups : Optional[int] = None
                    number of GQA groups in the transformer layer.
                    Grouped Query Attention is described in
                    `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                    This only affects the keys and values, not the queries.
                    GQA-1 is equivalent to Multi-Query Attention
                    (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                    is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
7311
7312
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
7313
    attn_mask_type: str, default = `causal`
7314
                   type of attention mask passed into softmax operation, options are "`no_mask`",
7315
7316
7317
7318
7319
7320
7321
7322
7323
                   "`padding`", "`causal`", "`padding,causal`", "`causal,padding`",
                   "`padding_causal`", "`causal_bottom_right`", "`padding_causal_bottom_right`", and
                   "`arbitrary`", where "`padding,causal`", "`causal,padding`" and "`padding_causal`"
                   are equivalent. This arg can be overridden by :attr:`attn_mask_type` in the
                   `forward` method. It is useful for cases involving compilation/tracing, e.g.
                   ONNX export, and the forward arg is useful for dynamically changing mask types,
                   e.g. a different mask for training and inference.
                   1. For "`no_mask`", no attention mask is applied.
                   2. For "`causal`", "`causal_bottom_right`", or the causal mask in
7324
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
7325
7326
7327
7328
7329
7330
7331
7332
7333
7334
7335
7336
7337
7338
                   calculates and applies an upper triangular mask to the softmax input.
                   No user input is needed. Causal masks without the "`bottom_right`" appendix align
                   the diagonal line to the top left corner of the softmax matrix. With
                   "`bottom_right`", the causal mask is aligned to the bottom right corner, which is
                   often used in inference/KV caching.
                   3. For "`padding`", or the padding mask in "`padding_causal`" and
                   "`padding_causal_bottom_right`", users need to provide the locations of padded
                   tokens, either via :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv` (both in shape
                   [batch_size + 1]), or via :attr:`attention_mask` (one tensor for self-attention
                   in shape [batch_size, 1, 1, max_seqlen_q], or two tensors in a tuple for
                   cross-attention in shapes [batch_size, 1, 1, max_seqlen_q] and
                   [batch_size, 1, 1, max_seqlen_kv]).
                   4. For "`arbitrary`", users need to provide a mask that is broadcastable to
                   the shape of softmax input [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
7339
7340
7341
7342
    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
7343
7344
7345
                window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
                map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
                `attn_mask_type`. Similar to :attr:`attn_mask_type`, `window_size` can
7346
                be overridden by :attr:`window_size` in `forward` as well.
7347
7348
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
7349
7350
7351
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
7352
7353
7354
    qkv_format: str, default = `sbhd`
               dimension format for `query_layer`, `key_layer` and `value_layer`,
               {`sbhd`, `bshd`, `thd`}. `s` stands for the sequence length, `b` batch size,
7355
               `h` the number of heads, `d` head size, and `t` the total number of tokens
7356
7357
7358
7359
7360
               in a batch, with `t = sum(s_i), for i = 0...b-1`. `sbhd` and `bshd` formats
               are used for when sequences in a batch are of equal length or padded to
               equal length, and the `thd` format is used for when sequences in a batch
               have different lengths. Please note that these formats do not reflect how
               tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
7361
               For that, please use `get_qkv_layout` to gain the layout information.
7362
7363
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
7364
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
7365
7366
7367
7368
7369
7370
7371
7372
7373

    Parallelism parameters
    ----------------------
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_size : int, default = 1
             tensor parallel world size.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
7374
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
7375
              context parallel process group.
7376
7377
7378
              ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
              List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
              and cp_group[1] are for a2a and p2p communications respectively.
7379
7380
7381
7382
7383
7384
7385
    cp_global_ranks : list of global rank IDs, default = `None`
                     global rank IDs of GPUs that are in cp_group.
    cp_stream : CUDA stream, default = `None`
               context parallelism splits flash attention into multiple steps for
               compute and communication overlapping. To address the wave quantization
               issue of each split step, we add an additional CUDA stream so that we
               can overlap two flash attention kernels.
7386
    cp_comm_type : str, default = `p2p`
7387
                  inter-gpu communication type for context parallelism.
7388
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7389
7390
7391
7392
7393
7394
                  "p2p": Exchange KV chunks with P2P communications in ring topology.
                         P2P is async and can be overlapped with attention compute.
                  "all_gather": All-gather to get full sequence of KV before attention.
                                The all-gather is not async, and cannot be overlapped.
                  "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                         group, and gather to get full sequence of QKV.
7395
7396
7397
                  "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                  across each CP sub-group (e.g., via NVLink), then exchanging KV with
                  p2p between sub-groups (e.g., via IBLink).
7398
7399
7400
7401
7402
    """

    def __init__(
        self,
        num_attention_heads: int,
7403
        kv_channels: Union[int, Tuple[int, int]],
7404
        num_gqa_groups: Optional[int] = None,
7405
        attention_dropout: float = 0.0,
7406
        qkv_format: str = "sbhd",
7407
        attn_mask_type: str = "causal",
7408
        window_size: Optional[Tuple[int, int]] = None,
7409
7410
7411
7412
7413
        sequence_parallel: bool = False,
        tp_size: int = 1,
        get_rng_state_tracker: Optional[Callable] = None,
        tp_group: Optional[dist_group_type] = None,
        layer_number: Optional[int] = None,
7414
        attention_type: str = "self",
7415
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7416
        cp_global_ranks: List[int] = None,
7417
        cp_stream: torch.cuda.Stream = None,
7418
        cp_comm_type: str = "p2p",
7419
        softmax_scale: Optional[float] = None,
7420
7421
7422
    ) -> None:
        super().__init__()

7423
        self.logger = logging.getLogger("DotProductAttention")
7424
7425
7426
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
7427
        self.qkv_format = qkv_format
7428
        attn_mask_type = attn_mask_type.replace(",", "_")
7429
7430
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
7431
        self.attn_mask_type = attn_mask_type
7432
        self.window_size = check_set_window_size(attn_mask_type, window_size)
7433
7434
7435
7436
7437
7438
7439
        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)
7440
        self.get_rng_state_tracker = get_rng_state_tracker
7441
        self.num_attention_heads = num_attention_heads
7442
        self.layer_number = 1 if layer_number is None else layer_number
7443
7444
7445
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7446
        self.cp_comm_type = cp_comm_type
7447

7448
7449
7450
7451
7452
7453
        self.hidden_size_per_attention_head_k = (
            kv_channels if isinstance(kv_channels, int) else kv_channels[0]
        )
        self.hidden_size_per_attention_head_v = (
            kv_channels if isinstance(kv_channels, int) else kv_channels[1]
        )
7454

7455
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
7456
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
7457

7458
7459
7460
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
7461

7462
        self.rng_states_tracker = None
7463
7464
7465
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
7466
7467
7468
            self.rng_states_tracker = get_rng_state_tracker()
            set_all_rng_states(self.rng_states_tracker.get_states())
            attention_dropout_ctx = self.rng_states_tracker.fork
7469

7470
        if softmax_scale is None:
7471
7472
7473
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
7474

7475
7476
7477
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
7478
        )
7479
7480
7481
7482
7483
7484
7485
7486
7487
7488
7489
7490
7491
7492
7493
7494
7495
7496
7497
        # To use the workspace optimization path for determinism, please
        # set NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT=1 for cuDNN >=8.9.5 and <9.0.0,
        # and set NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 for cuDNN >=9.0.0.
        cudnn_version = get_cudnn_version()
        if (8, 9, 5) <= cudnn_version < (9, 0, 0):
            if self.deterministic:
                os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] = "1"

            # CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT
            # - unset:       enables workspace optimization when required workspace is <= 256MB
            #                or when bias gradient needs to be computed
            # - n:           enables workspace optimization when required workspace is <= n bytes
            # - -1:          enables workspace optimization always
            # - 0:           disables workspace optimization always
            if "NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT" in os.environ:
                if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "0":
                    os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "0"
                if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "1":
                    os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "-1"
7498

7499
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
7500
7501
7502
7503

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

7504
7505
7506
7507
7508
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

7509
7510
7511
7512
7513
7514
7515
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7516

7517
        # Instantiating three types since use of flash-attn and FusedAttention
7518
        # might be ruled out due to forward inputs.
7519
7520
7521
7522
7523
7524
7525
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7526

7527
        self.unfused_attention = UnfusedDotProductAttention(
7528
7529
7530
7531
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
7532
        )
7533

7534
7535
7536
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
7537
7538
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
7539
7540
7541
7542
7543
7544
7545
            """
            for key in incompatible_keys.missing_keys:
                if "core_attention._extra_state" in key:
                    incompatible_keys.missing_keys.remove(key)

        self.register_load_state_dict_post_hook(remove_extra_states_check)

7546
7547
7548
7549
7550
7551
7552
7553
7554
7555
7556
7557
7558
7559
7560
7561
7562
7563
7564
7565
7566
7567
    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """
        This function helps to load Transformer Engine 1.6 and 1.7 checkpoints, where FP8 attention
        metadata is stored under the `core_attention.fused_attention._extra_state` key and not the
        `core_attention._extra_state` key. Please see `FP8 checkpoint compatibility
        <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_ for more details.
        """
        fused_attn_key = False
        dot_product_attn_key = False
        for k in state_dict.keys():
            if "core_attention.fused_attention._extra_state" in k:
                fused_attn_key = True
            if "core_attention._extra_state" in k:
                dot_product_attn_key = True
        if fused_attn_key and not dot_product_attn_key:
            prefix = prefix + "fused_attention."
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

7568
7569
7570
7571
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
7572
        **forward_kwargs: Dict[str, Any],
7573
7574
7575
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

7576
7577
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
7578
7579
7580

        hidden_states = checkpoint(
            custom_forward,
7581
7582
7583
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
7584
            *forward_args,
7585
            **forward_kwargs,
7586
7587
7588
7589
        )

        return hidden_states

7590
7591
    def set_context_parallel_group(
        self,
7592
        cp_group: Union[dist_group_type, List[dist_group_type], None],
7593
7594
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
7595
        cp_comm_type: str = "p2p",
7596
    ) -> None:
7597
7598
7599
7600
7601
7602
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
7603
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
7604
                  context parallel process group.
7605
7606
7607
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
7608
7609
7610
7611
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
7612
        cp_comm_type : str, default = `p2p`
7613
                      inter-gpu communication type for context parallelism.
7614
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7615
7616
7617
7618
7619
7620
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
7621
7622
7623
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
7624
        """
7625
7626
7627
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7628
        self.cp_comm_type = cp_comm_type
7629

7630
    @no_torch_dynamo(recursive=False)
7631
7632
7633
7634
7635
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7636
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7637
7638
7639
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7640
7641
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7642
7643
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7644
        attn_mask_type: Optional[str] = None,
7645
        window_size: Optional[Tuple[int, int]] = None,
7646
        checkpoint_core_attention: bool = False,
7647
7648
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
7649
        alibi_slopes: Optional[torch.Tensor] = None,
7650
        fast_zero_fill: bool = True,
7651
        inference_params: Optional[InferenceParams] = None,
7652
        is_first_microbatch: Optional[bool] = None,
7653
7654
7655
7656
7657
7658
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

7659
7660
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
7661

7662
7663
        .. note::

7664
7665
7666
7667
7668
7669
7670
7671
7672
7673
7674
7675
7676
            DotProductAttention supports three backends: 1) FlashAttention which calls
            HazyResearch/Dao-AILab's `flash-attn <https://arxiv.org/pdf/2305.13245.pdf>`_
            PyTorch API, 2) FusedAttention which has multiple fused attention implementations
            based on `cuDNN Graph API
            <https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#op-fusion>`_
            (see :attr:`FusedAttention` for more details on FusedAttention backends), and 3)
            UnfusedDotProductAttention which is the native PyTorch implementation
            with fused scaled masked softmax.

        .. note::

            Users can use environment variables :attr:`NVTE_FLASH_ATTN`, :attr:`NVTE_FUSED_ATTN`,
            and :attr:`NVTE_FUSED_ATTN_BACKEND` to control which DotProductAttention backend,
7677
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
7678
7679
7680
7681
            FlashAttention over FusedAttention and over UnfusedDotProductAttention.
            If FusedAttention is being used, users can also choose to switch to flash-attn's
            implementation for backward by setting :attr:`NVTE_FUSED_ATTN_USE_FAv2_BWD=1`
            (default: 0), because of the performance differences between various versions of
7682
7683
            flash-attn and FusedAttention. Further, :attr:`NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT`
            can be used to enable (:attr:`1`) or disable (:attr:`0`) the workspace related
7684
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
7685
7686
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
7687

7688
7689
7690
7691
7692
7693
7694
7695
7696
7697
7698
7699
7700
7701
7702
7703
7704
7705
7706
7707
7708
7709
7710
7711
7712
7713
7714
7715
7716
7717
7718
7719
7720
7721
7722
7723
7724
7725
7726
7727
7728
7729
7730
7731
7732
7733
7734
7735
7736
7737
7738
7739
7740
7741
        .. note::
            .. _cu_seqlens note:

            When training data has variable sequence lengths, users have two options.

            1. Manipulate the data and pad all sequences to the same length. Use
               :attr:`qkv_format` = {"bshd", "sbhd"} and
               :attr:`attn_mask_type` = {"padding", "padding_causal", "padding_causal_bottom_right"}.
               Pass in :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`, or :attr:`attention_mask`
               (which will be converted to :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`), to provide
               the real sequence length information. For example, a batch of 3 sequences
               [a a a b b c c c c] can be padded to [a a a PAD b b PAD PAD c c c c], and the cumulative
               sequence length tensors would be
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9] for self-attention.

            2. Do not perform padding on training data. Use :attr:`qkv_format` = "thd" and
               :attr:`attn_mask_type` = {"padding", "padding_causal", "padding_causal_bottom_right"}.
               Pass in :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`, or :attr:`attention_mask`,
               as in option 1. For example, a batch of 3 sequences [a a a b b c c c c] can be processed
               without any padding, and the sequence length tensors would be
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9] for self-attention.

               In certain use cases, a varying number of identifier tokens are inserted between
               sequences. These tokens do not participate in the attention calculation.
               :attr:`cu_seqlens_q_padded` and :attr:`cu_seqlens_kv_padded` must be specified
               in such cases to correctly identify the start and end of each sequence in a batch.
               For example, a batch of 3 sequences [a a a 1 b b 2 2 c c c c 3] would have
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9], and
               :attr:`cu_seqlens_q_padded` = :attr:`cu_seqlens_kv_padded` = [0, 4, 8, 13]
               for self-attention.

        .. note::
            .. _max_seqlen note:

            When :attr:`qkv_format` = {"bshd", "sbhd"}, sequences are of equal length in a batch.
            :attr:`max_seqlen_q` and :attr:`max_seqlen_kv` should be the same as the "s" dimension of
            :attr:`query_layer` and :attr:`key_layer` tensors. When unset, Transformer Engine will
            infer them as such.

            When :attr:`qkv_format` = "thd", sequences have varying lengths. :attr:`max_seqlen_q` and
            :attr:`max_seqlen_kv` should be the maximum query and key/value sequence length in a batch.
            When unset, Transformer Engine deduces them from :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`.
            This deduction costs a small kernel and some CPU-GPU synchronization, and to avoid this
            overhead, users are recommended to obtain the maximum sequence lengths from the data loaders
            and pass them in.

            - As the maximum sequence lengths, batch size, and number of tokens change from batch to batch,
              dynamic shapes need to be supported for tensor construction. FlashAttention and
              UnfusedDotProductAttention naturally do so, while FusedAttention requires parameters to be static
              to create graphs before performance heuristics analysis. To reduce the number of graphs created
              per run, Transformer Engine 1.13+ quantizes relevant parameters: for cuDNN < 9.6, {batch size,
              :attr:`max_seqlen_q`, :attr:`max_seqlen_kv`}, and for cuDNN >= 9.6, {"t" dimension of
              :attr:`query_layer`, "t" dimension of :attr:`key_layer`}.

7742
7743
7744
7745
7746
7747
7748
7749
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
7750
7751
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
7752
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
7753
7754
             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
7755
7756
7757
7758
             for cross-attention. For "`arbitrary`" mask, it should be in a shape broadcastable
             to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value means
             the corresponding position is masked out and a `False` means that position
             is allowed to participate in attention.
7759
7760
7761
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
7762
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
7763
                   with shape [batch_size + 1] and dtype torch.int32.
7764
                   See :ref:`note<cu_seqlens note>` for more details.
7765
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
7766
7767
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
7768
                   See :ref:`note<cu_seqlens note>` for more details.
7769
7770
7771
7772
7773
        cu_seqlens_q_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for
                   `query_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_q_padded = cu_seqlens_q`.
7774
                   See :ref:`note<cu_seqlens note>` for more details.
7775
7776
7777
7778
7779
        cu_seqlens_kv_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_kv_padded = cu_seqlens_kv`.
7780
                   See :ref:`note<cu_seqlens note>` for more details.
7781
7782
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
7783
                      See :ref:`note<max_seqlen note>` for more details.
7784
7785
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
7786
                       See :ref:`note<max_seqlen note>` for more details.
7787
7788
7789
7790
7791
7792
7793
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding,causal', 'causal,padding',
                       'padding_causal', 'causal_bottom_right', 'padding_causal_bottom_right',
                       'arbitrary'}, default = `None`. Type of attention mask passed into
                       softmax operation. 'padding,causal', 'causal,padding' and 'padding_causal'
                       are equivalent. By default, causal masks are aligned to the top left corner
                       of the softmax matrix. When "`bottom_right`" is specified in the mask type,
                       causal masks are aligned to the bottom right corner.
7794
        window_size: Optional[Tuple[int, int]], default = `None`
7795
                    Sliding window size for local attention.
7796
7797
7798
7799
7800
        checkpoint_core_attention : bool, default = `False`
                                   If true, forward activations for attention are recomputed
                                   during the backward pass in order to save memory that would
                                   otherwise be occupied to store the forward activations until
                                   backprop.
7801
        core_attention_bias_type: str, default = `no_bias`
7802
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
7803
        core_attention_bias: Optional[torch.Tensor], default = `None`
7804
7805
                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
7806
7807
7808
7809
        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
7810
        fast_zero_fill: bool, default = `True`
7811
                    Whether to use the fast path to set output tensors to 0 or not.
7812
7813
7814
7815
7816
7817
7818
7819
7820
7821
        inference_params: Optional[InferenceParams], default = `None`
            Optimizes execution performance during inference by caching Keys and Values of the
            current decoding iteration. These cached values are appended to the K and V values
            computed in previous iterations, eliminating the need to recalculate them for the
            entire sequence.
            Initialization of `inference_params` is required prior to use to ensure sufficient
            memory allocation.
            Adjustments of the sequence_len_offset should be done after a complete forward pass.
            If rotary positional embeddings (RoPE) are utilized, they must be prepared beforehand.
            Supports "sbhd" and "bshd" layouts, with the "sbhd" layout being more efficient.
7822
7823
7824
7825
7826
7827
7828
7829
7830
7831
7832
7833
7834
        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)
7835
        """
7836
7837
7838
7839
7840
7841
7842
7843
7844
7845
7846
        with self.prepare_forward(
            query_layer,
            is_first_microbatch,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:

            if self.fp8:
                if self.fp8_meta["recipe"].fp8_mha:
                    if not self.fp8_meta["recipe"].fp8_dpa:
                        self.fp8_meta["recipe"].fp8_dpa = True
7847
                        self.logger.warning(
7848
7849
7850
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
7851
7852
7853
7854
7855
7856
7857
7858
7859
7860
7861

            if self.fp8 and self.fp8_meta["recipe"].fp8_dpa:
                forward_dtype = get_fp8_te_dtype(self.fp8_meta["recipe"], fprop_tensor=True)
                backward_dtype = get_fp8_te_dtype(self.fp8_meta["recipe"], fprop_tensor=False)
                assert forward_dtype in [
                    tex.DType.kFloat8E4M3,
                    tex.DType.kFloat8E5M2,
                ] and backward_dtype in [
                    tex.DType.kFloat8E4M3,
                    tex.DType.kFloat8E5M2,
                ], """DotProductAttention only supports "E4M3" and "E5M2" FP8 data types."""
7862

7863
7864
7865
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
7866
7867
7868
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
7869
7870
7871
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
7872
7873
7874
7875
7876
7877
7878
7879
            assert (
                key_layer.shape[-1] == self.hidden_size_per_attention_head_k
            ), f"Keys have head_dim = {key_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
                value_layer.shape[-1] == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {value_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
7880

7881
7882
7883
7884
7885
7886
            if attn_mask_type is None:
                attn_mask_type = self.attn_mask_type
            else:
                attn_mask_type = attn_mask_type.replace(",", "_")
                if attn_mask_type == "causal_padding":
                    attn_mask_type = "padding_causal"
7887
            assert (
7888
7889
7890
7891
7892
7893
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
            if qkv_format == "thd":
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
7894

7895
7896
7897
7898
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

7899
7900
7901
7902
7903
7904
7905
            if self.rng_states_tracker is not None and is_graph_capturing():
                assert isinstance(
                    self.rng_states_tracker, CudaRNGStatesTracker
                ), "Unsupported RNG states tracker."
                assert (
                    graph_safe_rng_available()
                ), "Upgrade PyTorch version to get RNG manipulation support for cuda graph capture."
7906

7907
7908
            if qkv_format is None:
                qkv_format = self.qkv_format
7909

7910
7911
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
7912

7913
7914
7915
7916
7917
                # convert causal to causal_bottom_right in inference when KV-caching is in use
                # so users can run with the same attn_mask_type for training and inference
                if attn_mask_type in ["causal", "padding_causal"]:
                    attn_mask_type = attn_mask_type + "_bottom_right"

7918
7919
7920
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7921

7922
7923
7924
7925
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
7926

7927
7928
7929
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
7930

7931
7932
7933
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
7934

7935
7936
7937
7938
7939
7940
7941
7942
7943
                # Copy keys and values into KV-cache
                inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    key_layer
                )
                inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    value_layer
                )
                key_layer = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
                value_layer = inference_value_memory[:sequence_end, batch_start:batch_end, ...]
7944

7945
7946
7947
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7948

7949
7950
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
7951
7952

            assert (
7953
7954
                key_layer.shape[-2] == self.num_gqa_groups_per_partition
                and value_layer.shape[-2] == self.num_gqa_groups_per_partition
7955
7956
7957
7958
            ), (
                "Keys and values must have num_gqa_group ="
                f" {self.num_gqa_groups_per_partition} heads!"
            )
7959
7960
7961
7962
7963
7964
7965
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

            if qkv_format == "thd":
7966
                assert all(
7967
7968
7969
7970
7971
7972
7973
7974
7975
7976
7977
7978
7979
                    len(x.shape) == 3 for x in (query_layer, key_layer, value_layer)
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
                assert (
                    cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
                assert (
                    cu_seqlens_q.shape == cu_seqlens_kv.shape
                    and len(cu_seqlens_q.shape) == 1
                    and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
                assert (
                    cu_seqlens_q.dtype == torch.int32 and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
7980
                batch_size = len(cu_seqlens_q) - 1
7981
                if max_seqlen_q is None:
7982
7983
7984
7985
                    if cu_seqlens_q_padded is not None:
                        seqlens_q = cu_seqlens_q_padded[1:] - cu_seqlens_q_padded[:-1]
                    else:
                        seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
7986
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
7987
                if max_seqlen_kv is None:
7988
7989
7990
7991
                    if cu_seqlens_kv_padded is not None:
                        seqlens_kv = cu_seqlens_kv_padded[1:] - cu_seqlens_kv_padded[:-1]
                    else:
                        seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
7992
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
7993

7994
7995
7996
7997
7998
7999
            cp_size = 1
            if isinstance(self.cp_group, dist_group_type):
                cp_size = get_distributed_world_size(self.cp_group)
            elif isinstance(self.cp_group, list):
                for group in self.cp_group:
                    cp_size *= get_distributed_world_size(group)
8000
8001
            context_parallel = cp_size > 1

8002
            if qkv_format in ["sbhd", "bshd"]:
8003
                assert all(
8004
8005
8006
                    len(x.shape) == 4 for x in (query_layer, key_layer, value_layer)
                ), f"Queries, keys and values must be 4D tensors when qkv_format = {qkv_format}!"
                if qkv_format == "sbhd":
8007
8008
                    max_seqlen_q = query_layer.shape[0] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[0] if max_seqlen_kv is None else max_seqlen_kv
8009
                    batch_size = query_layer.shape[1]
8010
                else:
8011
8012
                    max_seqlen_q = query_layer.shape[1] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[1] if max_seqlen_kv is None else max_seqlen_kv
8013
                    batch_size = query_layer.shape[0]
8014
8015
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
8016
8017
8018
8019
8020
                if cu_seqlens_q is not None:
                    seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                    assert all(
                        seqlens_q <= max_seqlen_q
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
8021
                        the sequence dimension in 'query_layer'!"""
8022
8023
8024
8025
8026
                if cu_seqlens_kv is not None:
                    seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                    assert all(
                        seqlens_kv <= max_seqlen_kv
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
8027
                        the sequence dimension in 'key_layer' and 'value_layer'!"""
8028
8029
8030
8031
8032
                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
8033
                        if self.attention_type == "self":
8034
8035
8036
8037
8038
8039
8040
8041
8042
8043
8044
8045
8046
8047
8048
8049
                            cu_seqlens_q = get_cu_seqlens(attention_mask)
                            cu_seqlens_kv = cu_seqlens_q
                        else:
                            cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                            cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
                    else:
                        cu_seqlens_q = _get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
                        cu_seqlens_kv = _get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
8050

8051
8052
8053
8054
8055
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
8056
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
8057
8058
8059
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
8060
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
8061
8062
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
8063

8064
8065
8066
8067
8068
8069
8070
8071
            global _alibi_cache
            if alibi_slopes is not None:
                assert (
                    core_attention_bias_type == "alibi"
                ), "core_attention_bias_type must be alibi in order to use alibi_slopes!"
                if self.layer_number == 1:
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True
8072
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
8073
8074
8075
8076
8077
8078
8079
8080
            if core_attention_bias_type == "alibi":
                assert (
                    core_attention_bias is None
                ), "core_attention_bias must be None when core_attention_bias_type is alibi!"
                if (
                    _alibi_cache["_num_heads"] != query_layer.shape[-2]
                    or _alibi_cache["_max_seqlen_q"] != max_seqlen_q
                    or _alibi_cache["_max_seqlen_kv"] != max_seqlen_kv
8081
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
8082
8083
8084
8085
8086
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

8087
8088
            core_attention_bias_shape = None
            if core_attention_bias is not None:
8089
                if (
8090
8091
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
8092
                ):
8093
8094
8095
8096
8097
8098
8099
8100
8101
8102
8103
8104
8105
8106
8107
8108
8109
8110
8111
8112
8113
8114
8115
8116
                    core_attention_bias_shape = "bhss"
                elif (
                    core_attention_bias.shape[0] == 1
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
                ):
                    core_attention_bias_shape = "1hss"
                elif (
                    core_attention_bias.shape[0] == batch_size and core_attention_bias.shape[1] == 1
                ):
                    core_attention_bias_shape = "b1ss"
                elif core_attention_bias.shape[0] == 1 and core_attention_bias.shape[1] == 1:
                    core_attention_bias_shape = "11ss"
                else:
                    assert (
                        False
                    ), "core_attention_bias must be in one of {bhss, 1hss, b1ss, 11ss} shapes"

            pad_between_seqs = (
                cu_seqlens_q_padded is not None
                and not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
            ) or (
                cu_seqlens_kv_padded is not None
                and not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
            )
8117

8118
            attention_params = AttentionParams(
8119
8120
8121
8122
8123
8124
8125
8126
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
                num_heads=query_layer.shape[-2],
                num_gqa_groups=key_layer.shape[-2],
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
8127
8128
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
8129
8130
8131
8132
8133
8134
8135
8136
8137
8138
8139
                attn_mask_type=attn_mask_type,
                window_size=window_size,
                alibi_slopes_shape=alibi_slopes.shape if alibi_slopes is not None else None,
                core_attention_bias_type=core_attention_bias_type,
                core_attention_bias_shape=core_attention_bias_shape,
                core_attention_bias_requires_grad=(
                    core_attention_bias.requires_grad if core_attention_bias is not None else False
                ),
                pad_between_seqs=pad_between_seqs,
                attention_dropout=self.attention_dropout,
                context_parallel=context_parallel,
8140
8141
                deterministic=self.deterministic,
                is_training=self.training,
8142
8143
8144
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
8145
            global _attention_backends, _use_flash_attn_3
8146
8147
8148
8149
8150
8151
8152
            if (
                _attention_backends["attention_params"] is None
                or attention_params != _attention_backends["attention_params"]
            ):
                _attention_backends["attention_params"] = attention_params
                _attention_backends["backend_selection_requires_update"] = True
            if _attention_backends["backend_selection_requires_update"]:
8153
                _use_flash_attn_3 = _flash_attn_3_is_installed
8154
8155
8156
8157
8158
8159
8160
8161
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
8162
8163
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
8164
                        _flash_attn_version if not _use_flash_attn_3 else _flash_attn_3_version,
8165
                    )
8166
8167
8168
8169
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
8170
                    )
8171
8172
8173
8174
8175
8176
8177
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
8178

8179
8180
8181
8182
8183
8184
8185
8186
8187
8188
8189
8190
8191
8192
8193
8194
8195
8196
8197
8198
8199
8200
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
                    alibi_slopes, _ = get_alibi(
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                    )
                return self.flash_attention(
                    query_layer,
                    key_layer,
                    value_layer,
                    attention_mask=attention_mask,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
                    attn_mask_type=attn_mask_type,
                    window_size=window_size,
                    alibi_slopes=alibi_slopes,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
8201
                    cp_comm_type=self.cp_comm_type,
8202
8203
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8204
8205
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8206
                )
8207

8208
            if use_fused_attention:
8209
8210
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
8211
8212
8213
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
8214
8215
8216
8217
8218
8219
8220
                    fu_core_attention_bias_type = "post_scale_bias"
                    _, fu_core_attention_bias = get_alibi(
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
8221
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
8222
                    )
8223
8224
8225
8226
8227
8228
8229
8230
8231
                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.fused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
8232
8233
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8234
8235
8236
8237
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
8238
                        window_size=window_size,
8239
8240
8241
8242
8243
8244
8245
                        fused_attention_backend=fused_attention_backend,
                        core_attention_bias_type=fu_core_attention_bias_type,
                        core_attention_bias=fu_core_attention_bias,
                        fast_zero_fill=fast_zero_fill,
                        cp_group=self.cp_group,
                        cp_global_ranks=self.cp_global_ranks,
                        cp_stream=self.cp_stream,
8246
                        cp_comm_type=self.cp_comm_type,
8247
8248
8249
8250
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
8251
8252
8253
8254
8255
8256
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
8257
8258
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8259
8260
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8261
8262
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
8263
                    window_size=window_size,
8264
                    fused_attention_backend=fused_attention_backend,
8265
8266
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
8267
8268
8269
8270
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
8271
                    cp_comm_type=self.cp_comm_type,
8272
8273
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8274
                )
8275

8276
            from .cpu_offload import CPUOffloadEnabled
8277

8278
8279
8280
8281
8282
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
8283

8284
            if use_unfused_attention:
8285
8286
8287
8288
8289
8290
                if window_size is not None and (
                    window_size[0] != -1 or window_size[1] not in [-1, 0]
                ):
                    attn_mask_type, attention_mask = get_swa_mask(
                        window_size, max_seqlen_q, max_seqlen_kv, attn_mask_type, attention_mask
                    )
8291
8292
8293
8294
8295
8296
8297
8298
8299
8300
8301
8302
8303
8304
8305
8306
                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.unfused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
                    )
                return self.unfused_attention(
8307
8308
8309
                    query_layer,
                    key_layer,
                    value_layer,
8310
8311
8312
8313
8314
8315
8316
8317
8318
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
                )
8319

8320
            raise ValueError("No dot product attention support for the provided inputs!")
8321
8322


8323
8324
8325
8326
8327
8328
8329
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

8330
8331
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
8332

8333
8334
8335
8336
8337
8338
8339
8340
8341
8342
8343
8344
8345
8346
8347
8348
8349
8350
8351
8352
8353
8354
8355
8356
8357
    Parameters
    ----------
    hidden_size : int
                 size of each input sample.
    num_attention_heads : int
                         number of attention heads in the transformer layer.
    kv_channels: int, default = `None`
                number of key-value channels. defaults to
                :attr:`hidden_size` / :attr:`num_attention_heads` if `None`.
    attention_dropout: float, default = 0.1
                      dropout probability for the dropout op during multi-head attention.
    layernorm_epsilon : float, default = 1e-5
                       a value added to the denominator of layer normalization
                       for numerical stability.
    init_method : Callable, default = `None`
                 used for initializing weights of QKV and 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 weights of PROJ and FC2 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)`.
    layer_number: int, default = `None`
                 layer number of the current `TransformerLayer` when multiple such modules are
                 concatenated to form a transformer block.
8358
8359
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
8360
                   default = `causal`
8361
8362
8363
8364
8365
                   type of attention mask passed into softmax operation. Overridden by
                   :attr:`attn_mask_type` in the `forward` method. The forward
                   arg is useful for dynamically changing mask types, e.g. a different
                   mask for training and inference. The init arg is useful for cases
                   involving compilation/tracing, e.g. ONNX export.
8366
8367
8368
8369
    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
8370
8371
8372
                window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
                map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
                `attn_mask_type`. Similar to :attr:`attn_mask_type`, `window_size` can
8373
                be overridden by :attr:`window_size` in `forward` as well.
8374
8375
8376
8377
8378
8379
8380
8381
8382
8383
8384
8385
8386
    num_gqa_groups : int, default = `None`
                         number of GQA groups in the transformer layer.
                         Grouped Query Attention is described in
                         `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                         This only affects the keys and values, not the querys.
                         GQA-1 is equivalent to Multi-Query Attention
                         (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                         is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
    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.
8387
8388
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
8389
8390
8391
8392
8393
8394
8395
8396
8397
8398
8399
8400
8401
8402
8403
8404
8405
8406
8407
8408
    attention_type: { 'self', 'cross' }, default = 'self'
                   type of attention applied.
    zero_centered_gamma : bool, default = 'False'
                         if set to 'True', gamma parameter in LayerNorm is initialized to 0 and
                         the LayerNorm formula changes to

                         .. math::
                            y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                            (1 + \gamma) + \beta
    normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm'
                   type of normalization applied.
    qkv_weight_interleaved : bool, default = `True`
                            if set to `False`, the QKV weight is interpreted as a concatenation of
                            query, key, and value weights along the `0th` dimension. The default
                            interpretation is that the individual `q`, `k`, and `v` weights for each
                            attention head are interleaved. This parameter is set to `False` when
                            using :attr:`fuse_qkv_params=False`.
    bias : bool, default = `True`
          if set to `False`, the transformer layer will not learn any additive biases.
    device : Union[torch.device, str], default = "cuda"
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          The device on which the parameters of the model will be allocated. It is the user's
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          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
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    qkv_format: str, default = `sbhd`
            dimension format for `query_layer`, `key_layer` and `value_layer`,
            {`sbhd`, `bshd`}. `s` stands for the sequence length, `b` batch size,
            `h` the number of heads and `d` head size. `sbhd` and `bshd` formats
            are used for when sequences in a batch are of equal length or padded to
            equal length. Please note that these formats do not reflect how
            tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
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            For that, please use `get_qkv_layout` to gain the layout information.
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    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, QKV and FC1 layers are used as Column Parallel
                      whereas PROJ 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. 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.
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.
    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.
    fuse_qkv_params: bool, default = 'False'
                    if set to `True`, `TransformerLayer` module exposes a single fused
                    parameter for query-key-value. This enables optimizations such as QKV
                    fusion without concatentations/splits and also enables the argument
                    `fuse_wgrad_accumulation`.
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    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
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        kv_channels: Optional[int] = None,
        attention_dropout: float = 0.1,
        layernorm_epsilon: float = 1e-5,
        init_method: Optional[Callable] = None,
        output_layer_init_method: Optional[Callable] = None,
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        layer_number: Optional[int] = None,
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        attn_mask_type: str = "causal",
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        window_size: Optional[Tuple[int, int]] = None,
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        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
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        num_gqa_groups: Optional[int] = None,
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        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
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        params_dtype: Optional[torch.dtype] = None,
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        return_bias: bool = False,
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        return_layernorm_output: bool = False,
        input_layernorm: bool = False,
        attention_type: str = "self",
        set_parallel_mode: bool = False,
        fuse_qkv_params: bool = False,
        zero_centered_gamma: bool = False,
        qkv_weight_interleaved: bool = True,
        ub_bulk_wgrad: bool = False,
        ub_bulk_dgrad: bool = False,
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        ub_overlap_rs_dgrad: bool = False,
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        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
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        bias: bool = True,
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        normalization: str = "LayerNorm",
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        device: Union[torch.device, str] = "cuda",
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        qkv_format: str = "sbhd",
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    ) -> None:
        super().__init__()
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        self.qkv_format = qkv_format
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        self.attn_mask_type = attn_mask_type
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        self.window_size = check_set_window_size(attn_mask_type, window_size)
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        self.layer_number = layer_number
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        self.input_layernorm = input_layernorm
        self.attention_type = attention_type
        self.get_rng_state_tracker = get_rng_state_tracker
        self.tp_group = tp_group
        self.return_layernorm_output = return_layernorm_output
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        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
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        self.num_attention_heads = num_attention_heads
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        self.return_bias = return_bias
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        self.cp_size = 1
        self.cp_rank = 0
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        kv_channels = kv_channels if kv_channels else (hidden_size // num_attention_heads)

        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()
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        if not fuse_qkv_params:
            qkv_weight_interleaved = False
        self.qkv_weight_interleaved = qkv_weight_interleaved

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        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
        if layer_number is not None:
            assert layer_number > 0, "layer_number must be a positive integer"
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        tp_size = tp_size if tp_group is None else get_distributed_world_size(tp_group)
        self.tp_size = tp_size
        self.sequence_parallel = (tp_size > 1) and sequence_parallel

        self.num_attention_heads_per_partition = divide(num_attention_heads, tp_size)
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        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
        assert (
            self.num_gqa_groups % tp_size == 0
        ), "The number of GQA groups must be divisible by tensor parallel size!"
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        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
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        self.hidden_size_per_attention_head = kv_channels
        self.hidden_size_q = self.hidden_size_per_attention_head * num_attention_heads
        self.hidden_size_kv = self.hidden_size_per_attention_head * self.num_gqa_groups
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        common_gemm_kwargs = {
            "fuse_wgrad_accumulation": fuse_wgrad_accumulation,
            "tp_group": tp_group,
            "tp_size": tp_size,
            "get_rng_state_tracker": get_rng_state_tracker,
            "sequence_parallel": sequence_parallel,
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            "params_dtype": self.params_dtype,
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            "device": device,
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        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

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        if self.attention_type == "self":
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            parameters_split = None
            if not fuse_qkv_params:
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                parameters_split = collections.OrderedDict(
                    [
                        ("query", self.hidden_size_q),
                        ("key", self.hidden_size_kv),
                        ("value", self.hidden_size_kv),
                    ]
                )
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            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
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                    self.hidden_size_q + 2 * self.hidden_size_kv,
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                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    return_layernorm_output=return_layernorm_output,
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                    parameters_split=parameters_split,
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                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
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                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
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                    ub_overlap_ag=ub_overlap_ag,
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                    normalization=normalization,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
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                    self.hidden_size_q + 2 * self.hidden_size_kv,
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                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
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                    parameters_split=parameters_split,
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                    **common_gemm_kwargs,
                )
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        elif self.attention_type == "cross":
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            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
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                    self.hidden_size_q,
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                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
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                    parameters_split=("query",) if not fuse_qkv_params else None,
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                    return_layernorm_output=return_layernorm_output,
                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
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                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
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                    ub_overlap_ag=ub_overlap_ag,
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                    normalization=normalization,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
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                    self.hidden_size_q,
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                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
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                2 * self.hidden_size_kv,
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                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
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                parameters_split=("key", "value") if not fuse_qkv_params else None,
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                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
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            self.hidden_size_per_attention_head,
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            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
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            qkv_format=self.qkv_format,
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            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
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            layer_number=self.layer_number,
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            attention_type=self.attention_type,
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        )

        # Linear
        self.proj = Linear(
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            self.hidden_size_q,
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            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
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            return_bias=return_bias,
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            parallel_mode="row" if set_parallel_mode else None,
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            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
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            ub_name="proj",
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            **common_gemm_kwargs,
        )

    def _allocate_memory(
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        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
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    ) -> torch.Tensor:
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        """Allocates memory for KV cache."""
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        return torch.empty(
            inference_max_sequence_len,
            batch_size,
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            self.num_gqa_groups_per_partition,
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            self.hidden_size_per_attention_head,
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            dtype=dtype,
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            device=torch.cuda.current_device(),
        )

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

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

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    def set_context_parallel_group(
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        self,
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        cp_group: Union[dist_group_type, List[dist_group_type], None],
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        cp_global_ranks: List[int],
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        cp_stream: torch.cuda.Stream,
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        cp_comm_type: str = "p2p",
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    ) -> None:
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        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
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        cp_group : Union[ProcessGroup, List[ProcessGroup]]
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                  context parallel process group.
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                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
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        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
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        cp_comm_type : str, default = `p2p`
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                      inter-gpu communication type for context parallelism.
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                      Can be "p2p" or "all_gather" or "a2a", "a2a+p2p".
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                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
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                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
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        """
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        if isinstance(cp_group, dist_group_type):
            self.cp_size = get_distributed_world_size(cp_group)
            self.cp_rank = get_distributed_rank(cp_group)
        elif isinstance(cp_group, list):
            assert len(cp_group) == 2, "Current implementation only supports two-level CP groups!"
            assert (
                cp_comm_type == "a2a+p2p"
            ), "Only cp_comm_type of a2a+p2p requires hierarchical CP groups!"
            cp_size_a2a = get_distributed_world_size(cp_group[0])
            cp_rank_a2a = get_distributed_rank(cp_group[0])
            cp_size_p2p = get_distributed_world_size(cp_group[1])
            cp_rank_p2p = get_distributed_rank(cp_group[1])
            self.cp_size = cp_size_a2a * cp_size_p2p
            self.cp_rank = cp_size_a2a * cp_rank_p2p + cp_rank_a2a

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        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
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                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        encoder_output: Optional[torch.Tensor] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
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        inference_params: Optional[InferenceParams] = None,
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        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        fast_zero_fill: bool = True,
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    ) -> Tuple[Union[torch.Tensor, None], ...]:
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        """
        Forward propagation for MultiheadAttention layer.

        .. note::

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            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
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        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
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             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
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             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
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             for cross-attention. For "`arbitrary`" mask, it should be in a shape broadcastable to
             [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value means
             the corresponding position is masked out and a `False` means that position
             is allowed to participate in attention.
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                       'padding_causal_bottom_right','arbitrary'},
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                       default = `None`
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                       type of attention mask passed into softmax operation. By default,
                       causal masks are aligned to the top left corner of the softmax matrix.
                       When "`bottom_right`" is specified in the mask type, causal masks are
                       aligned to the bottom right corner.
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        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
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        encoder_output : Optional[torch.Tensor], default = `None`
             Output of the encoder block to be fed into the decoder block if using
             `layer_type="decoder"`.
        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)
        checkpoint_core_attention: bool, default = `False`
                                  If true, forward activations for core attention are recomputed
                                  during the backward pass in order to save memory that would
                                  otherwise be occupied to store the forward activations until
                                  backprop.
        rotary_pos_emb: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], default = `None`
                       Embeddings for query and key tensors for applying rotary position
                       embedding. By default no input embedding is applied.
        core_attention_bias_type: str, default = `no_bias`
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                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
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        core_attention_bias: Optional[torch.Tensor], default = `None`
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                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
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        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
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        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
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        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
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        # hidden_states: [sq, b, h]

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        if attn_mask_type is None:
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            attn_mask_type = self.attn_mask_type
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        if window_size is None:
            window_size = self.window_size
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        window_size = check_set_window_size(attn_mask_type, window_size)
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        if "padding" in attn_mask_type and attention_mask is not None:
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            for mask in attention_mask:
                assert mask.dtype == torch.bool, "Attention mask must be in boolean type!"
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        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
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        # =================================================
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        # Pre-allocate memory for key-values for inference
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        # =================================================

        if inference_params and self.layer_number is not None:
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            assert (
                self.qkv_format != "thd"
            ), "qkv_format == thd is not supported for an inference with KV-cache!"
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            if self.layer_number not in inference_params.key_value_memory_dict:
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                inf_max_seq_len = inference_params.max_sequence_length
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                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
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                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
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                )
                inference_value_memory = self._allocate_memory(
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                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
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                )
                inference_params.key_value_memory_dict[self.layer_number] = (
                    inference_key_memory,
                    inference_value_memory,
                )
            else:
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]

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        # ======================
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        # Query, Key, and Value
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        # ======================
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        fp8_mha = (
            FP8GlobalStateManager.is_fp8_enabled()
            and FP8GlobalStateManager.get_fp8_recipe().fp8_mha
        )

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        layernorm_output = None
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        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
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            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                    fp8_output=fp8_mha and rotary_pos_emb is None,
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                )
                if self.return_layernorm_output:
                    mixed_x_layer, layernorm_output = layernorm_qkv_outputs
                else:
                    mixed_x_layer = layernorm_qkv_outputs
            else:
                mixed_x_layer = self.qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                    fp8_output=fp8_mha and rotary_pos_emb is None,
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                )

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            num_queries_per_key_value = (
                self.num_attention_heads_per_partition // self.num_gqa_groups_per_partition
            )
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            if self.qkv_weight_interleaved:
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                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
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                new_tensor_shape = mixed_x_layer.size()[:-1] + (
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                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
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                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
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            else:
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, (np/ng + 2), ng, hn]
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
                    (num_queries_per_key_value + 2),
                    self.num_gqa_groups_per_partition,
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                    self.hidden_size_per_attention_head,
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                )
                # split along third last dimension
                split_dim = -3
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            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

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            # qkv_weight_interleaved:
            #  [sq, b, ng, (np/ng + 2), hn]
            #  --> [sq, b, ng, np/ng, hn], [sq, b, ng, 1, hn], [sq, b, ng, 1, hn]
            # not qkv_weight_interleaved:
            #  [sq, b, (np/ng + 2), ng, hn]
            #  --> [sq, b, np/ng, np, hn], [sq, b, 1, ng, hn], [sq, b, 1, ng, hn]
            if not is_in_onnx_export_mode():
                query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
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                )
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            else:
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                query_layer, key_layer, value_layer = torch.split(
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                    mixed_x_layer,
                    (num_queries_per_key_value, 1, 1),
                    dim=split_dim,
                )
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            if self.qkv_format == "thd":
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
            else:
                # query: -> [sq, b, np, hn]
                # key, value: -> [sq, b, ng, hn]
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), x.size(1), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
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        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
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            mixed_kv_layer = self.key_value(
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                encoder_output,
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                is_first_microbatch=is_first_microbatch,
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                fp8_output=fp8_mha and rotary_pos_emb is None,
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            )

            if self.qkv_weight_interleaved:
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                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
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                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
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                    self.num_gqa_groups_per_partition,
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                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
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                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
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                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
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                    2 * self.num_gqa_groups_per_partition,
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                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

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            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
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                    mixed_kv_layer,
                    split_dim,
                    mixed_kv_layer.shape[split_dim] // 2,
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                )
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            else:
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                key_layer, value_layer = torch.split(
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                    mixed_kv_layer,
                    mixed_kv_layer.shape[split_dim] // 2,
                    dim=split_dim,
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                )
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            key_layer, value_layer = (
                x.reshape(
                    x.size(0),
                    x.size(1),
                    -1,
                    self.hidden_size_per_attention_head,
                )
                for x in (key_layer, value_layer)
            )
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            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                    fp8_output=fp8_mha and rotary_pos_emb is None,
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                )
                if self.return_layernorm_output:
                    query_layer, layernorm_output = layernorm_query_outputs
                else:
                    query_layer = layernorm_query_outputs
            else:
                query_layer = self.query_layer(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                    fp8_output=fp8_mha and rotary_pos_emb is None,
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                )

            # [sq, b, hp] --> [sq, b, np, hn]
            new_tensor_shape = query_layer.size()[:-1] + (
                self.num_attention_heads_per_partition,
                self.hidden_size_per_attention_head,
            )
            query_layer = query_layer.view(*new_tensor_shape)

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        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
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        if rotary_pos_emb is not None:
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            assert not isinstance(query_layer, Float8Tensor) and not isinstance(
                key_layer, Float8Tensor
            ), "RoPE is not supported for Float8Tensors!"
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            # duplicate the pos_emb for self attention
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            if not isinstance(rotary_pos_emb, tuple):
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                rotary_pos_emb = (rotary_pos_emb,) * 2
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            q_pos_emb, k_pos_emb = rotary_pos_emb
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            # adjust key and value for inference
            if inference_params is not None:
                if self.qkv_format == "sbhd":
                    sequence_length = key_layer.size(0)
                elif self.qkv_format == "bshd":
                    sequence_length = key_layer.size(1)
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                else:
                    raise ValueError(f"QKV format {self.qkv_format} not supported for KV caching.")
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                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + sequence_length

                q_pos_emb = q_pos_emb[sequence_start:sequence_end, ...]
                k_pos_emb = k_pos_emb[sequence_start:sequence_end, ...]

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            query_layer = apply_rotary_pos_emb(
                query_layer,
                q_pos_emb,
                self.qkv_format,
                fused=True,
                cu_seqlens=cu_seqlens_q,
                cp_size=self.cp_size,
                cp_rank=self.cp_rank,
            )
            key_layer = apply_rotary_pos_emb(
                key_layer,
                k_pos_emb,
                self.qkv_format,
                fused=True,
                cu_seqlens=cu_seqlens_kv,
                cp_size=self.cp_size,
                cp_rank=self.cp_rank,
            )
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        # ===========================
        # Core attention computation
        # ===========================

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        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
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            qkv_format=self.qkv_format,
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            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_kv,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_kv=max_seqlen_kv,
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            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
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            window_size=window_size,
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            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
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            alibi_slopes=alibi_slopes,
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            fast_zero_fill=fast_zero_fill,
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            inference_params=inference_params,
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        )

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        # ===================
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        # Output. [sq, b, h]
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        # ===================
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        projection_output = self.proj(
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            context_layer,
            is_first_microbatch=is_first_microbatch,
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        )

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        if self.return_bias:
            attention_output, attention_bias = projection_output
        else:
            attention_output, attention_bias = projection_output, None

        outputs = (attention_output,)
        if self.return_bias:
            outputs += (attention_bias,)
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        if self.input_layernorm and self.return_layernorm_output:
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            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]