attention.py 382 KB
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# Copyright (c) 2022-2025, 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
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from transformer_engine.pytorch.utils import (
    get_cudnn_version,
    nvtx_range_pop,
    nvtx_range_push,
)
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
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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.tensor._internal.float8_tensor_base import Float8TensorBase
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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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)
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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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from transformer_engine.pytorch.tensor.quantized_tensor import (
    QuantizedTensor,
    prepare_for_saving,
    restore_from_saved,
)
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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_version_required_blackwell = PkgVersion("2.7.3")
_flash_attn_max_version = PkgVersion("2.7.3")
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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_2_7_0_plus = False
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flash_attn_cuda_bwd = None
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flash_attn_func = None
flash_attn_varlen_func = None
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_flash_attn_fwd = None
_flash_attn_bwd = None
_flash_attn_varlen_fwd = None
_flash_attn_varlen_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:
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    if torch.cuda.is_available() and get_device_compute_capability() >= (10, 0):
        if _flash_attn_version_required_blackwell <= _flash_attn_version <= _flash_attn_max_version:
            _flash_attn_is_installed = True
    elif _flash_attn_version_required <= _flash_attn_version <= _flash_attn_max_version:
        _flash_attn_is_installed = True

    if _flash_attn_is_installed:
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        from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
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        from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
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        from flash_attn.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd
        from flash_attn.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd
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        from flash_attn.flash_attn_interface import (
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            _flash_attn_varlen_forward as _flash_attn_varlen_fwd,
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        )
        from flash_attn.flash_attn_interface import (
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            _flash_attn_varlen_backward as _flash_attn_varlen_bwd,
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        )

        _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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        _flash_attn_2_7_0_plus = _flash_attn_version >= PkgVersion("2.7.0")
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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(
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                (
                    _flash_attn_version_required
                    if get_device_compute_capability() < (10, 0)
                    else _flash_attn_version_required_blackwell
                ),
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                _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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# TODO(cyang): update FA to 2.7.3 when its FA3 compilation issue is resolved
# https://github.com/Dao-AILab/flash-attention/issues/1452
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_flash_attn_3_installation_steps = """\
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(1) pip install "git+https://github.com/Dao-AILab/flash-attention.git@v2.7.2#egg=flashattn-hopper&subdirectory=hopper"
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(2) python_path=`python -c "import site; print(site.getsitepackages()[0])"`
(3) mkdir -p $python_path/flashattn_hopper
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(4) wget -P $python_path/flashattn_hopper https://raw.githubusercontent.com/Dao-AILab/flash-attention/v2.7.2/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_forward as _flash_attn_fwd_v3
    from flashattn_hopper.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd_v3
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    from flashattn_hopper.flash_attn_interface import (
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        _flash_attn_varlen_forward as _flash_attn_varlen_fwd_v3,
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    )
    from flashattn_hopper.flash_attn_interface import (
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        _flash_attn_varlen_backward as _flash_attn_varlen_bwd_v3,
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    )
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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

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    def __eq__(self, other):
        """
        Overwrite dataclass.__eq__ so that only fp8_meta["recipe"] is compared,
        since all other entries of fp8_meta are unused in get_attention_backend.
        """
        if not isinstance(other, self.__class__):
            return NotImplemented
        for field in fields(self):
            fname = field.name
            sf = getattr(self, fname)
            of = getattr(other, fname)
            if fname != "fp8_meta":
                if sf != of:
                    return False
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            elif sf.get("recipe", None) != of.get("recipe", None):
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                return False
        return True

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_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: 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:
489
        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)))
511
    ):
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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
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    # padding_causal_bottom_right | Same as "padding"                    | All
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    # 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
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            elif window_size[1] != 0 or attention_dropout != 0.0:
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                logger.debug(
                    "Disabling FusedAttention as it only supports sliding window attention "
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                    "with (left, 0) and no dropout"
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                )
                use_fused_attention = False
734
            elif max_seqlen_q > max_seqlen_kv:
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                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
737
                    "with s_q > s_kv for cross-attention"
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                )
                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
763
    if use_flash_attention and core_attention_bias_type == "alibi":
764
        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
772

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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"
787
        and (alibi_slopes_shape is not None or max_seqlen_q != max_seqlen_kv)
788
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790
    ):
        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],
839
        )
840
        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)
903
            )
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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"]
    ):
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        if device_compute_capability >= (9, 0):
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            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()
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def get_full_mask(
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    max_seqlen_q: int,
    max_seqlen_kv: int,
    attn_mask_type: str = "no_mask",
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    attention_mask: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = None,
    window_size: Tuple[int, int] = None,
    attention_type: str = "self",
    bottom_right_alignment: bool = True,
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) -> torch.Tensor:
    """
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    Get full attention mask in [..., max_seqlen_q, max_seqlen_kv] shape, based on `attn_mask_type`,
    `attention_mask`, and `window_size`. For sliding window attention, the diagonal alignment depends
    on both `attn_mask_type` and `bottom_right_alignment`, as detailed below.::

       attn_mask_type              output shape                                 diagonal alignment
       --------------------------------------------------------------------------------------------
       no_mask                     [1, 1, max_seqlen_q, max_seqlen_kv]          follow bottom_right_alignment
       causal                      [1, 1, max_seqlen_q, max_seqlen_kv]          always top left
       causal_bottom_right         [1, 1, max_seqlen_q, max_seqlen_kv]          always bottom right
       padding                     [batch_size, 1, max_seqlen_q, max_seqlen_kv] follow bottom_right_alignment
       padding_causal              [batch_size, 1, max_seqlen_q, max_seqlen_kv] always top left
       padding_causal_bottom_right [batch_size, 1, max_seqlen_q, max_seqlen_kv] always bottom right
       arbitrary                   same as attention_mask                       follow bottom_right_alignment

    .. note::

    For "padding_bottom_right" mask, or "padding" mask with `bottom_right_alignment` = True, the bottom right
    diagonal comes from the bottom right corner of the [actual_seqlens_q[i], actual_seqlens_kv[i]] matrix,
    i = 0,...,batch_size-1, not the [max_seqlen_q, max_seqlen_kv] matrix. For example, with max_seqlen_q = 4,
    max_seqlen_kv = 4, attn_mask_type = "padding", attention_type = "cross", and attention_mask = (
    [[False, False,  True, True], [False, False, False, False]],
    [[False, False, False, True], [False,  True,  True,  True]]), the returned full attention mask has [2, 4, 4]
    shape and is,::

      [[[False, False, False, True],
        [False, False, False, True],
        [ True,  True,  True, True],
        [ True,  True,  True, True]],
       [[False,  True,  True, True],
        [False,  True,  True, True],
        [False,  True,  True, True],
        [False,  True,  True, True]]]
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    Parameters
    ----------
    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`"}
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    attention_mask: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
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        default = `None`
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        Boolean tensor(s) used to mask out attention softmax input. Please see DotProductAttention
        for the requirements of `attention_mask` for different `attn_mask_type`s.
    window_size: 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
        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`.
    attention_type: str, default = "self"
        Attention type, {"self", "cross"}
    bottom_right_alignment: bool, default = `True`
        Whether to align the diagonal of the sliding window attention to the bottom right (`True`)
        or top left (`False`) corner of the softmax matrix. Ignored if `attn_mask_type` explicitly
        specifies "causal" or "causal_bottom_right".
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    Returns
    ----------
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    attn_mask_type: str
        For sliding window attention (>=0, >0), "arbitrary"; otherwise, the same as input `attn_mask_type`
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    attention_mask: torch.Tensor
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        The full attention mask based on `attn_mask_type`, `attention_mask` and `window_size`
    actual_seqlens_q: torch.Tensor
        For padding masks, the actual sequence lengths for queries, in shape [batch_size].
        For other masks, `None`.
    actual_seqlens_kv: Optional[torch.Tensor], default = `None`
        For padding masks, the actual sequence lengths for keys and values, in shape [batch_size].
        For other masks, `None`.
1124
    """
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    # perform basic checks
    change_type = window_size is not None and (
        window_size[0] != -1 or window_size[1] not in [-1, 0]
    )
    if window_size is None:
        window_size = (-1, -1)
    if "causal" in attn_mask_type:
        window_size = (window_size[0], 0)
    window_size = (
        max_seqlen_kv if window_size[0] == -1 else window_size[0],
        max_seqlen_q if window_size[1] == -1 else window_size[1],
    )

    # apply padding mask
    actual_seqlens_q = None
    actual_seqlens_kv = None
    if "padding" in attn_mask_type:
        if attention_type == "self":
            attention_mask = torch.logical_or(
                attention_mask.squeeze(1).unsqueeze(3), attention_mask
            )
        else:
            attention_mask = torch.logical_or(
                attention_mask[0].squeeze(1).unsqueeze(3), attention_mask[1]
            )
        m = attention_mask.logical_not()
        actual_seqlens_q = m[:, 0, :, 0].sum(dim=1)
        actual_seqlens_kv = m[:, 0, 0, :].sum(dim=1)

    # apply SWA mask
    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)
    swa_left = None
    swa_right = None
    if attn_mask_type == "causal_bottom_right" or (
        attn_mask_type in ["no_mask", "arbitrary"] and bottom_right_alignment
    ):
        swa_left = mask + max_seqlen_kv - max_seqlen_q - window_size[0]
        swa_right = mask + max_seqlen_kv - max_seqlen_q + window_size[1]
    elif attn_mask_type in ["causal", "padding_causal"] or (
        attn_mask_type in ["no_mask", "padding", "arbitrary"] and not bottom_right_alignment
    ):
        swa_left = mask - window_size[0]
        swa_right = mask + window_size[1]
    elif attn_mask_type == "padding_causal_bottom_right" or (
        attn_mask_type == "padding" and bottom_right_alignment
    ):
        batch_size = attention_mask.shape[0]
        swa_left = mask.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv) + (
            actual_seqlens_kv - actual_seqlens_q - window_size[0]
        ).view(batch_size, 1, 1, 1)
        swa_right = mask.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv) + (
            actual_seqlens_kv - actual_seqlens_q + window_size[1]
        ).view(batch_size, 1, 1, 1)
    swa_mask = torch.logical_not(
        torch.where(swa_left <= 0, 1, 0) - torch.where(swa_right < 0, 1, 0)
    )
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    if attention_mask is not None:
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        attention_mask = torch.logical_or(swa_mask, attention_mask)
    else:
        attention_mask = swa_mask

    # change mask type
    if change_type:
        attn_mask_type = "arbitrary"

    return attn_mask_type, attention_mask, actual_seqlens_q, actual_seqlens_kv
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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,
1204
    bottom_right_alignment: bool = True,
1205
) -> Tuple[torch.Tensor, torch.Tensor]:
1206
    """
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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`).
1226

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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:
1262
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
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        else:
            raise ValueError("ALiBi slopes cannot exceed 2 dimensions.")

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        bias = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
1267
            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
1270
        )
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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
1286
        _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

1319
    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)
1325
    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)
    )
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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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1349

    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

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_cu_seqlens_cache = {}
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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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1385
@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
    )
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    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)
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        gathered_data = torch.gather(tensor_data, 0, indices)
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        packed = Float8Tensor.make_like(tensor, data=gathered_data, shape=gathered_data.shape)
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    else:
        tensor = torch.cat((tensor, padding_indice), dim=0)

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


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@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


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@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


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@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)
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        unpacked_data = unpacked[0:-1, :, :]
        unpacked = Float8Tensor.make_like(tensor, data=unpacked_data, shape=unpacked_data.shape)
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    else:
        unpacked.scatter_(0, indices, tensor)
        unpacked = unpacked[0:-1, :, :]
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    return unpacked


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@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


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@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(
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        ctx, indices: torch.Tensor, *tensors: Tuple[torch.Tensor, ...]
1502
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
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        # pylint: disable=missing-function-docstring
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        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
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        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, ...]):
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        # pylint: disable=missing-function-docstring
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        (indices,) = ctx.saved_tensors
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        if len(grad_outputs) == 1:
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            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.
    """
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    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
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        # pylint: disable=missing-function-docstring
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        ctx.save_for_backward(indices)
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        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
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        # 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)
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    new_scale = max_scale + torch.log1p(torch.exp(min_scale - max_scale))
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    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


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def get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False):
    """Get the list of quantizers used in attention from the quantizers list."""
    if not fp8:
        num_of_nones = 8 if cp_specific_quantizers else 6
        return [None] * num_of_nones
    QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
    QKV_quantizer.internal = True
    QKV_quantizer.set_usage(rowwise=True, columnwise=False)
    O_quantizer = quantizers["scaling_fwd"][META_O]
    O_quantizer.set_usage(rowwise=True, columnwise=False)
    S_quantizer = quantizers["scaling_fwd"][META_S]
    S_quantizer.internal = True
    S_quantizer.set_usage(rowwise=True, columnwise=False)
    dQKV_quantizer = quantizers["scaling_bwd"][META_DQKV]
    dQKV_quantizer.interal = True
    dQKV_quantizer.set_usage(rowwise=True, columnwise=False)
    dO_quantizer = quantizers["scaling_bwd"][META_DO]
    dO_quantizer.set_usage(rowwise=True, columnwise=False)
    dO_quantizer.internal = True
    dP_quantizer = quantizers["scaling_bwd"][META_DP]
    dP_quantizer.set_usage(rowwise=True, columnwise=False)
    dP_quantizer.interal = True
    dQKV_CP_quantizer = quantizers["scaling_bwd"][META_DQKV_CP]
    dQKV_CP_quantizer.set_usage(rowwise=True, columnwise=False)
    dQKV_CP_quantizer.internal = True
    O_CP_quantizer = quantizers["scaling_fwd"][META_O_CP]
    O_CP_quantizer.set_usage(rowwise=True, columnwise=False)

    if cp_specific_quantizers:
        return (
            QKV_quantizer,
            O_quantizer,
            O_CP_quantizer,
            S_quantizer,
            dQKV_quantizer,
            dQKV_CP_quantizer,
            dO_quantizer,
            dP_quantizer,
        )

    return QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer


1800
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
1801
    """
1802
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1804
    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.
1805
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1809

    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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1812
    """

    @staticmethod
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1819
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
1820
        cu_seqlens_kv,
1821
        max_seqlen_q,
1822
        max_seqlen_kv,
1823
1824
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
1825
1826
1827
1828
1829
1830
1831
1832
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
1833
1834
        fp8,
        fp8_meta,
1835
1836
1837
        cp_group,
        cp_global_ranks,
        cp_stream,
1838
        quantizers,
1839
    ):
1840
        # pylint: disable=missing-function-docstring
1841
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
1842
1843
1844
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
        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

1862
1863
        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
1864
1865
        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]
1866
1867
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1868
1869
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
1870

1871
        seq_dim = None
1872
        if qkv_format in ["bshd", "sbhd"]:
1873
            seq_dim = qkv_format.index("s")
1874
1875
1876
1877
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

1878
1879
1880
1881
1882
1883
        pad_between_seqs_q = cu_seqlens_q_padded is not None and not torch.equal(
            cu_seqlens_q_padded[:-1], cu_seqlens_q[:-1]
        )
        pad_between_seqs_kv = cu_seqlens_kv_padded is not None and not torch.equal(
            cu_seqlens_kv_padded[:-1], cu_seqlens_kv[:-1]
        )
1884
1885
        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
1886
1887
1888
1889
1890
1891
        cu_seqlens_q_padded = (
            None if cu_seqlens_q_padded is None else cu_seqlens_q_padded // cp_size
        )
        cu_seqlens_kv_padded = (
            None if cu_seqlens_kv_padded is None else cu_seqlens_kv_padded // cp_size
        )
1892
1893
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
1894

1895
        fused_attn_backend = None
1896
        qkv_dtype = q.dtype
1897
1898
1899
        amax_per_step = None
        S_quantizer_per_step = [None for _ in range(cp_size)]
        O_CP_quantizer_per_step = [None for _ in range(cp_size)]
1900
1901
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
        is_output_fp8 = False

        (
            QKV_quantizer,
            O_quantizer,
            O_CP_quantizer,
            S_quantizer,
            dQKV_quantizer,
            dQKV_CP_quantizer,
            dO_quantizer,
            dP_quantizer,
        ) = get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=True)

1915
1916
1917
        if fp8:
            if use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
1918

1919
1920
1921
1922
                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)
1923
1924
1925
1926
1927
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
                if is_input_fp8:
                    QKV_quantizer = q._quantizer
                    q, k, v = q._data, k._data, v._data
                else:
1928
1929
                    q_f16, k_f16, v_f16 = q, k, v
                    if cp_size_a2a == 1 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1930
                        q = QKV_quantizer(q_f16)._data
1931
                    if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1932
1933
1934
1935
1936
1937
1938
1939
                        k, v = [QKV_quantizer(x)._data for x in [k_f16, v_f16]]
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                # partial result quantizer
                for i in range(cp_size):
                    S_quantizer_per_step[i] = S_quantizer.copy()
                    S_quantizer_per_step[i].amax = amax_per_step[0][i]
                    O_CP_quantizer_per_step[i] = O_CP_quantizer.copy()
                    O_CP_quantizer_per_step[i].amax = amax_per_step[1][i]
1940
1941
1942
1943
1944
1945
1946
1947
1948
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            q_f16 = q
            if use_fused_attention:
                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)
1949

1950
1951
1952
1953
1954
            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
1955
            elif not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1956
                q_f16 = q
1957
                q = QKV_quantizer(q_f16)._data
1958

1959
1960
1961
        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!"
1962
        if causal:
1963
1964
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
1965
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
1966
1967
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
1968
                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
1969
        if attn_bias is not None:
1970
            assert len(attn_bias.shape) == 4, (
1971
1972
1973
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
1974
1975
1976
            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
1977
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
1978
1979
1980
1981
1982
1983
            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),
1984
1985
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
1986
1987
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
1988
            )
1989
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
1990

1991
1992
1993
1994
1995
1996
1997
        softmax_lse_in_packed_format = False
        if qkv_format == "thd":
            if use_fused_attention:
                softmax_lse_in_packed_format = get_cudnn_version() >= (9, 6, 0)
            else:
                softmax_lse_in_packed_format = _flash_attn_2_6_0_plus or _use_flash_attn_3

1998
        flash_attn_fwd = None
1999
2000
2001
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
2002
2003
2004
2005
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
2006
2007
                fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
            else:
2008
2009
2010
2011
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2012
2013
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2014
                if (_flash_attn_2_3_plus and not _flash_attn_2_7_0_plus) or _use_flash_attn_3:
2015
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
2016
2017
2018
                elif _flash_attn_2_7_0_plus:
                    fa_forward_kwargs["window_size_left"] = -1
                    fa_forward_kwargs["window_size_right"] = 0 if causal else -1
2019
2020
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
2021
                if _flash_attn_2_5_7_plus and qkv_format == "thd":
2022
                    fa_forward_kwargs["block_table"] = None
2023
2024
                if _flash_attn_2_6_0_plus:
                    fa_forward_kwargs["softcap"] = 0.0
2025

2026
2027
2028
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
2029
        attn_bias_inputs = [None, None]
2030
2031
2032
2033
        # 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)]
2034
        attn_biases = [None for _ in range(cp_size)]
2035
2036
2037
2038
2039
2040
2041

        # 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)]
2042
        if qkv_format in ["bshd", "sbhd"]:
2043
2044
2045
            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)
2046
2047
        send_recv_reqs = [[], []]

2048
2049
        softmax_lse_ = None
        out = None
2050
        for i in range(cp_size + 1):
2051
            if i < cp_size:
2052
                with torch.cuda.stream(flash_attn_streams[i % 2]):
2053
                    # wait until KV is received
2054
                    for req in send_recv_reqs[(i + 1) % 2]:
2055
2056
                        req.wait()

2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
                    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,
                        )

2069
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
2070
2071
2072
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
2073
                        kv_inputs[i % 2] = QKV_quantizer(p2p_comm_buffers[i])._data
2074
2075
                    if causal:
                        if i == 0:
2076
2077
2078
2079
                            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
                                )
2080
                            elif use_fused_attention or qkv_format == "thd":
2081
2082
2083
2084
2085
                                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
                                )
2086
                            elif use_fused_attention or qkv_format == "thd":
2087
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [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:]
                                )
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
2104
                            if use_fused_attention:
2105
2106
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2107
2108
2109
2110
2111
2112
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
2113
                                    ).contiguous()
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
2126
                                fp8_meta_kwargs = {}
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
2137
2138
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
2139

2140
2141
2142
2143
2144
2145
                                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],
2146
2147
2148
2149
2150
                                    q_part,
                                    k_part,
                                    v_part,
                                    fake_dtype=qkv_dtype,
                                    fused_attention_backend=fused_attn_backend,
2151
2152
2153
2154
2155
2156
2157
2158
2159
                                    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,
2160
                                )
2161
2162
2163
2164
2165
                                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
2166
                            else:
2167
2168
2169
2170
2171
2172
2173
2174
                                fa_forward_args_thd = []
                                if qkv_format == "thd":
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q,
                                        max_seqlen_kv,
                                    ]
2175
                                fa_outputs = flash_attn_fwd(
2176
                                    q_inputs[i % 2],
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
2188
                                    causal=True,
2189
                                    **fa_forward_kwargs,
2190
                                )
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
                                if not _flash_attn_2_7_0_plus:
                                    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]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
                                    if not _use_flash_attn_3:
                                        rng_states[i] = fa_outputs[3]
2201
                        elif i <= rank:
2202
2203
2204
2205
                            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
                                )
2206
                            elif use_fused_attention or qkv_format == "thd":
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
                                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,
                                )
2217
                            elif use_fused_attention or qkv_format == "thd":
2218
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...]
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][0]
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
                                # [2, t, np, hn] -> [2, t/2, np, hn]
                                kv_inputs[i % 2] = tex.thd_read_half_tensor(
                                    kv_inputs[i % 2], cu_seqlens_kv_padded, 0
                                )
2235
                            if use_fused_attention:
2236
                                kv_inputs[i % 2] = kv_inputs[i % 2].contiguous()
2237
2238
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2239
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
2252
                                fp8_meta_kwargs = {}
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
2263
2264
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
2265
2266
2267
2268
2269
2270
                                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],
2271
2272
2273
2274
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
                                    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,
2289
                                )
2290
2291
2292
2293
2294
                                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
2295
                            else:
2296
                                fa_forward_args_thd = []
2297
                                if qkv_format == "thd":
2298
2299
2300
2301
2302
2303
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q,
                                        max_seqlen_kv // 2,
                                    ]
2304
2305
2306
                                if _use_flash_attn_3 or (
                                    _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                                ):
2307
                                    fa_forward_kwargs["window_size"] = (-1, -1)
2308
2309
2310
                                elif _flash_attn_2_7_0_plus:
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
2311
                                fa_outputs = flash_attn_fwd(
2312
                                    q_inputs[i % 2],
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
2324
                                    causal=False,
2325
                                    **fa_forward_kwargs,
2326
                                )
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
                                if not _flash_attn_2_7_0_plus:
                                    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]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
                                    if not _use_flash_attn_3:
                                        rng_states[i] = fa_outputs[3]
2337
                        else:
2338
2339
2340
2341
                            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
                                )
2342
                            elif use_fused_attention or qkv_format == "thd":
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
                                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,
                                )
2353
                            elif use_fused_attention or qkv_format == "thd":
2354
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                                q_inputs[i % 2] = q[:, 1, ...]
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                                q_inputs[i % 2] = q[1]
                                # [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:]
                                )
                            elif qkv_format == "thd":
                                # [t, np, hn] -> [t/2, np, hn]
                                q_inputs[i % 2] = tex.thd_read_half_tensor(
                                    q, cu_seqlens_q_padded, 1
                                )
2374
                            if use_fused_attention:
2375
                                q_inputs[i % 2] = q_inputs[i % 2].contiguous()
2376
2377
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2378
2379
2380
2381
2382
2383
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
2384
                                    ).contiguous()
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
2397
                                fp8_meta_kwargs = {}
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
2408
2409
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
2410
2411
2412
2413
2414
2415
                                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],
2416
2417
2418
2419
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
                                    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,
2434
                                )
2435
2436
2437
2438
2439
                                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
2440
                            else:
2441
                                fa_forward_args_thd = []
2442
                                if qkv_format == "thd":
2443
2444
2445
2446
2447
2448
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q // 2,
                                        max_seqlen_kv,
                                    ]
2449
2450
2451
                                if _use_flash_attn_3 or (
                                    _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                                ):
2452
                                    fa_forward_kwargs["window_size"] = (-1, -1)
2453
2454
2455
                                elif _flash_attn_2_7_0_plus:
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
2456
                                fa_outputs = flash_attn_fwd(
2457
                                    q_inputs[i % 2],
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
2469
                                    causal=False,
2470
                                    **fa_forward_kwargs,
2471
                                )
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
                                if not _flash_attn_2_7_0_plus:
                                    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]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
                                    if not _use_flash_attn_3:
                                        rng_states[i] = fa_outputs[3]
2482
                    else:
2483
2484
2485
2486
                        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
                            )
2487
                        elif use_fused_attention or qkv_format == "thd":
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
                            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,
                            )
2498
                        elif use_fused_attention or qkv_format == "thd":
2499
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2500
                        if use_fused_attention:
2501
2502
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
2503
2504
2505
2506
2507
2508
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
2509
                                ).contiguous()
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521

                            q_part = q
                            k_part = (
                                kv_inputs[i % 2][..., 0, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][0]
                            )
                            v_part = (
                                kv_inputs[i % 2][..., 1, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][1]
                            )
2522
                            fp8_meta_kwargs = {}
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
                            if fp8:
                                q_part = QKV_quantizer.create_tensor_from_data(
                                    q_part, fake_dtype=qkv_dtype, internal=True
                                )
                                k_part = QKV_quantizer.create_tensor_from_data(
                                    k_part, fake_dtype=qkv_dtype, internal=True
                                )
                                v_part = QKV_quantizer.create_tensor_from_data(
                                    v_part, fake_dtype=qkv_dtype, internal=True
                                )
2533
2534
                                fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
2535
2536
2537
2538
2539
2540
                            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],
2541
2542
2543
2544
                                q_part,
                                k_part,
                                v_part,
                                qkv_dtype,
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
                                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,
2555
                            )
2556
2557
2558
2559
2560
                            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
2561
                        else:
2562
2563
2564
2565
2566
2567
2568
2569
                            fa_forward_args_thd = []
                            if qkv_format == "thd":
                                fa_forward_args_thd = [
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                ]
2570
                            fa_outputs = flash_attn_fwd(
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
                                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]
                                ),
                                *fa_forward_args_thd,
2583
                                causal=False,
2584
                                **fa_forward_kwargs,
2585
                            )
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
                            if not _flash_attn_2_7_0_plus:
                                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]
                            else:
                                out_per_step[i] = fa_outputs[0]
                                softmax_lse_per_step[i] = fa_outputs[1]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[3]
2596
2597
2598
2599

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
2600
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
2601

2602
                if use_fused_attention:
2603
2604
                    # [b, np, sq, 1] -> [b, np, sq] or
                    # [t, np, 1] -> [t, np]
2605
                    softmax_lse_per_step[i - 1].squeeze_(-1)
2606
2607
2608
2609
                    if softmax_lse_in_packed_format:
                        softmax_lse_per_step[i - 1] = (
                            softmax_lse_per_step[i - 1].transpose(0, 1).contiguous()
                        )
2610

2611
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
2612
                    if fp8:
2613
                        out_per_step[i - 1] = out_per_step[i - 1].dequantize(dtype=torch.float32)
2614
                    if i == 1:
2615
                        out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
2616
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
2617
                        if causal and qkv_format != "thd":
2618
                            # [b, np, sq] -> [b, np, 2, sq//2]
2619
                            softmax_lse_ = softmax_lse.view(
2620
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
2621
                            )
2622
2623
2624
2625
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
2626
                    else:
2627
                        if qkv_format == "thd":
2628
                            tex.thd_second_half_lse_correction(
2629
2630
2631
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
2632
                                softmax_lse_in_packed_format,
2633
                            )
2634
                        else:
2635
2636
2637
                            flash_attn_fwd_softmax_lse_correction(
                                softmax_lse_[..., 1, :], softmax_lse_per_step[i - 1]
                            )
2638
2639

                if i < cp_size:
2640
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
2641
2642
2643

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

2644
2645
2646
2647
        second_half_lse_seqlen = None
        if causal and rank < (cp_size - 1):
            second_half_lse_seqlen = softmax_lse_per_step[-1].shape[-1]

2648
2649
        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
2650
            if i <= rank or not causal:
2651
                if qkv_format in ["bshd", "sbhd"]:
2652
2653
2654
2655
2656
                    flash_attn_fwd_out_correction(
                        out.view(*out_per_step[i].shape),
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2657
2658
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2659
                    )
2660
                elif qkv_format == "thd":
2661
2662
2663
2664
2665
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2666
                        cu_seqlens_q_padded,
2667
                        False,
2668
                        softmax_lse_in_packed_format,
2669
                    )
2670
            else:
2671
                if qkv_format in ["bshd", "sbhd"]:
2672
                    out_ = out.select(seq_dim, 1)
2673
2674
2675
2676
2677
                    flash_attn_fwd_out_correction(
                        out_,
                        out_per_step[i],
                        softmax_lse_[..., 1, :],
                        softmax_lse_per_step[i],
2678
2679
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2680
                    )
2681
                elif qkv_format == "thd":
2682
2683
2684
2685
2686
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2687
                        cu_seqlens_q_padded,
2688
                        True,
2689
                        softmax_lse_in_packed_format,
2690
                    )
2691
2692

        kv = p2p_comm_buffers[-1]
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
        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:
2713
            out = out.view(-1, *out.shape[-2:])
2714

2715
2716
2717
2718
2719
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
            S_quantizer.amax = amax_cp_fwd[0]
            O_CP_quantizer.amax = amax_cp_fwd[1]

2720
        out_fp8 = None
2721
        out_f16 = out.to(qkv_dtype)
2722

2723
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
2724
2725
2726
            out_fp8 = O_quantizer(out_f16)  # final result

        out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16
2727
2728

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
2729
            q_save, kv_save, out_save = q, kv, out_fp8._data
2730
        elif fp8 and is_input_fp8:
2731
            q_save, kv_save, out_save = q, kv, out_f16
2732
        else:
2733
            q_f16 = q_f16.view(q.shape)
2734
2735
            q_save, kv_save, out_save = q_f16, kv, out_f16

2736
        tensors_to_save, tensor_objects = prepare_for_saving(
2737
2738
2739
            q_save,
            kv_save,
            out_save,
2740
            softmax_lse,
2741
2742
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
2743
2744
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
2745
2746
            *rng_states,
            *attn_biases,
2747
        )
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

        ctx.qkv_dtype = qkv_dtype
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.O_CP_quantizer = O_CP_quantizer
        ctx.S_quantizer = S_quantizer
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dQKV_CP_quantizer = dQKV_CP_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer

2761
2762
2763
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
2764
2765
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
2766
        ctx.cp_stream = cp_stream
2767
2768
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
2769
        ctx.max_seqlen_kv = max_seqlen_kv
2770
        ctx.softmax_scale = softmax_scale
2771
        ctx.qkv_format = qkv_format
2772
        ctx.attn_mask_type = attn_mask_type
2773
2774
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
2775
        ctx.deterministic = deterministic
2776
        ctx.use_fused_attention = use_fused_attention
2777
        ctx.softmax_lse_in_packed_format = softmax_lse_in_packed_format
2778
        ctx.second_half_lse_seqlen = second_half_lse_seqlen
2779
2780
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
2781
2782
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
2783
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
2784

2785
        return out_ret
2786
2787
2788

    @staticmethod
    def backward(ctx, dout):
2789
        # pylint: disable=missing-function-docstring
2790
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
2791
2792
2793
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

2794
2795
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
2796
2797
        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]
2798
2799
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

2800
        q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded, *other_tensors = (
2801
            restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
2802
2803
2804
2805
2806
        )
        cu_seqlens_q_per_step = other_tensors[:cp_size]
        cu_seqlens_kv_per_step = other_tensors[cp_size : cp_size * 2]
        rng_states = other_tensors[cp_size * 2 : cp_size * 3]
        attn_biases = other_tensors[cp_size * 3 : cp_size * 4]
2807

2808
2809
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
2810
2811

        seq_dim = None
2812
        if ctx.qkv_format in ["bshd", "sbhd"]:
2813
            seq_dim = ctx.qkv_format.index("s")
2814
2815
2816
            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
2817

2818
        if attn_biases[0] is not None:
2819
2820
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2821
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2822
2823
2824
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2825
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2826
2827
2828
            )
        else:
            attn_dbias = None
2829
            attn_dbias_ = None
2830

2831
2832
        softmax_lse_ = None
        if causal and ctx.second_half_lse_seqlen is not None:
2833
            if ctx.qkv_format == "thd":
2834
                softmax_lse_ = tex.thd_read_second_half_lse(
2835
2836
2837
2838
                    softmax_lse,
                    cu_seqlens_q_padded,
                    ctx.softmax_lse_in_packed_format,
                    ctx.second_half_lse_seqlen,
2839
                )
2840
2841
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2842
2843
2844
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2845
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
2846
2847
2848
2849
2850
2851
            if ctx.use_fused_attention:
                if ctx.softmax_lse_in_packed_format:
                    softmax_lse_ = softmax_lse_.transpose(0, 1).contiguous()
                # [b, np, sq//2] -> [b, np, sq//2, 1] or
                # [t//2, np] -> [t//2, np, 1]
                softmax_lse_.unsqueeze_(-1)
2852
        if ctx.use_fused_attention:
2853
2854
2855
2856
            if ctx.softmax_lse_in_packed_format:
                softmax_lse = softmax_lse.transpose(0, 1).contiguous()
            # [b, np, sq] -> [b, np, sq, 1] or
            # [t, np] -> [t, np, 1]
2857
            softmax_lse.unsqueeze_(-1)
2858

2859
        dq = None
2860
        dout_dtype = dout.dtype
2861
2862
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
2863
2864
2865
        amax_per_step = None
        dP_quantizer_per_step = [None for _ in range(cp_size)]
        dQKV_CP_quantizer_per_step = [None for _ in range(cp_size)]
2866
2867
2868
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
2869

2870
2871
2872
2873
2874
2875
2876
2877
2878
                dqkv_fp8_torch_dtype = get_fp8_torch_dtype(
                    ctx.fp8_meta["recipe"], fprop_tensor=False
                )
                dq_fp8 = torch.empty(
                    (cp_size, *q.shape), dtype=dqkv_fp8_torch_dtype, device=q.device
                )
                dkv_fp8 = torch.empty(
                    (cp_size, *kv.shape), dtype=dqkv_fp8_torch_dtype, device=kv.device
                )
2879
                dkv_fp8_ = torch.empty_like(dkv_fp8)
2880
                if ctx.is_output_fp8:
2881
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
2882
                    ctx.dO_quantizer = dout._quantizer
2883
                else:
2884
                    dout = ctx.dO_quantizer(dout)
2885
2886
                fused_attn_dqkv_dtype = dout._fp8_dtype
                dout = dout._data
2887
2888
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
                fp8_meta_kwargs = {}
2889
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
2890
2891
2892
2893
2894
2895
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                for i in range(cp_size):
                    dP_quantizer_per_step[i] = ctx.dP_quantizer.copy()
                    dP_quantizer_per_step[i].amax = amax_per_step[0][i]
                    dQKV_CP_quantizer_per_step[i] = ctx.dQKV_CP_quantizer.copy()
                    dQKV_CP_quantizer_per_step[i].amax = amax_per_step[1][i]
2896
2897
2898
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
            if ctx.fp8_meta is not None:
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    kv = ctx.QKV_quantizer.create_tensor_from_data(
                        kv, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q = q.dequantize(dtype=ctx.qkv_dtype)
                    kv = kv.dequantize(dtype=ctx.qkv_dtype)
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    if cp_size_a2a == 1:
                        dout = dout.dequantize(dtype=dout_dtype)
                    else:
                        ctx.dO_quantizer = dout._quantizer
                        dout = dout._data
2916
2917
2918
2919
2920
2921
2922
2923
            dq = torch.empty_like(q)
            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 = {}
2924
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
2925
2926
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
        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,
            )
2941
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
2942
2943
2944
2945
                dout = ctx.dO_quantizer.create_tensor_from_data(
                    dout, fake_dtype=dout_dtype, internal=True
                )
                dout = dout.dequantize(dtype=dout_dtype)
2946

2947
2948
2949
2950
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

2951
        flash_attn_bwd = None
2952
2953
2954
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
2955
2956
2957
2958
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
2959
2960
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
2961
2962
2963
2964
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
2965
2966
2967
2968
2969
                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
2970
2971
                if _flash_attn_2_6_0_plus:
                    fa_backward_kwargs["softcap"] = 0.0
2972

2973
2974
2975
2976
2977
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2978
2979
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
            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
                )
3009

3010
            kv = p2p_comm_buffers[i % 2][0]
3011
3012
            q_, kv_, out_, dout_ = None, None, None, None
            dq_, dk_, dv_ = None, None, None
3013
            # In reversed order of fwd
3014
            if causal:
3015
                if i == (cp_size - 1):
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        q_, kv_, out_, dout_ = q, kv, out, dout
3030
                    if ctx.use_fused_attention:
3031
3032
3033
3034
3035
3036
3037
3038
                        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]]
3039
                        if attn_dbias is not None:
3040
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
3061
                                dout_part, fake_dtype=dout_dtype, internal=True
3062
                            )
3063
3064
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
3065
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3066
                            ctx.max_seqlen_q,
3067
3068
3069
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
3070
3071
3072
3073
3074
3075
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
3076
                            fused_attn_dqkv_dtype,
3077
                            aux_ctx_tensors,
3078
                            fused_attn_backend,
3079
3080
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
3081
3082
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
3083
                            qkv_layout=qkv_layout,
3084
                            attn_mask_type=ctx.attn_mask_type,
3085
                            attn_bias_type=ctx.attn_bias_type,
3086
3087
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
3088
                        )
3089
3090
3091
3092
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
3093
                    else:
3094
                        dq_ = torch.empty_like(q_)
3095
                        dkv_ = torch.empty_like(kv_)
3096
3097
3098
3099
3100
3101
3102
3103
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q,
                                ctx.max_seqlen_kv,
                            ]
3104
3105
3106
                        if _use_flash_attn_3 or (
                            _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                        ):
3107
                            fa_backward_kwargs["window_size"] = (-1, 0)
3108
3109
3110
                        elif _flash_attn_2_7_0_plus:
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = 0
3111
3112
3113
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
3114
3115
                            dout_,
                            q_,
3116
3117
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
3118
3119
3120
                            out_,
                            softmax_lse,
                            dq_,
3121
3122
3123
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
3124
3125
                            causal=True,
                            **fa_backward_kwargs,
3126
                        )
3127
                elif i >= (cp_size - rank - 1):
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                        kv_ = kv[:, 0]
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                        kv_ = kv[0]
                    elif ctx.qkv_format == "thd":
                        q_, out_, dout_ = q, out, dout
                        # [2, t, np, hn] -> [2, t/2, np, hn]
                        kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
3144
                    if ctx.use_fused_attention:
3145
                        kv_ = kv_.contiguous()
3146
3147
3148
3149
3150
3151
3152
3153
                        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]]
3154
                        if attn_dbias is not None:
3155
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
3176
                                dout_part, fake_dtype=dout_dtype, internal=True
3177
                            )
3178
3179
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
3180
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3181
                            ctx.max_seqlen_q,
3182
3183
3184
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
3185
3186
3187
3188
3189
3190
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
3191
                            fused_attn_dqkv_dtype,
3192
                            aux_ctx_tensors,
3193
                            fused_attn_backend,
3194
3195
3196
3197
                            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
                            ),
3198
3199
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
3200
                            qkv_layout=qkv_layout,
3201
                            attn_mask_type="padding" if padding else "no_mask",
3202
                            attn_bias_type=ctx.attn_bias_type,
3203
3204
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
3205
                        )
3206
3207
3208
3209
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
3210
                    else:
3211
                        dq_ = torch.empty_like(q_)
3212
                        dkv_ = torch.empty_like(kv_)
3213
3214
3215
3216
3217
3218
3219
3220
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q,
                                ctx.max_seqlen_kv // 2,
                            ]
3221
3222
3223
                        if _use_flash_attn_3 or (
                            _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                        ):
3224
                            fa_backward_kwargs["window_size"] = (-1, -1)
3225
3226
3227
                        if _flash_attn_2_7_0_plus:
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
3228
3229
3230
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
3231
3232
                            dout_,
                            q_,
3233
3234
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
3235
3236
3237
                            out_,
                            softmax_lse,
                            dq_,
3238
3239
3240
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
3241
3242
                            causal=False,
                            **fa_backward_kwargs,
3243
3244
                        )
                else:
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_, out_, dout_ = q[:, 1], out[:, 1], dout[:, 1]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_, out_, dout_ = q[1], out[1], dout[1]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        # [t, np, hn] -> [t/2, np, hn]
                        q_, out_, dout_ = [
                            tex.thd_read_half_tensor(x, cu_seqlens_q_padded, 1)
                            for x in [q, out, dout]
                        ]
                        kv_ = kv
3262
                    if ctx.use_fused_attention:
3263
                        q_, out_, dout_ = [x.contiguous() for x in [q_, out_, dout_]]
3264
3265
3266
3267
3268
3269
3270
3271
                        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]]
3272
                        if attn_dbias is not None:
3273
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294

                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
3295
                                dout_part, fake_dtype=dout_dtype, internal=True
3296
                            )
3297
3298
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
3299
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3300
                            ctx.max_seqlen_q // 2,
3301
3302
3303
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
3304
3305
3306
3307
3308
3309
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
3310
                            fused_attn_dqkv_dtype,
3311
                            aux_ctx_tensors,
3312
                            fused_attn_backend,
3313
3314
3315
3316
                            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,
3317
3318
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
3319
                            qkv_layout=qkv_layout,
3320
                            attn_mask_type="padding" if padding else "no_mask",
3321
                            attn_bias_type=ctx.attn_bias_type,
3322
3323
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
3324
                        )
3325
3326
3327
3328
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
3329
                    else:
3330
                        dq_ = torch.empty_like(q_)
3331
                        dkv_ = torch.empty_like(kv_)
3332
                        fa_backward_args_thd = []
3333
                        if ctx.qkv_format == "thd":
3334
3335
3336
3337
3338
3339
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q // 2,
                                ctx.max_seqlen_kv,
                            ]
3340
3341
3342
                        if _use_flash_attn_3 or (
                            _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                        ):
3343
                            fa_backward_kwargs["window_size"] = (-1, -1)
3344
3345
3346
                        elif _flash_attn_2_7_0_plus:
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
3347
3348
3349
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
3350
3351
                            dout_,
                            q_,
3352
3353
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
3354
3355
3356
                            out_,
                            softmax_lse_,
                            dq_,
3357
3358
3359
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
3360
3361
                            causal=False,
                            **fa_backward_kwargs,
3362
3363
3364
                        )
            else:
                if ctx.use_fused_attention:
3365
3366
3367
3368
                    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]]
3369
                    if attn_dbias is not None:
3370
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3371
3372
3373
3374
3375
3376
3377
3378
                    q_part = q
                    k_part = kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0]
                    v_part = kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1]
                    out_part = out
                    dout_part = dout

                    if ctx.fp8:
                        q_part = ctx.QKV_quantizer.create_tensor_from_data(
3379
                            q_part, fake_dtype=ctx.qkv_dtype, internal=True
3380
3381
                        )
                        k_part = ctx.QKV_quantizer.create_tensor_from_data(
3382
                            k_part, fake_dtype=ctx.qkv_dtype, internal=True
3383
3384
                        )
                        v_part = ctx.QKV_quantizer.create_tensor_from_data(
3385
                            v_part, fake_dtype=ctx.qkv_dtype, internal=True
3386
3387
                        )
                        out_part = ctx.O_quantizer.create_tensor_from_data(
3388
                            out_part, fake_dtype=ctx.qkv_dtype, internal=True
3389
3390
                        )
                        dout_part = ctx.dO_quantizer.create_tensor_from_data(
3391
                            dout_part, fake_dtype=dout_dtype, internal=True
3392
                        )
3393
3394
                        fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                        fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
3395
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3396
                        ctx.max_seqlen_q,
3397
3398
3399
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3400
3401
3402
3403
3404
3405
                        q_part,
                        k_part,
                        v_part,
                        out_part,
                        dout_part,
                        ctx.qkv_dtype,
3406
                        fused_attn_dqkv_dtype,
3407
                        aux_ctx_tensors,
3408
                        fused_attn_backend,
3409
3410
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
3411
3412
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
3413
                        qkv_layout=qkv_layout,
3414
                        attn_mask_type=ctx.attn_mask_type,
3415
                        attn_bias_type=ctx.attn_bias_type,
3416
3417
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
3418
                    )
3419
3420
3421
3422
3423
3424

                    if ctx.fp8:
                        dq_ = dq_._data
                        dk_ = dk_._data
                        dv_ = dv_._data

3425
                else:
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
                    dq_ = torch.empty_like(q)
                    dkv_ = torch.empty_like(kv)
                    fa_backward_args_thd = []
                    if ctx.qkv_format == "thd":
                        fa_backward_args_thd = [
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
                            ctx.max_seqlen_q,
                            ctx.max_seqlen_kv,
                        ]
3436
                    if _use_flash_attn_3 or (_flash_attn_2_3_plus and not _flash_attn_2_7_0_plus):
3437
                        fa_backward_kwargs["window_size"] = (-1, -1)
3438
3439
3440
                    elif _flash_attn_2_7_0_plus:
                        fa_backward_kwargs["window_size_left"] = -1
                        fa_backward_kwargs["window_size_right"] = -1
3441
3442
3443
                    if not _use_flash_attn_3:
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
3444
3445
3446
3447
3448
                        dout,
                        q,
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
                        out,
3449
3450
                        softmax_lse,
                        dq_,
3451
3452
3453
                        dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                        dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                        *fa_backward_args_thd,
3454
3455
                        causal=False,
                        **fa_backward_kwargs,
3456
3457
                    )

3458
3459
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
3460
3461
3462
            if causal and ctx.qkv_format in ["bshd", "sbhd"] and i >= (cp_size - rank - 1):
                # [b, sq, np, hn] -> [b, 2, sq//2, np, hn] or
                # [sq, b, np, hn] -> [2, sq//2, b, np, hn]
3463
                dq_ = dq_.view(*dq.shape)
3464

3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
            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:
3476
                if i > (cp_size - rank - 1):
3477
                    dq.add_(dq_)
3478
3479
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
3480
3481
                        dq.copy_(dq_)
                    else:
3482
3483
3484
3485
3486
3487
                        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])
3488
                        elif ctx.qkv_format == "thd":
3489
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
3490
                elif i > 0:
3491
3492
3493
3494
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
3495
                    elif ctx.qkv_format == "thd":
3496
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
3497
                else:
3498
3499
3500
3501
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
3502
                    elif ctx.qkv_format == "thd":
3503
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
3504
3505
3506
3507
3508
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
3509

3510
            if attn_dbias is not None:
3511
                idx = (rank + i + 1) % cp_size
3512
                if i == (cp_size - 1) or not causal:
3513
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
3514
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3515
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
3516
3517
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
3518
3519
3520
3521
                    # [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)]
3522
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3523
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
3524
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
3525

3526
3527
3528
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
3529

3530
3531
3532
3533
3534
3535
3536
            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]
3537
            if ctx.use_fused_attention:
3538
                if ctx.qkv_format in ["bshd", "sbhd"]:
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
                    dkv_ = _combine_tensors([dk_, dv_], -2)
                elif ctx.qkv_format == "thd":
                    dkv_ = torch.cat(
                        (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                    )  # pylint: disable=used-before-assignment
            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:])
                dkv_ = dkv_.movedim(-3, 0)
                if causal and (i < (cp_size - rank - 1) or i == (cp_size - 1)):
                    # [2, b, sk, np, hn] -> [2, b, 2, sk//2, np, hn] or
                    # [2, sk, b, np, hn] -> [2, 2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(*dkv.shape)
3553

3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
            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:
3565
                if i == (cp_size - 1):
3566
                    if rank == 0:
3567
3568
3569
3570
3571
3572
                        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, ...])
3573
                        elif ctx.qkv_format == "thd":
3574
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
3575
3576
                    else:
                        dkv.add_(dkv_)
3577
3578
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
3579
3580
3581
3582
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
3583
                        elif ctx.qkv_format == "thd":
3584
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
3585
                    else:
3586
3587
3588
3589
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
3590
                        elif ctx.qkv_format == "thd":
3591
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
3592
3593
3594
3595
3596
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
3597
3598
3599
3600
3601
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

3602
        if ctx.fp8 and ctx.use_fused_attention:
3603
3604
3605
            amax_cp_bwd = amax_per_step.amax(dim=1)
            ctx.dP_quantizer.amax = amax_cp_bwd[0]
            ctx.dQKV_CP_quantizer.amax = amax_cp_bwd[1]
3606
3607
3608
3609
            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:])
3610
3611
3612
3613
3614
3615
3616
            dq = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dq_fp8, fake_dtype=torch.float32, internal=True
            )
            dkv = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dkv_fp8, fake_dtype=torch.float32, internal=True
            )
            dq, dkv = [x.dequantize(dtype=torch.float32) for x in [dq, dkv]]
3617
3618
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

3619
        if causal:
3620
3621
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
3622
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
3623
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
3624
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
3625
3626
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
3627
                dq = dq.view(-1, *dq.shape[-3:])
3628
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
3629
3630
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

3631
3632
3633
        if ctx.qkv_format == "thd" and not ctx.use_fused_attention:
            dq[cu_seqlens_q_padded[-1] :].fill_(0)
            dkv[:, cu_seqlens_kv_padded[-1] :].fill_(0)
3634

3635
        if ctx.fp8 and ctx.is_input_fp8:
3636
3637
            assert torch.uint8 not in [dq.dtype, dkv.dtype]
            dq, dkv = [ctx.dQKV_quantizer(x)._data for x in [dq, dkv]]
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
        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]]

3656
3657
3658
        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)
3659
3660
        # converting torch.uint8 to float8tensor
        if ctx.fp8 and ctx.is_input_fp8:
3661
3662
3663
            dq = ctx.dQKV_quantizer.create_tensor_from_data(dq, fake_dtype=dout_dtype)
            dk = ctx.dQKV_quantizer.create_tensor_from_data(dk, fake_dtype=dout_dtype)
            dv = ctx.dQKV_quantizer.create_tensor_from_data(dv, fake_dtype=dout_dtype)
3664
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
3665

3666
3667
3668
        return (
            None,
            dq,
3669
3670
            dk,
            dv,
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3682
            attn_dbias,
3683
3684
3685
3686
3687
            None,
            None,
            None,
            None,
            None,
3688
3689
            None,
            None,
3690
            None,
3691
        )
3692
3693


3694
3695
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
3696
):
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
    """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)
3719
3720
3721
3722


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
3723
3724
    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>`_.
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
    """

    @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,
3747
3748
        cp_group,
        cp_stream,
3749
    ):
3750
        # pylint: disable=missing-function-docstring
3751
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
3752
3753
3754
3755
3756
3757
        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)

3758
3759
        qkv_dtype = q.dtype

3760
3761
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
3762
        assert not padding, f"{attn_mask_type} mask type is not supported!"
3763
3764
3765
3766
3767
3768
3769
        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!"
3770

3771
        flash_attn_fwd = None
3772
3773
3774
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
3775
3776
3777
3778
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
3779
            else:
3780
3781
3782
3783
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
3784
3785
3786
3787
                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
3788
                if _flash_attn_2_5_7_plus and qkv_format == "thd":
3789
                    fa_forward_kwargs["block_table"] = None
3790
3791
                if _flash_attn_2_6_0_plus:
                    fa_forward_kwargs["softcap"] = 0.0
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802

        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)
3803
3804
3805
3806
3807
        if use_fused_attention or qkv_format == "thd":
            cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
        cu_seqlens_q_padded = (
            None if cu_seqlens_q_padded is None else cu_seqlens_q_padded // (2 * cp_size)
        )
3808

3809
3810
3811
3812
        # [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]]
3813

3814
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3815
3816
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
3817
3818

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3819
3820
        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:])
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
        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]
3831
3832

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
3833
3834
3835
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
3836
3837
3838
3839
3840
3841
3842
3843
        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]):
3844
3845
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3846
3847
3848
3849
3850
3851
3852
3853
3854
                    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,
3855
                        )
3856
3857
3858
3859
3860
3861
                    )
                    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
3862
3863
3864
3865
                    if use_fused_attention or qkv_format == "thd":
                        cu_seqlens_kv_per_step[i] = _get_full_cu_seqlens(
                            k.shape[1], max_seqlen_kv_, k.device
                        )
3866
3867
3868
                    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_]]
3869
3870
3871
3872
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
3873
                            max_seqlen_kv_,
3874
                            cu_seqlens_q,
3875
                            cu_seqlens_kv_per_step[i],
3876
3877
3878
                            q_,
                            k_,
                            v_,
3879
                            qkv_dtype,
3880
3881
3882
3883
3884
3885
3886
3887
                            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,
3888
3889
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
3890
3891
                        )
                    else:
3892
3893
3894
3895
3896
3897
3898
3899
                        fa_forward_args_thd = []
                        if qkv_format == "thd":
                            fa_forward_args_thd = [
                                cu_seqlens_q,
                                cu_seqlens_kv_per_step[i],
                                max_seqlen_q,
                                max_seqlen_kv_,
                            ]
3900
3901
3902
3903
3904
3905
3906
                        if _use_flash_attn_3 or (
                            _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus
                        ):
                            fa_forward_kwargs["window_size"] = window_size_per_step[i]
                        elif _flash_attn_2_7_0_plus:
                            fa_forward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_forward_kwargs["window_size_right"] = window_size_per_step[i][1]
3907
3908
3909
3910
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
3911
                            *fa_forward_args_thd,
3912
3913
                            causal=causal,
                            **fa_forward_kwargs,
3914
                        )
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
                        if not _flash_attn_2_7_0_plus:
                            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]
                        else:
                            out_per_step[i] = fa_outputs[0]
                            softmax_lse_per_step[i] = fa_outputs[1]
                            if not _use_flash_attn_3:
                                rng_states[i] = fa_outputs[3]
3925
3926
3927
3928

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
3929
                        out[:, i - 1].copy_(out_per_step[i - 1])
3930
                    elif qkv_format == "sbhd":
3931
                        out[i - 1].copy_(out_per_step[i - 1])
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948

        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,
3949
            *cu_seqlens_kv_per_step,
3950
3951
3952
3953
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
3954
3955

        ctx.qkv_dtype = qkv_dtype
3956
3957
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
3958
3959
3960
3961
3962
3963
3964
        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
3965
        ctx.attn_mask_type = attn_mask_type
3966
3967
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
3968
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
3969
3970
3971
3972
        return out

    @staticmethod
    def backward(ctx, dout):
3973
        # pylint: disable=missing-function-docstring
3974
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
3975
3976
3977
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

3978
3979
3980
3981
3982
3983
        (*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]
3984
3985
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
3986

3987
        seq_dim = ctx.qkv_format.index("s")
3988
3989
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

3990
        dout = dout.view(q.shape)
3991
        dq = torch.empty_like(q)
3992
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
        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()

4003
        # [s, b, np, hn] -> [cp, s, b, np, hn]
4004
4005
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
4006
4007

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
4008
4009
        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:])
4010
4011
4012
4013
4014
4015
4016
        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())
4017
4018
4019

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

4020
        flash_attn_bwd = None
4021
4022
4023
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
4024
4025
4026
4027
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
4028
4029
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
4030
4031
4032
4033
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
4034
4035
4036
4037
4038
                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
4039
4040
                if _flash_attn_2_6_0_plus:
                    fa_backward_kwargs["softcap"] = 0.0
4041
4042
4043
4044

        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]):
4045
4046
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
4047
4048
4049
4050
4051
4052
4053
4054
4055
                    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_]]
4056
                    out_ = out_per_step[i]
4057
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
4058
4059
4060
4061
                    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,
4062
                            max_seqlen_kv,
4063
                            cu_seqlens_q,
4064
                            cu_seqlens_kv_per_step[i],
4065
4066
4067
4068
4069
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
4070
                            ctx.qkv_dtype,
4071
                            TE_DType[dout.dtype],
4072
4073
4074
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
4075
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
4076
4077
4078
4079
4080
                            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,
4081
4082
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
4083
4084
4085
4086
4087
                        )
                    else:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
4088
4089
4090
4091
4092
4093
4094
4095
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q,
                                cu_seqlens_kv_per_step[i],
                                ctx.max_seqlen_q,
                                max_seqlen_kv,
                            ]
4096
4097
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[i]
4098
4099
4100
4101
4102
                        if _flash_attn_2_3_plus and not _flash_attn_2_7_0_plus:
                            fa_backward_kwargs["window_size"] = window_size_per_step[i]
                        if _flash_attn_2_7_0_plus:
                            fa_backward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_backward_kwargs["window_size_right"] = window_size_per_step[i][1]
4103
                        flash_attn_bwd(
4104
4105
4106
4107
4108
4109
4110
4111
4112
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
                            dq_per_step[i],
                            dk_per_step[i],
                            dv_per_step[i],
4113
                            *fa_backward_args_thd,
4114
4115
                            causal="causal" in ctx.attn_mask_type,
                            **fa_backward_kwargs,
4116
4117
4118
4119
4120
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
4121
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
4122
                    elif ctx.qkv_format == "sbhd":
4123
4124
4125
4126
4127
4128
                        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]]
                    ]
4129
4130
4131
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
4132
4133
4134
4135
4136
4137
                    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])
4138
4139
4140
4141
4142
                    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)

4143
4144
4145
4146
4147
4148
4149
        # [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]
4150
4151
4152
4153
4154
        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)

4155
4156
4157
        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()
4158
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214

        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,
4215
        quantizers,
4216
    ):
4217
        # pylint: disable=missing-function-docstring
4218
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
4219
4220
4221
4222
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
4223
        qkv_dtype = q.dtype
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235

        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!"
4236

4237
        flash_attn_fwd = None
4238
4239
4240
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
4241
4242
4243
4244
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
4245
4246
                fa_forward_kwargs["window_size"] = window_size
            else:
4247
4248
4249
4250
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
4251
4252
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
4253
                if _use_flash_attn_3 or (_flash_attn_2_3_plus and not _flash_attn_2_7_0_plus):
4254
                    fa_forward_kwargs["window_size"] = window_size
4255
4256
4257
                elif _flash_attn_2_7_0_plus:
                    fa_forward_kwargs["window_size_left"] = window_size[0]
                    fa_forward_kwargs["window_size_right"] = window_size[1]
4258
4259
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
4260
                if _flash_attn_2_5_7_plus and qkv_format == "thd":
4261
                    fa_forward_kwargs["block_table"] = None
4262
4263
                if _flash_attn_2_6_0_plus:
                    fa_forward_kwargs["softcap"] = 0.0
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277

        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!"

4278
        fused_attn_backend = None
4279
4280
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
4281
4282
4283
4284
4285
4286
4287
        is_output_fp8 = False

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
            get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
        )
        if fp8:
            if use_fused_attention:
4288
                fused_attn_backend = FusedAttnBackend["FP8"]
4289
4290
4291
4292
                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)
4293
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
4294
                if is_input_fp8:
4295
                    QKV_quantizer = q._quantizer
4296
4297
4298
4299
                    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
4300
                    q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
4301
                fp8_meta_kwargs = {}
4302
4303
                fp8_meta_kwargs["s_quantizer"] = S_quantizer
                fp8_meta_kwargs["o_quantizer"] = O_quantizer  # partial result quantizer
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
4314
4315
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if use_fused_attention:
                fp8_meta_kwargs = {}
                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
        )

4316
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4317
            q_f16, k_f16, v_f16 = q, k, v
4318
            q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
4319
4320
4321

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
            q_part, k_part, v_part = q, k, v
            if fp8:
                q_part = QKV_quantizer.create_tensor_from_data(
                    q, fake_dtype=qkv_dtype, internal=True
                )
                k_part = QKV_quantizer.create_tensor_from_data(
                    k, fake_dtype=qkv_dtype, internal=True
                )
                v_part = QKV_quantizer.create_tensor_from_data(
                    v, fake_dtype=qkv_dtype, internal=True
                )
4333
4334
4335
4336
4337
4338
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
4339
4340
4341
4342
                q_part,
                k_part,
                v_part,
                qkv_dtype,
4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
                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,
            )
4355
4356
            if fp8:
                out = out._data
4357
        else:
4358
4359
4360
4361
4362
4363
4364
4365
            fa_forward_args_thd = []
            if qkv_format == "thd":
                fa_forward_args_thd = [
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
                ]
4366
            fa_outputs = flash_attn_fwd(
4367
4368
4369
                q,
                k,
                v,
4370
                *fa_forward_args_thd,
4371
                causal=causal,
4372
                **fa_forward_kwargs,
4373
            )
4374
4375
4376
4377
4378
4379
            if not _flash_attn_2_7_0_plus:
                out, softmax_lse = fa_outputs[4], fa_outputs[5]
                rng_state = fa_outputs[7] if not _use_flash_attn_3 else None
            else:
                out, softmax_lse = fa_outputs[0], fa_outputs[1]
                rng_state = fa_outputs[3] if not _use_flash_attn_3 else None
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
            aux_ctx_tensors = [softmax_lse, rng_state]

        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:
4396
            if is_output_fp8:
4397
4398
                out_fp8 = O_quantizer.create_tensor_from_data(
                    out, fake_dtype=qkv_dtype, internal=False
4399
4400
                )
                out_ret = out_fp8
4401
                out = out_fp8._data
4402
            else:
4403
                out_fp8 = O_quantizer.create_tensor_from_data(
4404
                    out, fake_dtype=qkv_dtype, internal=True
4405
                )
4406
                out_f16 = out_fp8.dequantize(dtype=qkv_dtype)
4407
4408
4409
4410
                out_ret = out_f16
        else:
            out_ret = out

4411
        if not fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4412
            q_save, k_save, v_save, out_save = q, k, v, out
4413
4414
4415
4416
4417
4418
4419
4420
4421
        else:
            if is_input_fp8:
                q_save, k_save, v_save = q, k, v
            else:
                q_save, k_save, v_save = q_f16, k_f16, v_f16
            if is_output_fp8:
                out_save = out
            else:
                out_save = out_f16
4422

4423
        tensors_to_save, tensor_objects = prepare_for_saving(
4424
4425
4426
4427
4428
4429
4430
4431
4432
4433
            q_save,
            k_save,
            v_save,
            out_save,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        )
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

        ctx.qkv_dtype = qkv_dtype
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.S_quantizer = S_quantizer
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer

4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458
4459
        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
4460
4461
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4462
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
4463
4464
4465
4466
        return out_ret

    @staticmethod
    def backward(ctx, dout):
4467
        # pylint: disable=missing-function-docstring
4468
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
4469
4470
        cp_size = get_distributed_world_size(ctx.cp_group)

4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
4482
4483
4484
4485
4486

        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")

4487
        dout_dtype = dout.dtype
4488
4489
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
4490
4491
4492
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
4493
                if ctx.is_output_fp8:
4494
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
4495
                    ctx.dO_quantizer = dout._quantizer
4496
                else:
4497
4498
4499
                    dout = ctx.dO_quantizer(dout)
                fused_attn_dqkv_dtype = dout._fp8_dtype
                dout = dout._data
4500
                fp8_meta_kwargs = {}
4501
4502
4503
4504
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
                fp8_meta_kwargs["dp_quantizer"] = ctx.dP_quantizer
                fp8_meta_kwargs["dqkv_quantizer"] = ctx.dQKV_quantizer

4505
4506
4507
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
            if ctx.fp8_meta is not None:
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.dO_quantizer = dout._quantizer
                    dout = dout._data
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    k = ctx.QKV_quantizer.create_tensor_from_data(
                        k, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    v = ctx.QKV_quantizer.create_tensor_from_data(
                        v, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q, k, v = [x.dequantize(dtype=ctx.qkv_dtype) for x in [q, k, v]]
4524
4525
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
4526
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
                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
        )
4537
4538
4539
4540
4541
4542
4543
4544
4545
        if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
            out = ctx.O_quantizer.create_tensor_from_data(
                out, fake_dtype=ctx.qkv_dtype, internal=True
            )
            dout = ctx.dO_quantizer.create_tensor_from_data(
                dout, fake_dtype=dout_dtype, internal=True
            )
            out = out.dequantize(dtype=ctx.qkv_dtype)
            dout = dout.dequantize(dtype=dout_dtype)
4546

4547
        flash_attn_bwd = None
4548
4549
4550
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
4551
4552
4553
4554
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
4555
4556
4557
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
4558
4559
4560
4561
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
4562
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
4563
                if _use_flash_attn_3 or (_flash_attn_2_3_plus and not _flash_attn_2_7_0_plus):
4564
                    fa_backward_kwargs["window_size"] = ctx.window_size
4565
4566
4567
                elif _flash_attn_2_7_0_plus:
                    fa_backward_kwargs["window_size_left"] = ctx.window_size[0]
                    fa_backward_kwargs["window_size_right"] = ctx.window_size[1]
4568
4569
4570
4571
                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
4572
4573
                if _flash_attn_2_6_0_plus:
                    fa_backward_kwargs["softcap"] = 0.0
4574
4575

        if ctx.use_fused_attention:
4576
4577
4578
4579
4580
4581
4582
4583
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
            q_part = q
            k_part = k
            v_part = v
            out_part = out
            dout_part = dout

            if ctx.fp8:
                q_part = ctx.QKV_quantizer.create_tensor_from_data(
                    q_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                k_part = ctx.QKV_quantizer.create_tensor_from_data(
                    k_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                v_part = ctx.QKV_quantizer.create_tensor_from_data(
                    v_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                out_part = ctx.O_quantizer.create_tensor_from_data(
                    out_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                dout_part = ctx.dO_quantizer.create_tensor_from_data(
4596
                    dout_part, fake_dtype=dout_dtype, internal=True
4597
4598
                )

4599
4600
4601
4602
4603
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
4604
4605
4606
4607
4608
4609
                q_part,
                k_part,
                v_part,
                out_part,
                dout_part,
                ctx.qkv_dtype,
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
                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,
            )
4624
4625
4626
4627
            if ctx.fp8:
                dq = dq._data
                dk = dk._data
                dv = dv._data
4628
4629
4630
        else:
            softmax_lse, rng_state = aux_ctx_tensors
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
4631
4632
4633
4634
4635
4636
4637
4638
            fa_backward_args_thd = []
            if ctx.qkv_format == "thd":
                fa_backward_args_thd = [
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    ctx.max_seqlen_q,
                    ctx.max_seqlen_kv,
                ]
4639
4640
4641
            if not _use_flash_attn_3:
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
4642
4643
4644
4645
4646
4647
4648
4649
4650
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
4651
                *fa_backward_args_thd,
4652
4653
                causal=causal,
                **fa_backward_kwargs,
4654
4655
4656
4657
4658
4659
4660
            )

        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
        )

4661
        if ctx.qkv_format == "bshd":
4662
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
4663
        elif ctx.qkv_format == "sbhd":
4664
4665
4666
            dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8:
4667
4668
4669
4670
4671
4672
4673
4674
4675
            dq = ctx.dQKV_quantizer.create_tensor_from_data(
                dq, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dk = ctx.dQKV_quantizer.create_tensor_from_data(
                dk, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dv = ctx.dQKV_quantizer.create_tensor_from_data(
                dv, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
4676
            if not ctx.is_input_fp8:
4677
                dq, dk, dv = [x.dequantize(dtype=dout_dtype) for x in [dq, dk, dv]]
4678
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
4697
4698
4699
4700
4701

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4702
4703
4704
            None,
            None,
            None,
4705
            None,
4706
4707
4708
        )


4709
def attn_forward_func_with_cp(
4710
4711
4712
4713
4714
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
4715
    cu_seqlens_kv,
4716
    max_seqlen_q,
4717
    max_seqlen_kv,
4718
4719
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
4720
4721
4722
4723
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
4724
    cp_comm_type,
4725
4726
4727
4728
4729
4730
4731
    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
4732
    window_size=None,
4733
4734
    fp8=False,
    fp8_meta=None,
4735
    quantizers=None,
4736
) -> torch.Tensor:
4737
4738
4739
4740
    """
    Attention implementation with context parallelism.
    """

4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
    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}!"

4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
4768
    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 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 qkv_format != "thd" or (
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        cu_seqlens_q_padded is not None and cu_seqlens_kv_padded is not None
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    ), "cu_seqlens_padded cannot be None with context parallelism + THD format!"
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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 in [
        "a2a",
        "all_gather",
    ], "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, quantizers]
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        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":
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        args += [window_size, fp8, fp8_meta, cp_group, cp_stream, quantizers]
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        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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        squeeze=False,
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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, Float8TensorBase) and not isinstance(
            mixed_x_layer, Float8Tensor
        ):
            return tuple(
                Float8TensorBase(
                    fp8_scale_inv=mixed_x_layer._scale_inv,
                    fp8_dtype=mixed_x_layer._fp8_dtype,
                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
                    quantizer=mixed_x_layer._quantizer,
                )
                for x in torch.split(
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
                    dim=split_dim,
                )
            )
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        if isinstance(mixed_x_layer, Float8Tensor):
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            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
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                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
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                )
                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,
                )
            )
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        out_list = torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
        if squeeze:
            out_list = [x.squeeze(split_dim) for x in out_list]
        return out_list
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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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                )
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                return (
                    Float8Tensor.make_like(grad_outputs[0], data=ret, shape=ret.shape),
                    None,
                    None,
                )
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            grad_outputs_data = [x._data for x in grad_outputs]
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            data = torch.cat(grad_outputs_data, dim=split_dim)
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            return (
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                Float8Tensor.make_like(grad_outputs[0], data=data, shape=data.shape),
                None,
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                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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        window_size: Optional[Tuple[int, int]] = 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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        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
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        attn_mask_type, attention_mask, actual_seqlens_q, actual_seqlens_kv = get_full_mask(
            max_seqlen_q,
            max_seqlen_kv,
            attn_mask_type=attn_mask_type,
            attention_mask=attention_mask,
            window_size=window_size,
            attention_type=self.attention_type,
        )
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        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
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        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
5246
5247
5248
5249
5250
5251
5252
5253
5254

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

5255
        if key_layer.shape[2] != query_layer.shape[2]:
5256
5257
5258
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
5259
            key_layer = key_layer.repeat_interleave(
5260
5261
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
5262
            value_layer = value_layer.repeat_interleave(
5263
5264
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
5265

5266
        # [sq, b, np, hn] -> [sq, b * np, hn]
5267
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
5268
5269
5270
5271
5272
5273
5274
5275
        # [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]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
5276
            dtype=query_layer.dtype,
5277
5278
5279
            device=torch.cuda.current_device(),
        )

5280
        scale = self.softmax_scale
5281
        if apply_qk_layer_scaling:
5282
            scale /= self.layer_number
5283
5284

        # Raw attention scores. [b * np, sq, sk]
5285
5286
5287
5288
5289
5290
        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,
5291
                alpha=scale,
5292
            ).view(*output_size)
5293
5294
5295
5296
5297
5298
5299

        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]
            )
5300
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
5301
            matmul_result *= scale
5302

5303
5304
5305
5306
        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":
5307
                _, core_attention_bias = get_alibi(
5308
5309
5310
                    output_size[1],
                    output_size[2],
                    output_size[3],
5311
5312
                    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,
5313
5314
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
5315
                )
5316
5317
5318
5319
5320
            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,
5321
                alpha=scale,
5322
            )
5323
5324
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
5325
            )
5326
5327
5328

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
5329
        attention_probs = self.scale_mask_softmax(
5330
            matmul_result, attention_mask, attn_mask_type, softmax_scale
5331
        )
5332

5333
5334
5335
5336
5337
        # 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)

5338
5339
5340
5341
5342
5343
5344
5345
5346
5347
5348
5349
5350
5351
5352
        # 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]
5353
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
5354
5355

        # change view [b * np, sq, sk]
5356
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
5357
5358
5359
5360
5361
5362
5363

        # 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)

5364
        if qkv_format == "sbhd":
5365
5366
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
5367

5368
5369
5370
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

5371
        if qkv_format == "bshd":
5372
5373
5374
5375
5376
            # [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)
5377
5378
5379
5380
5381
5382

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
5383
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
5384
5385

    @staticmethod
5386
5387
5388
5389
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
5390
        value_layer: torch.Tensor,
5391
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
5392
        # pylint: disable=missing-function-docstring
5393
5394
5395
5396
5397
5398
5399
5400
5401
5402
5403
        # 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
5404
5405
5406
5407
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
5408
        dv: torch.Tensor,
5409
    ) -> Tuple[Union[torch.Tensor, None], ...]:
5410
        # pylint: disable=missing-function-docstring
5411
5412
5413
5414
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

5415

5416
def get_qkv_layout(
5417
5418
5419
5420
5421
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
5422
    """Get qkv layout.
5423

5424
5425
5426
5427
5428
5429
5430
5431
5432
5433
5434
    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,
5435
        `d` head size, and `t` the total number of tokens in a batch, i.e.
5436
5437
5438
5439
5440
5441
5442
5443
5444
5445
5446
5447
5448
5449
5450
        `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`}
5451
5452
5453
5454
5455
5456
5457
5458
5459
    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.
5460
    """
5461

5462
5463
    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!"
5464

5465
    def run_iteratively(q, k, v):
5466
        # check data pointers
5467
5468
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
5469
        check_ptrs_qk = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k])
5470
5471
5472
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

5473
5474
5475
5476
5477
5478
5479
        # 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
5480
5481
        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
5482
5483
        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]
5484
        )
5485

5486
5487
5488
5489
5490
5491
        # 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])
        )
5492

5493
5494
5495
5496
5497
5498
        # 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])
5499
        )
5500
5501
        check_h2d_offsets = all(
            x.storage_offset() == (offset + i * k.shape[-1]) for i, x in enumerate([k, v])
5502
        )
5503

5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
        # 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]))
5514
        )
5515
5516
5517
5518
        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]))
5519
        )
5520

5521
        if check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_3hd_offsets:
5522
            # sb3hd, bs3hd, t3hd
5523
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-3 in qkv
5524
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
5525
        elif check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_h3d_offsets:
5526
            # sbh3d, bsh3d, th3d
5527
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-2 in qkv
5528
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
5529
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_2hd_offsets:
5530
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
5531
5532
5533
            # 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
5534
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
5535
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_h2d_offsets:
5536
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
5537
5538
5539
            # 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
5540
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
5541
5542
5543
5544
5545
        elif (
            check_strides_kv
            and check_shapes_kv
            and (check_hd_offsets_qkv or check_hd_offsets_kv or check_hd_offsets_qk)
        ):
5546
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
5547
5548
5549
            # 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
5550
            qkv_layout = "_".join(list([qkv_format]) * 3)
5551
        else:
5552
            qkv_layout = "not_supported"
5553
5554
5555
5556

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
5557
    if qkv_layout == "not_supported":
5558
5559
5560
        # 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)
5561
    if qkv_layout == "not_supported":
5562
        raise RuntimeError("The provided qkv memory layout is not supported!")
5563

5564
    return qkv_layout, q, k, v
5565

5566

5567
def check_set_window_size(
5568
5569
5570
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
5571
5572
5573
5574
5575
5576
5577
5578
    """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)
5579
    """
5580
    orig_window_size = window_size
5581
    if "causal" in attn_mask_type:
5582
        if orig_window_size is None:
5583
            window_size = (-1, 0)
5584
5585
5586
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
5587
5588
5589
5590
            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
            )
5591
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
5592
5593
5594
5595
            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"]:
5596
5597
5598
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
5599
            window_size = (-1, -1)
5600
5601
5602
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
5603
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
5604
5605
5606
5607
5608
            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
5609
    return window_size
5610

5611

5612
class FlashAttention(torch.nn.Module):
5613
    """Dot product attention, using HazyResearch flash-attn package:
5614
    https://github.com/Dao-AILab/flash-attention
5615
5616
5617
5618
    """

    def __init__(
        self,
5619
        softmax_scale: float,
5620
5621
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
5622
5623
        attention_type: str = "self",
        layer_number: Optional[int] = None,
5624
        deterministic: bool = False,
5625
5626
5627
    ) -> None:
        super().__init__()

5628
5629
5630
5631
5632
5633
5634
        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."
5635

5636
        self.softmax_scale = softmax_scale
5637
5638
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
5639
5640
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
5641
        self.deterministic = deterministic
5642
5643
5644
5645
        self.logger = logging.getLogger("FlashAttention")
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
5646
5647
5648
5649
5650
5651

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5652
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5653
5654
5655
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5656
5657
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5658
        attn_mask_type: str = "causal",
5659
        window_size: Optional[Tuple[int, int]] = None,
5660
        alibi_slopes: Optional[torch.Tensor] = None,
5661
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5662
        cp_global_ranks: List[int] = None,
5663
        cp_stream: torch.cuda.Stream = None,
5664
        cp_comm_type: str = "p2p",
5665
5666
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5667
        quantizers=None,
5668
5669
5670
    ) -> torch.Tensor:
        """flash-attn fprop"""

5671
5672
5673
5674
        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."
5675
5676
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5677
        ), "FlashAttention currently only supports CUDA tensors."
5678
5679
        assert (
            qkv_layout in QKVLayouts
5680
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
5681

5682
5683
5684
5685
5686
5687
        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)
5688
        context_parallel = cp_size > 1
5689

5690
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
5691

5692
5693
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
        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 = [
5705
                        x.transpose(0, 1) for x in (query_layer, key_layer, value_layer)
5706
                    ]
5707
            if context_parallel:
5708
                query_layer, key_layer, value_layer = [
5709
5710
5711
5712
5713
                    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 = [
5714
                    x.transpose(0, 1)
5715
5716
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
5717
                query_layer, key_layer, value_layer = [
5718
                    Float8Tensor.make_like(x, data=x._data, shape=x._data.shape)
5719
5720
                    for x in (query_layer, key_layer, value_layer)
                ]
5721
            if context_parallel:
5722
5723
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
5724
                ]
5725

5726
        batch_size = query_layer.shape[0]
5727

5728
        if qkv_format in ["sbhd", "bshd"]:
5729
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
5730
5731
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
5732
5733
5734

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
5735
5736
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
5737
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
5738
5739
5740
5741
5742
5743
5744
                    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."
5745
                    if cu_seqlens_q is None:
5746
5747
5748
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
5749
5750
5751
5752
5753
5754
                        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
5755
5756
                    )
                else:
5757
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
5758
5759
5760
5761
5762
                        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])
5763
5764
5765
5766
                    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)
5767
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
5768
            else:
5769
5770
5771
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
                # 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,
                    )
5782
5783
5784
5785
        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!"
5786
5787
5788
5789
5790
5791
            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()
5792

5793
5794
5795
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
5796
5797
5798
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
5799
            with self.attention_dropout_ctx():
5800
                output = attn_forward_func_with_cp(
5801
5802
5803
5804
5805
5806
5807
5808
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5809
5810
                    cu_seqlens_q if qkv_format == "thd" else None,
                    cu_seqlens_kv if qkv_format == "thd" else None,
5811
                    self.attention_dropout if self.training else 0.0,
5812
5813
5814
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5815
                    cp_comm_type,
5816
                    softmax_scale=self.softmax_scale,
5817
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
5818
                    attn_mask_type=attn_mask_type,
5819
                    deterministic=self.deterministic,
5820
                    window_size=window_size,
5821
                    quantizers=quantizers,
5822
5823
                )
        else:
5824
5825

            from .cpu_offload import CPUOffloadEnabled
5826

5827
5828
5829
5830
5831
5832
            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

5833
            with self.attention_dropout_ctx():
5834
                fa_optional_forward_kwargs = {}
5835
5836
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
5837
5838
5839
5840
                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
5841
5842
5843
5844
                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:
5845
5846
                    if _flash_attn_2_5_7_plus:
                        fa_optional_forward_kwargs["block_table"] = None
5847
5848
5849
5850
5851
5852
5853
5854
5855
5856
                    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:
5857
5858
5859
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
                    fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
5860
                    if fp8:
5861
                        QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
5862
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
5863
                        torch_orig_dtype = query_layer.dtype
5864
5865
5866
5867
5868
5869
5870
5871
5872
5873
5874

                        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

5875
5876
5877
5878
5879
                        # "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."
5880
                        if not isinstance(query_layer, Float8Tensor):
5881
                            query_layer, key_layer, value_layer = (
5882
                                QKV_quantizer(x) for x in [query_layer, key_layer, value_layer]
5883
                            )
5884
5885
                        fa_3_optional_forward_kwargs["descale_q"] = (
                            query_layer._scale_inv.unsqueeze(0)
5886
                        )
5887
5888
                        fa_3_optional_forward_kwargs["descale_k"] = key_layer._scale_inv.unsqueeze(
                            0
5889
                        )
5890
5891
                        fa_3_optional_forward_kwargs["descale_v"] = (
                            value_layer._scale_inv.unsqueeze(0)
5892
                        )
5893
5894
5895
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
5896
                        )
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
                    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]
                                + ". Please update your flash-attn v3 (beta) installation as it "
                                + "may have added more supported arguments to its API. \n"
                                + _flash_attn_3_installation_steps,
                            ) + e.args[1:]
                        raise

                    if fp8:
                        output = output.to(dtype=torch_orig_dtype)
                    if fp8 and fp8_meta["recipe"].fp8_mha:
                        O_quantizer = quantizers["scaling_fwd"][META_O]
                        output = O_quantizer(output)
5922
                else:
5923
5924
5925
5926
5927
5928
5929
5930
5931
                    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,
5932
                    )
5933

5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)

        if qkv_format == "sbhd":
            # (bs)hd -> bs(hd) -> sb(hd)
            if fp8 and fp8_meta["recipe"].fp8_mha:
                output_data = (
                    output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous()
                )
                output = Float8Tensor.make_like(
                    output,
                    data=output_data,
                    shape=output_data.shape,
                )
            else:
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
        elif qkv_format == "bshd":
            # (bs)hd -> bs(hd)
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
        elif qkv_format == "thd":
            # thd -> t(hd)
            output = output.reshape(output.shape[0], -1)

        return output.contiguous()


def _combine_tensors(
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    if isinstance(tensors[0], Float8Tensor):
        new_stride = list(tensors[0]._data.stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor, shape=new_shape)
    else:
        new_stride = list(tensors[0].stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
        combined_tensor.set_(
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
5988
5989
        )

5990
5991
    return combined_tensor

5992

5993
5994
5995
5996
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
5997
5998
5999
6000
6001
6002
6003
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
6004
6005
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
6017
        window_size,
6018
6019
6020
6021
6022
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
6023
        quantizers,
6024
        deterministic,
6025
    ):
6026
        # pylint: disable=missing-function-docstring
6027
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
6028
        is_input_fp8 = False
6029
6030
6031
6032
6033
6034
        is_output_fp8 = fp8_meta["recipe"].fp8_mha if "recipe" in fp8_meta else False
        fake_dtype = q.dtype

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
            get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
        )
6035
6036
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
6037
6038
6039
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
6040

6041
            is_input_fp8 = isinstance(q, Float8Tensor)
6042
            q_fp8, k_fp8, v_fp8 = None, None, None
6043
            if is_input_fp8:
6044
                q_fp8, k_fp8, v_fp8 = q, k, v
6045
6046
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6047
                qkv_group = len(qkv_layout.split("_"))
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
                match qkv_group:
                    case 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_fp8 = QKV_quantizer(qkv)
                        q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1], True)
                    case 2:
                        q_fp8 = QKV_quantizer(q)
                        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_fp8 = QKV_quantizer(kv_c)
                        k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1], True)
                    case 3:
                        q_fp8 = QKV_quantizer(q)
                        k_fp8 = QKV_quantizer(k)
                        v_fp8 = QKV_quantizer(v)
                    case _:
                        raise "Invalid qkv_layout " + qkv_layout
6068
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
6069
6070
6071
6072
6073
6074
6075
6076
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
6077
                fake_dtype,
6078
6079
                fused_attention_backend,
                attn_bias,
6080
6081
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6082
6083
                S_quantizer,
                O_quantizer,
6084
6085
6086
6087
6088
6089
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6090
                window_size,
6091
6092
                rng_gen,
            )
6093
            if is_output_fp8:
6094
                out_ret = out_fp8
6095
            else:
6096
                out_ret = out_fp8.dequantize().view(out_fp8.shape)
6097
6098
            out_save = out_ret

6099
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
6100
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6101
6102
6103
6104
6105
6106
                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])
6107
6108
                        qkv_no_fp8 = qkv_c.dequantize().view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1], True)
6109
                    if qkv_group == 2:
6110
                        q = q.dequantize()
6111
6112
6113
                        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])
6114
6115
                        kv_no_fp8 = kv.dequantize()
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1], True)
6116
                    if qkv_group == 3:
6117
6118
6119
                        q = q.dequantize()
                        k = k.dequantize()
                        v = v.dequantize()
6120
                if is_output_fp8:
6121
6122
6123
                    out_save = out_fp8.dequantize()

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8)
6124
        else:
6125

6126
            out_ret, aux_ctx_tensors = fused_attn_fwd(
6127
6128
6129
6130
6131
6132
6133
6134
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
6135
                fake_dtype,
6136
6137
                fused_attention_backend,
                attn_bias,
6138
6139
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6140
6141
                None,  # s_quantizer
                None,  # o_quantizer
6142
6143
6144
6145
6146
6147
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6148
                window_size,
6149
6150
                rng_gen,
            )
6151
            out_save = out_ret
6152
            fp8_tensors = (None, None, None, None)
6153

6154
6155
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

6156
        from .cpu_offload import CPUOffloadEnabled
6157

6158
        if CPUOffloadEnabled:
6159
6160
6161
6162
6163
6164
6165
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

6166
            qkv_layout = "sbhd_sbhd_sbhd"
6167
6168
6169
6170
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

6171
6172
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6173
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
6174
6175
        tensors_to_save, tensor_objects = prepare_for_saving(
            *fp8_tensors,
6176
6177
6178
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6179
6180
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6181
6182
            *aux_ctx_tensors,
        )
6183
6184
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects
6185
        ctx.fp8_meta = fp8_meta
6186
6187
6188
6189
6190
6191

        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.S_quantizer = S_quantizer

6192
6193
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
6194
        ctx.fake_dtype = fake_dtype
6195
6196
6197
6198
6199
6200
6201
        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
6202
        ctx.window_size = window_size
6203
        ctx.fused_attention_backend = (
6204
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6205
        )
6206
        ctx.use_FAv2_bwd = use_FAv2_bwd
6207
        ctx.deterministic = deterministic
6208

6209
        return out_ret
6210
6211
6212

    @staticmethod
    def backward(ctx, d_out):
6213
        # pylint: disable=missing-function-docstring
6214
        if ctx.is_output_fp8:
6215
6216
6217
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6218

6219
        d_out = d_out.contiguous()
6220
        (
6221
6222
6223
6224
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
6225
6226
6227
6228
6229
6230
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6231
6232
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6233
6234
6235
6236
6237
            *other_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)

        aux_ctx_tensors = other_tensors

6238
6239
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6240
        rest = [None]
6241
        if ctx.use_FAv2_bwd:
6242
            softmax_lse, rng_state = aux_ctx_tensors
6243
6244
6245
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
6246
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
6247
            flash_attn_cuda_bwd(
6248
6249
6250
6251
6252
6253
6254
6255
6256
6257
6258
6259
6260
6261
6262
6263
6264
6265
6266
                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,
6267
            )
6268
6269
6270
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
6271
        else:
6272
6273
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
6274
                    if ctx.is_output_fp8:
6275
6276
                        d_out_fp8 = d_out
                    else:
6277
                        d_out_fp8 = ctx.dO_quantizer(d_out)
6278
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
6279
6280
6281
6282
6283
6284
6285
6286
6287
                        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,
6288
6289
                        ctx.fake_dtype,
                        ctx.qkv_dtype,
6290
                        aux_ctx_tensors,
6291
                        ctx.fused_attention_backend,
6292
6293
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6294
6295
6296
                        ctx.S_quantizer,
                        ctx.dP_quantizer,
                        ctx.dQKV_quantizer,
6297
6298
6299
6300
6301
6302
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6303
6304
                        ctx.window_size,
                        ctx.deterministic,
6305
                    )
6306

6307
                    if not ctx.is_input_fp8:
6308
                        qkv_group = len(ctx.qkv_layout.split("_"))
6309
                        if qkv_group == 1:
6310
                            dim = ctx.qkv_layout.find("3")
6311
6312
                            dqkv_fp8_data = _combine_tensors(
                                [dq_fp8._data, dk_fp8._data, dv_fp8._data], dim
6313
                            )
6314
6315
6316
6317
6318
                            dqkv_fp8 = dq_fp8.make_like(
                                tensor=dq_fp8, data=dqkv_fp8_data, shape=dqkv_fp8_data.shape
                            )
                            dqkv = dqkv_fp8.dequantize()
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1], True)
6319
                        if qkv_group == 2:
6320
                            dq = dq_fp8.dequantize()
6321
6322
6323
6324
6325
                            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]
                            )
6326
6327
                            dkv = dkv_c_fp8.dequantize()
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1], True)
6328
                        if qkv_group == 3:
6329
6330
6331
6332
6333
                            dq = dq_fp8.dequantize()
                            dk = dk_fp8.dequantize()
                            dv = dv_fp8.dequantize()
                    else:
                        dq, dk, dv = dq_fp8, dk_fp8, dv_fp8
6334
                else:
6335
6336
                    if isinstance(d_out, QuantizedTensor):
                        d_out = d_out.dequantize()
6337
                    dq, dk, dv, *rest = fused_attn_bwd(
6338
6339
6340
6341
6342
6343
6344
6345
6346
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
6347
                        ctx.fake_dtype,
6348
6349
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
6350
                        ctx.fused_attention_backend,
6351
6352
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6353
6354
6355
6356
6357
6358
6359
6360
6361
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6362
6363
                        ctx.window_size,
                        ctx.deterministic,
6364
                    )
6365

6366
6367
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6368
6369
6370
6371
6372
6373
6374
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
6386
6387
6388
6389
6390
6391
6392
6393
            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,
6394
6395
                None,
                None,
6396
                None,
6397
            )
6398
        # else, return (dqkv, dbias)
6399
6400
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
6416
6417
6418
6419
6420
6421
6422
6423
6424
        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,
6425
6426
            None,
            None,
6427
            None,
6428
        )
6429

6430

6431
class FusedAttention(torch.nn.Module):
6432
6433
6434
6435
6436
6437
6438
6439
6440
    """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:

6441
6442
6443
6444
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
6445
    | attn_type     | self/cross              | self/cross                     |
6446
    | qkv_layout    |                         |                                |
6447
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
6448
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
6449
6450
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
6451
6452
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
6453
    | dropout       | yes                     | yes                            |
6454
6455
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
6456
    | output dtype  | fp16/bf16               | fp16/bf16                      |
6457
6458
6459
6460
    """

    def __init__(
        self,
6461
        softmax_scale: float,
6462
6463
6464
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
6465
6466
        layer_number: Optional[int] = None,
        deterministic: bool = False,
6467
6468
6469
    ) -> None:
        super().__init__()

6470
        self.softmax_scale = softmax_scale
6471
6472
6473
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
6474
6475
6476
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
6477
        self.layer_number = 1 if layer_number is None else layer_number
6478
        self.deterministic = deterministic
6479

6480
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
6481
6482
            """
            Temporarily remove fused_attention._extra_state as a missing key
6483
            or an unexpected key when loading Transformer Engine checkpoints.
6484
6485
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
6486
            phased out in Transformer Engine 2.0.
6487
6488
            """
            for key in incompatible_keys.missing_keys:
6489
                if "fused_attention._extra_state" in key:
6490
                    incompatible_keys.missing_keys.remove(key)
6491
6492
6493
6494
6495
6496
6497
            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."
                    )
6498

6499
6500
        self.register_load_state_dict_post_hook(remove_extra_states_check)

6501
    @no_torch_dynamo()
6502
6503
6504
6505
6506
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
6507
6508
6509
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
6510
6511
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
6512
6513
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
6514
        attn_mask_type: str = "causal",
6515
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6516
        window_size: Optional[Tuple[int, int]] = None,
6517
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
6518
6519
6520
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
6521
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
6522
6523
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
6524
        cp_comm_type: str = "p2p",
6525
6526
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
6527
        quantizers=None,
6528
6529
    ) -> torch.Tensor:
        """fused attention fprop"""
6530
6531
6532
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
6533
6534
6535
6536
        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."
6537
6538
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
6539
        ), "FusedAttention only supports CUDA tensors."
6540
6541
        assert (
            qkv_layout in QKVLayouts
6542
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
6543

6544
6545
6546
6547
6548
6549
        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)
6550
        context_parallel = cp_size > 1
6551

6552
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
6553

6554
6555
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
6556
                batch_size, max_seqlen_q, max_seqlen_kv = (
6557
6558
6559
6560
6561
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
6562
                batch_size, max_seqlen_q, max_seqlen_kv = (
6563
6564
6565
6566
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
6567
6568
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
6569
            if "padding" in attn_mask_type:
6570
6571
                assert not context_parallel, "Padding mask not supported with context parallelism!"

6572
6573
6574
6575
6576
                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!"
                        )
6577
                    if self.attention_type == "self":
6578
6579
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
6580
                    else:
6581
6582
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
6583
            else:
6584
6585
6586
6587
6588
6589
6590
6591
6592
6593
6594
6595
                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,
                    )
6596
6597
6598
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
6599
6600
6601
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
6602
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
6603

6604
        if qkv_format == "thd" and (cu_seqlens_q_padded is None or cu_seqlens_kv_padded is None):
6605
6606
            cu_seqlens_q_padded = cu_seqlens_q
            cu_seqlens_kv_padded = cu_seqlens_kv
6607
6608
6609

        qkv_dtype = TE_DType[query_layer.dtype]

6610
6611
6612
6613
6614
        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)
        )
6615

6616
6617
6618
6619
6620
6621
6622
6623
6624
6625
6626
        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!"
            )

6627
        if context_parallel:
6628
            assert (
6629
6630
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
6631
6632
6633
6634
6635
6636
6637
            ), 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)
            ]
6638
6639
6640
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
6641
6642
6643
6644
6645
6646
6647
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
6648
6649
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
6650
                    self.attention_dropout if self.training else 0.0,
6651
6652
6653
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
6654
                    cp_comm_type,
6655
                    softmax_scale=self.softmax_scale,
6656
                    qkv_format=qkv_format,
6657
                    attn_mask_type=attn_mask_type,
6658
6659
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
6660
                    deterministic=self.deterministic,
6661
                    use_fused_attention=True,
6662
                    window_size=window_size,
6663
6664
                    fp8=fp8,
                    fp8_meta=fp8_meta,
6665
                    quantizers=quantizers,
6666
6667
                )
        else:
6668
6669
6670
6671
6672
6673
6674
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
6675
6676
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
6677
6678
6679
6680
6681
6682
6683
6684
6685
6686
6687
                    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,
6688
                    window_size,
6689
6690
6691
6692
6693
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
6694
                    quantizers,
6695
                    self.deterministic,
6696
                )
6697

6698
6699
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
6700
6701


6702
class DotProductAttention(TransformerEngineBaseModule):
6703
6704
6705
6706
6707
6708
    """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::

6709
        Argument :attr:`attention_mask` in the `forward` call is only used when
6710
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6711
6712
6713

    .. warning::

6714
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
6715
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
6716
6717
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
6718

6719
6720
6721
6722
6723
6724
6725
    .. 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>`_).


6726
6727
6728
6729
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
6730
6731
6732
    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.
6733
6734
6735
6736
6737
6738
6739
6740
    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`.
6741
6742
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
6743
    attn_mask_type: str, default = `causal`
6744
                   type of attention mask passed into softmax operation, options are "`no_mask`",
6745
6746
6747
6748
6749
6750
6751
6752
6753
                   "`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
6754
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
6755
6756
6757
6758
6759
6760
6761
6762
6763
6764
6765
6766
6767
6768
                   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].
6769
6770
6771
6772
    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
6773
6774
6775
                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
6776
                be overridden by :attr:`window_size` in `forward` as well.
6777
6778
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
6779
6780
6781
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
6782
6783
6784
    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,
6785
               `h` the number of heads, `d` head size, and `t` the total number of tokens
6786
6787
6788
6789
6790
               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.
6791
               For that, please use `get_qkv_layout` to gain the layout information.
6792
6793
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
6794
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
6795
6796
6797
6798
6799
6800
6801
6802
6803

    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.
6804
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
6805
              context parallel process group.
6806
6807
6808
              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.
6809
6810
6811
6812
6813
6814
6815
    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.
6816
    cp_comm_type : str, default = `p2p`
6817
                  inter-gpu communication type for context parallelism.
6818
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
6819
6820
6821
6822
6823
6824
                  "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.
6825
6826
6827
                  "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).
6828
6829
6830
6831
6832
    """

    def __init__(
        self,
        num_attention_heads: int,
6833
        kv_channels: Union[int, Tuple[int, int]],
6834
        num_gqa_groups: Optional[int] = None,
6835
        attention_dropout: float = 0.0,
6836
        qkv_format: str = "sbhd",
6837
        attn_mask_type: str = "causal",
6838
        window_size: Optional[Tuple[int, int]] = None,
6839
6840
6841
6842
6843
        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,
6844
        attention_type: str = "self",
6845
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
6846
        cp_global_ranks: List[int] = None,
6847
        cp_stream: torch.cuda.Stream = None,
6848
        cp_comm_type: str = "p2p",
6849
        softmax_scale: Optional[float] = None,
6850
6851
6852
    ) -> None:
        super().__init__()

6853
        self.logger = logging.getLogger("DotProductAttention")
6854
6855
6856
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
6857
        self.qkv_format = qkv_format
6858
        attn_mask_type = attn_mask_type.replace(",", "_")
6859
6860
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
6861
        self.attn_mask_type = attn_mask_type
6862
        self.window_size = check_set_window_size(attn_mask_type, window_size)
6863
6864
6865
6866
6867
6868
6869
        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)
6870
        self.get_rng_state_tracker = get_rng_state_tracker
6871
        self.num_attention_heads = num_attention_heads
6872
        self.layer_number = 1 if layer_number is None else layer_number
6873
6874
6875
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
6876
        self.cp_comm_type = cp_comm_type
6877

6878
6879
6880
6881
6882
6883
        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]
        )
6884

6885
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
6886
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
6887

6888
6889
6890
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
6891

6892
        self.rng_states_tracker = None
6893
6894
6895
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
6896
6897
6898
            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
6899

6900
        if softmax_scale is None:
6901
6902
6903
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
6904

6905
6906
6907
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
6908
        )
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
6922
6923
6924
6925
6926
6927
        # 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"
6928

6929
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
6930
6931
6932
6933

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

6934
6935
6936
6937
6938
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

6939
6940
6941
6942
6943
6944
6945
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
6946

6947
        # Instantiating three types since use of flash-attn and FusedAttention
6948
        # might be ruled out due to forward inputs.
6949
6950
6951
6952
6953
6954
6955
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
6956

6957
        self.unfused_attention = UnfusedDotProductAttention(
6958
6959
6960
6961
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
6962
        )
6963

6964
6965
6966
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
6967
6968
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
6969
6970
6971
6972
6973
6974
6975
            """
            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)

6976
6977
6978
6979
6980
6981
6982
6983
6984
6985
6986
6987
6988
6989
6990
6991
6992
6993
6994
6995
6996
6997
    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
        )

6998
6999
7000
7001
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
7002
        **forward_kwargs: Dict[str, Any],
7003
7004
7005
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

7006
7007
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
7008
7009
7010

        hidden_states = checkpoint(
            custom_forward,
7011
7012
7013
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
7014
            *forward_args,
7015
            **forward_kwargs,
7016
7017
7018
7019
        )

        return hidden_states

7020
7021
    def set_context_parallel_group(
        self,
7022
        cp_group: Union[dist_group_type, List[dist_group_type], None],
7023
7024
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
7025
        cp_comm_type: str = "p2p",
7026
    ) -> None:
7027
7028
7029
7030
7031
7032
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
7033
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
7034
                  context parallel process group.
7035
7036
7037
                  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.
7038
7039
7040
7041
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
7042
        cp_comm_type : str, default = `p2p`
7043
                      inter-gpu communication type for context parallelism.
7044
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7045
7046
7047
7048
7049
7050
                      "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.
7051
7052
7053
                      "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).
7054
        """
7055
7056
7057
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7058
        self.cp_comm_type = cp_comm_type
7059

7060
    @no_torch_dynamo(recursive=False)
7061
7062
7063
7064
7065
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7066
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7067
7068
7069
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7070
7071
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7072
7073
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7074
        attn_mask_type: Optional[str] = None,
7075
        window_size: Optional[Tuple[int, int]] = None,
7076
        checkpoint_core_attention: bool = False,
7077
7078
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
7079
        alibi_slopes: Optional[torch.Tensor] = None,
7080
        fast_zero_fill: bool = True,
7081
        inference_params: Optional[InferenceParams] = None,
7082
7083
7084
7085
7086
7087
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

7088
7089
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
7090

7091
7092
        .. note::

7093
7094
7095
7096
7097
7098
7099
7100
7101
7102
7103
7104
7105
            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,
7106
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
7107
7108
7109
7110
            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
7111
7112
            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
7113
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
7114
7115
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
7116

7117
7118
7119
7120
7121
7122
7123
7124
7125
7126
7127
7128
7129
7130
7131
7132
7133
7134
7135
7136
7137
7138
7139
7140
7141
7142
7143
7144
7145
7146
7147
7148
7149
7150
7151
7152
7153
7154
7155
7156
7157
7158
7159
7160
7161
7162
7163
7164
7165
7166
7167
7168
7169
7170
        .. 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`}.

7171
7172
7173
7174
7175
7176
7177
7178
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
7179
7180
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
7181
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
7182
7183
             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]
7184
7185
7186
7187
             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.
7188
7189
7190
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
7191
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
7192
                   with shape [batch_size + 1] and dtype torch.int32.
7193
                   See :ref:`note<cu_seqlens note>` for more details.
7194
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
7195
7196
                   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.
7197
                   See :ref:`note<cu_seqlens note>` for more details.
7198
7199
7200
7201
7202
        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`.
7203
                   See :ref:`note<cu_seqlens note>` for more details.
7204
7205
7206
7207
7208
        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`.
7209
                   See :ref:`note<cu_seqlens note>` for more details.
7210
7211
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
7212
                      See :ref:`note<max_seqlen note>` for more details.
7213
7214
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
7215
                       See :ref:`note<max_seqlen note>` for more details.
7216
7217
7218
7219
7220
7221
7222
        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.
7223
        window_size: Optional[Tuple[int, int]], default = `None`
7224
                    Sliding window size for local attention.
7225
7226
7227
7228
7229
        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.
7230
        core_attention_bias_type: str, default = `no_bias`
7231
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
7232
        core_attention_bias: Optional[torch.Tensor], default = `None`
7233
7234
                    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.
7235
7236
7237
7238
        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.
7239
        fast_zero_fill: bool, default = `True`
7240
                    Whether to use the fast path to set output tensors to 0 or not.
7241
7242
7243
7244
7245
7246
7247
7248
7249
7250
        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.
7251
        """
7252

7253
7254
7255
7256
7257
7258
7259
7260
7261
        with self.prepare_forward(
            query_layer,
            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
7262
                        self.logger.warning(
7263
7264
7265
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
7266
7267
7268
7269
7270
7271
7272
7273
7274
7275
7276

            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."""
7277

7278
7279
7280
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
7281
7282
7283
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
7284
7285
7286
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
7287
7288
7289
7290
7291
7292
7293
7294
            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}!"
7295

7296
7297
7298
            if qkv_format is None:
                qkv_format = self.qkv_format

7299
7300
7301
7302
7303
7304
            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"
7305
            assert (
7306
7307
7308
7309
7310
7311
                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!"
7312

7313
7314
7315
7316
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

7317
7318
7319
7320
7321
7322
7323
            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."
7324

7325
7326
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
7327

7328
7329
7330
7331
7332
                # 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"

7333
7334
7335
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7336

7337
7338
7339
7340
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
7341

7342
7343
7344
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
7345

7346
7347
7348
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
7349

7350
7351
7352
7353
7354
7355
7356
7357
7358
                # 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, ...]
7359

7360
7361
7362
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7363

7364
7365
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
7366
7367

            assert (
7368
7369
                key_layer.shape[-2] == self.num_gqa_groups_per_partition
                and value_layer.shape[-2] == self.num_gqa_groups_per_partition
7370
7371
7372
7373
            ), (
                "Keys and values must have num_gqa_group ="
                f" {self.num_gqa_groups_per_partition} heads!"
            )
7374
7375
7376
7377
7378
7379
7380
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

            if qkv_format == "thd":
7381
                assert all(
7382
7383
7384
7385
7386
7387
7388
7389
7390
7391
7392
7393
7394
                    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!"
7395
                batch_size = len(cu_seqlens_q) - 1
7396
                if max_seqlen_q is None:
7397
7398
7399
7400
                    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]
7401
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
7402
                if max_seqlen_kv is None:
7403
7404
7405
7406
                    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]
7407
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
7408

7409
7410
7411
7412
7413
7414
            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)
7415
7416
            context_parallel = cp_size > 1

7417
            if qkv_format in ["sbhd", "bshd"]:
7418
                assert all(
7419
7420
7421
                    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":
7422
7423
                    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
7424
                    batch_size = query_layer.shape[1]
7425
                else:
7426
7427
                    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
7428
                    batch_size = query_layer.shape[0]
7429
7430
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
7431
7432
7433
7434
7435
                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
7436
                        the sequence dimension in 'query_layer'!"""
7437
7438
7439
7440
7441
                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
7442
                        the sequence dimension in 'key_layer' and 'value_layer'!"""
7443
7444
7445
7446
7447
                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!"
7448
                        if self.attention_type == "self":
7449
7450
7451
7452
7453
7454
7455
7456
7457
7458
7459
7460
7461
7462
7463
7464
                            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,
                        )
7465

7466
7467
7468
7469
7470
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
7471
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
7472
7473
7474
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
7475
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
7476
7477
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
7478

7479
7480
7481
7482
7483
7484
7485
7486
            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
7487
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
7488
7489
7490
7491
7492
7493
7494
7495
            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
7496
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
7497
7498
7499
7500
7501
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

7502
7503
            core_attention_bias_shape = None
            if core_attention_bias is not None:
7504
                if (
7505
7506
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
7507
                ):
7508
7509
7510
7511
7512
7513
7514
7515
7516
7517
7518
7519
7520
7521
7522
7523
7524
7525
7526
                    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
7527
                and not torch.equal(cu_seqlens_q_padded[:-1], cu_seqlens_q[:-1])
7528
7529
            ) or (
                cu_seqlens_kv_padded is not None
7530
                and not torch.equal(cu_seqlens_kv_padded[:-1], cu_seqlens_kv[:-1])
7531
            )
7532

7533
            attention_params = AttentionParams(
7534
7535
7536
7537
7538
7539
7540
7541
                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,
7542
7543
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
7544
7545
7546
7547
7548
7549
7550
7551
7552
7553
7554
                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,
7555
7556
                deterministic=self.deterministic,
                is_training=self.training,
7557
7558
7559
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
7560
            global _attention_backends, _use_flash_attn_3
7561
7562
7563
7564
7565
7566
7567
            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"]:
7568
                _use_flash_attn_3 = _flash_attn_3_is_installed
7569
7570
7571
7572
7573
7574
7575
7576
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
7577
7578
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
7579
                        _flash_attn_version if not _use_flash_attn_3 else _flash_attn_3_version,
7580
                    )
7581
7582
7583
7584
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
7585
                    )
7586
7587
7588
7589
7590
7591
7592
                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"]
7593

7594
7595
7596
7597
7598
7599
7600
7601
7602
7603
7604
7605
7606
7607
7608
7609
7610
7611
7612
7613
7614
7615
            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,
7616
                    cp_comm_type=self.cp_comm_type,
7617
7618
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
7619
7620
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
7621
                    quantizers=self.quantizers,
7622
                )
7623

7624
            if use_fused_attention:
7625
7626
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
7627
7628
7629
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
7630
7631
7632
7633
7634
7635
7636
                    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,
7637
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
7638
                    )
7639
7640
7641
7642
7643
7644
7645
7646
7647
                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,
7648
7649
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
7650
7651
7652
7653
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
7654
                        window_size=window_size,
7655
7656
7657
7658
7659
7660
7661
                        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,
7662
                        cp_comm_type=self.cp_comm_type,
7663
7664
7665
7666
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
7667
7668
7669
7670
7671
7672
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
7673
7674
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
7675
7676
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
7677
7678
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
7679
                    window_size=window_size,
7680
                    fused_attention_backend=fused_attention_backend,
7681
7682
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
7683
7684
7685
7686
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
7687
                    cp_comm_type=self.cp_comm_type,
7688
7689
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
7690
                    quantizers=self.quantizers,
7691
                )
7692

7693
            from .cpu_offload import CPUOffloadEnabled
7694

7695
7696
7697
7698
7699
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
7700

7701
7702
7703
7704
7705
7706
7707
7708
7709
7710
7711
7712
            if use_unfused_attention:
                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,
7713
                        window_size=window_size,
7714
7715
7716
7717
7718
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
                    )
                return self.unfused_attention(
7719
7720
7721
                    query_layer,
                    key_layer,
                    value_layer,
7722
7723
7724
7725
7726
                    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,
7727
                    window_size=window_size,
7728
7729
7730
7731
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
                )
7732

7733
            raise ValueError("No dot product attention support for the provided inputs!")
7734
7735


7736
7737
7738
7739
7740
7741
7742
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

7743
7744
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
7745

7746
7747
7748
7749
7750
7751
7752
7753
7754
7755
7756
7757
7758
7759
7760
7761
7762
7763
7764
7765
7766
7767
7768
7769
7770
    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.
7771
7772
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
7773
                   default = `causal`
7774
7775
7776
7777
7778
                   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.
7779
7780
7781
7782
    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
7783
7784
7785
                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
7786
                be overridden by :attr:`window_size` in `forward` as well.
7787
7788
7789
7790
7791
7792
7793
7794
7795
7796
7797
7798
7799
    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.
7800
7801
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
7802
7803
7804
7805
7806
7807
7808
7809
7810
7811
7812
7813
7814
7815
7816
7817
7818
7819
7820
7821
    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"
7822
          The device on which the parameters of the model will be allocated. It is the user's
7823
7824
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
7825
7826
7827
7828
7829
7830
7831
    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.
7832
            For that, please use `get_qkv_layout` to gain the layout information.
7833
7834
7835
7836
7837
7838
7839
7840
7841
7842
7843
7844
7845
7846
7847
7848
7849
7850
7851
7852
7853
7854
7855
7856
7857
7858
7859
7860
7861
7862
7863
7864
7865
7866
7867
7868
7869
7870
7871
7872

    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`.
7873
7874
7875
7876
7877
7878
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
7879
7880
7881
7882
7883
        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,
7884
        layer_number: Optional[int] = None,
7885
        attn_mask_type: str = "causal",
7886
        window_size: Optional[Tuple[int, int]] = None,
7887
7888
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
7889
        num_gqa_groups: Optional[int] = None,
7890
7891
7892
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
7893
        params_dtype: Optional[torch.dtype] = None,
7894
        return_bias: bool = False,
7895
7896
7897
7898
7899
7900
7901
        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,
7902
        ub_overlap_ag: bool = False,
7903
7904
7905
7906
        ub_overlap_rs: bool = False,
        ub_overlap_rs_dgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: 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)
8273

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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]
8343
                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]
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            query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
            )
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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]
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            key_layer, value_layer = _SplitAlongDim.apply(
                mixed_kv_layer,
                split_dim,
                mixed_kv_layer.shape[split_dim] // 2,
            )
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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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            fp8_grad=isinstance(context_layer, QuantizedTensor),
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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]